Internet of things

Pax Technica: How the Internet of Things May Set Us Free or Lock Us Up
by Philip N. Howard
Published 27 Apr 2015

International tensions over competing technology standards are only going to increase as governments and firms identify the engineering protocols, licensing arrangements, and telecommunications standards that will allow them to use the internet of things to advance their goals. Being purposeful about the design of the internet of things is the safest way to export democracy. However, the internet of things is being built over the internet we have now. For each of the premises about the political internet in the previous chapter, there’s a consequence for the emerging internet of things. 1. Major governments and firms will hold back on inflicting real damage to rival device networks for fear of suffering consequences themselves—a kind of cyberdeterrence against debilitating attacks. 2.

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Even more communities will be able to replace their failing governments with institutional arrangements that provide distinct governance goods over the internet of things. 3. The primary fissures of global politics will be among rival device networks and the competing technology standards and media ecosystems that entrench the internet of things. 4. People will use the internet of things for connective action, especially for those crypto-clans organized over networks of trust and reciprocity established by people and mediated by their devices. 5. The great new flows of data from the internet of things will make it much easier for security services to stop crime and terrorism, but unless civil society groups also have access to such data, it will be difficult to know how pervasive censorship and surveillance really is.

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Moreover, there’s no guarantee that you will have access to the data about your behavior. Putting the Civic into the Internet of Things, Domestically In this day and age, you either set the technology standards or you follow them. Many brilliant civic projects provide governance through the open, considered, and deliberate use of the internet. So we need an internet of things that allows expression and experimentation. Brett Frischmann makes this same argument in Infrastructure: all public works like the internet of things should be open and nondiscriminatory.25 We need to make sure the internet of things is designed for civic engagement. These days, it’s normal for civil-society groups to have an internet strategy or a social-media strategy.

The Silent Intelligence: The Internet of Things
by Daniel Kellmereit and Daniel Obodovski
Published 19 Sep 2013

As Kelly Venturini of M2M Certified put it, “Everybody wants to get connected and they don’t freaking know how to do it.” Much has been written about the Internet of Things in publications such as the Economist, Financial Times, the New York Times, McKinsey Quarterly, and Harvard Business Review, as well as multiple research studies and white papers by Harbor Research, ABI, and Frost & Sullivan, to name a few. We wanted to build upon these sources, but also go further and deeper in our examination, to answer these fundamental questions: What is the Internet of Things? How is it coming about? What are the key trends? What is the potential? What needs to be done to succeed in this space?

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In chapter 1, we start by defining the subject and what impact it might have on our everyday lives. Here we also talk about the nature of the terms Machine-to-Machine and the Internet of Things and others, such as embedded computing and smart services. Then we examine the history of the topic. We followed the advice of Peggy Smedley of Connected World, who suggested we look back before we look forward. It was important for us to understand where the Internet of Things came from, which trends preceded it, and, more importantly, which trends are aligning now to facilitate the exponential growth of new services, businesses, and opportunities in this space.

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Here we also talk about the main technical challenges of the space and the opportunities that present themselves in addressing these challenges. Chapter 3 looks into the future of M2M and the Internet of Things and focuses on what this brave new world may look like. We ask some provocative questions: What role will humans play when a lot of decision-making is done by machines, and might humans ever become a bottleneck to realizing the Internet of Things vision? We also take a peek at what the ubiquitous connectivity between various devices may look like in real terms. Chapter 4 is dedicated to the core industries of M2M.

Industry 4.0: The Industrial Internet of Things
by Alasdair Gilchrist
Published 27 Jun 2016

Industry 4.0 The Industrial Internet of Things ― Alasdair Gilchrist INDUSTRY 4.0 THE INDUSTRIAL INTERNET OF THINGS Alasdair Gilchrist Industry 4.0: The Industrial Internet of Things Alasdair Gilchrist Bangken, Nonthaburi Thailand ISBN-13 (pbk): 978-1-4842-2046-7 ISBN-13 (electronic): 978-1-4842-2047-4 DOI 10.1007/978-1-4842-2047-4 Library of Congress Control Number: 2016945031 Copyright © 2016 by Alasdair Gilchrist This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed.

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Gilchrist, Industry 4.0, DOI 10.1007/978-1-4842-2047-4_1 2 Chapter 1 | Introduction to the Industrial Internet of them all, the Industrial Internet of Things, which encompasses a vast amount of disciplines such as energy production, manufacturing, agriculture, health care, retail, transportation, logistics, aviation, space travel and many more. Figure 1-1. Horizontal and vertical aspects of the Internet of Things In this book to avoid confusion we will follow GE’s lead and use the name Industrial Internet of Things (IIoT) as a generic term except where we are dealing with conceptually and strategically different paradigms, in which case it will be explicitly referred to by its name, such as Industry 4.0.

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CHAPTER 1 Introduction to the Industrial Internet GE (General Electric) coined the name “Industrial Internet” as their term for the Industrial Internet of Things, and others such as Cisco termed it the Internet of Everything and others called it Internet 4.0 or other variants. However, it is important to differentiate the vertical IoT strategies (see Figure 1-1), such as the consumer, commercial, and industrial forms of the Internet from the broader horizontal concept of the Internet of Things (IoT), as they have very different target audiences, technical requirements, and strategies. For example, the consumer market has the highest market visibility with smart homes, personal connectivity via fitness monitors, entertainment integrated devices as well as personal in-car monitors.

The Zero Marginal Cost Society: The Internet of Things, the Collaborative Commons, and the Eclipse of Capitalism
by Jeremy Rifkin
Published 31 Mar 2014

The coming together of the Communications Internet with the fledgling Energy Internet and Logistics Internet in a seamless twenty-first-century intelligent infrastructure—the Internet of Things (IoT)—is giving rise to a Third Industrial Revolution. The Internet of Things is already boosting productivity to the point where the marginal cost of producing many goods and services is nearly zero, making them practically free. The result is corporate profits are beginning to dry up, property rights are weakening, and an economy based on scarcity is slowly giving way to an economy of abundance. The Internet of Things The Internet of Things will connect every thing with everyone in an integrated global network.

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Big Data, in turn, will be processed with advanced analytics, transformed into predictive algorithms, and programmed into automated systems to improve thermodynamic efficiencies, dramatically increase productivity, and reduce the marginal cost of producing and delivering a full range of goods and services to near zero across the entire economy. The Internet of Things European Research Cluster, a body set up by the European Commission, the executive body of the European Union, to help facilitate the transition into the new era of “ubiquitous computing,” has mapped out some of the myriad ways the Internet of Things is already being deployed to connect the planet in a distributed global network. The IoT is being introduced across industrial and commercial sectors. Companies are installing sensors all along the commercial corridor to monitor and track the flow of goods and services.

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New studies, however, including one conducted by my global consulting group, show that with the shift to a Third Industrial Revolution infrastructure, it is conceivable to increase aggregate energy efficiency to 40 percent or more in the next 40 years, amounting to a dramatic increase in productivity beyond what the economy experienced in the twentieth century.8 The Internet of Things The enormous leap in productivity is possible because the emerging Internet of Things is the first smart-infrastructure revolution in history: one that will connect every machine, business, residence, and vehicle in an intelligent network comprised of a Communications Internet, Energy Internet, and Logistics Internet, all embedded in a single operating system.

Future Crimes: Everything Is Connected, Everyone Is Vulnerable and What We Can Do About It
by Marc Goodman
Published 24 Feb 2015

Smith, “Most ‘Hackable’ Vehicles Are Jeep, Escalade, Infiniti, and Prius,” Network World, Aug. 3, 2014. 36 In a nod: Ina Fried, “Tesla Hires Hacker Kristin Paget to, Well, Secure Some Things,” Re/code, Feb. 7, 2014. 37 “expected to reach”: Transparency Market Research, “Home Automation Market (Lighting, Safety and Security, Entertainment, HVAC, Energy Management)—Global Industry Analysis, Size, Share, Growth, Tends, and Forecast, 2013–2019,” Sept. 30, 2013. 38 Many such systems: Kashmir Hill, “When ‘Smart Homes’ Get Hacked: I Haunted a Complete Stranger’s House via the Internet,” Forbes, July 26, 2013. 39 A July 2014 study: Daniel Miessler, “HP Study Reveals 70 Percent of Internet of Things Devices Vulnerable to Attack,” HP, July 29, 2014. 40 Major toy makers: Arrayent, “Internet of Things Toys with Mattel,” http://​www.​arrayent.​com/​internet-​of-​things-​case-​studies/​connecting-​toys-​with-​mattet/​Disney Research, “CALIPSO: Internet of Things.” http://​www.​disneyresearch.​com/​project/​calipso-​internet-​of-​things/. 41 But toys too can be subverted: Heather Kelly, “ ‘Smart Homes’ Are Vulnerable, Say Hackers,” CNN, Aug. 2, 2013. 42 They allow hackers to turn off: Dan Goodin, “Welcome to the ‘Internet of Things,’ Where Even Lights Aren’t Hacker Safe,” Ars Technica, Aug. 13, 2013. 43 Additional systems: Jane Wakefield, “Experts Hack Smart LED Light Bulbs,” BBC News, July 8, 2014; Leo King, “Smart Home?

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Indeed, one such firm, the smartthermostat company Nest Labs, was acquired in 2014 for an astounding $3.2 billion just 854 days after the launch of its first product. And while there is undoubtedly big money to be made in the IoT, its social implications may even outstrip its economic impact. Imagining the Internet of Things The Internet of Things is a way of saying that more of the world will become part of the network … We are assimilating more and more of the world into the computer. GORDON BELL, MICROSOFT RESEARCHER The promise of the Internet of Things sounds rosy. Because chips and sensors will be embedded in everyday objects, we will have much better information and convenience in our lives. So, for example, because your alarm clock is connected to the Internet, it will be able to access and read your calendar.

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At these stores and elsewhere online, a whole new array of digital devices are vying for a position on our home networks—things such as Internet-enabled thermostats, lightbulbs, music speakers, baby monitors, and security systems. Together they represent the first steps in a rapidly emerging new paradigm of computing known as the Internet of Things (IoT), and when it takes off, it may very well change the world we live in forever. The Pew Research Center defines the Internet of Things as “a global, immersive, invisible, ambient networked computing environment built through the continued proliferation of smart sensors, cameras, software, databases, and massive data centers in a world-spanning information fabric.”

Click Here to Kill Everybody: Security and Survival in a Hyper-Connected World
by Bruce Schneier
Published 3 Sep 2018

One of the premises of this book is that the Internet is a singular connected network—that any part of it can affect any other part of it—and needs to be viewed in this way to properly talk about security. 5“the network of physical objects”: Gartner (accessed 24 Apr 2018), “Internet of Things,” Gartner IT Glossary, https://www.gartner.com/it-glossary/internet-of-things. 5In 2017, there were 8.4 billion things: Gartner (7 Feb 2017), “Gartner says 8.4 billion connected ‘things’ will be in use in 2017, up 31 percent from 2016,” https://www.gartner.com/newsroom/id/3598917. 5By 2020, there are likely to be: Tony Danova (2 Oct 2013), “Morgan Stanley: 75 billion devices will be connected to the Internet of Things by 2020,” Business Insider, http://www.businessinsider.com/75-billion-devices-will-be-connected-to-the-internet-by-2020-2013-10. Peter Brown (25 Jan 2017), “20 billion connected Internet of Things devices in 2017, IHS Markit says,” Electronics 360, http://electronics360.globalspec.com/article/8032/20-billion-connected-internet-of-things-devices-in-2017-ihs-markit-says. Julia Boorstin (1 Feb 2016), “An Internet of Things that will number ten billions,” CNBC, https://www.cnbc.com/2016/02/01/an-internet-of-things-that-will-number-ten-billions.html. Statista (2018), “Internet of Things (IoT) connected devices installed base worldwide from 2015 to 2025 (in billions),” https://www.statista.com/statistics/471264/iot-number-of-connected-devices-worldwide. 6your T-shirt someday will: Michael Sawh (26 Sep 2017), “The best smart clothing: From biometric shirts to contactless payment jackets,” Wareable, https://www.wareable.com/smart-clothing/best-smart-clothing. 6“The ‘Smart Everything’ Trend”: J.

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There are advantages to computerizing everything—some that we can see today, and some that we’ll realize only once these computers have reached critical mass. The Internet of Things will embed itself into our lives at every level, and I don’t think we can predict the emergent properties of this trend. We’re reaching a fundamental shift that is due to scale and scope; these differences in degree are causing a difference in kind. Everything is becoming one complex hyper-connected system in which, even if things don’t interoperate, they’re on the same network and affect each other. There is more to this trend than the Internet of Things. Take the Internet of Things. Start with the IoT or, more generally, cyberphysical systems.

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One of the premises of this book is that the Internet is a singular connected network—that any part of it can affect any other part of it—and needs to be viewed in this way to properly talk about security. 5“the network of physical objects”: Gartner (accessed 24 Apr 2018), “Internet of Things,” Gartner IT Glossary, https://www.gartner.com/it-glossary/internet-of-things. 5In 2017, there were 8.4 billion things: Gartner (7 Feb 2017), “Gartner says 8.4 billion connected ‘things’ will be in use in 2017, up 31 percent from 2016,” https://www.gartner.com/newsroom/id/3598917. 5By 2020, there are likely to be: Tony Danova (2 Oct 2013), “Morgan Stanley: 75 billion devices will be connected to the Internet of Things by 2020,” Business Insider, http://www.businessinsider.com/75-billion-devices-will-be-connected-to-the-internet-by-2020-2013-10.

The Driver in the Driverless Car: How Our Technology Choices Will Create the Future
by Vivek Wadhwa and Alex Salkever
Published 2 Apr 2017

It will probably release many new products, all controllable from the Nest application. Technology companies say they will use the Internet of Things in the same way: to reduce our energy usage, improve our health, make us more secure, and nudge us toward better lifestyles. Of course, the I.o.T., they say, will save us money too. The ability to collect such data will have a profound effect on the economy. The McKinsey Global Institute, in a report titled The Internet of Things: Mapping the Value beyond the Hype, says that the economic impact of the Internet of Things could be $3.9 to $11.1 trillion per year by 2025: up to 11 percent of the global economy.2 Much of the value of the I.o.T. is hard for us to comprehend, because it will be machines talking to other machines to enable different A.I. systems to work together and make better decisions.

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They will become the drivers in the driverless car. 13 When Your Scale Talks to Your Refrigerator: The Internet of Things Your refrigerator will talk to your toothbrush, your gym shoes, your car, and your bathroom scale. They will all have a direct line to your smartphone and tell your digital doctor whether you have been eating right, exercising, brushing your teeth, or driving too fast. I have no idea what they will think of us or gossip about; but I know that many more of our electronic devices will soon be sharing information about us—with each other and with the companies that make or support them. The Internet of Things (I.o.T.) is a fancy name for the increasing array of sensors embedded in our commonly used appliances and electronic devices, our vehicles, our homes, our offices, and our public places.

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The 200-mile-per-hour ride in a Google car will be controlled by a transportation subset of the Internet of Things, a web of sensors on the roadways and embedded in the vehicles that will allow them to speak the same language. The McKinsey report also assigns value to the I.o.T. by including the economic impact of reductions in disease, accidents, and deaths. Those are real economic benefits even if they are hard to calculate today, with few of those systems in place. McKinsey believes that the I.o.T. will monitor and help manage a huge swath of activity on Earth: the natural world, people, and animals. The Internet of Things should not only change our interactions with devices and improve their efficiencies but also create entirely new ways of understanding the global economic engine.

The Internet of Us: Knowing More and Understanding Less in the Age of Big Data
by Michael P. Lynch
Published 21 Mar 2016

First there was Web 1.0 (the ancient days of “Wow! You should check out this email thing!”). Then, starting in the early 2000s, there was Web 2.0. (“Wow! You should check out this Facebook thing!”). Now we have Web 3.0 (the “smart Web”) and, most significantly, the so-called Internet of Things (“Wow! You should check out my smart … watch, refrigerator, lamp, socks!”). In essence, the “Internet of Things” is a way of describing the phenomenon of networked objects—objects that are embedded with data-streaming sensors and software that connect them to the Net. The “things” in question run the gamut from autonomous connected devices like smartphones to the tiny radio-frequency identification (RFID) microchips and other sorts of sensors attached to everything from UPS trucks and cargo containers to pets, farm animals, cars, thermostats, and NFL helmets.

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By 2007 there were already 10 million sensors of all sorts connected to the Internet, and some projections have that number rising to 100 trillion by 2030 if not before.4 These sensors are being used not only for economic purposes but for scientific ones (to track migratory animals, for example), and for security and military purposes (such as tracking human beings). According to Jeremy Rifkin, a leading economist of the digital world, the Internet of Things is even giving rise to a “Third Industrial Revolution,” precipitating huge changes in how human beings around the globe interact with one another, economically and otherwise.5 The Internet of Things is made possible by—and is also producing—big data. The term “big data” has no fixed definition, but rather three connected uses. First, it names the ever-expanding volume of data that surrounds us.

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Thermostats, refrigerators, children’s toys, tools and washing machines can be (and are) connected digitally to the Web, sending and receiving information, emails, locations, updates. This is the Internet of Things. As Floridi notes, “With interfaces becoming progressively less visible, the threshold between here (analogue, carbon-based offline) and there (digital, silicon-based, online) is fast becoming blurred, although this is as much to the advantage of there as it is to here.’ ”5 But the blurring of the distinction between online and offline isn’t just due to things like smart watches. For the Internet is not just the Internet of Things. It is also composed of social artifacts. And these emerging social constructs are intertwined with the literal constructs.

Radical Technologies: The Design of Everyday Life
by Adam Greenfield
Published 29 May 2017

What links these wildly different circumstances is a vision of connected devices now being sold to us as the “internet of things,” in which a weave of networked perception wraps every space, every place, every thing and every body on Earth. The technologist Mike Kuniavsky, a pioneer and early proponent of this vision, characterizes it as a state of being in which “computation and data communication [are] embedded in, and distributed through, our entire environment.”1 I prefer to see it for what it is: the colonization of everyday life by information processing. Like the smartphone, the internet of things isn’t a single technology, but an unruly assemblage of protocols, sensing regimes, capabilities and desires, all swept under a single rubric for the sake of disciplinary convenience.

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The quest to instrument the body, monitor its behavior and derive actionable insight from these soundings is known as the “quantified self”; the drive to render interior, domestic spaces visible to the network “the smart home”; and when this effort is extended to municipal scale, it is known as “the smart city.” Each of these scales of activity illuminates a different aspect of the challenge presented to us by the internet of things, and each of them has something distinct to teach us. At the most intimate scale, the internet of things manifests in the form of wearable biometric sensors: devices that collect the various traces of our being in the world, and submit them to the network for inspection and analysis. The simplest of these are little more than networked digital pedometers.

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For me, many years of thinking and working in this domain have left behind a clear and vivid picture of that world. It seems strange to assert that anything as broad as a class of technologies might have a dominant emotional tenor, but the internet of things does. That tenor is sadness. When we pause to listen for it, the overriding emotion of the internet of things is a melancholy that rolls off of it in waves and sheets. The entire pretext on which it depends is a milieu of continuously shattered attention, of overloaded awareness, and of gaps between people just barely annealed with sensors, APIs and scripts.

Platform Capitalism
by Nick Srnicek
Published 22 Dec 2016

These platforms already are strong revenue sources for the companies: Predix currently brings GE $5 billion and is expected to triple this revenue by 2020.49 Predictions are that the sector will be worth $225 billion by 2020 – more than both the consumer internet of things and enterprise cloud computing.50 Nevertheless, demonstrating the power of monopolies, GE continues to use AWS for its internal needs.51 Product Platforms Importantly, the preceding developments – particularly the internet of things and cloud computing – have enabled a new type of on-demand platform. They are two closely related but distinct business models: the product platform and the lean platform.

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‘Britain’s Lonely High-Flier’ (Editor’s Note). 2009. The Economist, 8 January. http://www.economist.com/node/12887368 (accessed 4 June 2016). Bughin, Jacques, Michael Chui, and James Manyika. 2015. ‘An Executive’s Guide to the Internet of Things’. McKinsey&Company.August.http://www.mckinsey.com/business-functions/business-technology/our-insights/an-executives-guide-to-the-internet-of-things (accessed 4 June 2016). Burrington, Ingrid. 2016. ‘Why Amazon’s Data Centers Are Hidden in Spy Country’. The Atlantic, 8 January. http://www.theatlantic.com/technology/archive/2016/01/amazon-web-services-data-center/423147 (accessed 4 June 2016).

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‘Uber Drivers, if Employees, Owed $730 Million More: US Court Papers’. Reuters. 10 May. http://www.reuters.com/article/us-uber-tech-driverslawsuit-idUSKCN0Y02E8 (accessed 22 May 2016). Löffler, Markus, and Andreas Tschiesner. 2013. ‘The Internet of Things and the Future of Manufacturing’. McKinsey & Company. http://www.mckinsey.com/insights/business_technology/the_internet_of_things_and_the_future_of_manufacturing (accessed 22 May 2016). Manyika, James, Susan Lund, Kelsey Robinson, John Valentino, and Richard Dobbs. 2015. ‘A Labor Market That Works: Connecting Talent with Opportunity in the Digital Age’.

The Green New Deal: Why the Fossil Fuel Civilization Will Collapse by 2028, and the Bold Economic Plan to Save Life on Earth
by Jeremy Rifkin
Published 9 Sep 2019

Only after sealing the building envelope to make it more energy efficient can the smart IoT infrastructure be embedded, transforming the building into a smart node, ready to engage its neighbors locally and globally in collective endeavors. Early on, the Internet of Things was viewed more as an ancillary aid to industries to help them increase their surveillance of equipment and improve performance across assembly lines and supply chains—for example, embedding sensors in airplanes that could alert a company when a component needed to be replaced before standard maintenance checkups. While the term “Internet of Things” was coined by Kevin Ashton back in 1999, the prospects for its widespread application remained unexplored for another thirteen years because of the high cost of sensors and actuators.

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Willis Towers Watson, Thinking Ahead Institute, Global Pension Assets Study 2018, https://www.thinkingaheadinstitute.org/en/Library/Public/Research-and-Ideas/2018/02/Global-Pension-Asset-Survey-2018 (accessed April 5, 2019), 9. 24.  “1,000+ Divestment Commitments,” Fossil Free, https://gofossilfree.org/divestment/commitments/ (accessed March 15, 2019). CHAPTER 1   1.  Brian Merchant, “With a Trillion Sensors, the Internet of Things Would Be the ‘Biggest Business in the History of Electronics,’” Motherboard, October 29, 2013, https://motherboard.vice.com/en_us/article/8qx4gz/the-internet-of-things-could-be-the-biggest-business-in-the-history-of-electronics (accessed February 6, 2019).   2.  “Wikipedia.org Traffic Statistics,” Alexa, https://www.alexa.com/siteinfo/wikipedia.org (accessed February 6, 2019).   3.  

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“Questions and Answers: Energy Efficiency Tips for Buildings and Heating,” Federal Ministry for the Environment, Nature Conservation and Nuclear Safety (Germany), https://www.bmu.de/en/topics/climate-energy/energy-efficiency/buildings/questions-and-answers-energy-efficiency-tips-for-buildings-and-heating/ (accessed February 1, 2019); John Calvert and Kaylin Woods, “Climate Change, Construction and Labour in Europe: A Study of the Contribution of Building Workers and Their Unions to ‘Greening’ the Built Environment in Germany, the United Kingdom and Denmark,” paper presented at the Work in a Warming World (W3) Researchers’ Workshop “Greening Work in a Chilly Climate,” Toronto, November 2011, http://warming.apps01.yorku.ca/wp-content/uploads/WP_2011-04_Calvert_Climate-Change-Construction-Labour-in-Europe.pdf (accessed March 23, 2019), 15. 35.  The Internet of Things Business Index: A Quiet Revolution Gathers Pace, Economist Intelligence Unit, 2013, http://fliphtml5.com/atss/gzeh/basic (accessed May 9, 2019), 10. 36.  Jeremy Rifkin, The Zero Marginal Cost Society: The Internet of Things, the Collaborative Commons, and the Eclipse of Capitalism (New York: Palgrave Macmillan, 2014). 37.  Haier, “Haier Group Announces Phase 2.0 of Its Cornerstone ‘Rendanheyi’ Business Model,” Cision PR Newswire, September 21, 2015, https://www.prnewswire.com/news-releases/haier-group-announces-phase-20-of-its-cornerstone-rendanheyi-business-model-300146135.html (accessed March 5, 2019). 38.  

The Age of Surveillance Capitalism
by Shoshana Zuboff
Published 15 Jan 2019

Kevin Ashton, a former Procter and Gamble brand manager who pioneered the marriage of radio-enabled microchips and physical products, birthed the term “internet of things,” and helped drive RFID innovation at MIT’s Media Lab, criticizes the US government for its lack of a comprehensive vision for the “internet of things” and the leadership of private firms in this domain. See Kevin Ashton, “America Last?” Politico, June 29, 2015, http://www.politico.com/agenda/story/2015/06/kevin-ashton-internet-of-things-in-the-us-000102. 20. See Nick Statt, “What the Volkswagen Scandal Means for the Future of Connected Devices,” Verge, October 21, 2015, http://www.theverge.com/2015/10/21/9556153/internet-of-things-privacy-paranoia-data-volkswagen-scandal. 21.

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“Overcoming Speed Bumps on the Road to Telematics,” Deloitte University Press, April 21, 2014, https://dupress.deloitte.com/dup-us-en/industry/insurance/telematics-in-auto-insurance.html. 34. “Overcoming Speed Bumps on the Road to Telematics.” 35. Leslie Scism, “State Farm Is There: As You Drive,” Wall Street Journal, August 5, 2013. 36. “Insurers Need to Plug into the Internet of Things.” 37. Joseph Reifel, Alyssa Pei, Neeti Bhardwaj, and Shamik Lala, “The Internet of Things: Opportunity for Insurers,” ATKearney, 2014, https://www.atkearney.co.uk/documents/10192/5320720/internet+of+Things+-+Opportunity+for +Insurers.pdf/4654e400-958a-40d5-bb65-1cc7ae64bc72. 38. Steve Johansson, “Spireon Reaches 2.4 Million Subscribers, Becoming Industry’s Largest Aftermarket Vehicle Telematics Company,” BusinessWire, August 17, 2015, http://www.businesswire.com/news/home/20150817005365/en/Spireon-Reaches-2.4-Million-Subscribers-Industry%E2%80%99s-Largest. 39.

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Brad Jarvis et al., Operator benefits and rewards through sensory tracking of a vehicle, US20150019270 A1, published January 2015, 2015, http://www.google.com/patents/US20150019270. 42. Joao Lima, “Insurers Look Beyond Connected Cars for IOT Driven Business Boom,” Computer Business Review, December 9, 2015, http://www.cbronline.com/news/internet-of-things/insurers-look-beyond-connected-cars-for-iot-driven-business-boom-4748866. 43. Sam Ramji, “Looking Beyond the Internet of Things Hype: Here’s What’s in Store,” VentureBeat, March 28, 2014, http://venturebeat.com/2014/03/28/looking-beyond-the-internet-of-things-hype-heres-whats-in-store. 44. “Overcoming Speed Bumps on the Road to Telematics.” 45. Corin Nat, “Think Outside the Box—Motivate Drivers Through Gamification,” Spireon, August 11, 2015, https://web.archive.org/web/20150811014300/spireon.com/motivate-drivers-through-gamification; “Triad Isotopes,” 2017, http://www.triadisotopes.com. 46.

Blockchain Revolution: How the Technology Behind Bitcoin Is Changing Money, Business, and the World
by Don Tapscott and Alex Tapscott
Published 9 May 2016

Interview with Michelle Tinsley, June 25, 2015. 19. Ibid. 20. McKinsey Global Institute, “The Internet of Things: Mapping the Value Beyond the Hype,” June 2015. 21. Interview with Eric Jennings, July 10, 2015. 22. IBM Institute for Business Value, “The Economy of Things: Extracting New Value from the Internet of Things,” 2015. 23. Cadie Thompson, “Apple Has a Smart Home Problem: People Don’t Know They Want It Yet,” Business Insider, June 4, 2015; www.businessinsider.com/apple-homekit-adoption-2015-6. 24. McKinsey Global Institute, “The Internet of Things.” 25. Interview with Eric Jennings, July 10, 2015. 26.

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Ibid., 13. 28. McKinsey Global Institute, “The Internet of Things.” MGI defined nine settings with value potential. 29. www.wikihow.com/Use-Uber. 30. http://consumerist.com/tag/uber/page/2/. 31. Mike Hearn, “Future of Money,” Turing Festival, Edinburgh, Scotland, August 23, 2013, posted September 28, 2013; www.youtube.com/watch?v=Pu4PAMFPo5Y&feature=youtu.be. 32. McKinsey, “An Executive’s Guide to the Internet of Things,” August 2015; www.mckinsey.com/Insights/Business_Technology/An_executives_guide_to_the_Internet_of_Things?cid=digital-eml-alt-mip-mck-oth-1508. Chapter 7: Solving the Prosperity Paradox: Economic Inclusion and Entrepreneurship 1. http://datatopics.worldbank.org/financialinclusion/country/nicaragua. 2. www.budde.com.au/Research/Nicaragua-Telecoms-Mobile-and-Broadband-Market-Insights-and-Statistics.html. 3.

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The network could alert the nearest repair facility to dispatch the necessary parts and equipment. Blockchain technology is critical. This Internet of Things (IoT) application depends on a Ledger of Things. With tens of thousands of smart poles collecting data through numerous sensors and communicating that data to another device, computer, or person, the system needs to continually track everything—including the ability to identify each unique pole—to ensure its reliability. “Nothing else works without identity,” said Jennings. “The blockchain for identity is the core for the Internet of Things. We create a unique path for each device. That path, that identity, is then stored in the bitcoin blockchain assigned to Filament.

Digital Bank: Strategies for Launching or Becoming a Digital Bank
by Chris Skinner
Published 27 Aug 2013

This means that everything will be intelligently and wirelessly communicating with everything through what is now called the internet of things. The internet of things delivers a new wireless augmented world of digital reality where, in the very near future, fifty billion devices will be communicating with each other. The internet of things The internet of things is where internet communication – both wired and wireless – are placed into everyday objects from cars to refrigerators, keys to key rings, jewellery to watches and more. Anything that can have a chip placed inside in fact. We will all soon be wearing and watching and being monitored by chips in everything, and the vision of the internet of things is just that: ubiquitous connectivity with everything communicating and transacting non-stop.

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The reason the latter is my favourite is that mobile is rapidly moving from devices to wearable, and so we will soon have mobile chips embedded in jewellery, watches, handbags, shoes and fashion times. Yes, it’s back to the internet of things, but it goes beyond the internet of things to the knowledge of everything. Intellisensing and locating customers and verifying and authenticating them through the internet of things will become the norm. It will be the case of knowing who is where doing what in real-time, and being able to check it is who you think it is without forcing an action – a token or PIN being activated – but by sensing it who you think it is through the network.

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We will all soon be wearing and watching and being monitored by chips in everything, and the vision of the internet of things is just that: ubiquitous connectivity with everything communicating and transacting non-stop. The key point about the internet of things is that it will be the next big wave of change. It may take ten years or so but, just as we were talking about internet banking in the early 1990s and it became the next big wave of change in the 2000’s, the internet of things is going to be our next big wave of change and opportunity. This everything, everywhere connected world where everything can trade and transact is a huge opportunity and change for banks, and the banks that change today will win. When you can put a chip inside anything and everything, you can track, trace, communicate and trade with anything and everything.

The Smart Wife: Why Siri, Alexa, and Other Smart Home Devices Need a Feminist Reboot
by Yolande Strengers and Jenny Kennedy
Published 14 Apr 2020

The Internet of Shit is a Twitter handle and website established in 2015 by an anonymous user (with over four hundred thousand followers as of early 2020), and is devoted to parodying Internet of Things devices that either serve no useful purpose or don’t work as intended. See Internet of Shit, “The Internet of Things Has a Dirty Little Secret: It’s Not Really Yours,” Circuit Breaker (blog), Verge, July 12, 2016, https://www.theverge.com/circuitbreaker/2016/7/12/12159766/internet-of-things-iot-internet-of-shit-twitter. 84. Kate Crawford and Vladan Joler, Anatomy of an AI System: The Amazon Echo as an Anatomical Map of Human Labor, Data and Planetary Resources (New York: AI Now Institute and Share Lab, September 7, 2018), https://anatomyof.ai. 85.

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Lopez-Neira et al., “Internet of Things.” 81. Anthony Cuthbertson, “Amazon Ordered to Give Alexa Evidence in Double Murder Case,” Independent, November 14, 2018, https://www.independent.co.uk/life-style/gadgets-and-tech/news/amazon-echo-alexa-evidence-murder-case-a8633551.html. 82. Lopez-Neira et al., “Internet of Things,” 25. 83. Roxanne Leitão, “Digital Technologies and Their Role in Intimate Partner Violence,” in Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems (New York: ACM, 2018). 84. Lopez-Neira et al., “Internet of Things.” 85. For a critique of this idea, see Clementine Ford, “It’s Been ‘a Hard Year to Be a Man,’ Clementine Ford Has Tips,” Sydney Morning Herald, December 11, 2018, https://www.smh.com.au/lifestyle/life-and-relationships/it-s-been-a-hard-year-to-be-a-man-clementine-ford-has-tips-20181210-p50lav.html. 86.

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Consider, for example, image-based sexual abuse—mainly perpetrated by men toward intimate and ex partners, family members, and friends—which is one of the fastest-growing areas of cybercrime.37 Could the Look facilitate this trend through its unique cataloging and categorizing of images of women’s bodies, which are also likely to be accessible by an intimate partner? In addition, the Look could easily fall into the continuing catalog of devices within the Internet of Things that experience security breaches and vulnerabilities. There is already an established research community documenting these security concerns. One 2014 study revealed that the amount of Internet of Things devices with vulnerabilities that could be exploited stands at about 70 percent.38 Still in its infancy, the Look is marked by much hype and commentary with little research yet available. As such, we can only speculate as to what may unfold.

The Truth Machine: The Blockchain and the Future of Everything
by Paul Vigna and Michael J. Casey
Published 27 Feb 2018

CHAPTER FIVE World Economic Forum founder Klaus Schwab says: Klaus Schwab, The Fourth Industrial Revolution (Crown, 2017). Security expert Bruce Schneier laid it all bare: Bruce Schneier, “The Internet of Things Will Turn Large-Scale Hacks into Real World Disasters,” Motherboard, July 25, 2016, https://motherboard.vice.com/en_us/article/qkjzwp/the-internet-of-things-will-cause-the-first-ever-large-scale-internet-disaster. In a widely read paper titled “Device Democracy…”: Veena Pureswaran and Paul Brody, “Device Democracy: Saving the Future of the Internet of Things,” September 2014, http://www-01.ibm.com/common/ssi/cgi-bin/ssialias?htmlfid=GBE03620USEN. Demonstrating the extent of this challenge, researchers at the University of Michigan: Andy Greenberg, “This ‘Demonically Clever’ Backdoor Hides in a Tiny Slice of a Computer Chip,” Wired, June 1, 2016, https://www.wired.com/2016/06/demonically-clever-backdoor-hides-inside-computer-chip/.

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“In the post-Snowden era, it is evident that trust in the Internet is over. The notion of IoT solutions built as centralized systems with trusted partners is now something of a fantasy.” Pureswaran and Brody argue that the blockchain offers the only way to build the Internet of Things to scale while ensuring that no one entity has control over it. A blockchain-based system becomes the Internet of Things’ immutable seal. In an environment where so many machine-to-machine exchanges become transactions of value, we will need a blockchain in order for each device’s owner to trust the others. Once this decentralized trust structure is in place, it opens up a world of new possibilities.

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Here is sampling of possible use cases, and it is by no means an exhaustive list: •  Inviolable property registries, which people can use to prove that they own their houses, cars, or other assets; •  Real-time, direct, bank-to-bank settlement of securities exchanges, which could unlock trillions of dollars in an interbank market that currently passes such transactions through dozens of specialized institutions in a process that takes two to seven days; •  Self-sovereign identities, which don’t depend on a government or a company to assert a person’s ID; •  Decentralized computing, which supplants the corporate business of cloud computing and Web hosting with the hard drives and processing power of ordinary users’ computers; •  Decentralized Internet of Things transactions, where devices can securely talk and transact with each other without the friction of an intermediary, making possible big advances in transportation and decentralized energy grids; •  Blockchain-based supply chains, in which suppliers use a common data platform to share information about their business processes to greatly improve accountability, efficiency, and financing with the common purpose of producing a particular good; •  Decentralized media and content, which would empower musicians and artists—and, in theory, anyone who posts information of value to the Net—to take charge of their digital content, knowing they can track and manage the use of this “digital asset.”

Future Politics: Living Together in a World Transformed by Tech
by Jamie Susskind
Published 3 Sep 2018

(Not to mention that the application used to control the vibrator was ‘barely secured’, meaning ‘anyone within Bluetooth range’ could ‘seize control’ of it.)32 Law enforcement officials have made no secret of their interest in the internet of things as a means of gathering information. Says the US Director of National Intelligence:33 In the future, intelligence services might use the [internet of things] for identification, surveillance, monitoring, location tracking, and targeting for recruitment, or to gain access to networks or user credentials. Imperishable We tend to think of forgetting as a vice. We curse our poor memories when we lose our keys or forget to call our mother on her birthday (a mistake the wise son only makes once).

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Andrew Keen, The Internet is Not the Answer (London: Atlantic Books, 2015), 13; Richard Susskind and Daniel Susskind, The Future of the Professions: How Technology Will Transform the Work of Human Experts OUP CORRECTED PROOF – FINAL, 30/05/18, SPi РЕЛИЗ ПОДГОТОВИЛА ГРУППА "What's News" VK.COM/WSNWS Notes 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 377 (Oxford: Oxford University Press, 2015), 175; Gartner Newsroom, ‘Gartner Says By 2020, a Quarter Billion Connected Vehicles Will Enable New In-vehicle Services and Automated Driving Capabilities’, Gartner, 26 January 2015 <http://www.gartner.com/newsroom/ id/2970017> (accessed 30 November 2017). Samuel Greengard, The Internet of Things (Cambridge, Mass: MIT Press, 2015), 13. Greenfield, Everyware, 1. Greengard, Internet of Things; Greenfield, Everyware; Kitchin, Data Revolution. NYC Mayor’s Office of Technology and Innovation, ‘Preparing for the Internet of Everything’ (undated) <https://www1.nyc.gov/site/ forward/innovations/iot.page> (accessed 6 December 2017). Mat Smith, ‘Ralph Lauren Made a Great Fitness Shirt that Also Happens to Be “Smart”’, Engadget, 18 March 2016 <https://www. engadget.com/2016/03/18/ralph-lauren-polotech-review/> (accessed 6 December 2017).

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Wallach, Dangerous Master, 220; Bryant Walker Smith, ‘Human Error as a Cause of Vehicle Crashes’, Stanford Center for Internet and Society, 18 December 2013 <http://cyberlaw.stanford.edu/blog/ 2013/12/human-error-cause-vehicle-crashes> (accessed 30 November 2017). 92. Greengard, Internet of Things, 161. 93. Boden, AI, 102. 94. Wyss Institute <http://wyss.harvard.edu/viewpage/457> (accessed 30 November 2017). 95. CBC, ‘Cockroach-inspired Robots Designed for Disaster Search and Rescue’, CBC The Associated Press, 8 February 2016 <http://www. cbc.ca/beta/news/technology/robot-roach-1.3439138> (accessed 30 November 2017). 96. Greengard, Internet of Things, 162. 97. Paul Ratner, ‘Harvard Scientists Create a Revolutionary Robot Octopus’, Big Think, 2016 <http://bigthink.com/paul-ratner/harvardteam-creates-octobot-the-worlds-first-autonomous-soft-robot> (accessed 30 November 2017). 98.

The Industries of the Future
by Alec Ross
Published 2 Feb 2016

Cisco Systems chairman John Chambers: “Cisco Keynote Highlights from CES 2014,” YouTube, January 10, 2014, http://www.youtube.com/watch?v=TepUznT42ro. From 2015 to 2020, the number: “The Internet of Things Will Drive Wireless Connected Devices to 40.9 Billion in 2020,” ABI Research, August 20, 2014, https://www.abiresearch.com/press/the-internet-of-things-will-drive-wireless-connect. Chambers predicts that the Internet of Things: Don Clark, “Cisco CEO Chambers Still Biggest ‘Internet of Things’ Cheerleader,” Wall Street Journal, January 7, 2014, http://blogs.wsj.com/digits/2014/01/07/cisco-ceo-john-chambers-Internet-of-everything-ces-2014/.

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Cisco Systems chairman John Chambers has said, “We will look back one decade from today [2014] and you’ll look at the impact of the Internet of Everything, and I predict it will be five to ten times more impactful in one decade than the whole Internet to date has been.” From 2015 to 2020, the number of wireless connected devices is going to grow from an estimated 16 billion to 40 billion. Chambers predicts that the Internet of Things will grow to be a $19 trillion global market. For context, the GDP of the entire world is currently just a little more than $100 trillion. The growth of the Internet of Things is motivated by four main drivers. The first is the number of Internet-connected cars on the road, expected to grow from 23 million in 2015 to 152 million in 2020. The second driver is the advent of wearable technology, which doubled in use between 2013 and 2014.

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According to a Juniper research report, revenues generated from smart home services are expected to reach a global market value of $71 billion by 2018. The fourth driver is in manufacturing. A McKinsey report projects that by 2025, Internet of Things applications could have an economic impact of $900 billion to $2.3 trillion a year in manufacturing alone. McKinsey bases this estimate on potential savings of 2.5 to 5 percent in operating costs, the integration of the Internet of Things into the power grid, and its applications in public-sector services like waste, heating, and water systems that they believe could cut waste by 10 to 20 percent annually. There’s one huge catch: with the rapid growth of these technologies, we are also creating an almost unimaginable new set of vulnerabilities and openings for cybersecurity hacks.

The Costs of Connection: How Data Is Colonizing Human Life and Appropriating It for Capitalism
by Nick Couldry and Ulises A. Mejias
Published 19 Aug 2019

As Canadian political economist Vincent Mosco puts it, “The commodified self is a contested terrain in business battles that are only just beginning.”125 The Internet of Things provides the perfect cover for converting all streams of human life into raw material for capitalism, in the process capitalizing everything and everyone. Simply put, the Internet of Things operationalizes the capitalization of life without limit. We can also capture the Internet of Things through the lens of colonialism. Whereas the extractivism of historical colonialism “relat[ed] to the world as a frontier of conquest—rather than as home,”126 data colonialism brings extraction home, literally into the home and the farthest recesses of everyday life.

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The language of capture implies an unending series of territorial expansion: from the Internet of Things to “the internet of medical things,” from “the internet of services” to “the internet of everything.”122 Actors in the data sector are not shy about pursuing these opportunities for behavioral influence. Indeed, as one marketer notes, “One of the major value-adds for businesses implementing IoT solutions is the behavioural data they unlock. . . . Businesses should be able to segment who is using an object in order to accurately contextualize its usage.”123 A report on “The Internet of Things: Opportunities for Insurers” noted that insurers could “use IoT-enriched relationships to connect more holistically to customers and influence their behaviors.”124 It is easy to imagine how the evidence of our home life transmitted by fridges, heating systems, and the like could license external judgments and discrimination—all without our knowledge or ability to comment.

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title=Criticism_of_Facebook. 42. Global Voices, “Can Facebook Connect?” 43. All figures come from Google’s own corporate reports. 44. Krazit, “Public Cloud.” 45. Schiller, Digital Depression, 81–82. 46. Turow, Aisles Have Eyes. 47. IHS Markit, “Internet of Things: A Movement, Not a Market.” 2017. https://ihsmarkit.com/Info/1017/internet-of-things.html. 48. Khatchadourian, “We Know.” 49. Christl and Spiekermann, “Networks of Control,” 82–83. 50. Congressional Testimony: What Information Do Data Brokers Have on Consumers, and How Do They Use It?, Statement of Pam Dixon before the Senate Comm. on Commerce, Science, and Transportation, 113th Cong., 1st sess.

The Stack: On Software and Sovereignty
by Benjamin H. Bratton
Published 19 Feb 2016

Cory Doctorow, “Metacrap: Putting the Torch to Seven Straw-men of the Meta-Utopia,” Well, August 26, 2011. 21.  Payam Barnaghi, Cory Henson, Kerry Taylor, and Wei Wang, “Semantics for the Internet of Things: Early Progress and Back to the Future,” International Journal on Semantic Web and Information System 8, no. 1 (2012): 1–21, http://knoesis.org/library/download/IJSWIS_SemIoT.pdf. 22.  Yann Moulier-Boutang, Cognitive Capitalism (London: Polity Press, 2012). 23.  Open Internet of Things Assembly, “Bill of Rights” http://postscapes.com/open-internet-of-things-assembly. (July 17, 2012). 24.  See, for example, Saul A. Kripke, Naming and Necessity (Cambridge, MA: Harvard University Press, 1980).

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As a result, every request from a user need not result in a transmission cross-country and through the Internet backbone; network activity may be more evenly balanced and confined to local areas.” 18.  Cisco proudly estimated the number of “things” connected to the Internet of Things as 50 billion by 2020. See Dave Evans, “The Internet of Things: How the Next Evolution of the Internet Is Changing Everything,” April 2011, https://www.cisco.com/web/about/ac79/docs/innov/IoT_IBSG_0411FINAL.pdf. To say nothing of the more or less charted waters of the Dark Net, accessible only through tools like the Tor browser. 19. 

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In the hype, it's easy to forget that the Internet of Things is also an Internet for Things (or for any addressable entity, however immaterial). Control of this multitude of chattering things would represent enormous power, and the danger of overcentralization paired with a monetized opacity of data flows is real. The capture of the “general intellect” by search and other mechanisms of “cognitive capitalism” is one lens through which to imagine a future in which tracing objective knowledge about the appearance and disappearance of material culture is a proprietary narrative.22 At the same time, Internet of Things scenarios that prioritize human Users sensing and interacting with their responsive habitats, as masters of the data that appear in their midst, divert discussions of the politics of ubiquitous computing toward an overly local frame of reference within a larger landscape of humans and nonhuman associations.

Virtual Competition
by Ariel Ezrachi and Maurice E. Stucke
Published 30 Nov 2016

The sale and ownership of information and risks associated with disclosure were key concerns with 71 per cent of users’ concerns with information being sold to a third party and 59 per cent concerned about a lack of information on where their data goes and who owns it.10 Extraction and Capture 163 With the rise of the Internet of Things, mobile platforms will become the key gateway to the flow of personal data. Google’s underlying operating system for the Internet of Things, dubbed “Brillo,” is based on its Android operating system.11 As our smartphones are always near us (except perhaps when we shower or swim), they will assist the super-platforms, governments,12 and others in tracking our behavior, harvesting our data, and targeting us with behavioral ads.13 This data trove will also attract hackers and criminals. Thus we should expect Frenemies to support the Internet of Things, to the extent that the sensors can effectively track and collect data on us when we are offline—data that can be used to fuel their advertising-supported business model.

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For instance, Amazon in 2015 launched its “IoT platform,” which “lets connected devices easily and securely interact with cloud applications and other devices.”63 The platform is designed to process trillions of messages from billions of devices “and can process and route those messages to [Amazon Web Ser vice] endpoints and to other devices reliably and securely.”64 The research firm International Data Corp estimated the “global market for Internet of Things” to nearly triple to $1.7 trillion by 2020.65 The firm also New Economic Reality 19 notes how technology firms, like Google, Intel Corp, Cisco Systems, Samsung Electronics and the major telecoms such as Vodafone and Verizon, “are betting heavily on it to drive revenue and profit in the future.” 66 Whereas traditional data is harvested through our interaction with online sellers and our digitalized environment, the Internet of Things would widen the scope of data for the algorithms. As more products have sensors, the interfaces will include anything from household appliances, clothing, cars, and bicycles, to streetlights, airports, smart building materials, and human-embedded sensors.

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Danny Palmer, “Amazon follows Microsoft and Google with AI tools in Amazon Machine Learning Ser vice,” Computing, April 10, 2015, http://www .computing.co.uk /ctg/news/2403533/amazon-follows-microsoft-and-google -into-offering-customers-ai-tools-with-amazon-machine-learning-service. 63. Ingrid Lunden, “Amazon Launches AWS IoT—A Platform for Building, Managing and Analyzing the Internet Of Things,” Tech Crunch, October 8, 2015, http://techcrunch.com/2015/10/08/amazon-announces-aws-iot-a -platform-for-building-managing-and-analyzing-the-internet-of-things/# .gfgxjj:0nTE. 64. Ibid. Amazon Web Ser vices is a collection of cloud computing ser vices offered by Amazon. “Amazon Web Ser vices offers a broad set of global compute, storage, database, analytics, application, and deployment ser vices that help organizations move faster, lower IT costs, and scale applications”; Amazon Web Ser vices, Cloud Products (2015), https://aws.amazon.com /products/?

The Business of Platforms: Strategy in the Age of Digital Competition, Innovation, and Power
by Michael A. Cusumano , Annabelle Gawer and David B. Yoffie
Published 6 May 2019

For example, Siemens, GE’s biggest industrial competitor, offered its own platform, called MindSphere. Siemens described MindSphere as an “open operating system for the Internet of things.”75 MindSphere’s architecture and technology were much like that of Predix: It was a cloud-based platform-as-a-service product, built on the SAP Cloud, which in turn was based on the Pivotal Cloud Foundry.76 IBM and Microsoft also planned to compete head-to-head with GE and Siemens. In 2015, IBM announced it was launching an Internet of things division that would bring the analytical capabilities of its Watson cognitive computing service to the analysis of data generated by billions of devices that make up the IoT.

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article_id=1948 (accessed October 30, 2018). 70.Danny Palmer, “GE Opens Paris ‘Digital Foundry’ in International Industrial IoT Push,’ ZD Net, June 14, 2016; and Adrian Bridgewater, “GE Builds ‘Digital Foundry’ Locations, Where Physics + Analytics Intersect,” Forbes, October 22, 2016. 71.Lal and Johnson, “GE Digital,” 6. 72.Winig, “GE’s Big Bet.” 73.“Siemens and General Electric Gear Up for the Internet of Things,” Economist, December 3, 2016. 74.Quoted in Winig, “GE’s Big Bet.” 75.Siemens, “MindSphere—The Internet of Things (IoT) Solution,” https://www.siemens.com/global/en/home/products/software/mindsphere.html (accessed October 30, 2018). 76.Siemens and SAP, “Delivering an Open Cloud for Industrial Customers,” https://cloudplatform.sap.com/success/siemens.html (accessed October 30, 2018). 77.

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See also entries beginning with “European” Google Alphabet holding company, 8, 176, 185 choosing market sides, 70 data captured by, 10 defending dominance in Internet search, 52–53 and European Commission case for vertical search and Android OS, 177 European Union fine for anti-competitive behavior, 9–10 Google Play Store and European antitrust regulators, 100 and Motorola Mobility, 87 as one of “the Frightful Five,” 176–77 quantum computer strategy, 227 Waze, 51–52 YouTube, 10, 203–5 Google AdWords, 85 Google Android building an ecosystem around, 65 as core product, 72–73 European Commission antitrust lawsuit, 183–85 give away software and make money on mobile search, 79 Google Play Store as applications platform, 100, 259n38 as market-share winner in smartphone platforms, 130–31 Open Handset Alliance, viii, 45–46 popularity of, 10 restrictions on access to Google Play, 99–100 and voice recognition, 221–23 Google Chrome, 127–29, 128f, 263n51 Google Gmail, 10 Google Maps app, 87–88 Google Search, 40–41, 183–85 governments and regulations overview, 180 and categorizing “technology companies” that provide services, 179–80 decisions that drive network effects, 33 regulatory influence on market share, 41 regulatory scrutiny of platforms, 111, 118 and Uber, 147–48 See also platform governance Great Britain black cabs of London, 148–51 Central Arbitration Committee, London, 196–97 Deliveroo, 83–84, 195–96 Gett, 150–51 Hailo, 149–50 Independent Workers Union of Great Britain, 195 private hire vehicle operators in London, 148–50 Green, Logan, 224 Grove, Andy, 138 guarantees or insurance for quality of services, 92–93 Hagiu, Andrei, 82–83, 250nn21–22, 251n1 Hailo (ride-sharing platform in London), 149–50 Handy, 193–95 Hanrahan, Oisin, 194 Harvard Graphics, 87 hate speech blocking efforts, 187, 203–4, 205–6 EU allows self-regulation, 205–6 and social media responsibility, 189 on YouTube, 204 See also violence Hawkins, Jeff, 69 Herrera, Dennis, 198 Homejoy, 195 Hong Kong, 123 Hong Kong and Uber, 148 HTC Corporation (Taiwan), 142–43 hubris (dismissing the competition), 124–29, 137–38 Hungary and Uber, 148 Huynh, Tri, 158 Hyatt Hotels, 142 hybrid platforms overview, 94–95, 103–4 conglomerate hybrid strategy, 95 innovation + transaction, 94–96 integrated hybrid strategy, 94–95 integrated vs. conglomerate hybrids, 98–101 as next phase in evolution of platform thinking, 103–4 transaction + innovation, 96–98 utilizing transaction platforms and innovation platforms, 19f, 20–21 “I am Rich” app, Apple’s App Store, 85–86 IBM attempt to control PCs, 72 contract with Microsoft for MS-DOS, 3–4 launching an IIoT division, 166 quantum computer strategy, 227 Watson AI technology, 103 IDC (market research firm), 133 ideological platforms, 12 IIoT (industrial Internet of things), 161–62. See also Predix Immelt, Jeffrey, 161 incumbent firms. See traditional business Independent Workers Union of Great Britain (IWGB), 195 India, 158–59 indirect or cross-side network effects, 17 industrial Internet of things (IIoT), 161–62. See also Predix industry-wide platforms overview, 14–15, 234–35, 250n21 and analysis of value of digital platforms, 21–22, 23t, 24–25, 251nn23–24 emergence of, 13 engaging multiple sides of a market, 15–16, 41–42 generating network effects, 16–18 solving the chicken-or-egg problem, 17–18 See also innovation platforms infinite launching loop, 77–78 Inktomi, 70 innovation platforms overview, 18–19, 19f, 101–3, 250n21 and barriers to entry, 47–48 building your business model, 77–80 choosing your market sides, 68–69 countering multi-homing by users, 43–44 designing your business model, 78–80 desire to manage the customer experience, 218 establishing and enforcing ecosystem rules, 85–90 Facebook as, 6–7 Microsoft giving away SDKs as means to, 4 risks and expenses associated with startups, 102 solving the “chicken-or-egg” problem, 71–74, 72–74 transaction platforms vs., 19f, 20, 22, 23t, 24 value associated with, 21–22, 23t, 24–25 winner’s curse phenomenon, 90 See also Predix Instagram choosing market sides, 70–71 roll out on Windows phone, 135 Twitter blocks “Find Friends” feature, 89 Zuckerberg’s purchase of, 53, 55–56 instant messaging, 41–42 integrated hybrid strategy, 94–95 Internet services, value of platforms associated with, 21–22, 23t, 24–25, 251nn23–24 Invisible Engines (Evans, Hagiu, and Schmalensee), 82–83, 250nn21–22 iPhone (Apple), 24, 46, 130–31, 135 iVillage, 110 Jet.com and Walmart, 156–58 Jobs, Steve, 74.

Smart Cities, Digital Nations
by Caspar Herzberg
Published 13 Apr 2017

Dan Kaplan, “Black Hat: Assessing Smart Meters for Hacker Footprints, Vulnerabilities,” SC Magazine, July 25, 2012, http://www.scmagazine.com/black-hat-assessing-smartmeters-for-hacker-footprints-vulnerabilities/article/251947/. 8 In addition to Cisco’s dedicated focus on security, there are many independent groups highlighting the vulnerabilities of devices and how consumers can protect themselves, e.g. “Abusing the Internet of Things: Blackouts. Freakouts. Stakeouts,” (Blackhat.com; https://www.blackhat.com/docs/asia-14/materials/Dhanjani/Asia-14-Dhanjani-Abusing-The-Internet-Of-Things-Blackouts-Freakouts-And-Stakeouts.pdf), which describes how several household devices can be compromised. 9 Dave Evans, “End of the Human Race?” LinkedIn, May 8, 2015, https://www.linkedin.com/pulse/end-humanrace-david-evans CONCLUSION AS THE AUTHOR THINKS BACK on a decade of work and its implications for the future of digital cities, there is the inescapable fact that very few details remain in place for long.

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These new smart cities engage high-tech industrial pioneers to provide the digital infrastructure, and companies such as Cisco are finding success providing the Internet “plumbing” in this age of massive digital expansion. The countries and cities explored in this book are perfect examples with which to trace the development of smart cities and analyze the lessons learned in making them work. This, in turn, forms the basis for the “Internet of Things” (IoT), a network that enables physical objects to collect and exchange data, and the “Internet of Everything” (IoE), a future wherein devices, appliances, people, and process are connected via the global Internet. The IoE is a value proposition that is estimated to be worth trillions of dollars to the technology industry and the early adopters in business and the public sector.

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THE FUTURE: SEEN FROM THE EAST, COMING TO THE WEST History repeatedly tells us what happens when too many people are trying to live with insufficient land, resources, or jobs. And while data may be a new concern with regard to population growth, it poses unforeseen threats, in addition to the opportunities it creates. It could be argued that there has never been a time in history when “too much information” was even possible at the city level. But as the Internet of Things becomes the Internet of Everything, we will enter a new, in-formation-laden reality. The systems and platforms implemented today will harness more or less of that data, depending on how systematically it is captured and how aware people are of its potential uses. Data, like fire, is agnostic when it comes to humans—its value, or danger, depends wholly on who uses it and how they use it.

Demystifying Smart Cities
by Anders Lisdorf

In this chapter, we will consider what a device is and how devices connect in distributed smart city solutions. This is what is typically referred to as the Internet of Things. We look at the challenges of managing thousands or even millions of devices and what it takes to secure them. We will also consider examples of how to build standards around the use of devices in a city context. Even the casual observer of the world of technology has heard about the Internet of Things or IoT for short. To many people, IoT is this magical thing that will manage our homes, mow our lawns, and bring us the food we need when we need it.

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About the Technical Reviewer Ahmed Bakir is an iOS author, teacher, and entrepreneur. He has worked on over 30 mobile projects, ranging from advising startups to architecting apps for Fortune 500 companies. In 2014, he published his first book,Beginning iOS Media App Development , followed by the first edition ofProgram the Internet of Things with Swift for iOS in 2016 and the second edition in 2018. In 2015, he was invited to develop courses and teach iOS development at UCSD Extension. He is currently building cool stuff in Tokyo! You can find him online at www.devatelier.com . © Anders Lisdorf 2020 A. LisdorfDemystifying Smart Citieshttps://doi.org/10.1007/978-1-4842-5377-9_1 1.

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Whereas they don’t sell their hardware directly, the implementation and use of it is critically tied to cities allowing them to do it and using them for solution deployments. Software vendors – Only sell their products embedded with hardware in exceptional cases like appliances or when they supply peripheral hardware like AWS and Microsoft offering an Internet of Things (IoT) button. Consequently, these vendors are interested in solutions where their software generates license, support, or subscription fees, which means they are interested in embedding their software in lasting solutions or as is the case with cloud vendors, to become the main platform for any type of solution.

Architecting For Scale
by Lee Atchison
Published 25 Jul 2016

system improvement, Improve Your Systems the nines, The Nines availability percentage, Measure and Track Your Current Availability availability pool, The “Magic” of Usage-Based Resource Allocation Availability Zones (AZs), AWS Availability Zoneartificial remapping of, Availability Zones Are Not Data Centers AWS Regions and, AWS Region data center architecture and, Architecture Overview-Architecture Overview data centers vs., Availability Zones Are Not Data Centers-Availability Zones Are Not Data Centers outages and, Availability Zones Are Not Data Centers AWS (Amazon Web Services)API Gateway, Mobile Backend architecture, AWS Architecture-Architecture Overview AutoScaling, Changing Allocations Availability Zones (see Availability Zones) data centers, Data Center, Availability Zones Are Not Data Centers-Availability Zones Are Not Data Centers DynamoDB, Allocated-Capacity Resource Allocation EC2 (see Amazon EC2) ecosystem terms, AWS Architecture-Data Center Elastic Load Balancer, Changing Allocations Kinesis, Internet of Things Data Intake Lambda (see AWS Lambda) maintaining location diversity for availability reasons, Maintaining Location Diversity for Availability Reasons overview, Architecture Overview-Architecture Overview Regions (see AWS Regions) S3 (see Amazon S3) SLAs, What are Service-Level Agreements? AWS Lambda, AWS Lambda-Advantages and Disadvantages of Lambdaadvantages/disadvantages, Microcompute, Advantages and Disadvantages of Lambda event processing, Event Processing Internet of Things data intake, Internet of Things Data Intake mobile backend, Mobile Backend picture management application, Event Processing using, Using Lambda-Internet of Things Data Intake AWS Regions, AWS Region, Architecture Overview B business requirements, service boundaries and, Guideline #1: Specific Business Requirements C call latency, Performance Measurements for SLAs-Latency Groups capabilities, shared, Guideline #4: Shared Capabilities/Data capacity units, Allocated-Capacity Resource Allocation cascading service failures, Cascading Service Failures Chaos Monkey, Concerns with Running Game Days in Production circuit breakers, Focus #1: Build with Failure in Mind, Determining Failures cloud-based servers, Cloud-Based Servers cloud-based services, Cloudallocated capacity resource allocation, Allocated-Capacity Resource Allocation-Reserved Capacity, The Pros and Cons of Resource Allocation Techniques application management, Greater Focus on the Application AWS architecture, AWS Architecture-Architecture Overview AWS Availability Zones, AWS Availability Zone, Availability Zones Are Not Data Centers-Availability Zones Are Not Data Centers(see also Availability Zones (AZs)) AWS Lambda, Microcompute, AWS Lambda-Advantages and Disadvantages of Lambda AWS Region, AWS Region changes in, Change and the Cloud-Change Continues choosing scalable computing options, Now What?

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G game days, Game Days-Game Day TestingChaos Monkey, Concerns with Running Game Days in Production staging vs. production environments, Staging Versus Production Environments-Staging Versus Production Environments testing recovery plans in production environment, Concerns with Running Game Days in Production Google App Engines, Optimized Use Cases graceful backoff, Graceful Backoff graceful degradation, Graceful Degradation, Critical Dependency H Heroku Dynos, Compute Slices human error, Operational Processes I icon failure, Five Focuses to Improve Application Availability idempotent interfaces, Redundancy independence, risk mitigation and, Independence internal SLAs, External Versus Internal SLAs, How Many and Which Internal SLAs? Internet of Things, Internet of Things Data Intake K key-based partitioning, Data Partitioning-Data Partitioning L Lambda (see AWS Lambda) latency, Performance Measurements for SLAs-Latency Groups latency groups, Latency Groups likelihood, riskand changes in risk matrix reviews, Review Regularly, Maintaining the Risk Matrix as risk component, Likelihood Versus Severity severity vs., Likelihood Versus Severity-T-Shirt Photos: High Likelihood, High Severity Risk limit SLAs, Limit SLAs load balancing, Availability Zones Are Not Data Centers localization, data, Where’s the Data?

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The scripts perform the operations necessary, in conjunction with some form of database, to handle the cloud backend for the mobile game. This architecture is shown in Figure 25-2. Figure 25-2. Mobile backend lambda usage In this model, no servers are needed on the backend, and all scaling is handled automatically. Internet of Things Data Intake Consider an application that takes data from a huge quantity of data sensors deployed around the world. Data from these sensors arrives regularly. On the server side, this results in an enormous quantity of data being regularly presented to the application for storage in some form of data store.

Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia
by Anthony M. Townsend
Published 29 Sep 2013

Instead of making funny videos to promote their invention, students would have spent their evenings holding smoking soldering irons, staring bleary-eyed into a tangle of wires. But Botanicalls is just one of thousands of projects that are exploiting a new approach to prototyping networked objects, allowing civic hackers, students, and artists around the world to invent their own visions of the Internet of Things. Botanicalls, like many objects on the Internet of Things, is powered by an unsung but utterly ubiquitous kind of computer called a microcontroller. Microcontrollers are the brains of the modern mechanical world, governing the operations of everything from elevators to the remote control on your TV. Like a personal computer, they contain a processor, memory, and input/output systems.

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By changing people’s behavior, it could stanch the need for hundreds of millions of dollars of retrofits to the city’s sewage infrastructure. Projects like dontflush.me suggest a future where citizens decide what gets connected to the Internet of Things, and why. Instead of being merely a system for remote monitoring and management, as industry visionaries see it today, the Internet of Things could become a platform for local, citizen microcontrol of the physical world. And that’s what’s so disruptive about Arduino’s growing reach. Torrone suggests more prosaic applications for which Arduino is also the clear technology of choice.

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The promise is that we’ll build the hardware of smart cities just like we built the web, by empowered users one little piece at a time. Botanicalls showed simultaneously how silly but also how incredibly useful and social the Internet of Things could be but, more importantly, it hinted at the creative possibilities that lie ahead. Don’t let Igoe hear you call it an “Internet of Things.” It’s true that things are being connected and rigged with tiny little electronic brains, eyes, and motors, but for him it is a social technology, a creative catalyst that harkens back to Red Burns’s enchantment with portable video, one that lets us pay attention to people instead of technology.

The Fourth Industrial Revolution
by Klaus Schwab
Published 11 Jan 2016

Consider the unlimited possibilities of having billions of people connected by mobile devices, giving rise to unprecedented processing power, storage capabilities and knowledge access. Or think about the staggering confluence of emerging technology breakthroughs, covering wide-ranging fields such as artificial intelligence (AI), robotics, the internet of things (IoT), autonomous vehicles, 3D printing, nanotechnology, biotechnology, materials science, energy storage and quantum computing, to name a few. Many of these innovations are in their infancy, but they are already reaching an inflection point in their development as they build on and amplify each other in a fusion of technologies across the physical, digital and biological worlds.

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The recent discovery of new classes of recyclable thermosetting polymers called polyhexahydrotriazines (PHTs) is a major step towards the circular economy, which is regenerative by design and works by decoupling growth and resource needs.8 2.1.2 Digital One of the main bridges between the physical and digital applications enabled by the fourth industrial revolution is the internet of things (IoT) – sometimes called the “internet of all things”. In its simplest form, it can be described as a relationship between things (products, services, places, etc.) and people that is made possible by connected technologies and various platforms. Sensors and numerous other means of connecting things in the physical world to virtual networks are proliferating at an astounding pace.

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This suggests that businesses will become increasingly organized around distributed teams, remote workers and dynamic collectives, with a continuous exchange of data and insights about the things or tasks being worked on. An emerging workplace scenario that reflects this change builds on the rapid rise of wearable technology when combined with the internet of things, which is progressively enabling companies to blend digital and physical experiences to benefit workers as well as consumers. For example, workers operating with highly complex equipment or in difficult situations can use wearables to help design and repair components. Downloads and updates to connected machinery ensure that both workers in the field and the capital equipment they use are kept up to date with the latest developments.

Platform Revolution: How Networked Markets Are Transforming the Economy--And How to Make Them Work for You
by Sangeet Paul Choudary , Marshall W. van Alstyne and Geoffrey G. Parker
Published 27 Mar 2016

With designers and engineers finding more and more ways to usefully link the machines, gadgets, and other devices people interact with daily, a vast new layer of data infrastructure is emerging that has been dubbed the Internet of things. This new universe of networks will have a profound impact on the power of tomorrow’s platforms. A wide range of companies is deeply engaged in the effort to build the Internet of things—and, if possible, to control both the new infrastructure and the ultra-valuable data it will provide. As we’ve mentioned, industrial firms like GE, Siemens, and Westinghouse are moving to create information links among the turbines, engines, motors, heating and cooling systems, and manufacturing plants they build and operate, hoping to enable tremendous new efficiencies and cost savings.

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Digital technology firms like IBM, Intel, and Cisco are racing to design the tools and connections that will make the vast new networks possible. And Internet-centered companies like Google and Apple are designing interfaces and operating systems that will enable both technology experts and ordinary people to have easy access to the Internet of things and use it in countless ways we’re only beginning to imagine and explore. What’s more, the potential power of the Internet of things will only continue to grow as the varieties of devices available to us and their capabilities continue to expand. To mention just a few examples, consider the transformative power of such just-around-the-corner technologies as driverless cars, cheap and powerful electrical storage batteries for the home, and easy-to-use 3D printers for quickly replicating useful objects.

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To mention just a few examples, consider the transformative power of such just-around-the-corner technologies as driverless cars, cheap and powerful electrical storage batteries for the home, and easy-to-use 3D printers for quickly replicating useful objects. As these and other new tools become widely available, they’ll also quickly be linked to the Internet of things, making even more powerful value-creating platforms possible. Applied to the Internet of things, platform economics will dramatically alter the business models associated with countless familiar goods and services. Take, for example, the familiar lightbulb. Originally patented by Thomas Edison in 1878, the basic engineering of the incandescent bulb has scarcely changed since then, which is why the typical bulb retails for just 40 cents and provides its maker with virtually no profit margin.

Blockchain: Blueprint for a New Economy
by Melanie Swan
Published 22 Jan 2014

Radar (O’Reilly), October 24, 2011. http://radar.oreilly.com/2011/10/post-pc-revolution.html. 9 Gartner. “Gartner Says the Internet of Things Installed Base Will Grow to 26 Billion Units By 2020.” Gartner Press Release, December 12, 2013. http://www.gartner.com/newsroom/id/2636073. 10 Omohundro, S. “Cryptocurrencies, Smart Contracts, and Artificial Intelligence.” Submitted to AI Matters (Association for Computing Machinery), October 22, 2014. http://steveomohundro.com/2014/10/22/cryptocurrencies-smart-contracts-and-artificial-intelligence/. 11 Dawson, R. “The New Layer of the Economy Enabled by M2M Payments in the Internet of Things.” Trends in the Living Networks, September 16, 2014. http://rossdawsonblog.com/weblog/archives/2014/09/new-layer-economy-enabled-m2m-payments-internet-things.html. 12 Petschow, K.

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The current emerging paradigm for this decade could be the connected world of computing relying on blockchain cryptography. The connected world could usefully include blockchain technology as the economic overlay to what is increasingly becoming a seamlessly connected world of multidevice computing that includes wearable computing, Internet-of-Things (IoT) sensors, smartphones, tablets, laptops, quantified self-tracking devices (i.e., Fitbit), smart home, smart car, and smart city. The economy that the blockchain enables is not merely the movement of money, however; it is the transfer of information and the effective allocation of resources that money has enabled in the human- and corporate-scale economy.

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Disruptive computing paradigms: Mainframe, PC, Internet, Social-Mobile, Blockchain8 M2M/IoT Bitcoin Payment Network to Enable the Machine Economy Blockchain is a revolutionary paradigm for the human world, the “Internet of Individuals,” and it could also be the enabling currency of the machine economy. Gartner estimates the Internet of Things will comprise 26 billion devices and a $1.9 trillion economy by 2020.9 A corresponding “Internet of Money” cryptocurrency is needed to manage the transactions between these devices,10 and micropayments between connected devices could develop into a new layer of the economy.11 Cisco estimates that M2M (machine-to-machine) connections are growing faster than any other category (84 percent), and that not only is global IP traffic forecast to grow threefold from 2012 to 2018, but the composition is shifting in favor of mobile, WiFi, and M2M traffic.12 Just as a money economy allows for better, faster, and more efficient allocation of resources on a human scale, a machine economy can provide a robust and decentralized system of handling these same issues on a machine scale.

Subscribed: Why the Subscription Model Will Be Your Company's Future - and What to Do About It
by Tien Tzuo and Gabe Weisert
Published 4 Jun 2018

manufacturers contributed $2.2 trillion National Association of Manufacturers, “Top 20 Facts About Manufacturing,” www.nam.org/Newsroom/Facts-About-Manufacturing. And if you sell technology to help sense conditions Scott Pezza, “How to Make Money with the Internet of Things,” Blue Hill Research, May 18, 2015, http://bluehillresearch.com/how-to-make-money-with-the-internet-of-things. most of our factories look the same Olivier Scalabre, “The Next Manufacturing Revolution Is Here,” TED talk, May 2016, www.ted.com/talks/olivier_scalabre_the_next_manufacturing_revolution_is_here/transcript. We recently hosted Gytis Barzdukas “Gytis Barzdukas, GE Digital,” Zuora Subscribed conference, www.youtube.com/watch?

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Everything that we formerly electrified Kevin Kelly, The Inevitable: Understanding the 12 Technological Forces That Will Shape Our Future (New York: Viking, 2016). This ‘as-a-service’ approach can give the supplier McKinsey & Company, “Unlocking the Potential of the Internet of Things,” www.mckinsey.com/business-functions/digital-mckinsey/our-insights/the-internet-of-things-the-value-of-digitizing-the-physical-world. CHAPTER 8: THE END OF OWNERSHIP digitally enhanced products, services, and experiences International Data Corporation, “IDC Sees the Dawn of the DX Economy and the Rise of the Digitally Native Enterprise, International Data Corporation,” November 1, 2016, www.idc.com/getdoc.jsp?

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See continuous innovation insurance industry, 117–18 Intel, 108 international sales, 168–70 Internet of Things (IOT), 101–13 construction industry and, 98–100 data inherent in connected devices, sales of, 109–10 defined, 102 digital twins of physical machinery and, 104–6 efficiency stage of, 102 focusing on outcomes instead of products, 106–11 manufacturing industry and, 103–13 new business models in response to, 112–13 sensors, collection and transmission of data from, 101–2 service-level agreements and, 97–98, 106 Invoca, 172 IOT. See Internet of Things (IOT) iPhone, 3 IT department, 129–30, 189–99 business insights and, 198 evolving IT architecture to meet subscription economy needs, 197–99 financials and, 192 legacy IT architecture, structure of and problems associated with, 189–97 pricing and packaging and, 190–91, 199 renewals and, 191 sales to different customer groups and, 191–92 subscribers/customer metrics and, 190, 198 “It Doesn’t Matter” (Carr), 83 Jankowski, Simona, 26, 27 Janzer, Anne, 130 Jaws (film), 38 JCPenney, 22 Jobs, Steve, 39, 47 Johnny Walker Blue Label, 107 Johnson, Kevin, 33 just in time inventory, 16 Kaplan, 117 Kaplan, Ethan, 30–31 Katzenberg, Jeffrey, 46 Kelly, Kevin, 111 Kern, Mac, 60–61 Kmart, 22 Komatsu, 98–99 Kramer, Kelly, 95–96 Kreisky, Peter, 78 Lah, Thomas, 85–86, 96 Lean Startup method, 48 leasing versus subscription model, for automobiles, 52–53 Lemonade, 118 Lessin, Jessica, 66, 68 Levie, Aaron, 167–68, 198–99 Life of Pablo, The (album), 48, 136–37 linear order-to-cash systems, 192–97 livestreaming, 42 LL Cool J, 101 LO3 Energy, 119 Loot Crate, 28 Lotto, Mark, 75 Lucas, George, 136 Lyft, 3, 54–55 Lynda.com, 31, 117 MacKenzie, Angus, 72 McGraw-Hill, 12–13 McKinsey, 11, 34, 98, 112–13, 165, 173, 218, 221 Macy’s, 14 Magellan Health, 115 Main, Andy, 121–22 malls, 17, 22, 34–35 Manifesto for Agile Software Development, 135–36 manufacturing industry, 100–101, 103–13 digital twins of physical machinery and, 104–6 focusing on outcomes instead of products, 106–11 future of, 111–13 margins, 15 marketing, 130–31, 143–55 experiences, communicating brand through, 145, 149 one-on-one marketing, 145–46 optimizing growth within service itself, 145 place (channels) and, 146, 147–48 pricing and packaging and, 146, 151–54 promotion and, 146, 149–51 subscriber IDs and, 146 Three Rooms mental model of storytelling and, 149–51 traditional role and techniques of, 143–44 Marketo, 190 MarketTools, 171 Marshall, John, 68 Martin-Flickinger, Gerri, 141 Mashable, 66 mass production, 37 media industry, 37–50 community, building, 43 content creation, investment in, 41 continuous innovation and, 136–37 Hollywood, historical business model of, 37–38 livestreaming, 42 mass production of movies in, 37 music industry, historical business model of, 38–39 music streaming services, 46–50 Netflix show, business model for, 41 portfolio effect and, 37, 41 streaming services and, 39–50 subscription video on demand (SVOD), 42–46 Meeker, Mary, 21 Membership Economy, The (Baxter), 29 Merry Christmas (album), 38 Metallica, 39 Metromile, 118 Microsoft, 56, 83, 89 minimum viable product, 48 ModCloth, 23 Moffett, Craig, 45 Molotov, 46 monetizing longtail content business model, 38 Money element, of PADRE operating model, 204 MOOCs (massive open online courses), 117 Mooney, Andy, 31–32 Motor Trend, 72–73, 79 MoviePass, 2 Mukherjee, Subrata, 74 multiple of three factors, for gauging reader engagement, 74 music streaming services, 46–50 BowieNet and, 47 minimum viable product and, 48 Prince’s NPG Music Club and, 47–48, 49–50 virtuous feedback loop, creating, 48–49 My Royal Canin, 118 Napster, 39 NCR, 13 negative option model, 28–30 Nest, 119 net account growth, 211–13 Netflix, 2, 3, 13, 18–19, 39, 40–41, 69, 139–40, 145, 161, 198 Newman, Nic, 69 New Relic, 166–67 newspaper industry, 65–79 ad-based business model, decline of, 66–70 digital subscribers, growth in, 65–66 enthusiast networks, 72–73 freemium model and, 76 multiple of three factors, for gauging reader engagement, 74 New York Times, subscription-first model of, 75–79 pricing agility and, 73–74 print versus digital myths, 70–71 reader’s wants and needs, prioritizing, 70–71 subscription/ad revenue mix, flipping, 75–76 New Yorker, The, 65, 66–67 New York Times, The, 65, 72–73, 75–79 Ngenic, 109–11 Nichols, Jim, 52 Nordstrom, 33 NPG Music Club, 47–48, 49–50 O’Brien, Mike, 51 Okta, 3 One Medical, 115 one-on-one marketing, 145–46 OnStar, 55–56, 148 Oracle, 4, 83, 190 Pacioli, Luca, 176–78 packaging.

The End of Ownership: Personal Property in the Digital Economy
by Aaron Perzanowski and Jason Schultz
Published 4 Nov 2016

Classification: LCC K783 .P47 2016 | DDC 346.04/8—dc23 LC record available at http://lccn.loc.gov/2016013180 ePub Version 1.0 For Clem, who at least gets my record collection—AP For Kate & Elliott, my two favorite booksneaks—JS Table of Contents Series page Title page Copyright page Dedication Acknowledgments 1 Introduction 2 Property and the Exhaustion Principle 3 Copies, Clouds, and Streams 4 Ownership and the Fine Print 5 The “Buy Now” Lie 6 The Promise and Perils of Digital Libraries 7 DRM and the Secret War inside Your Devices 8 The Internet of Things You Don’t Own 9 Patents and the Ordinary Pursuits of Life 10 Ownership’s Uncertain Future Index List of Illustrations Figure 5.1 An example of a MediaShop product page Figure 5.2 Percentage of respondents who believe the “Buy Now” button confers rights Figure 5.3 Percentage of respondents who express a strong or moderate preference for rights Figure 5.4 Examples of MediaShop short notices Figure 5.5 Percentage of respondents who believe the short notice confers rights Acknowledgments This project grew out of a series of our academic articles: “Digital Exhaustion,” UCLA Law Review 58 (2011): 889–946; “Copyright Exhaustion and the Personal Use Dilemma,” Minnesota Law Review 96 (2012): 2067–2143; “Legislating Digital Exhaustion,” Berkeley Technology Law Journal 28 (2015): 1535–1557; and “Reconciling Personal and Intellectual Property,” Notre Dame Law Review 90 (2015): 1213–1263.

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From there, we turn our attention from individuals to the implications of the licensing model for an important group of institutional actors, public libraries. Next, we look at how the licensing model, which was largely confined to digital media for decades, has been exported to the world of physical goods. That transition starts with DRM technology and the laws that protect it. But with the emergence of the Internet of Things, the question of our relationship with the devices around us—and sometimes in us—is more pressing than ever. Then we explore another legal avenue for exerting control over how we use the objects we buy—the patent system—and how the ongoing fight over so-called post-sale restrictions threatens ownership.

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Park & Sons Co., 220 U.S. 373 (1911); Van Houweling, “The New Servitudes.” But see Glen O. Robinson, “Personal Property Servitudes,” University of Chicago Law Review 71 (Fall 2004): 1449–1523 (arguing in favor of servitudes on personal property). 7. Christina Mulligan, “Personal Property Servitudes on the Internet of Things,” Georgia Law Review (forthcoming). 8. John D. Park & Sons Co. v. Hartman, 153 F. 24, 39 (6th Cir. 1907). 9. J. K. Rowling, Harry Potter and the Deathly Hallows (New York: Arthur A. Levine Books, 2007), 417–418. 10. Exhaustion also plays a role in trademark law, where it permits the resale of authentic goods without the trademark holder’s permission.

The Data Revolution: Big Data, Open Data, Data Infrastructures and Their Consequences
by Rob Kitchin
Published 25 Aug 2014

European Commission (2012) Commission Proposes a Comprehensive Reform of the Data Protection Rules, 25 January, http://ec.europa.eu/justice/newsroom/data-protection/news/120125_en.htm (last accessed 6 August 2013). Farber, D. (2013) ‘Counting the Internet of things in real time’, CNet, 30 July, http://news.cnet.com/8301-11386_3-57596162-76/counting-the-internet-of-things-in-real-time/ (last accessed 18 September 2013). Farber, M., Cameron, M., Ellis, C. and Sullivan, J. (2011) Massive Data Analytics and the Cloud: A Revolution in Intelligence Analysis. Booz Allen Hamilton. http://www.boozallen.com/media/file/MassiveData.pdf (last accessed 16 July 2013).

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Rooney, B. (2012) ‘Big data’s big problem: little talent’, Wall Street Journal: Tech Europe, 26 April, http://blogs.wsj.com/tech-europe/2012/04/26/big-datas-big-problem-little-talent/ (last accessed 12 November 2012). Rose, A. (2013) ‘The internet of things has arrived – and so have massive security issues’, Wired, 11 January, http://www.wired.com/opinion/2013/01/securing-the-internet-of-things/ (last accessed 7 August 2013). Rose, N. (1996) Inventing Our Selves: Psychology, Power and Personhood. Cambridge University Press, Cambridge. Rosenberg, D. (2013) ‘Data before the fact’, in L. Gitelman (ed.), ‘Raw Data’ is an Oxymoron.

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A data revolution is underway, one that is already reshaping how knowledge is produced, business conducted, and governance enacted. This revolution is founded on the latest wave of information and communication technologies (ICTs), such as the plethora of digital devices encountered in homes, workplaces and public spaces; mobile, distributed and cloud computing; social media; and the internet of things (internetworked sensors and devices). These new technical media and platforms are leading to ever more aspects of everyday life – work, consumption, travel, communication, leisure – and the worlds we inhabit to be captured as data and mediated through data-driven technologies. Moreover, they are materially and discursively reconfiguring the production, circulation and interpretation of data, producing what has been termed ‘big data’ – vast quantities of dynamic, varied digital data that are easily conjoined, shared and distributed across ICT networks, and analysed by a new generation of data analytics designed to cope with data abundance as opposed to data scarcity.

The Future Is Faster Than You Think: How Converging Technologies Are Transforming Business, Industries, and Our Lives
by Peter H. Diamandis and Steven Kotler
Published 28 Jan 2020

(Author note: Peter’s VC firm is an investor.) John Romkey: Ryan Nagelhout, Smart Machines and the Internet of Things (Rosen Publishing, 2016). Neil Gross: Neil Gross, “The Earth Will Don an Electronic Skin,”BusinessWeek, August 29, 1999. In 2009, the number of devices connected to the Internet: Dave Evans, “The Internet of Things,” Cisco.com, April 2011. See: https://www.cisco.com/c/dam/en_us/about/ac79/docs/innov/IoT_IBSG_0411FINAL.pdf. By 2015, all this progress added up to 15 billion: Louis Columbus, “Roundup of Internet of Things Forecasts and Market Estimates, 2016,” Forbes.com, no. 27 (2016). See: https://www.forbes.com/sites/louiscolumbus/2016/11/27/roundup-of-internet-of-things-forecasts-and-market-estimates-2016/#32a1a5ba292d.

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See: https://www.forbes.com/sites/louiscolumbus/2016/11/27/roundup-of-internet-of-things-forecasts-and-market-estimates-2016/#32a1a5ba292d. Nor will we stop there: For a full breakdown of the Accenture report, see: https://newsroom.accenture.com/subjects/management-consulting/industrial-internet-of-things-will-boost-economic-growth-but-greater-government-and-business-action-needed-to-fulfill-its-potential-finds-accenture.htm. Steven Sasson: Steven Kotler and Peter Diamandis, BOLD (Simon & Schuster, 2015), pp 4–6. LIDAR sensors: Sean Higgins, “Livox Announces $600 Lidar for Autonomous Vehicles,” Spar3-D.com, January 23, 2019.

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New York Times describes passing through the store’s turnstiles: Wingfield, “Inside Amazon Go.” McKinsey estimates automated checkout will save retailers: “The Internet of Things: Mapping the Value Beyond the Hype,” McKinsey & Company, June 2015. See: https://www.mckinsey.com/~/media/McKinsey/Business%20Functions/McKinsey%20Digital/Our%20Insights/The%20Internet%20of%20Things%20The%20value%20of%20digitizing%20the%20physical%20world/The-Internet-of-things-Mapping-the-value-beyond-the-hype.ashx. the San Francisco startup v7labs: “This AI Startup Wants to Automate Every Store Like Amazon Go,” Fast Company, November 9, 2017.

Thinking Machines: The Inside Story of Artificial Intelligence and Our Race to Build the Future
by Luke Dormehl
Published 10 Aug 2016

As with many newer smart gadgets, XCoffee was connected to the Web, and was therefore part of the so-called ‘Internet of Things’. But to me it is closer to an example of what hardware geeks would call a ‘hack’ – a term which colloquially refers to a clever solution to a tricky problem. The prerequisite of what we would today think of as a smart device (fondly described by MIT’s Media Lab as a ‘thing that thinks’) is that it exists as a self-governing feedback loop, capable of operating autonomously without a lot of human intervention. The Internet of Things is not simply about ‘things’ connected to the Internet. The traditional Internet was there to allow humans to carry out tasks, such as searching, downloading music, or reading information.

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, Daedalus, 1988. 6 Hernandez, Daniela, ‘Meet the Man Google Hired to Make AI a Reality’, Wired, 16 January 2014: wired.com/2014/01/geoffrey-hinton-deep-learning/ 7 McCarthy, John, ‘Computer-Controlled Cars’, 1968: formal.stanford.edu/jmc/progress/cars.ps http://www-formal.stanford.edu/jmc/progress/cars.ps 8 idcdocserv.com/1678 9 Lohr, Steve, Data-Ism: Inside the Big Data Revolution (London: Oneworld Publications, 2015). 10 Allen, Kate, ‘How a Toronto Professor’s Research Revolutionized Artificial Intelligence’, The Star, 17 April 2015: thestar.com/news/world/2015/04/17/how-a-toronto-professors-research-revolutionized-artificial-intelligence.html 11 https://plus.google.com/u/0/102889418997957626067/posts 12 Metz, Cade, ‘New Tool Analyzes a Video’s Sound for Better Search Results’, Wired, 24 September 2015: wired.com/2015/09/new-tool-analyzes-videos-sound-better-search-results/ 13 Taigman, Yaniv et al., ‘DeepFace: Closing the Gap to Human-Level Performance in Face Verification’, Facebook Research, 24 June 2014: research.facebook.com/publications/deepface-closing-the-gap-to-human-level-performance-in-face-verification/ 14 Yang, Yezhou et al., ‘Robot Learning Manipulation Action Plans by “Watching” Unconstrained Videos from the World Wide Web’, AAAI, 2015: umiacs.umd.edu/~yzyang/paper/YouCookMani_CameraReady.pdf 15 Rashid, Rick, ‘How Technology Can Bridge Language Gaps’, Microsoft Research, 2012: research.microsoft.com/en-us/research/stories/speech-to-speech.aspx Chapter 3: Intelligence Is all Around Us 1 Warwick, Kevin, ‘Cyborg 1.0’, Wired, 1 February 2000: archive.wired.com/wired/archive/8.02/warwick.html 2 Hutchings, Emma, ‘Lenovo’s Smart Shoes Display Your Mood on Tiny Screen’, PSFK, 1 June 2015: psfk.com/2015/06/lenovo-smart-shoes-lenovo-tech-world.html 3 Dormehl, Luke, ‘Internet of Things: It’s All Coming Together for a Tech Revolution’, Guardian, 8 June 2015: theguardian.com/technology/2014/jun/08/internet-of-things-coming-together-tech-revolution 4 http://americanhistory.si.edu/lighting/19thcent/consq19.htm 5 Stafford-Fraser, Quentin, ‘The Trojan Room Coffee Pot: A (Non-Technical) Biography’: cl.cam.ac.uk/coffee/qsf/coffee.html 6 Woods, Michael and Woods, Mary: Ancient Machines: From Wedges to Waterwheels (Minneapolis: Runestone Press, 2000). 7 Wiener, Norbert, The Human Use of Human Beings (New York: Doubleday, 1954). 8 Freeman, Walter, ‘W.

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By 1938, the former US president Franklin Roosevelt, speaking in Barnesville, Georgia, proclaimed electricity ‘a modern necessity of life’. Could we be at the start of a similarly transformative journey for smart devices? Perhaps so. Certainly, the rise of mobile wireless networks means that devices are more portable than ever. The dream of what is sometimes (and quite clumsily) termed the ‘Internet of Things’ is that intelligent hardware will become as much a ‘modern necessity of life’ in the twenty-first century as electricity did 100 years ago. Where once we electrified, now we will cognitise. Right now, hype is so strong around the field of smart devices that analysts at Ericsson predict that there will be in the region of 50 billion smart devices around the world by 2020: a figure that works out as approximately 6.8 per person.

The Economic Singularity: Artificial Intelligence and the Death of Capitalism
by Calum Chace
Published 17 Jul 2016

They are all driven at least in part by AI, and they will all impact the way our societies evolve. Because they will all unfold in different ways and at different speeds, it is impossible to predict exactly what the impact of these interlacing technologies will be, other than that it will be profound. The Internet of Things The Internet of Things (IoT) has been talked about for years – the term was coined by British entrepreneur Kevin Ashby back in 1999.[cxxxiii] Indeed it has been around for long enough to have acquired a selection of synonyms. GE calls it the Industrial Internet, Cisco calls it the Internet of Everything, and IBM calls it Smarter Planet.

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The information revolution does the same, providing farmers with crops that are more resilient in the face of weather, pests and weeds, and allowing them to sow, cultivate and harvest their crops far more accurately with satellite navigation. Along with the uncertainty about the start date of the information revolution, there is disagreement about how distinct it is from the industrial revolution. The Internet of Things (IoT) is a phenomenon of the information revolution which we will look at in more detail in chapter 3.7. Klaus Schwab, founder and executive chairman of the World Economic Forum which hosts the annual meeting of the global elite in Davos, calls the IoT the fourth industrial revolution.[x] This seems to me to under-state the importance of the IoT, and also to separate it from all the other digital revolutions which comprise the information revolution, including, of course, artificial intelligence. 2.3 – The Automation story so far The mechanisation of agriculture The particular aspect of the industrial and information revolutions which concerns us in this book is automation.

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[cxli] They span Siri out of a DARPA-funded research project, taking the name from Sigrid, a Scandinavian word meaning both “victory” and “beauty”, and sold it to Steve Jobs in 2011. Artificial intelligences will govern most things in our environment, and something like Siri will be our intermediary, negotiating with and filtering out most of the Internet of Things. Although we may not notice it, this will be a blessed relief. Imagine having to negotiate a world where every AI-enabled device has direct access to you, with every chair and handrail pitching their virtues to you, and every shop screaming at you to buy something. This dystopia was captured in the famous shopping mall scene in the 2002 film “Minority Report”, and more laconically in Douglas Adam's peerless “Hitchhiker's Guide to the Galaxy” series, where the Corporation that produces the eponymous guide has installed talking lifts, known as happy vertical people transporters.

Places of the Heart: The Psychogeography of Everyday Life
by Colin Ellard
Published 14 May 2015

The next frontier in the cybernetic transformation of space and place is not just focused on relationships between people, or even between people and the landscapes they inhabit. In the much-vaunted Internet of Things, places themselves are entirely penetrated by devices and sensors, still ostensibly in the service of human beings, but now with the central focus on the things themselves and their connections, rather than the flesh-and-blood actors who animate the scene. Many news media accounts might lead us to believe that what is new about the Internet of Things is that the appliances and gadgets of our lives will begin to talk to one another. Our carbon monoxide detectors will commune with our furnaces, knowing enough to shut things down when a lethal gas is detected in the air in our houses.

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The built world has become increasingly flooded with sensors. Surveillance cameras have been around for years, but now they can be combined with technology that can measure our facial expressions, our patterns of gaze, our heart and breathing rates, and our body temperature. The burgeoning “Internet of Things”13 joins together every kind of device and structure from the home thermostat to traffic control devices and mass transit ticket systems in a massive electronic skein of information that persistently watches, measures, and adjusts the relationships between people and their everyday settings.

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In a very real sense, we are no longer there as we used to be, and our physical surroundings are no longer as real as they used to be. The trend toward hybridization of real and virtual spaces in urban environments also has ideological roots. Indeed, though some are touting the new trends toward wired cities and the Internet of Things as ushering in the bare beginnings of a new kind of merger between information technologies and architecture, this trend has actually been under way for some time. Just as electronic connectedness enables globalization by discounting the importance of physical space and dimension in many of our everyday dealings with life, the homogenization of architectural design parallels this trend in the arena of bricks, steel, and concrete.

Modern Monopolies: What It Takes to Dominate the 21st Century Economy
by Alex Moazed and Nicholas L. Johnson
Published 30 May 2016

However, the FDA may be trying to expand HIPAA to also cover user-generated health data, which would severely limit the utility of many platforms in the sector. Still, as technology (and regulations) changes, there will be many more opportunities for platform business models to help improve patient outcomes. The next sector where big changes are happening is the Internet of Things. If you’re not familiar with it, the Internet of Things is an envisioned future where machines communicate directly with other machines rather than people. The idea is that Internet-enabled devices, such as cars, buildings, or electronics (really, any device you can imbed with a sensor), will exchange data directly with other devices.

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The challenge is getting most users to agree on one platform, as there are many platform competitors out there currently. (This situation is a classic example of excess fragmentation leading to lost value.) The growth of the Internet of Things will have a big impact that spreads beyond just the platforms themselves. Insurance companies are starting to offer better rates based around users’ ability to drive safely or their ability to monitor users’ homes. Farther down the line, driverless cars will be a big part of the Internet of Things. And they will likely be connected by one or two dominant development platforms. Apple and Google have both made big moves in this space over the last year, so they are currently leading contenders.

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The potential in the industrial sector of the economy to improve efficiency is enormous. Industry reports on the Internet of Things typically make astronomical predictions. For example, Gartner predicts that we’ll have 25 billion connected “things” in use by 2020, not including PCs, laptops, and smartphones, which it predicts will make up an additional 7 billion devices. This total will be up more than 500 percent from an estimated 4.9 billion things at the end of 2015. Gartner also suggests that the Internet of Things will generate an additional $1.9 trillion in economic value by 2020, while market research and consulting firm International Data Corporation places this number at a more sanguine $8.9 trillion.44 However, most of this value won’t be created or captured by the makers of the “things” themselves.

The Network Imperative: How to Survive and Grow in the Age of Digital Business Models
by Barry Libert and Megan Beck
Published 6 Jun 2016

They enable people to share information about the activities of relief agencies and the locations of food, supplies, shelter, and charging stations. Cloud technology helps people access their important information and documents wherever they are. By tracking the best routes between countries via global positioning, smartphones have even become part of the internet of things. This story of refugees and their smartphones contains a key message for business leaders: for most of the world, technology is as essential to life as food and water, and it is changing everything. No industry is untouched by the technical revolution, and technology is transforming the back end, the front end, and everything in between—not only manufacturing, not only resource planning, not only marketing and customer relationships, but also the very business models that companies use to create value.

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You can’t expect to build a great business without it. However, even though many leaders are beginning to incorporate technology piecemeal into various parts of their organizations, few are creating business models that take advantage of digital technology such as social, mobile, cloud, big data analytics, and the internet of things. Digitally enabled business models offer many advantages to organizations and those they serve. Here are a few of them. Convenience. When customers are served through digital means, such as online or through an app, they can interact with the organization on their own terms and at their own convenience.

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It enables people and businesses to improve utilization of resources, scale rapidly (both up and down), and access data and services through multiple channels and devices. Big data analytics. This refers to our ability to capture and analyze enormous sets of data, often in real time. Big data helps companies understand their users and themselves and make better decisions. The internet of things. This web of interconnected, internet-enabled devices lets us collect and use data in order to understand the world, accomplish new tasks, or improve our lives. For example, smart thermostats can learn our daily schedules and make the house toasty warm when we roll out of bed. Each of these technologies has value in its own right.

Age of Context: Mobile, Sensors, Data and the Future of Privacy
by Robert Scoble and Shel Israel
Published 4 Sep 2013

Soon the smartest operators of supermarkets, dry cleaners and other merchants will Uberize their services as well. Connecting All the Things We’ve talked about the Internet of Things. We believe that a part of it will be households of connected things. Anything in your home that has an on-off switch will be interconnected. All glass objects will be connected as well. Ubiquitous sensors will be a part of it as well, as, of course, will your front door. All of these things will communicate with you wherever you are, through some form of PCA. They will also connect to emergency services, utilities and the entire Internet of Things. Everywhere we looked we found companies that were building little pieces of the new contextual household.

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Not just the mobile phones in our pockets, but different kinds of computers—our watches, our cameras, our cars, our refrigerators, our toothbrushes. Every aspect of our lives is somehow on the network, a wireless network, and in the cloud. This is the third wave of computing. Research firm IDC reports that there will be 3.5 billion networked products by 2015. Compare that to 1.7 billion networked PCs and it’s clear that the “Internet of Things” has arrived. With it, and with everything connected to the network, we enter an amazing new world of possibilities. The big change here is that technology is becoming intuitive. It is starting to understand where you are and where you are likely to be going, and it can help you on your way.

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Through the use of many different types of sensors, our mobile devices now emulate three of our five senses. Camera sensors give them eyes, and microphone sensors serve as ears; capacitive sensors enable them to feel our touch on their screens. They can’t yet detect fragrance—but our guess is that such a capability is coming soon. The so-called Internet of Things enables many common appliances, fixtures and devices to communicate with systems due to the availability of radical new low-cost and miniaturized sensors. Microsoft Kinect for Xbox, for example, has a 3D sensor that can see your heartbeat just by looking at your skin. When we talk about “the system knowing about you,” that knowledge depends on machine learning and database computation breakthroughs that couldn’t be imagined when Microsoft researcher Jim Gray turned on Microsoft’s first terabyte database back in December 1997.

The Autonomous Revolution: Reclaiming the Future We’ve Sold to Machines
by William Davidow and Michael Malone
Published 18 Feb 2020

IBM’s revenue increased from less than $2 billion in 1960 to more than $26 billion in 1980, a 14 percent growth rate.9 In the form of the microprocessor, the integrated circuit also spawned industries and applications that had never existed before—including cellular communications, personal computers, tablets, and the Internet of Things.10 New jobs followed. Today, the worldwide market for smartphones is approaching a half-trillion dollars.11 For personal computers and laptops, it’s about $200 billion.12 IHS Markit, a technology consulting firm, forecasts that the installed base of Internet of Things devices will reach 75.4 billion units by 2025, or about ten devices for every person on the planet. Estimates of the potential economic impact of these devices and associated services fall in the wide range of $2.7 to $6.2 trillion.13 Just as remarkable is that the semiconductor industry was able to achieve this mind-boggling level of growth while experiencing precipitous declines in the price per transistor.

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“Global Revenue from Smartphone Sales from 2013 to 2018,” Statista, https://www.statista.com/statistics/237505/global-revenue-from-smartphones-since-2008/ (accessed June 26, 2019). 12. “Worldwide PC Spending Forecast,” Statista, https://www.statista.com/statistics/380434/worldwide-pc-spending-forecast/ (accessed June 26, 2019). 13. Louis Columbus, “Roundup of Internet of Things Forecasts and Market Estimates, 2016,” Forbes.com, November 27, 2016, https://www.forbes.com/sites/louiscolumbus/2016/11/27/roundup-of-internet-of-things-forecasts-and-market-estimates-2016/#2c6bdf93292d (accessed June 26, 2019). 14. Rachel Courtland, “How Much Did Early Transistors Cost?,” IEEE Spectrum, April 16, 2015, https://spectrum.ieee.org/tech-talk/semiconductors/devices/how-much-did-early-transistors-cost (accessed June 26, 2019); and “Handel Jones: Cost per Transistor Flat from 28 to 7nm,” AnandTech, June 15, 2016, https://forums.anandtech.com/threads/handel-jones-cost-per-transistor-flat-from-28-to-7nm.2476904/ (accessed June 26, 2019). 15.

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In a work studded with arresting insights, few are more unsettling than the following, offered in the context of the authors’ discussion of artificial intelligence, and the “Autonomous Revolution” that is utterly upending the world of work, and therefore education and even human memory: The Autonomous Revolution will embed functional intelligence in autonomous machines. In practice, this means that the systematic development of human knowledge through education and work experience will have less value than it did in the past. Humans may continue to expand their knowledge and skills by accessing databases on the Internet, interfacing directly with the IoT (Internet of Things), or eventually having devices implanted in their bodies that will enhance their physical and mental abilities. But the real repositories of practical knowledge will shift to autonomous devices, which will learn much more quickly than people can. In situation after situation, automatons will substitute for humans.

The Fifth Domain: Defending Our Country, Our Companies, and Ourselves in the Age of Cyber Threats
by Richard A. Clarke and Robert K. Knake
Published 15 Jul 2019

The professional staff at the Federal Communications Commission was so concerned about the possibility of 5G being susceptible to hacking that they publicly published 132 questions to the communications industry about 5G security. They asked all about 5G security and authentication, encryption, physical security, DDoS attacks, patch management, and risk segmentation. Then they got to 5G and the Internet of Things. The FCC professionals began by noting the obvious: “It is widely expected that 5G networks will be used to connect the myriad devices, sensors and other elements that will form the Internet of Things (IoT). The anticipated diversity and complexity of these networks, how they interconnect, and the sheer number of discrete elements they will comprise raise concerns about the effective management of cyber threats.”

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Intercontinental Ballistic Missile (ICBM): A land-based, guided missile capable of traveling in excess of five thousand kilometers to deploy and detonate one or more nuclear weapons on an enemy target(s). Internet of Things (IoT): The expanding network of devices that are internet connected. This includes, but is not limited to, devices such as “smart” appliances, networked health-care equipment, and infrastructure monitoring electronics. In the context of cybersecurity, Internet of Things devices are notoriously insecure, and when used in an enterprise or otherwise sensitive setting, can present a significant security risk to an organization.

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On the sidewalk there may be what looks like one of those Postal Service relay boxes where the letter carrier picks up the mail to be delivered on her route. The box will not, however, belong to the Postal Service. It will belong to a “phone company.” When this happens, 5G will have arrived near you. So too will a new set of cyber risks. The fifth generation of mobile telephony technology (5G) will supercharge the Internet of Things (IoT), and neither will be secure. If Verizon, AT&T, Sprint, and other carriers move ahead with their plans, they will initially spend a quarter trillion dollars dotting U.S. cities with these new 5G transmitters on poles and accompanying electrical transformers in what look like mailboxes.

The Art of Invisibility: The World's Most Famous Hacker Teaches You How to Be Safe in the Age of Big Brother and Big Data
by Kevin Mitnick , Mikko Hypponen and Robert Vamosi
Published 14 Feb 2017

Customer security is very important to us, and our highest priority is on remote vulnerabilities. One of your best defenses is to buy a Dropcam Pro so you can monitor your home when you’re not there.”5 With the advent of the Internet of Things, companies like Google are eager to colonize parts of it—to own the platforms that other products will use. In other words, these companies want devices developed by other companies to connect to their services and not someone else’s. Google owns both Dropcam and Nest, but they want other Internet of Things devices, such as smart lightbulbs and baby monitors, to connect to your Google account as well. The advantage of this, at least to Google, is that they get to collect more raw data about your personal habits (and this applies to any large company—Apple, Samsung, even Honeywell).

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Each packet—or unit of data between source and destination—of voice, text, or data sent over 2G GSM could be decrypted in just a few minutes using the published table of keys.5 This was an extreme example, but the team considered it necessary; when Nohl and others had previously presented their findings to the carriers, their warnings fell on deaf ears. By demonstrating how they could crack 2G GSM encryption, they more or less forced the carriers to make the change. It is important to note that 2G still exists today, and carriers are considering selling access to their old 2G networks for use in Internet of Things devices (devices other than computers that connect to the Internet, such as your TV and refrigerator), which only need occasional data transmission. If this happens, we will need to make sure the devices themselves have end-to-end encryption because we know that 2G will not provide strong enough encryption by itself.

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The advantage of this, at least to Google, is that they get to collect more raw data about your personal habits (and this applies to any large company—Apple, Samsung, even Honeywell). In talking about the Internet of Things, computer security expert Bruce Schneier concluded in an interview, “This is very much like the computer field in the ’90s. No one’s paying any attention to security, no one’s doing updates, no one knows anything—it’s all really, really bad and it’s going to come crashing down.… There will be vulnerabilities, they’ll be exploited by bad guys, and there will be no way to patch them.”6 To prove that point, in the summer of 2013 journalist Kashmir Hill did some investigative reporting and some DIY computer hacking.

Life After Google: The Fall of Big Data and the Rise of the Blockchain Economy
by George Gilder
Published 16 Jul 2018

In 2014, Google summoned Jeremy Rifkin to its lecture series to sum it all up. He heralded a “zero marginal cost society.” Under the new regime, the price of every incremental good and service, from search to software, from news to energy, will plunge toward “free” as every device and entity in the world is subsumed in an Internet of Things, where exponential network effects yield a new economy of leisure and abundance.3 Rifkin assured his audience that it is indeed a Google world. But not only is “free” a lie, as we’ve seen, but a price of zero signifies a return to the barter system, a morass of incommensurable exchanges that the human race left behind in the Stone Age.

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Here more and more ads were needed to prop up a dwindling supply of content, and the chief winners were charismatic un-Googly talkers, such as Rush Limbaugh. Now Google Assistant is winning plaudits as the best of the speech recognizers, and LG has enlisted it for all ninety of its home appliances. Pioneering voice in the Internet of Things, Google and LG envisage people confiding their inner ids and desires to their washing machines, ovens, refrigerators, gas ranges, heating-and-air-conditioning systems, dishwashers, and lighting panels. No longer will Google be restricted to data about online purchases. When Amazon’s Whole Foods loads up the refrigerator, Google will know.

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He wrote three major research papers, earned vital recommendations, and gained a precarious hold on the other side of the bridge. The thousand dollars ran out just as Ali secured a research job in the Netherlands with the co-chairmen of the European Community standards body for the then-futuristic “Internet of Things” (IoT). He worked on the media access control layer for the IoT project, necessarily concerned with the security problems of connecting the “things” to the network. Having won more glowing recommendations, he then ascended to the summit of computer science studies in the United States—doctoral studies at Princeton during the school year and Stanford in the summers.

Designing Search: UX Strategies for Ecommerce Success
by Greg Nudelman and Pabini Gabriel-Petit
Published 8 May 2011

Mobile picture search is certainly emerging as the input device of choice for connecting the real and the virtual worlds to create the Internet of Things (see sidebar). Internet of Things Internet of Things, also known as Internet of Objects, refers to a self-configuring wireless network connecting regular everyday objects to one another. Internet of things is a term attributed to Auto-ID Center, originally based at Massachusetts Institute of Technology (MIT). Eventually, Internet of things will connect 50-100 trillion of objects and be able to track their movement and state through the use of computers. Companies like Arrayent, Inc. have already developed practical, low-cost solutions that connect everyday things like thermostats to mobile phones and tablets.

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Chapter 17, “Search on Tablet Devices: The Flight of Discovery” discusses some intriguing possibilities offered by the next generation of the tablet devices. In his book Ambient Findability, Peter Morville talks about the sensory overload, trust issues, and bad decisions that are sure to result from interactions with the Internet of things. However, it is hard to elude the siren’s call of such technology. It is now almost possible to use technology similar to that of Like.com to analyze every image and frame of video a mobile phone captures and tag it with text, GPS coordinates, time, and author, while at the same time cross-referencing this image with other images of the same place or similar things along with all the text tags, content, and links the entire world has added to this collection of images.

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—Pabini Gabriel-Petit References [1] Linden, Greg, Brent Smith, and Jeremy York. “Amazon.com Recommendations: Item-to-Item Collaborative Filtering.” [http://www.win.tue.nl/~laroyo/2L340/resources/Amazon-Recommendations.pdf] IEEE Internet Computing, January-February 2003. References Dodson, Sean. “The Internet of Things.” The Guardian, October 9, 2003. Jung, Carl Gustav. Man and His Symbols. New York: Dell, 1968. Morville, Peter. Ambient Findability. Sebastopol, California: O’Reilly, 2005. Nudelman, Greg. “Making $10,000 a Pixel: Optimizing Thumbnail Images in Search Results.” UXmatters, May 11, 2009. Retrieved July 26, 2009.

How Democracy Ends
by David Runciman
Published 9 May 2018

This ‘pax technica’ comes after the ‘pax Americana’, when one very powerful state was needed to keep the world at peace. That era seems to be over anyway, thanks to Trump. Howard thinks we will do fine without it. ‘The internet of things,’ he writes, ‘will probably strengthen social cohesion to such a degree that when regular government structures break down or weaken, they can be repaired or substituted. In other words, people will continue using the internet of things to provide governance even when government is absent.’ 86 Libertarian, revolutionary or technocratic, these visions of the future have some features in common. One is their impatience to get there.

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They originate with things that are already happening. Howard, showing the true impatience of someone who has seen the possibility of political transformation, dates the arrival of the politics of the future to around 2020, when the internet of things will kick into gear. That’s pretty much now. Yet Howard recognises this as only one possibility. There are many others. The subtitle of his book is ‘How the Internet of Things May Set Us Free or Lock Us Up’. Contained in the technology that has the power to liberate us are the worst-case scenarios, too, involving vast abuses of power, growing inequality and political paralysis.

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Mason recognises the utopian strain in this way of thinking. The Marx-was-right-after-all rhetoric will put many readers off. Haven’t we heard that one too many times before? But there are non-Marxist variants on the same line of thought. In Pax Technica (2015), a much less bombastic book than Mason’s, Philip N. Howard argues that the ‘internet of things’ – whereby machines come to share vast quantities of data directly with each other – will entirely transform contemporary politics. Once your fridge can talk to your light bulb, we will be in a different political world, whether we like it or not. A lot of decisions will be out of our hands because machines will be taking them for us, in the name of greater efficiency.

Mastering Blockchain: Unlocking the Power of Cryptocurrencies and Smart Contracts
by Lorne Lantz and Daniel Cawrey
Published 8 Dec 2020

Regulations are coming into place that will require that health-care providers enable patients to access all of their digital data. Google is working on something called a verifiable data audit, a ledger-based system that will cryptographically verify data records. Internet of Things Billions of smart devices, from power strips to light bulbs, can run more efficiently when cooperating with a larger network. To date, businesses are still struggling with ways to pay for all of these devices to connect into the Internet of Things (IoT) and provide verifiable information. Blockchain, with accounts and even payments in a controlled infrastructure, may be part of the solution. IBM’s artificial intelligence platform Watson interacts with IoT devices and securely stores data with the IBM Blockchain Platform, which is based on Hyperledger Fabric.

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Gox-Bitfinex jurisdiction over cryptocurrency exchanges, Jurisdiction order types in cryptocurrency exchanges, The Role of Exchanges risks of, in cryptocurrency trading, Exchange Risk types of cryptocurrency exchanges, Jurisdiction externally owned account (EOA) wallets, Multisignature Contracts F Fabric (Hyperledger), Hyperledger FacebookLibra Association, The Libra Association Novi wallet, Novi false stake attacks, Proof-of-Stake faucets (Ethereum testnets), Authoring a smart contract Federal Reserve (see US Federal Reserve) federated sidechains, Sidechains fiat currencies, Electronic Systems and Trustblockchain-based assets pegged to, Stablecoins mint-based model, The Whitepaper file storage in web applications, Web 3.0 Financial Action Task Force (FATF), Travel Rule, The FATF and the Travel Rule Financial Crimes Enforcement Network (FinCEN), FinCEN Guidance and the Beginning of Regulation financial crisis of 2008, Electronic Systems and Trust, The 2008 Financial Crisis financial transactions, reliance on trust, Electronic Systems and Trust flash loans, Flash Loans-The Fulcrum Exploitcreating a smart contract for, Creating a Flash Loan Contract-Deploying the Contract deploying the smart contract, Deploying the Contract executing, Executing a Flash Loan-Executing a Flash Loan floatconfiguration 1, Float Configuration 1 configuration 2, Float Configuration 2 configuration 3, Float Configuration 3 timing and managing, Timing and Managing Float Force, Carl, Skirting the Laws forks, Understanding Forks-Replay attacks, Altcoins(see also altcoins) contentious hard forks, Contentious Hard Forks-Replay attacksfork of Bitcoin Cash into Bitcoin SV, The Bitcoin Cash Fork replay attacks vulnerability, Replay attacks different types of, Understanding Forks Ethereum Classic, The Ethereum Classic Fork, Forking Ethereum and the creation of Ethereum Classic fork choice rule in Ethereum 2.0, Ethereum Scaling other Ethereum forks, Other Ethereum forks in proof-of-stake networks, Proof-of-Stake fraud risk as seen by banking audits, Banking Risk Fulcrum attack, The Fulcrum Exploit full nodes (Libra), How the Libra Protocol Works funding amount, Lightning funding transactions, Funding transactions fungible tokens, Fungible and Nonfungible TokensERC-20 standard for, ERC-20 ERC-777 proposed standard for, ERC-777 futures, Derivatives G gambling, on Web 3.0, Web 3.0 gamingpermissioned ledger uses of blockchain, Gaming tracking virtual goods in games, ERC-1155 Garza, Homero Joshua, Skirting the Laws gas, Ether and GasETH Gas Station, Gas and Pricing list of gas prices by opcode, Gas and Pricing GAW Miners, Skirting the Laws GeistGeld, Altcoins Gemini, arbitrage trading on, Arbitrage Trading-Exchange APIs and Trading BotsAPI example, BTC/USD ticker call, Exchange APIs and Trading Bots Genesis block (Bitcoin), Achieving Consensus Gitcoin, Web 3.0 Gnosis, Tokenize Everything government-backed currencies (see fiat currencies) graphics processing units (GPUs), Mining Is About Incentives Grin, Mimblewimble, Beam, and Grin H halting problem, Ether and Gas hard forks, Understanding Forks hardware wallets, Wallet Type Variations, Wallets hash algorithms, Proof-of-Work hash power, Block discovery, How Omni Layer works hash rates, Proof-of-Work Hashcash, Hashcash hashes, Hashcash, Hashes-Custody: Who Holds the KeysBitcoin hash function, double SHA-256, The Merkle Root block, Storing Data in a Chain of Blocks, Block Hashes-Custody: Who Holds the Keys of information generated by transactions in Bitcoin, Introducing the Timestamp Server MD5 password hashes, Zero-Knowledge Proof Merkle root, The Merkle Root-The Merkle Root in proof-of-work cryptocurrency mining, Proof-of-Work public key hash on Bitcoin, Public and Private Keys in Cryptocurrency Systems in Satoshi Nakamoto's whitepaper, The Whitepaper health care, permissioned ledger implementations of blockchain, Health Care height number (block), Storing Data in a Chain of Blocks hex value arguments to smart contract calls, Custody and counterparty risk Honest validator framework, Ethereum Scaling Hong Kong, regulatory arbitrage, Hong Kong hot or cold storage wallets, Counterparty Risk hot wallets, Wallet Type Variations HotStuff algorithm, Borrowing from Existing Blockchains Hyperledger, Hyperledger I IBMIoT interaction by Watson and data storage in Blockchain Platform, Internet of Things toolset offering support for Hyperledger Fabric, Blockchain as a Service identifyverification of, Security Fundamentals identityand dangers of hacking, Identity and the Dangers of Hacking associating with Bitcoin addresses, The Evolution of Crypto Laundering identification services, Private Keys IDEX decentralized exchange, Decentralized Exchange Contracts illiquidity, signs of, Counterparty Risk infinite recursion, Forking Ethereum and the creation of Ethereum Classic information on blockchain industry, Information Infura, Interacting with Code initial coin offerings (ICOs), Mastercoin and Smart Contracts, Tokenize Everything, Initial Coin Offerings-Whitepaperas example of regulatory arbitrage, Initial Coin Offerings DAOs and, Decentralized Autonomous Organizations Ethereum, Tokenize Everything founder intentions, Founder Intentions funds collected into multisignature wallets, Multisignature Contracts illegal activities in, Skirting the Laws legal, regulatory, and other problems with, Tokenize Everything Mastercoin, Tokenize Everything motivations for founders versus venture-funding startups, Whitepaper other terms for, Initial Coin Offerings spectrum of ICO viability, Initial Coin Offerings token economics, Token Economics use of Ethereum platform, Use Cases: ICOs whitepaper, Whitepaper intermediary trust, Electronic Systems and Trust internetdata exchange protocols, evolution of, The More Things Change dot-com crash, Tulip Mania or the internet? evolution of, Electronic Systems and Trust Internet of Things (IoT), permissioned ledger implementations of blockchain, Internet of Things interoperability between different blockchains, Interoperability Interplanetary File System (IPFS), Web 3.0 issuance trust, Electronic Systems and Trust IT systems, permissioned ledger uses, IT Ixcoin, Altcoins J Java, Corda language JPMorgan, JPMorganinterbank payments using permissioned ledger, Payments jurisdiction over cryptocurrency exchanges, Jurisdiction K Keccak-256 hash algorithm, Hashes Know Your Customer (KYC) rules, Banking Risk, DAIon centralized and decentralized exchanges, Know your customer crypto laundering and, The Evolution of Crypto Laundering implementation in Novi wallet, Novi in Singapore, Singapore stablecoins requiring/not requiring, KYC and pseudonymity L LBFT consensus protocol, How the Libra Protocol Works Ledger wallet, Wallets ledgers, Storing Data in a Chain of Blocks, Databases and LedgersCorda, Corda ledger distributed verifiable, key properties of, Key Properties of Distributed Verifiable Ledgers Hyperledger Fabric technology, Hyperledger permissioned ledger uses of blockchain, Permissioned Ledger Uses-Payments Ripple, Ripple legal industry, permissioned ledger uses, Legal legal requirements, cryptocurrency and blockchain technology skirting the laws, Skirting the Laws lending services (DeFi), Lending less than 5% rule, Counterparty Risk Libra, Libra-Summaryborrowing from existing blockchains, Borrowing from Existing Blockchains centralization challenges, Novi how the Libra protocol works, How the Libra Protocol Works-Transactionsblocks, Blocks transactions, Transactions Libra Association, The Libra Association Novi wallet and other third-party wallets, Novi Lightning, Lightning, Lightningfunding transactions, Funding transactions nodes and wallets, Lightning nodes and wallets off-chain transactions, Off-chain transactions solving scalability issues on Blockchain, Lightning Liquid multisignature wallet, Liquid liquidity, Arbitrageor depth in a market, Hunting for Bart Litecoin, Litecoin longest chain rule, The mining process lottery-based consensus, Alternative methods M MaidSafe, Understanding Omni LayerICO for, Use Cases: ICOs Maker project's DAI, DAIsavings rates for DAI, Savings Malta, regulatory arbitrage, Malta man in the middle attacks, Zero-Knowledge Proof margin/leveraged products, Derivatives market capitalization, low, cryptocurrencies with, Whales market depthconsiderations in cryptocurrency trading, Basic Mistakes lacking in cryptocurrency market, Cryptocurrency Market Structure market infrastructure, Market Infrastructure-Summaryanalysis, Analysis-Hunting for Bartfundamental cryptocurrency analysis, Fundamental Cryptocurrency Analysis-Tools for fundamental analysis technical cryptocurrency analysis, Technical Cryptocurrency Analysis-Hunting for Bart arbitrage trading, Arbitrage Trading-Float Configuration 3 cryptocurrency market structure, Cryptocurrency Market Structure-Transaction flowsaribtrage, Arbitrage counterparty risk, Counterparty Risk market data, Market Data-Transaction flows depth charts, Depth Charts derivatives, Derivatives exchange APIs and trading bots, Exchange APIs and Trading Bots-Market Aggregatorsmarket aggregators, Market Aggregators open source trading tech, Open Source Trading Tech rate limiting, Rate Limiting REST versus WebSocket APIs, REST Versus WebSocket testing trading bot in sandbox, Testing in a Sandbox exchanges, The Role of Exchanges-The Role of Exchanges order books, Order Books regulatory challenges, Regulatory Challenges-Basic Mistakes slippage in cryptocurrency trading, Slippage wash trading, Wash Trading ways to buy and sell cryptocurrency, Evolution of the Price of Bitcoin whales, Whales market size, Order Books Mastercoin, Mastercoin and Smart Contracts, Tokenize EverythingEthereum and, Ethereum: Taking Mastercoin to the Next Level raising cryptocurrency funds to launch a project, Use Cases: ICOs Meetup.com, Information mempool, unconfirmed transactions on Bitcoin, Transaction life cycle Merkelized Abstract Syntax Trees (MAST), Privacy Merkle roots, Storing Data in a Chain of Blocks, The Merkle Root-The Merkle Rootin block hashes, Block Hashes Merkle trees, The Merkle Root MetaMask wallet, ConsenSys, Walletsusing in writing smart contracts, Writing a smart contract Middleton, Reggie, Skirting the Laws Mimblewimble, Mimblewimble, Beam, and Grin mining, Mining-Block Generation, Evolution of the Price of BitcoinBitcoin, problems with, Ripple and Stellar block generation, Block Generation GAW Miners, Skirting the Laws impacts on market data, Slippage incentives for, Mining Is About Incentives miners discovering new block at same time, The mining process process on Bitcoin for block discovery, The mining process Scrypt, Altcoins transactions confirmed by miner on Bitcoin, Transaction life cycle mint-based currency model, The Whitepaper minting, Important Definitions MKR token, DAI mobile wallets, Wallet Type Variations Moesif’s binary encoder/decoder, Custody and counterparty risk Monero, Monero, Ring Signatures, The Evolution of Crypto Laundering, Blockchains to Watchhow it works, How Monero Works-How Monero Works money laundering, Banking Risk(see also Anti-Money Laundering (AML) rules) evolution of crypto laundering, The Evolution of Crypto Laundering-The Evolution of Crypto Laundering Money Services Business (MSB) standards, The FATF and the Travel Rule MoneyGram, Ripple Mt.

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Gox-Bitfinex multisignature wallet contracts, Multisignature Contracts-Multisignature Contracts N Namecoin, Altcoins naming services, Naming Services network hash rate, Block discovery networkscentralized versus decentralized versus distributed design, Distributed Versus Centralized Versus Decentralized Corda, The Corda networknodes having visibility into transactions, Corda ledger DAG design, DAGs Libra's centralization challenge, Novi transactions confirmed by network on Bitcoin, Transaction life cycle New York Department of Financial Services (NYDFS), FinCEN Guidance and the Beginning of Regulation NiceHash, NiceHash Nightfall blockchain, Nightfall nodes, Distributed Versus Centralized Versus Decentralizedin Avalance consensus mechanism, Avalanche Libra, validator and full nodes, How the Libra Protocol Works Lightning, Lightning nodes and wallets in proof-of-stake networks, Proof-of-Stake nonces, The mining processin block discovery on Bitcoin, The mining process running out of nonce space or overflow, The mining process in Satoshi Nakamoto's whitepaper, The Whitepaper noncustodial wallets, Wallet Types: Custodial Versus Noncustodial(see also wallets) nonfungible tokens, Fungible and Nonfungible TokensERC-721 standard for, ERC-721 Nothing-at-Stake problem, Proof-of-Stake Novi wallet, Novi NuBits, NuBits NXT blockchain, NXT O oligarchical model dominating the web, Web 3.0 Omni Core, Understanding Omni Layerlimitations of, Deploying and Executing Smart Contracts in Ethereum Omni Layer, Understanding Omni Layer-Adding custom logicadding custom logical operations to Bitcoin, Adding custom logic-Adding custom logic how it works, How Omni Layer works limitations of, Deploying and Executing Smart Contracts in Ethereum technical stack, overview of, Understanding Omni Layer Tether project built on, Tether opcodes, Gas and Pricing Open Systems Interconnection (OSI) model, The More Things Change operating system platform (EOS), Blockchains to Watch operators, ERC-777, ERC-1155 Optimistic Rollups, Other Altchain Solutions, Lightning nodes and wallets options, Derivatives OP_RETURN field, Adding custom logictranslation of metadata in, Adding custom logic Oracle, Blockchain Platform, Blockchain as a Service oracles, Important Definitionsmanipulation in Fulcrum attack, The Fulcrum Exploit order books, Order Booksthin, slippages and, Slippage over-the-counter (OTC) market, Slippage P paper wallets, Wallet Type Variations Parity, Parity Parity hack (2017), Parity participants, Participants passwordssecurity vulnerabilities, Zero-Knowledge Proof Thinbus Secure Remote Password protocol, Zero-Knowledge Proof pay-to-play, Tools for fundamental analysis payment channels, Lightningnode dropping or losing connection to, Lightning nodes and wallets opening by sending funding transaction, Funding transactions withdrawing funds from, Off-chain transactions payment systemsLibra, Borrowing from Existing Blockchains permissioned ledger uses of blockchain, Payments physical cash versus digital, Electronic Systems and Trust Permacoin, Alternative methods permissioned ledger uses of blockchain, Permissioned Ledger Uses-Paymentsbanking, Banking central bank digital currencies, Central Bank Digital Currencies gaming, Gaming health care, Health Care Internet of Things, Internet of Things IT systems, IT payments systems, Payments permissioned ledgers, Databases and Ledgers permissionless ledgers, Databases and Ledgers person-to-person trading of cryptocurrency, Evolution of the Price of Bitcoin phishing attacks, Security Fundamentals Plasma implementation of sidechains, Other Altchain Solutions Ponzi schemes in cryptocurrency, Skirting the Laws PotCoin, More Altcoin Experiments precompilation of zk-SNARKs, zk-SNARKs preminingissues with, Litecoin premined altcoin, Ixcoin, Altcoins prices (gas), Gas and Pricing Primecoin, Altcoins privacyand censorship resistance with dapps, Use Cases Ethereum-based privacy implementations, Ethereum-Based Privacy Implementations future developments in blockchains, Privacy information security in decentralizing finance and the web, Privacy-Ring Signaturesring signatures, Ring Signatures Zcash, Zcash zero-knowledge proof, Zero-Knowledge Proof zk-SNARKs, zk-SNARKs insufficient anonymity on Bitcoin, The Evolution of Crypto Laundering paired with scalability, Mimblewimble blockchain protocol, Mimblewimble, Beam, and Grin privacy-focused blockchains, PrivacyMonero, Blockchains to Watch-How Monero Works Zcash, Zcash privacy-focused cryptocurrencies, Privacy-Focused CryptocurrenciesDash, Dash Monero, Monero Zcash, Zcash private blockchain networks, Privacy private blockchains, The Enterprise Ethereum Alliance private keys, Public/private key cryptography(see also public/private key cryptography) products/services, buying or selling, Evolution of the Price of Bitcoin proof-of-history, Alternative methods proof-of-stake, Proof-of-Stake-Proof-of-StakeByzantine fault-tolerant algorithm, HotStuff, Borrowing from Existing Blockchains Casper algorithm in Ethereum 2.0, Ethereum Scaling proof-of-stake velocity, More Altcoin Experiments proof-of-storage, Alternative methods proof-of-work, Block Generation, Proof-of-Work-Confirmationsbit gold's client puzzle function type, Bit Gold block discovery, Block discovery confirmations by miners of blocks to include in blockchain, Confirmations criticisms of, Proof-of-Stake, Ripple and Stellar CryptoNote protocol, Monero Ethereum's Ethash protocol, Ethereum: Taking Mastercoin to the Next Level longest chain rule, The mining process mining process for block discovery on Bitcoin, The mining process mining process on Bitcoin, The mining process in Satoshi Nakamoto's whitepaper, The Whitepaper transaction life cycle, Transaction life cycle use by B-Money, B-Money use by Hashcash, Hashcash X11 ASIC-resistant, Dash protocols, Electronic Systems and Trust pseudonimity, KYC rules and, KYC and pseudonymity public keys, Public/private key cryptography(see also public/private key cryptography) public/private key cryptographyBitcoin's use of, Public/private key cryptography examples of public and private keys, Naming Services generating keys, Generating keys private key storage for digital wallets, Authoring a smart contract private keys for wallets, Private Keys public and private keys in cryptocurrency systems, Public and Private Keys in Cryptocurrency Systems-Public and Private Keys in Cryptocurrency Systems unauthorized access to private key, Bitcoin Transaction Security use in controlling access to personal information, Identity and the Dangers of Hacking pull transactions, Bitcoin Transaction Security, ERC-777 push transactions, Bitcoin Transaction Security, ERC-777 Q Quantum Ledger Database (QLDB), Blockchain as a Service Quorum blockchain, Quorum, JPMorgan R ransomware, CryptoLocker and, CryptoLocker and Ransomware rate limiting, Exchange Risk, Rate Limiting real estate transactions, using tokens on a blockchain, Tokens on the Ethereum Platform recovery seed, Recovery Seed recursive call vulnerability, Forking Ethereum and the creation of Ethereum Classic regulationof cryptocurrency exchanges, Jurisdiction FATF and the Travel Rule, The FATF and the Travel Rule FinCEN guidance and beginnings of, FinCEN Guidance and the Beginning of Regulation-FinCEN Guidance and the Beginning of Regulation regulatory challenges in cryptocurrency market, Regulatory Challenges-Basic Mistakes regulatory issues with ICOs, Tokenize Everything regulatory arbitrage, Avoiding Scrutiny: Regulatory Arbitrage-Crypto-Based StablecoinsICOs as example of, Initial Coin Offerings relational databases, Databases and Ledgers replay attacks, Replay attacksprotecting against, on Ethereum and Ethereum Classic, The Ethereum Classic Fork replication systems, Databases and Ledgers REST APIsEthereum network, Interacting with Code WebSocket versus, REST Versus WebSocket ring confidential transactions, Blockchains to Watch, How Monero Works ring signatures, Monero, Ring Signatures, Blockchains to Watchhiding public address of sender on Monero, How Monero Works Ripple, Other Concepts for Consensus, Rippleblock times, Float Configuration 2 Robinhood mobile app, Brokerages Rollups, Zero Knowledge (ZK) and Optimistic, Other Altchain Solutions, Lightning nodes and wallets Royal Mint, The Royal Mint S Santander, blockchain-issued bonds, Banking SAP, Blockchain as a Service, Blockchain as a Service satoshi, Gas and Pricing Satoshi Nakamotobitcoin address related to, The Evolution of Crypto Laundering efforts to establish identity of, Storing Data in a Chain of Blocks identity, guesses at, Bahamas Satoshi's Vision group (Bitcoin SV), The Bitcoin Cash Fork whitepaper, The Whitepaper savings services (DeFi), Savings scalabilitycentralized versus decentralized exchanges, Scalability discontent over Bitcoin network's scaling, The Bitcoin Cash Fork EOS solution to blockchain issues, Tokenize Everything privacy paired with, Mimblewimble blockchain potocol, Mimblewimble, Beam, and Grin Scalable Transparent ARguments of Knowledge (STARKs), STARKs scaling blockchains, Scaling Blockchains-Other Altchain Solutions, The Scaling Problem-Ethereum ScalingAvalanche consensus mechanism, Avalanche DAG network design, DAGs Ethereum, Ethereum Scaling-Ethereum Scaling Lightning solution, Lightning, Lightning-Lightning nodes and wallets Liquid multisignature wallet, Liquid other altchain solutions, Other Altchain Solutions SegWit, SegWit sharding, Sharding sidechains, Sidechains STARKs, STARKs Schnorr algorithm, Privacy Scott, Mark, Skirting the Laws SCP consensus protocol, Stellar scripted money, Improving Bitcoin’s Limited Functionality Scrypt mining, Altcoins, Litecoin Secret Network, Privacy securitiestokens proposed in ICOs, Different Token Types unregistered securities offerings, Skirting the Laws Securities and Exchange Commission (SEC), FinCEN Guidance and the Beginning of Regulation securityBitcoin transaction security, Bitcoin Transaction Security custody infrastructure for exchanges, Counterparty Risk detection of blockchain tampering with Merkle roots, The Merkle Root early vulnerability on Bitcoin, An Early Vulnerability exchanges taking care of private keys, Counterparty Risk flash loans exploiting vulnerabilities in DeFi platforms, The Fulcrum Exploit fundamentals for cryptocurrencies, Security Fundamentals-Recovery Seed identity and dangers of hacking, Identity and the Dangers of Hacking information security in decentralizing finance and the web, Privacy Lightning Network vulnerabilities, Lightning proof-of-stake consensus algorithm, criticisms of, Proof-of-Stake recursive call vulnerability, Forking Ethereum and the creation of Ethereum Classic replay attacks vulnerability, Replay attacks, The Ethereum Classic Fork sharding, vulnerabilities with, Other Altchain Solutions theft of cryptocurrencies in exchange hacks, Exchange Hacks-NiceHash theft of cryptocurrencies in other hacks, Other Hacks-Summary transaction malleability vulnerability, Lightning nodes and wallets security token offerings (STOs), Different Token Types security tokens, Token Economics seeds (recovery), Recovery Seedstorage of, Authoring a smart contract SegWit (Segregated Witness), SegWit, Lightning nodes and wallets self-sovereign identity, Identity and the Dangers of Hacking SHA-256 hash algorithm, Introducing the Timestamp Server, Hashes SHA256 and RIPEMD160 functions, Generating keys shadow market for disinformation, Tools for fundamental analysis sharding, Other Altchain Solutions, Shardingin Ethereum 2.0, Ethereum Scaling Shavers, Trendon, Skirting the Laws Shrem, Charlie, Skirting the Laws sidechains, Other Altchain Solutions, SidechainsLiquid technology and, Liquid Optimistic Rollups and, Lightning nodes and wallets Silk Road, Catch Me If You Cancriminal investigation tracking bitcoin address to operator, The Evolution of Crypto Laundering provision of bitcoin to users without KYC/AML, Skirting the Laws SIM swapping, SIM Swapping-SIM Swapping Singapore, regulatory arbitrage, Singapore single-shard takeover attacks, Other Altchain Solutions slashing algorithms, Proof-of-Stake slippage, Slippage smart contracts, Mastercoin and Smart ContractsDAML language for distributed applications, DAML for decentralized exchanges, Decentralized Exchange Contracts, Custody and counterparty risk deploying and executing in Ethereum, Deploying and Executing Smart Contracts in Ethereum-Interacting with Codeauthoring a smart contract, Authoring a smart contract deployment, Deploying a smart contract-Deploying a smart contract Ethereum Virtual Machine (EVM), The Ethereum Virtual Machine executing a smart contract, Executing a smart contract gas and pricing, Gas and Pricing interacting with a smart contract, Interacting with a smart contract programmatically interacting with Ethereum, Interacting with Code reading a smart contract, Reading a smart contract writing a smart contract, Writing a smart contract deployment for dapps, Challenges in Developing Dapps EOS platform, Blockchains to Watch ERC-20 compliantevents supported by, ERC-20 example of, ERC-20-ERC-20 methods implemented, ERC-20 ERC-compliant, library of, Decentralized Exchange Contracts flash loanscreating the contract, Creating a Flash Loan Contract-Deploying the Contract deploying the contract, Deploying the Contract manipulation of oracles in Fulcrum attack, The Fulcrum Exploit steps in process, Flash Loans Libra support for, Borrowing from Existing Blockchains Omni Layer providing, Understanding Omni Layer publicly viewable record of method call to Uniswap smart contract, Custody and counterparty risk-Exchange rate sending tokens to via push and pull transactions, ERC-777 third-party auditors of, Fungible and Nonfungible Tokens Uniswap contract viewable on Ethereum, Infrastructure social media, campaigns to influence cryptocurrencies, Tools for fundamental analysis soft forks, Understanding Forks software development, changes from use of cryptcurrency and blockchain, Web 3.0 software forks, Understanding Forks software wallets, Wallets Solidcoin, Altcoins Solidity language, Authoring a smart contract South Korean exchanges, Regulatory Challenges speculation in cryptocurrency, Market Infrastructure, Tulip Mania or the internet?

Bezonomics: How Amazon Is Changing Our Lives and What the World's Best Companies Are Learning From It
by Brian Dumaine
Published 11 May 2020

Clement, “Number of Full-Time Facebook Employees from 2007 to 2018,” Statista, August 14, 2019, https://www.statista.com/statistics/273563/number-of-facebook-employees/. By 2022, there will be: “Growth of the Internet of Things and in the Number of Connected Devices Is Driven by Emerging Applications and Business Models, and Supported by Standardization and Falling Device Costs,” Internet of Things Forecast, Ericsson.com, https://www.ericsson.com/en/mobility-report/internet-of-things-forecast. When Henry Ford proved: “Celebrating the Moving Assembly Line in Pictures,” Ford Media Center, September 12, 2013, https://media.ford.com/content/fordmedia/fna/us/en/features/celebrating-the-moving-assembly-line-in-pictures.html.

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For the most part, Alphabet, Facebook, Baidu, and other big tech firms hire the kind of workers whose jobs are not likely to be threatened by automation. By contrast, Amazon operates not only in cyberspace but also in the realm of the tangible. The company is a leader in the adoption of the Internet of Things—which at its heart is the digitization of much of what we do in the real world. Devices such as cell phones, Amazon Echo smart speakers, Amazon microwaves, earbuds, and thermostats connect to the Internet, making them smarter and easier for us to control. (And as we saw in the previous chapter, easier for the companies that make them to collect data on our buying habits.)

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Alphabet, Facebook, Netflix, Alibaba, JD.com, and Tencent have built huge, powerful businesses based on their ability to collect and analyze data, and keep applying those learnings to make their businesses smarter and their offerings to customers more attractive. In their pursuit of AI-driven technologies such as voice and facial recognition, the Internet of Things, and robotics, they’re creating automated business models that will crush traditional businesses that fail to adapt to this new world. And the emergence of 5G technology, which will replace our current digital networks, will only widen the gap. Experts predict that this next generation of Internet connectivity will be as much as a hundred times faster than today’s web.

Makers at Work: Folks Reinventing the World One Object or Idea at a Time
by Steven Osborn
Published 17 Sep 2013

Yeah, I haven’t heard of that site, but I’m definitely seeing more tool sharing services. One for CNCs and pick and place machines makes a lot of sense. Osborn: Let’s see. So another theme getting some attention is the “Internet of Things,” which some people see as being the next big evolution of the Internet—monitoring and controlling everyday things remotely over the Internet. In your words, can you describe what that is and the impact you see it having? Seidle: The Internet of Things? I’m an electrical engineer, and so everything looks like an electrical engineering problem. We’ve been building a lot of stuff for a large number of years. As the processors get cheaper, and the development boards get cheaper, it’s easier and easier to hook up Ethernet and push some data around.

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I knew they were developing FLORA and they brought me on as director of wearables to sort of spearhead projects that people would make with the FLORA, and to help work on it late in its development there. Osborn: There seems to be a lot of interest lately—I think it’s more buzzword—in the whole “Internet of Things.” I was wondering if you’ve seen some projects that are interesting or that incorporated some wireless technology or biosensing that is interesting? Stern: So my favorite biosensing Internet of Things project is the chair that tweets when you fart. Osborn: Of course. Stern: That’s by my friend Randy Sarafan, who works at Instructables and is also a member of the FAT. He put the methane sensor in the chair and the Arduino in the XBee and the other part of the XBee that connects to the computer and used the Twitter API, and all that kind of stuff.

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Osborn: Oh man, I don’t want to take that any further. Stern: You might have seen Internet of Things printers. We have one for Raspberry Pi and Arduino. Those are really fun, just little a receipt printer that prints out whatever you want—like the weather. It’s really fun when it’s just sitting on your desk and it finds a tweet about you and prints it out. So instead of having to check my tabs with all of my Twitter tools in it or whatever, it just sort of prints them all out. That can be fun. Osborn: So let’s see. There’s the Internet of Things. There’s 3D printing. You do a lot of work with wearable technologies.

Data and Goliath: The Hidden Battles to Collect Your Data and Control Your World
by Bruce Schneier
Published 2 Mar 2015

Google Glass is the first wearable device: Jenna Wortham (8 Mar 2013), “Meet Memoto, the lifelogging camera,” New York Times Blogs, http://bits.blogs.nytimes.com/2013/03/08/meet-memoto-the-lifelogging-camera. Internet of Things: Ken Hess (10 Jan 2014), “The Internet of Things outlook for 2014: Everything connected and communicating,” ZDNet, http://www.zdnet.com/the-internet-of-things-outlook-for-2014-everything-connected-and-communicating-7000024930. smart cities: Georgina Stylianou (29 Apr 2013), “Idea to have sensors track everything in city,” Press (Christchurch), http://www.stuff.co.nz/the-press/business/the-rebuild/8606956/Idea-to-have-sensors-track-everything-in-city. Victoria Turk (Jul 2013), “City sensors: the Internet of Things is taking over our cities,” Wired, http://www.wired.co.uk/magazine/archive/2013/07/everything-is-connected/city-sensors.

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Schechter (11 May 2006), “Milk or wine: Does software security improve with age?” MIT Lincoln Laboratory, https://research.microsoft.com/pubs/79177/milkorwine.pdf. economics of software development: This is even worse with embedded devices and the Internet of Things. Bruce Schneier (6 Jan 2014), “The Internet of Things is wildly insecure—and often unpatchable,” Wired, http://www.wired.com/2014/01/theres-no-good-way-to-patch-the-internet-of-things-and-thats-a-huge-problem. how the NSA and GCHQ think: James Ball, Julian Borger, and Glenn Greenwald (5 Sep 2013), “Revealed: How US and UK spy agencies defeat internet privacy and security,” Guardian, http://www.theguardian.com/world/2013/sep/05/nsa-gchq-encryption-codes-security.

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smart pill bottles: Valentina Palladino (8 Jan 2014), “AdhereTech’s smart pill bottle knows when you take, and miss, your medication,” Verge, http://www.theverge.com/2014/1/8/5289022/adheretech-smart-pill-bottle. smart clothing: Econocom (19 Sep 2013), “When fashion meets the Internet of Things,” emedia, http://blog.econocom.com/en/blog/when-fashion-meets-the-internet-of-things. Michael Knigge (28 Aug 2014), “Tagging along: Is Adidas tracking soccer fans?” Deutsche Welle, http://www.dw.de/tagging-along-is-adidas-tracking-soccer-fans/a-1788463. because why not?: We’ve seen this trend before. Digital clocks first became popular in the 1970s.

Snowden's Box: Trust in the Age of Surveillance
by Jessica Bruder and Dale Maharidge
Published 29 Mar 2020

In a Connecticut murder case, prosecutors obtained the victim’s FitBit records to build a case against her husband, who claimed a masked intruder had shot her when the device showed she was still walking around. The internet of things is a gold mine for police. Researchers are working to expand its applications for law enforcement. At Champlain University in Vermont, graduate students dedicated a semester to “Internet of Things Forensics,” studying the Nest thermostat and other devices to see how they could help criminal investigations. A program description praised the “diversity and usefulness” of networked objects — ranging from “routers that connect a laptop to the internet” to “a crockpot (from WEMO) and slippers (from 24eight).”

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That sounds alarmist until you look back at 2006, when federal agents got permission to use a cellphone as a “roving bug.” What would prevent them from making a similar request involving an Amazon Echo or any other smart device with a microphone or sensors? The spread of networked devices — the so-called internet of things — could someday give police easy access to the most private parts of our lives. Law enforcement already has a formidable array of surveillance technologies, ranging from license plate readers to the cell site simulators nicknamed “stingrays” that mimic mobile phone towers to facial recognition and access to credit card transactions — an area of data that is mushrooming as some areas of the country move towards a cashless economy.

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These gizmos add comfort to our lives, but they also stalk us relentlessly — both online and in the physical world, often without our consciously consenting to this digital home invasion. Read the fine print. Decide how much of your privacy you’re willing to sacrifice in the name of convenience. Keep in mind that the burgeoning field of digital forensics has turned its attention to the internet of things. Nowadays, there are entire college programs dedicated to mining the data gathered by our digital devices. Carry this approach over to social media. Facebook, Twitter, Instagram, and other platforms may keep you virtually connected with friends and family. Their primary purpose, however, is mapping your patterns of consumption and even your political preferences, which may be sold to the highest bidder.

12 Bytes: How We Got Here. Where We Might Go Next
by Jeanette Winterson
Published 15 Mar 2021

Not everything in this world is destined to be replaced by something else. * * * What about humans though? Are we going to be replaced – or at least become less and less relevant – or are we evolving? In the next decade – 2020 onwards – the internet of things will start the forced evolution and gradual dissolution of Homo sapiens as we know it. But before we get to the internet of things and a world of connected devices – and some directly connected humans – let’s go back to the internet itself – to see how far we have come, and where we might be going. * * * Back in late-1960s America, soon after the Summer of Love, the Advanced Research Projects Agency Network (ARPANET) adopted a British packet-switching system to transmit limited data between research institutions.

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Forster put it in 1910, in his novel Howards End: ‘Only connect.’ * * * Or, as Ada put it, ‘A uniting link is established between the operations of matter and the abstract mental processes …’ * * * In 2021 Google’s next goal is ambient computing: that’s wrap-around connectivity. Hardware/software/user experience/ machine-human interaction. The Internet of Things. All integrated. From cat-flaps to coffee machines. Voice-activated digital assistants. 3-D printers. Smart homes. All working together. Invisibly. Permanently. No need for clicks. Think and it will appear. Magic lamp. Operations of matter. Abstract mental processes. * * * Ultimately – and Ada was right about this – the uniting link between the operations of matter and abstract mental processes is to reimagine – completely – what we call ‘real’.

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When we consider that the earth is about 4.5 billion years old, and that the oldest Homo sapiens fossil, discovered in Morocco in 2017, is 300,000 years, then what has happened in the last 250 years is time on a tiny scale – my 1780s house in London has lived that long. Not the next 250 years but the next 25 years will take us into a world where intelligent machines and non-embodied AI are as much a part of everyday life as humans are. Many of the separate strings we are developing now – the Internet of Things, blockchain, genomics, 3D printing, VR, smart homes, smart fabrics, smart implants, driverless cars, voice-activated AI assistants – will work together. Google calls it ambient computing: it’s all around you. It’s inside you. This future isn’t about tools or operating systems; the future is about co-operating systems.

The Sharing Economy: The End of Employment and the Rise of Crowd-Based Capitalism
by Arun Sundararajan
Published 12 May 2016

Two contemporary developments illustrate how the same three invariant forces, and thus the same economics, that led to the consumerization of digital may reshape our everyday physical objects: the Internet of Things and the emergence of additive manufacturing. The Internet of Things In the not-so-distant future, every “thing” will have the potential to be digitized and networked. In an iconic example (although perhaps not the most cost-effective), a milk carton nearing or getting close to its expiration date will communicate with your refrigerator, which will in turn communicate with your FreshDirect grocery list. Cartons of fresh milk will subsequently be delivered to your home, allowing you to focus your attention on more important things. This is the Internet of Things—a world where objects of all kinds from milk cartons to household appliances to items of clothing have a little bit of embedded digital intelligence, and are part of the network.

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The refrigerator will register this information and add milk to the grocery list at an online delivery service.7 In other words, in the near future, a growing number of quotidian objects will be able to talk to each other over a network. This is not, to be clear, the stuff of science fiction. After all, the Internet of Things does not promise to help us have intelligent conversations with our refrigerators or milk cartons (at least not anytime soon). Elevators imbued with a little intelligence are unlikely, as the humorist Douglas Adams posited, to get bored with their mundane jobs of traveling up and down and take to sulking in building basements. Yet the Internet of Things—though not yet delivering articulate appliances or portending device depression—will inevitably expand crowd-based capitalism.

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Put differently, a physical object will know where it is, how much it is being used, and will be able to arrange automated, digitally enabled transport for itself to its renter without human intervention.8 A physical object becomes, in a sense, like an intelligent iTunes movie file. As a consequence, the “rentability” of objects also expands. On-demand services of all kinds become more viable, more efficient, and more ubiquitous with the Internet of Things. 3-D Printing and Additive Manufacturing Until recently, if you wanted to get into the business of making and selling physical objects, you had to acquire the capabilities of manufacturing and find some way of distributing and selling objects (by connecting, for example, with a wholesaling or retailing network).

Future War: Preparing for the New Global Battlefield
by Robert H. Latiff
Published 25 Sep 2017

Unsurprisingly, the military has its own Internet of things. For several years, the Army issued helmets with built-in sensors to help diagnose brain injuries. Even individual munitions have Internet addresses. Almost everything—conventional bombs and nuclear weapons, soldier communications and satellites, simple vehicles and advanced armor systems, lasers and navigation systems—depends on computers and their components. The Internet of things and Wi-Fi-enabled devices, with all of their advantages, also come with some serious downsides. Always-on sensors are threats to privacy and civil liberties, and the Internet of things was recently determined to have provided the hardware basis for a massive denial-of-service attack on East Coast Internet service providers.

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The term “information technology” is a broad one, encompassing a range of areas including microelectronic devices such as microprocessors and transistors, the Internet, high-performance computing, algorithms, data collection and storage, data transmission, data mining and analysis, and the “Internet of things”—a term used to describe the growing trend of putting sensors on everything and tying them to the Internet. Computing power and memory have grown astronomically, as has the amount of data generated. The number of transistors that can now be placed on a chip lies in the hundreds of billions. The amount of data now gathered in two days exceeds all of the data created from the dawn of civilization to 2003. The Internet of things has grown enormously, with the number of connected devices worldwide predicted to be almost forty billion in 2020.

Exponential Organizations: Why New Organizations Are Ten Times Better, Faster, and Cheaper Than Yours (And What to Do About It)
by Salim Ismail and Yuri van Geest
Published 17 Oct 2014

And this is just beginning. Ten years ago we had five hundred million Internet-connected devices. Today there are about eight billion. By 2020 there will be fifty billion and a decade later we’ll have a trillion Internet-connected devices as we literally information-enable every aspect of the world in the Internet of Things. The Internet is now the world’s nervous system, with our mobile devices serving as edge points and nodes on that network. Think about that for a second: we’ll be jumping from eight billion Internet-connected devices today to fifty billion by 2025, and to a trillion a mere decade later. We like to think that thirty or forty years into the Information Revolution we are well along in terms of its development.

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As far back as 2005, writer and publisher Tim O’Reilly stated that, “Data is the new Intel Inside.” And that was when there were just a half-billion Internet-connected devices in the world. As we noted in Chapter One, that number is set to grow to a trillion devices as we prepare to embrace the Internet of Things. In the face of that explosion, the need for algorithms has become mission critical. Consider for a moment that the last two years have seen nine times more data created than in the entire history of humanity. Then consider that the Computer Sciences Corporation believes that by 2020 we’ll have created a total 73.5 zettabytes of data—in Stephen Hawking’s phraseology, that’s seventy-three followed by twenty-one zeros.

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Although the Milkmaid was just a pilot [Experimentation], the project was deemed a huge success, and in 2013, GE and Quirky announced the next stage of their innovative new partnership: GE gave Quirky’s 900,000 community members open access to GE’s most promising patents and technologies. It also started a co-branded Internet of Things initiative called “Wink: Instantly Connected,” dedicated to building a line of smart home devices. GE, which invested $30 million in Quirky, chose to open up its patents in order to accelerate the creation of new, innovative products—something GE determined the crowd could accomplish more quickly than it could do on its own.

The Internet Is Not the Answer
by Andrew Keen
Published 5 Jan 2015

Reporting about CES, the Guardian’s Dan Gillmor warned that networked televisions that “watch us” are “closing in on Orwell’s nightmarish Big Brother vision.”32 Even industry executives are fearful of the Internet of Things’s impact on privacy, with Martin Winterkorn, the CEO of Volkswagen, warning in March 2014 that the connected car of the future “must not become a data monster.”33 But there is one fundamental difference between the Internet of Things and Erich Mielke’s twentieth-century Big Brother surveillance state, one thing distinguishing today’s networked society from Orwell’s 1984. Mielke wanted to create crystal man against our will; in today’s world of Google Glass and Facebook updates, however, we are choosing to live in a crystal republic where our networked cars, cell phones, refrigerators, and televisions watch us.

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An Ericsson white paper predicts that, by 2020, there will be 50 billion intelligent devices on the network.4 Homes, cars, roads, offices, consumer products, clothing, health-care devices, electric grids, even those industrial cutting tools once manufactured in the Musto Steam Marble Mill company, will all be connected on what now is being called the Internet of Things. The number of active cellular machine-to-machine devices will grow 3 to 4 times between 2014 and 2019. “The physical world,” a McKinsey report confirms, “is becoming a type of information system.”5 The economics of this networked society are already staggering. Another McKinsey report studying thirteen of the most advanced industrial economies found that $8 trillion is already being spent through e-commerce.

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Every decade there’s a major revolution in Silicon Valley. In the mid-1990s, it was the original Web 1.0 revolution of free websites like Netscape, Yahoo, and Craigslist. In 2005, it was Tim O’Reilly’s Web 2.0 user-generated-content revolution of Google, Wikipedia, and YouTube. And today, in 2014, it’s the “Internet of Things” revolution of 3-D printing, wearable computing, driverless cars, and intelligent drones. To learn more about today’s revolution, I had returned to the scene of my original disenchantment with the Internet. I’d once again come to the O’Reilly Media offices in Sebastopol, the little town up in Sonoma County, California, some fifty miles north of San Francisco.

Warnings
by Richard A. Clarke
Published 10 Apr 2017

The same SCADA software used by the Iranians is used in thousands of U.S. manufacturing and operating plants and facilities and is susceptible to the same exploits that the U.S. used to destroy those Iranian centrifuges. This computer network, transparent to most of us, is a critical part of the global infrastructure. It is known as the Internet of Things, the IoT. “The Internet of Things is just a marketing term that somebody thought up long after millions of machines were already networked,” Weiss explained to us. “And most of them are networked in ways that can be accessed, perhaps indirectly, from the public Internet.” It is that understanding, that everything is connected, that keeps Joe Weiss up at night.

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Who now among us may be accurately warning us of something we are ignoring, perhaps at our own peril? We look at contemporary individuals and their predictions, and examine the ongoing public reaction to them. Our cases here include artificial intelligence, genetic engineering, sea level rise, pandemic disease, a new risk of nuclear winter, the Internet of Things, and asteroid impacts. Finally, we end this volume with some thoughts about how society and government might reduce the frequency of ignoring Cassandras when it comes to some of the major issues of our time. While we will not endorse the predictions of the possible contemporary Cassandras (we leave it to the reader to decide), we will apply our framework to their cases, evaluating each element—the individual, the receiver of the warning, and the threat itself—to determine the Cassandra Coefficient.

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The utility, PG&E, was hit with a $1.6 billion fine. The accident investigation report blamed the disaster on a substandard segment of pipe and technical errors. There was no suggestion that the software error was intentional, no indication that malicious actors were involved. “But that’s just the point,” Joe Weiss argues. “The Internet of Things introduces new vulnerabilities even without malicious actors.” The problem, Weiss claims, is using Internet software for things that it was never intended to run, like industrial controls, or linking solid industrial control software over Internet communications networks. The icon of the IoT and the darling of Silicon Valley techies and entrepreneurs is a round, wall-mounted gadget called Nest.

Zucked: Waking Up to the Facebook Catastrophe
by Roger McNamee
Published 1 Jan 2019

What I have in mind is an independent company that represents the interest of users at login, providing the minimum information required for each transaction. With each new generation of technology, entrepreneurs and engineers have an opportunity to profit from designing products that serve rather than exploit the needs of their users. Virtual reality, artificial intelligence, self-driving cars, and the Internet of Things (IoT)—smart speakers and web-enabled televisions, automobiles, and appliances—all present opportunities to create bicycles for the mind. Unfortunately, I see no evidence yet that the designers in those categories are thinking that way. The term you hear instead is “Big Data,” which is code for extracting value rather than creating it.

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Platforms and merchants will be unhappy to lose access to data from users who choose private log-in, but that is their own fault. They should not have abused the trust of users. The Next Big Thing would also include smartphones that are less addictive and do not share private data, devices in the Internet of Things that are respectful of data privacy, and applications that are useful and/or fun without causing harm. One way to think about the opportunity for human-driven technology is in terms of decentralization. If antitrust action creates room for competition, the Next Big Thing could see the pendulum of innovation swing back from centralized cloud systems to devices at the edge.

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If the latter, there may still be hope for startups. Will the Next Big Thing be the next generation of wireless technology, the standard known as 5G? The magic of 5G is not going to be more bandwidth to phones; it will be in enabling pervasive 4G-level bandwidth at one-tenth the cost. It will power the Internet of Things. The standard has been set, and we should anticipate lots of startup activity. However, IoT already exists, and the early products pose a range of issues that demand attention, including data privacy and security. Critics have expressed alarm about the ability of Amazon’s Alexa and Google Home to snoop on users.

Platform Scale: How an Emerging Business Model Helps Startups Build Large Empires With Minimum Investment
by Sangeet Paul Choudary
Published 14 Sep 2015

Additionally, the platform also pools data from many users to create network-level insights. Wearables benefit from implicit network effects. Platform Stack Figure 6d • Nest Thermostat And The Internet Of Things. The Nest thermostat uses a data platform to aggregate data from multiple thermostats. This aggregation of data enables analytics for thermostat users and powers services to the city’s utilities board. The Internet of Things will give rise to new business models in similar ways through the creation of data platforms. • Industrial Internet. GE’s focus on the industrial Internet is another example of a data platform.

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Platform Scale explains the design of a family of emerging digital business models that enables today’s startups to achieve rapid scale: the platform business model. The many manifestations of the platform business model - social media, the peer economy, cryptocurrencies, APIs and developer ecosystems, the Internet of things, crowdsourcing models, and many others - are becoming increasingly relevant. Yet, most new platform ideas fail because the business design and growth strategies involved in building platforms are not well understood. Platform Scale is a builder’s manual for anyone building a platform business today.

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Consumers moved to platform phones whose functionality could easily be extended using apps created by external developers. The disruption of Nokia and BlackBerry demonstrates that firms must leverage platforms for innovation. Today, banks, retailers, and businesses across diverse industries are following the Android playbook to use platforms for innovation. d. The intelligent Internet of Things Nest’s thermostats constantly create data, as do GE’s machines and Nike’s shoes. These products aren’t merely physical products anymore; they plug in to platforms. These objects feed data into central platforms, and every individual object connected to the platform learns from the community of other objects connected to the platform.

Death Glitch: How Techno-Solutionism Fails Us in This Life and Beyond
by Tamara Kneese
Published 14 Aug 2023

Communicative traces point to the networked nature of data production, which includes ambient metadata in addition to intentionally made profiles or other content. Communicative traces merge recent discourses on networked publics and mediated affective labor with anthropological theories of inheritance and exchange. With the rise of wearable devices, the Internet of Things, and the integration of smart objects with digital platforms, personal data maps onto embodied life by likening people’s digital remains to their physical ones. Both digital and physical remains reveal the traces of people and relationships that once were. Communicative traces reflect the financial and affective value of data but also their social value.

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As the media theorist John Durham Peters once put it, “Every new medium is a machine for the production of ghosts.”2 From the first days of nineteenth-century spirit photography and the Victorian curiosity of the spiritual telegraph, technology has offered the possibility of an afterlife.3 Material information networks and electronic technologies have long contained spiritual or even explicitly religious components. The first telegraphic message was a biblical verse, “What hath God wrought?”4 With the growing popularity of the Internet of Things—a coffee maker that knows when it is time to start brewing a pot or blinds that open and close at certain times of the day—digital remains are not merely the profiles, accounts, and other communicative traces that individuals leave behind when they die. Thanks to Google Home, Nest, and Amazon Echo and their invisible, feminized virtual assistants, a person can leave behind a cluster of smart objects: a self-contained universe of efficiency.

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In June 2022, Amazon advertised its new Alexa speakers by claiming the devices could manifest deepfaked voices of dead relatives, making it so a dead grandmother could read a story to her grandchild.5 Such technologies have emotional and ethical repercussions. Do smart objects have afterlives? Or, rather, what happens when the Internet of Things breaks down? What fantasies about transcendence and immortality exist in boring appliances like the Roomba? Discarded smart objects persist, even if the patterns, habits, and networks they are connected to disappear. Here I examine the disconnect between the fantasies that individual technologists might have about their digital afterlives versus the perpetual upkeep demanded by brittle smart technologies.

The Future of the Professions: How Technology Will Transform the Work of Human Experts
by Richard Susskind and Daniel Susskind
Published 24 Aug 2015

id=2646> (accessed 6 March 2015). 25 Sarah Neville, ‘Hospital takes the pulse of nursing by video’, Financial Times, 5 Oct. 2014 <http://www.ft.com/> (accessed 6 March 2015). 26 Mark Scott, ‘Novartis Joins With Google to Develop Contact Lens That Monitors Blood Sugar’, New York Times, 15 July 2014 <http://www.nytimes.com> (accessed 27 March 2015). 27 <https://www.bluestardiabetes.com>. 28 <http://www.eyenetra.com> (accessed 6 March 2015). 29 ‘Mobile Medical Applications’, FDA, 6 Apr. 2014 <http://www.fda.gov/MedicalDevices/ProductsandMedicalProcedures/ConnectedHealth/MobileMedicalApplications/ucm255978.htm> (accessed 6 March 2015). 30 Roy Kessels, ‘Patients memory for medical information’, Journal of the Royal Society of Medicine, 96: 5 (2003), 219–22. 31 David Cutler and Wendy Everett, ‘Thinking Outside the Pillbox—Medication Adherence as a Priority for Health Care Reform’, New England Journal of Medicine, 362: 17 (2010), 1553–5. 32 Tara Hovarth et al., ‘Mobile phone text messaging for promoting adherence to antiretroviral therapy in patients with HIV infection’, Cochrane Database of Systematic Reviews, 3 (2012): <doi: 10.1002/14651858.CD009756> (accessed 27 March 2015). 33 Caroline Jones et al., ‘ “Even if You Know Everything You Can Forget”: Health Worker Perceptions of Mobile Phone Text-Messaging to Improve Malaria Case-Management in Kenya’ PLoS ONE, 7: 6 (2012): <doi: 10.1371/journal.pone.0038636> (accessed 27 March 2015). 34 David Rose, Enchanted Objects: Design, Human Desire, and the Internet of Things (2014). It increases drug adherence by 23 percentage points (to 94%) compared with standard vials. See p. 130. 35 ‘Emory University Hospital Explores “Intensive Care Unit of the Future”’, IBM, 4 November 2013 <http://www-03.ibm.com/press/us/en/pressrelease/42362.wss> (accessed 6 March 2015). 36 Nick Bilton, ‘Disruptions: Medicine that Monitors You’, New York Times, 23 June 2013 <http://www.nytimes.com> (accessed 27 March 2015). 37 <http://www.patientslikeme.com> (accessed 27 March 2015). 38 Christina Farr and Alexei Oreskovic, ‘Exclusive: Facebook plots first steps into healthcare’, Reuters, 3 Oct. 2014 <http://www.reuters.com> (accessed 27 March 2015). 39 David Bray et al., ‘Sermo: A Community-Based, Knowledge Ecosystem’ (2008), <http://dx.doi.org/10.2139/ssrn.1016483> and <http://www.sermo.com> (accessed 27 March 2015). 40 <https://secure.quantiamd.com> (accessed 27 March 2015). 41 <https://www.doximity.com> (accessed 27 March 2015). 42 Daniel Gaitan, ‘Crowdsourcing the answers to medical mysteries’, Reuters, 1 Aug. 2014 <http://www.reuters.com> (accessed 27 March 2015). 43 <http://www.innocentive.com>. 44 <https://watsi.org>. 45 Jerome Groopman, ‘Print Thyself: How 3-D Printing is Revolutionizing Medicine’, New Yorker, 24 Nov. 2014. 46 e.g.

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But as computing becomes more portable and increasingly affordable in this way, this group will steadily diminish. Already in the United Kingdom and United States, for example, most people now have access to the Internet.75 This avalanche of hand-helds may seem pervasive in its own right. But when we speak of ‘increasingly pervasive devices’, we also include the phenomenon known as the ‘Internet of Things’.76 Alternatively referred to as ‘ubiquitous’ or ‘pervasive’ computing, the idea here is to embed processors, sensors, and Internet connectivity into physical objects.77 It is as if we have tiny connected computers planted inside everyday things: an alarm clock that can check train times online and let its owner sleep longer if there are delays; an umbrella that is able to check online weather forecasts and light up at the front door when rain is predicted; electronic books that can update one another; plant-pots that can monitor moisture in soil and refill as appropriate; refrigerators that can detect when the amount of some foodstuffs has fallen below a prescribed level and reorder accordingly; boilers, lights, and thermostats that can be switched on and adjusted remotely.

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Miniaturized circuits can be introduced into flesh and blood, of humans and animals—measuring, monitoring, dispensing, capturing, and transmitting information to specialists, patients, or to other systems. Similar technologies are being used in the corporate world. For example, GE calls this the ‘industrial Internet’—embedding sensors in their machines and sending large bodies of data into the ‘cloud’, and so bringing together the Internet of Things and Big Data.83 This, then, is what we mean by ‘increasingly pervasive devices’. In the first instance, there is a surge in the number of tablets and hand-held machines, meaning that more people can be the beneficiaries of online practical expertise. Secondly, and as dramatically, very small processing and communicating components are being embedded in machinery, buildings, people, animals, clothes, and other everyday objects, and this has application in the work of various professionals (certainly for doctors, dentists, vets, opticians, and architects).

Super Continent: The Logic of Eurasian Integration
by Kent E. Calder
Published 28 Apr 2019

Such a pattern prevails on only the largest of continents, with Eurasia naturally representing the most extreme case on earth. (3) The techno-political context is distinctive. Technological change, and regulatory adjustment with it, is uncommonly rapid today, in sectors of special relevance to Eurasia’s reintegration such as land transport and telecommunications. The Logistics Revolution, accelerated by digitalization and the Internet of Things, is proceeding at warp speed early in the twenty-first century, with greater implications for Eurasia than for other regions due to that Super Continent’s geography and to the pace of its economic advance. Public policy and private effort, especially China’s Belt and Road Initiative (BRI), together with the efforts of firms like COSCO, Huawei, Ericsson, Alibaba, and Deutsche 6 chapter 1 N Rotterdam Shanghai 0 0 1000 2000 mi 1000 2000 3000 km map 1.1 Land vs. sea routes Bahn, are capitalizing on these long-term trends, with BRI and private efforts complementing one another in synergistic fashion.

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Reflecting recent advances in intermodal transport technology and customs clearance, there are important prospective synergies between two distinct elements of the BRI: the overland Belt and the maritime Road. Shifting from rail or road transport to maritime traffic and back again is growing cheaper, faster, and more efficient. These developments, accelerated by cooperative ventures like the China-Singapore Internet of Things project in Chongqing and the rapid evolution of e-commerce, are making transcontinental supply chains in electronics, precision machinery, and fine chemicals increasingly plausible, giving BRI powerful new transcontinental geo-economic stimulus.67 Silk Road Economic Belt 21st-Century Maritime Silk Road Moscow Rotterdam Duisburg Istanbul Samarkand Athens Urumqi Almaty Bishkek Venice Khorgos Dushanbe Lanzhou Tehran Xi’an Fuzhou Kolkata Hanoi Haikou Colombo Kuala Lumpur Nairobi Jakarta 0 0 map 2.2 China’s Belt and Road Initiative 1000 1000 2000 2000 mi 3000 km N 46 chapter 2 Since Xi Jinping unveiled the BRI in general terms, other Chinese leaders and analysts have worked to clarify details of the initiative and to situate the BRI in global context.68 One spectacular demonstration of this effort was the first Belt and Road Forum, convened in May 2017 and attended by leaders of twenty-nine member nations, excluding President Xi, and representatives of fifty-six countries in all, together with their technical advisors.69 Top representatives of all the key global intergovernmental organizations (IGOs), including the World Bank, the International Monetary Fund, the World Trade Organization, and the United Nations, were also in attendance and were pressed to integrate their efforts with those of China, accenting the PRC’s nascent ambitions to transform the BRI into a “parallel structure” of global economic governance.

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Some of the related areas for innovation were insurance, customs clearance, freight forwarding, and intermodal transfer. Although containerization was an earlier development, technologies 86 chapter 4 in the latter areas, especially intermodal transfer, have been evolving rapidly over the past decade and promise to evolve still further with the introduction of Internet of Things (IoT) and 5th-Generation (5G) wireless network technology, giving promise of further disruptive innovation in the coming years. IoT allows, for example, the real-time monitoring of goods as well as assets from individual cases to the whole company. It also allows organizations to automate procedures that were previously manual and to optimize how multiple logistics systems work together.

In Our Own Image: Savior or Destroyer? The History and Future of Artificial Intelligence
by George Zarkadakis
Published 7 Mar 2016

And what about all the smart things with which we are all beginning to furnish our homes: intelligent thermostats, fridges that order food when it runs out, telemedicine devices that monitor our health? The evangelists of the ‘internet of things’ proclaim that our lives will be simpler and more productive when the things we use can take decisions on our behalf. This is happening already, but will explode in the next few years. According to Cisco CEO John Chambers there are some thirteen billion devices connected to the Internet today, a number predicted to grow to fifty billion by 2020, and 500 billion by 2030.30 The Internet of things will result in US$19 trillion in profits and cost savings in the private and public sector, and will be ten to fifteen times larger than the Internet today in terms of number of connections.

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It begins with the formulation of logic by Aristotle, and goes on to show how his ideas were developed further in the nineteenth and early twentieth centuries, until they led to the birth of computer languages and Artificial Intelligence. I will explore how ancient automata evolved into mechanical calculating machines, to Babbage’s Analytical Engine, and all the way to modern supercomputers and the Internet of things; and speculate about futuristic alternative computer architectures that mimic the neural networks of the brain. I will ask how close computers are to achieving self-awareness, and what might happen once they do. This book aspires to incite a fresh look at Artificial Intelligence by bridging the ‘two cultures’ gap, and illustrating the interconnection between literary narratives, philosophy and technology in defining and addressing the two most important scientific questions of all time: whence our minds and can we recreate them?

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Although computing machines began – as their name suggests – as contraptions that automated arithmetic operations, they quickly became applied to just about everything. What are the unique characteristics of computers that make them so flexible, adaptable and intrusive? How did the transformation from the physical to the digital come about? Where does it lead us? And, finally, in the age of big data, search engines, social media, mobile apps and the Internet of things, what role is there for Artificial Intelligence? ‘War is the father and king of all’ Daring to complement Heraclitus’ famous quote,2 I would add that ballistics and encryption were the mothers and queens of all computers. The world war of 1939–1945 was fought with aircraft that often had to be shot down from the ground or from a moving ship at sea, and with encoded signals that coordinated sophisticated military movements of naval, land and aerial forces.

Age of Discovery: Navigating the Risks and Rewards of Our New Renaissance
by Ian Goldin and Chris Kutarna
Published 23 May 2016

In the sky, aerospace improvements have extended the range that aircraft can fly and lowered their operating and environmental costs. Now, no two cities on the globe are more than a day apart, and more of us can afford to fly between them. In the United States, the cost of flying has fallen by as much as 40 percent over the past 30 years.71 On land, the emerging “Internet of Things”—tagging everything from cars to Coke machines with little chips and computers that can link to data networks—means that more and more objects in the physical world are adopting digital properties. Orchestrated by computers and robots, such objects can start to move about in volumes, at speeds and with efficiency far beyond human capabilities.

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Today they number 15 billion; by 2020, there will be 50 billion such objects in the world.72 In Seoul, Korea, for example, the entire public transportation system—every bus, taxi, train and public bicycle—is now networked.73 The expectation is that travel times will quicken and road congestion will fall as every user and “device” on the network starts to make computer-aided traffic management choices. The Internet of Things will transform the volume and variety of physical flows on land. We know this, because it has already helped to do so at sea. So far, that is where new technologies have done the most to enable new global flows. “Containerization” has digitized shipping by putting everything from cars to crayons into identical, traceable boxes.

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These crimes injure us personally, through the theft and ransom of identities, login information, webcam videos or Snapchat photos. They also use us to injure others, by making us unwitting accomplices in spam, phishing and email attacks, or by using our computers as web servers for malware and child pornography. And as more smart devices, from appliances to automobiles to the locks on our house, connect to the “Internet of Things,” the range of injuries that cyber criminals can cause us will only widen. In July 2015, some 1.4 million Jeeps were recalled when researchers proved they could exploit a bug to hack, and crash, the vehicles remotely over the Internet.81 Cybercrime also steals intellectual property and other secrets from institutions.

Succeeding With AI: How to Make AI Work for Your Business
by Veljko Krunic
Published 29 Mar 2020

Other minds: The octopus, the sea, and the deep origins of consciousness. New York: Farrar, Straus and Giroux; 2016. Brockman J. Know this: Today’s most interesting and important scientific ideas, discoveries, and developments. New York, NY: Harper Perennial; 2017. Wikimedia Foundation. Internet of things. Wikipedia. [Cited 2018 Jul 2]. Available from: https://en.wikipedia.org/wiki/Internet_of_things Wikimedia Foundation. Nicolas-Joseph Cugnot. [Cited 2019 Jul 15]. Available from: https://en.wikipedia.org/wiki/Nicolas-Joseph_Cugnot Wikimedia Foundation. History of the automobile. [Cited 2019 Jul 15]. Available from: https://en.wikipedia.org/wiki/History_of_the_automobile Gulshan V, et al.

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It also introduces the technique of sensitivity analysis and demonstrates how to interpret its results, as well as how to account for the passage of time in a long-running AI project. Chapter 8 focuses on trends in AI and how they’ll affect you. This chapter introduces you to trends such as AutoML (automation of the work that data scientists do in AI) and explores how AI relates to causality and Internet of Things (IoT) systems. It also contrasts AI system errors with the typical errors humans make and shows you how to account for those differences in your project. Some further comments about the organization of the book:  The material in this book is multidisciplinary and requires a combination of both theory and practice to understand.

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Automated data analysis is a recent development? Even uses of fully automated and rapid Sense/Analyze/React loops using complicated and computerized analysis are nothing new. Capital markets, especially combined with algorithmic trading, implement this pattern on a large scale. With the further advancement of the Internet of Things [46] and robotics, these large-scale, fully automated, closed control loops will become much more prevalent within the physical world. 2.3 What’s new with AI? The advance of AI broadened the applicability of the Sense/Analyze/React loop, because AI brought to the table new analytical capabilities.

Cybersecurity: What Everyone Needs to Know
by P. W. Singer and Allan Friedman
Published 3 Jan 2014

The final trend that will likely have serious cybersecurity implications builds on both cheaper computation and a more mobile world. The future blurring of cyber and physical will come to fruition when digital systems are fully embedded in the real world, also known as the “Internet of Things.” Like so many aspects of cyberspace, the Internet of Things can best be illustrated with a cat. Steve Sande was a man living in Colorado who worried about Ruby, his feline companion, when he was away. His particular concern was that Ruby might get too hot in his home that lacked air conditioning. However, Steve was environmentally conscious (or cheap) and didn’t want to waste power on a fan when it wasn’t needed.

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So he linked his fan to an Internet-connected device called a WeMo and wrote a script that monitored an online weather website. Whenever the website said the weather was over 85 degrees Fahrenheit, the WeMo switched the fan on. With no direct human instruction, the “things” in Steve’s house worked together via the Internet to keep Ruby the cat cool. More broadly, the Internet of Things is the concept that everything can be linked to a web-enabled device to collect or make use of data. So many physical objects in our lives, from cameras to cars, already have computer chips built in. What happens when they can all “talk” to each other? And then, what happens when literally anything from the wristband you wear to wall of your bathroom to fruit at the grocery store can have tiny cheap chips put on them, and also join the conversation?

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In this vision, distributed sensors can detect street traffic, enabling your GPS to route your car home from work, while communicating to your home’s thermostat how far away you are, so that it can power back up the heat from its most efficient setting, determined off its link to the smart power grid; sensors can detect how crowded different restaurants are to make you a reservation, and your exercise bike at the gym will talk to your credit card to find out what you ordered at that restaurant, and decide how long you have to work out the next day to burn that cheesecake off. One of the main obstacles to this vision is interoperability. The Internet exploded because of shared, open standards that anyone could build on. Without the unruly but effective governance structures, however, the many other devices that may be linked into the Internet of Things still lack standardized, open inputs and outputs that share and interpret instructions and data in seamless, automated exchanges. Common formats are required to understand data, and some mechanism is needed to gather and interpret data in the first place, which can be an expensive proposition.

The Inevitable: Understanding the 12 Technological Forces That Will Shape Our Future
by Kevin Kelly
Published 6 Jun 2016

The taxonomy of minds must reflect the different ways in which minds are engineered with these trade-offs. In the short list below I include only those kinds of minds that we might consider superior to us; I’ve omitted the thousands of species of mild machine smartness—like the brains in a calculator—that will cognify the bulk of the internet of things. Some possible new minds: A mind like a human mind, just faster in answering (the easiest AI mind to imagine). A very slow mind, composed primarily of vast storage and memory. A global supermind composed of millions of individual dumb minds in concert. A hive mind made of many very smart minds, but unaware it/they are a hive.

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Although it seems like wizardry now, this will be considered a very mechanical request in a decade, not very different from asking Google to find something—which would have been magical 20 years ago. Still, the picture is not big enough. We—the internet of people—will track ourselves, much of our lives. But the internet of things is much bigger, and billions of things will track themselves too. In the coming decades nearly every object that is manufactured will contain a small sliver of silicon that is connected to the internet. One consequence of this wide connection is that it will become feasible to track how each thing is used with great precision.

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The electronics industry expects a billion wearable devices in five years, tracking our activities, feeding data into the stream. We can expect another 13 billion appliances, like the Nest thermostat, animating our smarthomes. There will be 3 billion devices built into connected cars. And 100 billion dumb RFID chips embedded into goods on the shelves of Walmart. This is the internet of things, the emerging dreamland of everything we manufacture that is the new platform for the improbable. It is built with data. Knowledge, which is related, but not identical, to information, is exploding at the same rate as information, doubling every two years. The number of scientific articles published each year has been accelerating even faster than this for decades.

Frenemies: The Epic Disruption of the Ad Business
by Ken Auletta
Published 4 Jun 2018

Keith Weed of Unilever, who Kassan says was the first client to ask him to arrange a tour of the exhibition floors, explains why he comes to CES annually: “If you’re going to get to the future, first you better have an idea of the future. . . . If I can stay ahead, it gives me an advantage.” Plus, he adds, “Everyone is here.” Like other clients, Weed told MediaLink what he was most interested in doing at CES. This year he wanted a curated exhibition tour to explore three subjects: artificial intelligence, virtual reality, and the Internet of things. In addition to Unilever, MediaLink had a total of 100 clients attending and would curate 582 floor tours and meetings with advertisers, agencies, and digital companies. A staff of forty was in attendance, each person responsible for accompanying a group of clients—JPMorgan Chase, GE, McDonald’s, NBC, Hearst, Gawker, the New York Times—as well as organizing public session panels for agency clients like Digitas, Publicis’s digital media agency.

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Writing about CES 2016, Farhad Manjoo of the New York Times observed, “If news from CES feels especially desultory this year, it might not be the show that’s at fault. Instead, blame the tech cycle. We’re at a weird moment in the industry: The best new stuff is not all that cool, and the coolest stuff”—AI, virtual reality, the Internet of things, drones—“isn’t quite ready.” * * * ■ ■ ■ If CES had been a warm bath of relationship building, the American Association of Advertising Agencies conference at the Loews Miami Beach Hotel two months later centered more on conflict. The much-anticipated ANA report on agency kickbacks sparked by Jon Mandel’s speech a year earlier hovered over anxious agency executives.

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And with video becoming the principal way for advertisers to reach consumers on mobile devices, and with just the first two to three seconds of that video to win the consumers attention, he concludes, “Creativity becomes more important. So Math Men and Mad Men are joined.” The other potentially disruptive technology is what’s come to be called the Internet of things, or IoT, “smart devices” with Bluetooth connections—refrigerators, light bulbs, watches, thermostats, washing machines, coffeepots, cars, baby pacifiers, and so on. In 2016, Gartner, Inc., a technology research firm, estimated that there were 6.4 billion connected “things,” and this number would jump to 20.8 billion in four years.

Hit Refresh: The Quest to Rediscover Microsoft's Soul and Imagine a Better Future for Everyone
by Satya Nadella , Greg Shaw and Jill Tracie Nichols
Published 25 Sep 2017

We will think about customers as “dual users,” people who use technology for their work, their school, and their personal digital life. In the email I inserted the image of a target and in its center appeared the words, “digital work and life experiences,” surrounded by our cloud platform and computer devices. Soon there will be 3 billion people connected to the Internet, sensors, and the Internet of Things (IoT). Yes, PC sales were slowing, and so we needed to convert Nietzsche’s “courage in the face of reality” into “courage in the face of opportunity.” We needed to win the billions of connected devices, not fret about a shrinking market. Employees responded immediately. In just the first twenty-four hours I heard from hundreds of employees in every part of the company and in every part of the world.

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In the end, we still had to resolve some of our issues through the courts, but we also continued to show respect. “Microsoft values and respects our partnership,” we wrote in a statement. “Unfortunately, even partners sometimes disagree.” Today Microsoft apps are popular on Samsung smartphones; Windows 10 powers Samsung tablets and its ambitions for the far-flung Internet of Things. Around the same time, we were embroiled in a contentious dispute with Yahoo, which used the Bing search engine as its exclusive search partner. Microsoft and Yahoo shared in the revenue from the searches performed by Bing. But, as with Samsung, our relationship with Yahoo was deteriorating as Yahoo’s business model came under pressure, and lawsuits were being threatened.

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On horizon one, our customers and partners will continue to see quarter-by-quarter, year-by-year innovations in all of our businesses. On horizon two, we’re already investing in some exciting nearer-term platform shifts, such as new user interfaces with speech or digital ink, new applications with personal assistants and bots, and Internet of Things experiences for everything from factories to cars to home appliances. On horizon three, Microsoft is highly focused in areas that only a few years ago sounded distant, but today are frontiers of innovation—mixed reality, artificial intelligence, and quantum computing. Mixed reality will become an essential tool in medicine, education, and manufacturing.

Walled Culture: How Big Content Uses Technology and the Law to Lock Down Culture and Keep Creators Poor
by Glyn Moody
Published 26 Sep 2022

We can’t have this when companies can cut off compatible products, or use the law to prevent competitors from reverse-engineering their products to ensure compatibility across brands. For the Internet of Things to provide any value, what we need is a world that looks like the automotive industry, where you can go to a store and buy replacement parts made by a wide variety of different manufacturers. Instead, the Internet of Things is on track to become a battleground of competing standards, as companies try to build monopolies by locking each other out.”646 Copyright legislation makes it easy for manufacturers to thwart this kind of necessary interoperability.

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article=6939&amp;context=jclc 630 https://web.archive.org/web/20220617104420/https://www.copyright.gov/about/ 631 https://web.archive.org/web/20220617104443/https://www.techdirt.com/2022/01/06/top-disney-lawyer-to-become-top-copyright-office-lawyer-because-who-cares-about-public-interest/ 632 https://web.archive.org/web/20220617104505/https://publicknowledge.org/policy/captured-systemic-bias-at-the-u-s-copyright-office/ 633 https://web.archive.org/web/20220619010942/https://walledculture.org/unleashing-the-power-of-online-sharing-for-all-the-birth-and-rise-of-creative-commons/ 634 https://web.archive.org/web/20220616121321/https://walledculture.org/interview-lawrence-lessig-internet-architecture-remix-culture-creative-commons-nfts-aaron-swartz-and-the-internet-archive/ 635 https://web.archive.org/web/20220616121340/https://doctorow.medium.com/a-bug-in-early-creative-commons-licenses-has-enabled-a-new-breed-of-superpredator-5f6360713299 636 https://web.archive.org/web/20220616121359/https://en.wikipedia.org/wiki/Copyright_troll 637 https://web.archive.org/web/20220616121340/https://doctorow.medium.com/a-bug-in-early-creative-commons-licenses-has-enabled-a-new-breed-of-superpredator-5f6360713299 638 https://web.archive.org/web/20220616121412/https://en.wikipedia.org/wiki/Protection_of_Broadcasts_and_Broadcasting_Organizations_Treaty 639 https://web.archive.org/web/20220616121437/https://www.keionline.org/31905 640 https://web.archive.org/web/20220616121455/https://www.eff.org/deeplinks/2013/04/only-thing-broadcasting-treaty-good-crushing-innovation 641 https://web.archive.org/web/20220616121517/https://www.deere.com/en/index.html 642 https://web.archive.org/web/20220616121544/https://copyright.gov/1201/2015/comments-032715/class%2021/John_Deere_Class21_1201_2014.pdf 643 https://web.archive.org/web/20220616121604/https://www.wired.com/2015/04/dmca-ownership-john-deere/ 644 https://web.archive.org/web/20220616122417/https://en.wikipedia.org/wiki/Internet_of_things 645 https://web.archive.org/web/20220704120923/https://en.wikipedia.org/wiki/Bruce_Schneier 646 https://web.archive.org/web/20220616122437/https://www.theatlantic.com/technology/archive/2015/12/internet-of-things-philips-hue-lightbulbs/421884/ 647 https://web.archive.org/web/20220616122459/https://walledculture.org/interview-cory-doctorow-part-1-newspapers-big-tech-link-tax-drm-and-right-to-repair/ 648 https://web.archive.org/web/20220616122519/https://filmschoolrejects.com/fetishizing-celluloid-is-bad-for-film-preservation/ 649 https://web.archive.org/web/20220616122545/https://nofilmschool.com/missing-movies 650 https://web.archive.org/web/20220616121321/https://walledculture.org/interview-lawrence-lessig-internet-architecture-remix-culture-creative-commons-nfts-aaron-swartz-and-the-internet-archive/ 651 https://web.archive.org/web/20220616122617/https://www.theguardian.com/film/2021/oct/07/film-lost-netflix-the-afterlight-london-film-fesitval-digital-media 652 https://web.archive.org/web/20220616122643/https://fullstackeconomics.com/why-streaming-content-keeps-vanishing-and-how-to-stop-it/ 653 https://web.archive.org/web/20220616122643/https://fullstackeconomics.com/why-streaming-content-keeps-vanishing-and-how-to-stop-it/ 654 https://web.archive.org/web/20220616122710/https://en.wikipedia.org/wiki/Cloud_storage 655 https://web.archive.org/web/20220616122732/https://www.theesa.com/ 656 https://web.archive.org/web/20220616123532/https://www.washingtonpost.com/video-games/2022/01/12/video-game-preservation-emulation/ 657 https://web.archive.org/web/20220616123630/https://en.wikipedia.org/wiki/P-value 658 https://web.archive.org/web/20220616123645/https://papers.ssrn.com/sol3/papers.cfm?

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Because copyright-marauding farmers are very busy and need to multitask by simultaneously copying Taylor Swift’s 1989 and harvesting corn?”643 Given that software is covered by copyright, any device containing software can potentially be protected by legislation such as the DMCA and the European Union’s Information Society Directive. The consequences of this will affect objects that form the Internet of Things (IoT),644 which links together a range of domestic devices from thermostats to home security systems by imbuing them with computational power. As security expert Bruce Schneier645 wrote in The Atlantic in 2015: “We’ll want our light bulbs to communicate with a central controller, regardless of manufacturer.

Frugal Innovation: How to Do Better With Less
by Jaideep Prabhu Navi Radjou
Published 15 Feb 2015

Unlike concept testing, which requires people explicitly to articulate to researchers what they need and want, immersion labs allow researchers to observe customers as they play with prototypes and infer what needs to be done to improve product design and the user experience. Make use of big data analytics Consumer and industrial products of all kinds are increasingly connected to the internet. Mobile phones and the Internet of Things (identifiers for different physical objects) allow researchers to collect large amounts of detailed data to predict customer needs and respond with tailored solutions. This approach, called predictive analytics, has particular power in industrial contexts. Philips Lighting, which produces commercial lighting systems for large installations, provides a good example of its capabilities.

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They take advantage of the increasing ubiquity of smartphones, sensors in devices, the internet and social media to create apps that enable real-time monitoring and visualisation of behaviour. All this in turn enables consumers to become more aware of the causes and consequences of their behaviour and compare it with that of others. The most significant development here is the “Internet of Things”, that is, the equipping of everyday objects – watches, fridges, cars – with tiny, interconnected identifying devices that allow continuous, unobtrusive measuring, monitoring and regulation of behaviour on the web. Most efforts to shape consumer behaviour use a combination of both approaches, and are becoming increasingly widespread in areas as diverse as energy, education, finance and health.

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In the coming years, GE’s toughest competitors will not be other industrial powerhouses such as Siemens or Schneider Electric, but the so-called GAFAs (Google, Apple, Facebook and Amazon). Indeed, as more physical devices – from giant power turbines to modest light bulbs – are connected to the Internet of Things, a torrent of big data will be unleashed on the world. If the GAFAs can gain access to the data generated by GE’s industrial products, they can glean insights from that data to offer value-adding services to GE’s customers. As the saying goes: “Whoever owns the customer’s data owns the customer.”

Intertwingled: Information Changes Everything
by Peter Morville
Published 14 May 2014

The machine view was so successful during the industrial revolution, we find it astonishingly hard to let go, even as the information age renders it obsolete and counterproductive in a growing set of contexts. It’s not that the old model is all wrong. We’re not about to throw away hierarchy or specialization. But our world is changing, and we must adjust. The information age amplifies connectedness. Each wave of change – web, social, mobile, the Internet of Things – increases the degree and import of connection and accelerates the rate of change. In this context, it’s vital to see our organizations as ecosystems. This is not meant figuratively. Our organizations are ecosystems, literally. And while each community of organisms plus environment may function as a unit, the web of connections and consequences extends beyond its borders.

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Our strength in structural design must be joined by an aptitude for managing information flows, feedback loops, and motivational metrics. What matters most isn’t what we build but the change we make. That’s why I’m writing this book. I want to study, understand, and clarify the nature of information in systems. In part, it’s about going beyond the Web. Mobile and the Internet of Things are tearing down the walls between physical and digital, creating new information flows and loops. It’s also about seeing old sites with fresh eyes. Our websites aren’t just channels for marketing and communication. They’ve become rich, dynamic places where work gets done. Websites are extensions of the organization that change its nature.

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There are more dimensions to architecture than Tetris, so it’s even more vital we use models in the world to shift minds. Planning is making. Maps, sketches, words, and wireframes are still essential, but it’s also vital that we design in the medium of construction. How else will we imagine cross-channel experiences and the Internet of Things into life? Last year, I worked on a responsive redesign for a database publisher. Our team built wireframes and design comps to conduct quick, cheap experiments, and then an HTML prototype to enable new loops of build-measure-learn. Each of these cognition amplifiers is unique. Together they teach us that one way is the wrong way.

Dual Transformation: How to Reposition Today's Business While Creating the Future
by Scott D. Anthony and Mark W. Johnson
Published 27 Mar 2017

Innov8 scoured the globe to find interesting investment opportunities that fit emerging themes around mobile advertising, big data analytics, cyber security, the internet of things, and more. By 2015, Innov8 had invested in more than fifty companies and was well established as a go-to investor in both the region and the industry. One of Innov8’s early investments was in Viki, a video-streaming website based in Singapore that offers on-demand video of TV shows, movies, and music videos from around the world. Rakuten Group of Japan acquired that business for $200 million. In 2014, Innov8 backed Jasper, a hyped company that created a platform to help companies manage services related to the internet of things. In 2016 Cisco Systems acquired Jasper (the name refers to the operating system created by Tony Stark, otherwise known as Iron Man) for $1.4 billion.

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Moore’s Law improvement trajectory: The theory, based on an observation by Intel cofounder Gordon Moore in 1965, that the number of transistors on a chip were doubling regularly, holds that computing power doubles every eighteen months. See Investopedia, “Moore’s Law,” http://www.investopedia.com/terms/m/mooreslaw.asp. Nestlé and Samsung partnership: Samsung, “Samsung and Nestlé Collaborate on the Internet of Things and Nutrition to Advance Digital Health,” Samsung.com, July 28, 2016, https://news.samsung.com/global/samsung-and-nestle-collaborate-on-the-internet-of-things-and-nutrition-to-advance-digital-health. TechCrunch on platforms: Tom Goodwin, “The Battle Is For The Customer Interface ,” TechCrunch.com, March 3, 2015, https://techcrunch.com/2015/03/03/in-the-age-of-disintermediation-the-battle-is-all-for-the-customer-interface/.

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Andy’s plan promises to minimize sales declines and dramatically boost profit margins and cash flow. It requires radically reconfiguring the organization, however, and laying off about 30 percent of staff. Bernadette is driving transformation B. After considering several options, she and her team have decided to focus on the internet of things. She has recommended acquiring a sensor company and an analytics company, with the intent of stitching them together to offer unique services based on the data generated by Partzelg’s components. That all sounds sensible, but let’s see what happens when key leaders begin questioning Partzelg’s commitment. the organization still care about A?

Bold: How to Go Big, Create Wealth and Impact the World
by Peter H. Diamandis and Steven Kotler
Published 3 Feb 2015

Chapter Three: Five to Change the World 1 Adrian Kingsley-Hughes, “Mobile gadgets driving massive growth in touch sensors,” ZDNet, June 18, 2013, http://www.zdnet.com/mobile-gadgets-driving-massive-growth-in-touch-sensors-7000016954/. 2 Peter Kelly-Detwiler, “Machine to Machine Connections—The Internet of Things—And Energy,” Forbes, August 6, 2013, http://www.forbes.com/sites/peterdetwiler/2013/08/06/machine-to-machine-connections-the-internet-of-things-and-energy/. 3 See http://www.shotspotter.com. 4 Clive Thompson, “No Longer Vaporware: The Internet of Things Is Finally Talking,” Wired, December 6, 2012, http://www.wired.com/2012/12/20-12-st_thompson/. 5 Brad Templeton, “Cameras or Lasers?,” Templetons, http://www.templetons.com/brad/robocars/cameras-lasers.html. 6 See http://en.wikipedia.org/wiki/Passenger_vehicles_in_the_United_States. 7 Commercial satellite players include: PlanetLabs (already launched), Skybox (launched and acquired by Google), Urthecast (launched), and two still-confidential companies still under development (about which Peter Diamandis has firsthand knowledge). 8 Stanford University, “Need for a Trillion Sensors Roadmap,” Tsensorsummit.org, 2013, http://www.tsensorssummit.org/Resources/Why%20TSensors%20Roadmap.pdf. 9 Rickie Fleming, “The battle of the G networks,” NCDS.com blog, June 28, 2014, http://www.ncds.com/ncds-business-technology-blog/the-battle-of-the-g-networks. 10 AI with Dan Hesse, 2013–14. 11 Unless otherwise noted, all IoT information and Padma Warrior quotes come from an AI with Padma, 2013. 12 Cisco, “2013 IoE Value Index,” Cisco.com, 2013, http://internetofeverything.cisco.com/learn/2013-ioe-value-index-whitepaper. 13 NAVTEQ, “NAVTEQ Traffic Patterns,” Navmart.com, 2008, http://www.navmart.com/pdf/NAVmart_TrafficPatterns.pdf. 14 Juho Erkheikki, “Nokia to Buy Navteq for $8.1 Billion, Take on TomTom (Update 7),” Bloomberg, October 1, 2007, http://www.bloomberg.com/apps/news?

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That’s nearly 7 million per day, 2.5 billion per year. In 2014, the number reached almost 100 per second. By 2020, it’ll grow to more than 250 per second, or 7.8 billion per year. Add all of these numbers up and that’s more than 50 billion things connected to the Internet by 2020.” And it’s this explosion of connectivity that is building the Internet-of-Things (IoT). A recent study by Cisco estimated that between 2013 and 2020, this uber-network will generate $19 trillion in value (net profit).12 Think about this for a moment. The U.S. economy hovers around $15 trillion a year. Cisco is saying that over the ten-year period, this new net will have an economic impact greater than America’s GDP.

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From a technological perspective, what makes JARVIS extraordinary is both its pervasiveness in Stark’s life and its ability to understand natural-language instructions, even when the banter is laden with irony or humor. More technically, JARVIS is a software shell that interfaces between Stark’s every desire and the rest of the world, able both to gather data from billions of sensors and to take action through any system or robotic device connected to the AI. In this way, the Internet of Things serves as JARVIS’s eyes, ears, arms, and legs. For sure, JARVIS has dethroned HAL, now holding the title for most recognizable AI in the world, but what makes his dominance more spectacular is that unlike the never-actualized HAL, key elements of JARVIS are starting to come into existence in laboratories and companies around the world.

MegaThreats: Ten Dangerous Trends That Imperil Our Future, and How to Survive Them
by Nouriel Roubini
Published 17 Oct 2022

If China becomes a leader in the technologies and industries of the future—starting with AI—it could offer to many emerging markets deals they’d find hard to refuse: e-commerce platforms; digital payment systems platforms; its currency as a means of payment, unit of account, and store of value; its surveillance systems as a way for autocrats to control their restless masses; and its 5G networks, big data, and Internet of Things solution for widespread adoption of new technologies. The role of the dollar could decline if China delivers a competitive economic, trading, investment, technological, monetary, financial, social, and political model. Chilling implications would follow. Speaking with the Financial Times, Sir Jeremy Fleming, who heads Britain’s national cyber and security agency, expressed a dire warning.

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The cryptocurrency industry leverages a network of shady business connections, bought influencers and pay-for-play media outlets to perpetuate a cult-like ‘get rich quick’ funnel designed to extract new money from the financially desperate and naive.”30 Indeed, Dogecoin after a remarkable rally in 2021 lost about 90 percent of its value. A lot of crypto consists of mostly manipulative Ponzi schemes. If we want to revamp a centralized financial system with safeguards and supervision, we don’t need crypto or blockchain. Artificial intelligence, machine learning, big data, 5G, and the Internet of Things can speed transactions, lower costs, and increase reliability. These centralized fintech tools and firms collect and process detailed financial data at blistering speeds without any use of blockchain. Hundreds of firms worldwide have entered the fray with payment systems that handle billions of daily consumer and business-to-business transactions.

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Today the US government says we don’t want 5G technology from Huawei for our telecom systems because it has a back door for the Chinese government to keep tabs on Americans. We’re also urging Europeans and other allies to avoid that 5G. But tomorrow, guess what? Every consumer product will have a 5G chip. First you track it in the production system, and then to operate anything in the Internet of Things it must have a 5G chip. Access only begins with smartphones. Eventually, every home appliance—a toaster, a microwave, a coffee machine—will have a 5G chip, will be a potential listening device, and might furnish information—to what purpose I can’t say. Will ubiquitous technology force us to eventually impose restrictions on everything?

Autonomous Driving: How the Driverless Revolution Will Change the World
by Andreas Herrmann , Walter Brenner and Rupert Stadler
Published 25 Mar 2018

Chief Executive Officer, ThyssenKrupp, Düsseldorf, Germany Lutz Junge Principal Engineer, Electronics Research Lab, Volkswagen Group of America, San Francisco, California, USA Acknowledgements Kristin Kolodge xi Executive Director, Driver Interaction and Human Machine Interface, J. D. Power, Westlake Village, California, USA Martin Kolmar, Dr. Professor of Economics, University of St. Gallen, Switzerland Hartmut Kremling Engineering Consultant for 5G, Internet of Things and autonomous and connected Driving, Dresden, Germany Brett Lantz Associate Director of Analytics, University of Michigan, Ann Arbor, Michigan, USA Patrick Little Senior Vice President and General Manager, Automotive, Qualcomm Technologies Inc., San Diego, California, USA Jun Ma, Dr. Professor and Director, School of Automotive Studies, Tongji University, Shanghai, China Andreas Meyer Chief Executive Officer, Swiss Railway Corporation, Bern, Switzerland Julian Nida-Rümelin, Professor of Philosophy, Ludwig-Maximilian Dr.

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As automation has a positive impact on energy efficiency, increasing vehicle automation will also significantly extend the range of electric vehicles [148]. The essence of autonomous driving is the development of vehicles into cyber-physical systems that comprise a combination of mechanical and electronic components. A vehicle’s hardware and software exchange certain data about the infrastructure (the Internet of Things), and the vehicle is controlled or monitored by a processing unit. In the future, each vehicle will communicate with the infrastructure: parking garages, parking spaces, traffic lights, traffic signs and a traffic control centre (vehicle-to-infrastructure communication or V-to-I). Data on factors such as traffic flow, available parking spaces Autonomous Driving 10 and traffic-light phases, will allow the processing unit in the vehicle to select the best route and decide on a suitable speed.

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Data transfer rate in MBit/s <10 GBit 5G <1 GBit <0.4 MBit <7.2 MBit 2G 3G 3G GSM UMTS HSPA 1996 2004 2006 <0.2 MBit Source: LTE.info. <150 MBit 4G 4G LTE Adv. <42 MBit 3G LTE HPSA+ 2009 2010 2014 2020 The Connected Car 131 Box 12.1. Statement by Hartmut Kremling Hartmut Kremling, Consultant Engineer for 5G, Internet of Things and Autonomous and Connected Driving LTE-V and 5G play a crucial role for the functioning of the ecosystem of the automotive industry consisting of numerous digital services. Building an ecosystem for autonomous driving needs intensive collaboration between the car industry, infrastructure vendors like Ericsson, Huawei, Intel, Nokia and Qualcomm, and telecommunications operators like Vodafone.

The Non-Tinfoil Guide to EMFs
by Nicolas Pineault
Published 6 Dec 2017

Icaro Publishing. huffingtonpost.com antennasearch.com/ The website seems to be down at the time of this writing, unfortunately. rcrwireless.com stopglobalwifi.org © 2017 N&G Media Inc. 15 But why just connect every human being to the Internet? Our new smart electronics need it too! Experts in the development of what’s called the “Internet of Things” (IoT) predict that by 2020, there will be around 50 billion devices, people or sensors connected with each other.27 These include your Bluetooth dimmer switches and home appliances, but also wireless traffic lights, light bulbs, cars (GPS, satellite radio, etc.), FM-emitting posters28 and yes!

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Instead of doing just that, we’re exponentially increasing the amount of EMFs we’re all exposed to, we’re right about to upgrade all networks to 5G which means multiplying the number of cellphone antennas we use tremendously, we’re connecting 50 billion new devices by 2020328 thanks to the “Internet Of Things” and we’re even planning to blast a constant EMF signal to every plant, animal and living thing on the entire planet using satellites or freaking giant balloons. Google’s project Loon: while we figure out whether EMFs can screw up your health or not, let’s blast them on the entire planet! May I innocently suggest none of these sounds like being “cautious”?

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Sneaky Sources Of Wifi - Level 1 Cheap & Easy Aside from the very sneaky public wifi that’s installed on a third of all private routers, there are a ton of electronics in your home that constantly emit invisible RF signals right under your nose. How rude. As companies continue to push the idea of a smart, hyperconnected home and the Internet of Things (IoT) — where everything ranging from your light switches to your plants will be connected to the Internet — it will become harder and harder to identify these sources of EMFs unless you have a meter like mine, or hire an EMF expert to do a home assessment (always highly recommended). Scrap The Baby Monitor How can I put this bluntly?

How to Fix the Future: Staying Human in the Digital Age
by Andrew Keen
Published 1 Mar 2018

Although there might not be any single solution, any magic bullet for creating an ideal network society, what unites all these people is their determination—what I call “agency”—to shape their own fate in the face of technological forces that often seem both uncontrollable and unaccountable. There is today much hype, some of it justified, about the “internet of things”—the network of smart objects that is the newest new thing in Silicon Valley. Rather than an internet of things, however, this book showcases an internet of people. I show that instead of smart technology, it’s smart human beings, acting as they’ve always done throughout history—as innovators, regulators, educators, consumers, and, above all, as engaged citizens—who are fixing the twenty-first-century future.

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But one of the unavoidable consequences of the digital revolution is the massive explosion of personal data on the network. Like it or not, this data is only going to grow exponentially with the development of smart homes, smart cars, smart cities, and, above all, all the other smart objects driving the internet of things. We don’t have a choice about any of this. But what we do have a choice about is the amount of transparency we demand of the governments or corporations that have access to our personal data. That’s why Viik’s attempt to create a blockchain-like transparency within the Estonian government’s database of information about its citizens is so important.

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The German business consultancy group Roland Berger estimates that the German economy will lose 220 billion euros of annual value if it fails to successfully transform itself digitally.4 Today, only four of the world’s 174 unicorns—privately held companies like Uber or Airbnb that have a valuation of more than a billion dollars—are German. Every key German manufacturing sector—particularly its 361-billion-euro automotive industry, which makes up 20 percent of total German industrial revenue and employs more than 750,000 workers—is now vulnerable to the revolution of the internet of things, with its billions of connected devices flooding onto the market each year. Fifty billion of these smart things by 2020, according to Cisco. And many more billions throughout the 2020s and 2030s. So what Germans call Industrie 4.0—the fourth stage of industrial evolution after water and steam power, mass production, and the information technology revolution—is of vital importance to the future of the world’s leading engineering power.

Rule of the Robots: How Artificial Intelligence Will Transform Everything
by Martin Ford
Published 13 Sep 2021

The same sort of explosion is likely coming to artificial intelligence, and more specifically to deep learning. The emergence of AI as the new electricity will, for the foreseeable future, be driven by an ever-expanding spectrum of specific applications rather than any more general machine intelligence. AN INTERCONNECTED WORLD AND THE “INTERNET OF THINGS” The final piece of the “artificial intelligence as the new electricity” puzzle is vastly improved connectivity. The most important driver of this is likely to be the roll out of fifth-generation wireless service (or 5G) in the coming years. 5G is expected to boost mobile data speeds by at least a factor of ten—and perhaps as much as one hundred—while dramatically increasing network capacity so that bottlenecks are largely eliminated.19 This will lead inevitably to a far more interconnected world where communication happens almost instantaneously.

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We can imagine that virtually everything—including devices, appliances, vehicles, industrial machinery and a great many elements of our physical infrastructure—will all be interconnected and often monitored and controlled by smart algorithms running in the cloud. This vision of the future has been dubbed the “Internet of Things” and is poised to usher in a world where, for example, sensors in your refrigerator or elsewhere in your kitchen detect that you’re running low on a particular item and then relay that information to an algorithm that alerts you or perhaps even automatically places the necessary online order.

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If the refrigerator isn’t running optimally, another algorithm will often be able to accomplish an automated or remote resolution. A part that is about to fail will be identified and flagged for replacement. Scaling this model across our entire economy and society is likely to deliver enormous efficiency gains as machines, systems and infrastructure automatically diagnose, and often fix, problems as they arise. The Internet of Things will, in many ways, be like unleashing the algorithms that currently operate cloud data centers with a superhuman level of efficiency to run the wider world. All this will, however, also bring with it some very real risks, especially in the areas of security and privacy, and we will focus on these critical issues in Chapter 8.

Augmented: Life in the Smart Lane
by Brett King
Published 5 May 2016

That’s how strong IBM’s branding around “PC” became back then. 7 At the History of Science auction held at Bonhams New York on 22nd October 2014, one of the 50 original Apple-I computers (and one of only about 15 or so that are operational) was sold to The Henry Ford for a staggering US$905,000. 8 Cisco—Internet of Things (IoT) 9 Minecraft is a trademark owned by Mojang/Microsoft. 10 Globally, the term ECG is most common in which the Greek word for “heart” cardia or kardia is central to the acronym (elektro-cardia-graph, literally “electric-heart-writing”). The US common usage is EKG, using the original Greek spelling term rather than the English transliteration (cardio). 11 R.W.

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Sensors, Wearables, Ingestibles and Feedback Loops As mentioned in chapter 3, heart disease is one of the single most common causes of death in the developed world. As a result, heart health is one of the biggest disciplines in medicine globally today, second only to cancer and cancer research. It is just one of the areas set to be fundamentally changed by the technology of sensors and the Internet of Things. A Parisian doctor named René Théophile Hyacinthe Laënnec (1781–1826) invented the first stethoscope in 1816 to “assist with auscultation”, or listening to a patient’s heartbeat. In 1851, the stethoscope went binaural, and since has had minor adjustments, including even electronic amplification.

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There will be very few instances where a human who can give you advice at a future time with inferior data will compete with technologically embedded, contextual advice in real time. Machines Will Be Better at Learning about You Machine learning has been limited in the past by pattern recognition, natural speech and other deficiencies, but machines are beginning to catch up quickly. The advantage that machines connected to the Internet of Things and sensors will have is that they will be able to learn about your behaviour much more efficiently than service organisations today. How do service organisations today learn about your preferences? There are really only four ways: • demographic-based assumptions • surveys, marketing databases and user panels • data you’ve previously entered into the system or on a form • preferences you might input into an app, online portal or other configurator All of these are imprecise ways of measuring your preferences and behaviour, and at a very minimum depend on both your diligence and honesty in answering, and the effectiveness of the organisation in collecting and synthesizing that data.

Don't Be Evil: How Big Tech Betrayed Its Founding Principles--And All of US
by Rana Foroohar
Published 5 Nov 2019

These apps alone represent a $21 billion industry of snooping, and it’s not only the largest tech companies that benefit (though they certainly do; Google’s Android system has 1,200 apps that do such tracking), but a host of companies that you probably don’t even think about, from Goldman Sachs to the Weather Channel.38 And that’s just the consumer side of things. The old commercial Internet is shifting to an industrial “Internet of things” that will push data harvesting out into the physical world—into design firms, manufacturing plants, insurance companies, financial houses, hospitals, schools, and even our homes. Name any successful company: Starbucks, Johnson & Johnson, Goldman Sachs…and it’s likely that successful data mining plays an important role in their business strategy.

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I often think about those disastrous years and wonder what has changed in the tech world, and what hasn’t. Today’s tech market is so much more developed, with vastly better infrastructure and truly game-changing innovations. We are only just beginning to move into artificial intelligence, the Internet of things, and other areas that many businesses are counting on to propel revenue growth in the future. Whether they will or not remains to be seen. But it’s a fair bet that machines talking to one another will have a heck of a lot more practical and productive applications than online gossip websites did.

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Patents, as I covered extensively in chapter 5, is another. Over the past few years, I’ve heard disparate complaints from a variety of quarters—from start-up biotech firms, semiconductor and electronics firms, clean-tech companies, data analysis groups, universities, and innovators working on the Internet of things, as well as some of the venture capitalists that invest in these areas—that the patent system and the debate about how to structure it has been hijacked by the interests of the largest tech firms in the country. Indeed, the only ones who seem not to be complaining about the current system are Google, Apple, Intel, Cisco, and other Silicon Valley giants.

Who’s Raising the Kids?: Big Tech, Big Business, and the Lives of Children
by Susan Linn
Published 12 Sep 2022

Machine learning algorithms make predictions or classifications by inventing “learning” rules from stored data that was fed to them or from data extracted from real-time human/ computer interactions. Predictive algorithms are machine learning algorithms that use previously collected data to “learn” to predict something that has yet to happen. Research has shown that the rules these algorithms invent to predict the future often reproduce biases in their training data. Internet of things (IoT) is a catchall phrase for the billions of smart devices—toys, microwaves, vacuum cleaners, thermostats, toothbrushes, etc.—with internet connectivity. While some of the data these devices capture through their sensors is useful to device owners, most of it becomes monetizable by device providers once it is uploaded to a cloudbased platform like Salesforce IoT.

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Each digital “reward” triggers a little squirt of dopamine, a powerful neurotransmitter that triggers the seductive combination of enjoyment or excitement and desire.48 Once captured in this loop of pleasure and longing, we and our children are subject to unprecedented surveillance. At minimum, most of our behavior online is tracked. Increasingly, our behavior offline is tracked as well through location devices on our phones and through the Internet of Things (IoT), a catchall phrase describing “smart” toys, appliances, watches, and more. More and more, children are vulnerable to surveillance when they play. Sales of smart toys, including the physical toys and their associated apps, are expected to reach almost $70 billion in 2026.49 Tech companies call the process of continually collecting information about us as we use their offerings “data mining.”

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In her book Reclaiming Conversation: The Power of Talk in a Digital Age, psychologist Sherry Turkle highlights the importance of conversation for deepening relationships. “In family conversations,” she says, “children learn what can matter most is not the information shared, but the relationship sustained.”26 I find myself reflecting on Turkle’s quote a lot as I think about the Internet of Things—so-called smart objects that connect to the internet. These can be everything from toasters to vibrators to toys for children.27 They are marketed as desirable because they “learn” our preferences and habits by tracking what we do with them, analyzing the data they collect and altering their behavior accordingly.

Designing Great Web APIs: Creating Business Value Through Developer Experience
by James Higginbotham
Published 14 Apr 2015

Let’s examine the reasons that gave rise to the API economy. Reason #1 – Higher Demand Historically, APIs were used to integrate different software systems or even different organizations. Web APIs are now in high demand due to three key factors: the modern browser, mobile devices, and the Internet of Things. Years ago, modern browsers were limited to displaying content and limited scripting capabilities using JavaScript. Modern browsers have moved beyond this, allowing rich web applications to be built using a combination of HTML, CSS, and modern JavaScript frameworks. As a result, we no longer require servers to generate complete web pages.

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In addition to modern browsers, there has been explosive growth in mobile devices such as phones and tablets. These devices have access to the Internet from most locations and offer GPS location and app-store distribution. Applications no longer have to be web pages in a browser. Instead, they can use APIs to access data and business logic to get things done. Finally, the Internet of Things (IoT) is moving the world of devices, previously requiring human intervention, into autonomous replacements that combine the physical world with the world of software. As a result, APIs enable IoT devices to broadcast their telemetry data and receive commands from other systems. IoT is an emerging domain that will greatly benefit from integrating and providing APIs.

50 Future Ideas You Really Need to Know
by Richard Watson
Published 5 Nov 2013

Perhaps the best way to think about the Internet in the future is to see it as something that you no longer “do,” but as something that simply “is.” When this happens the Internet will seem to have vanished. “The Internet of Things is also triggering new questions on ownership and consumption … we grow into an access-based economy, where IOI makes a pay-what-you-use system possible on an individual level.” Alexander Bassi, Institute for Internet and Society The Internet of things is not quite the same as ubiquitous or pervasive computing, but like most things in the future it’s connected. In the past, information was scarce and tended to be tightly controlled by governments or large corporations.

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ISBN 978-1-62365-195-4 Distributed in the United States and Canada by Random House Publisher Services c/o Random House, 1745 Broadway New York, NY 10019 www.quercus.com Contents Introduction POLITICS & POWER 01 Ubiquitous surveillance 02 Digital democracy 03 Cyber & drone warfare 04 Water wars 05 Wane of the West ENERGY & ENVIRONMENT 06 Resource depletion 07 Beyond fossil fuels 08 Precision agriculture 09 Population change 10 Geo-engineering THE URBAN LANDSCAPE 11 Megacities 12 Local energy networks 13 Smart cities 14 Next-generation transport 15 Extra-legal & feral slums TECHNOLOGICAL CHANGE 16 An internet of things 17 Quantum & DNA computing 18 Nanotechnology 19 Gamification 20 Artificial Intelligence HEALTH & WELL-BEING 21 Personalized genomics 22 Regenerative medicine 23 Remote monitoring 24 User-generated medicine 25 Medical data mining SOCIAL & ECONOMIC DIMENSIONS 26 Living alone 27 Dematerialization 28 Income polarization 29 What (& where) is work?

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the condensed idea Slums the size of cities timeline 2012 Parents hire private security guards to escort teenagers in London 2014 25 percent more helipads in São Paulo than New York due to no-go areas 2022 CEO of General Electric visits outskirts of Nairobi to learn about recycling 2026 Indian rubbish pricing and distribution system copied in USA 2030 Soldiers outnumber police on some city streets 2070 After the collapse of the mines, Western Australia becomes a prison colony 16 An internet of things According to Cisco Systems, there will be 50 billion “things” connected to the Internet by 2020. That’s seven for every man, woman and child on the planet. So what are some of these “things” and what are the consequences of an Internet that’s increasingly made up of physical objects embedded with sensors?

Aiming High: Masayoshi Son, SoftBank, and Disrupting Silicon Valley
by Atsuo Inoue
Published 18 Nov 2021

The next issues to be addressed were things like whether to charge a flat rate for broadband or charge for use, waiving the basic fees. Tsutsui continues, ‘With things like photovoltaic cells, devices capable of capturing electricity are completely connected to the internet. The internet of things was just around the corner, in the very near future.’ The 21st century would see the dawn of the internet of things and the historical turning point in this respect would be the year 2001, the first year that broadband became available. The two geniuses would celebrate the day they created a new era. On 11 September of that same year, however, the world order would be shaken to the core with the Al-Qaeda terrorist attacks on multiple sites in the United States.

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Son quizzed Segars on all manner of things and spoke about what he thought the future would be like, his voice suddenly spiking when speaking about things he was excited about, just like a young boy would do. Even before meeting with Segars and when musing on what the ‘internet of things’ would be like going forwards, Son realised a new wave was imminent with the technology driving smaller, albeit interconnected, devices. At the present time around 1 billion mobiles were all interconnected but this would soon become 1 trillion devices and Son began thinking that the internet of things could be the next phase of the digital information revolution. So when Son finally met the Englishman he had already formed an image of what this future would be like in his head.

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So when Son finally met the Englishman he had already formed an image of what this future would be like in his head. Masa said to Simon: ‘You are a public company, if you want to have the same success in that you have gotten in mobile device that’s where you got such enormous penetration of mobile device. And what you are doing with the same kind of process of technology moving into Internet of Things, there has to be investments. You have to invest, make these investments to really grow the next generation of devices.’ Simon agreed with him, whilst explaining some of the challenges Arm faced. ‘You are right. The problem that we have is we are a public company. So we are very limited in the kinds of investments that we make.

Industrial Internet
by Jon Bruner
Published 27 Mar 2013

With a network connection and an open interface that masks its underlying complexity, a machine becomes a Web service, ready to be coupled to software intelligence that can ingest broad context and optimize entire systems of machines. The industrial internet is this union of software and big machines — what you might think of as the enterprise Internet of Things, operating under the demanding requirements of systems that have lives and expensive equipment at stake. It promises to bring the key characteristics of the Web — modularity, abstraction, software above the level of a single device — to demanding physical settings, letting innovators break down big problems, solve them in small pieces, and then stitch together their solutions.

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Think of a car’s odometer: the move to digital mileage counts, stored in software, makes it more difficult to tamper with the readout, but it expands the prospective target of an exploit from just one car (for mechanical odometers) to every car that uses the same software. Tools like Shodan[9], a search engine for the Internet of Things, and Digital Bond’s Basecamp[10], a database of industrial control exploits, illustrate the scale of the industrial internet and its vulnerabilities. Shodan is a search engine for Internet-connected devices, including some industrial control systems and Internet switches. Here it reveals several computers that return a default password field in their HTTP responses.

Mastering Blockchain, Second Edition
by Imran Bashir
Published 28 Mar 2018

With the invention of Bitcoin, the concept of blockchain was introduced for the very first time, but it was not until 2013 that the true potential of blockchain technology was realized with its possible application in many different industries, other than cryptocurrencies. Since then many use cases of blockchain technology in various industries have been proposed, including but not limited to finance, the Internet of Things, digital rights management, government, and law. In this chapter, four main industries namely the Internet of Things, government, health, and finance, have been selected, with the aid of use cases, for discussion. In 2010, discussion started regarding BitDNS, a decentralized naming system for domains on the internet. Then Namecoin (https://wiki.namecoin.org/index.php?

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title=History) started in April 2011 with a different vision as compared to Bitcoin whose sole purpose is to provision electronic cash. This can be considered first example of blockchain usage other than purely cryptocurrencies. After this by 2013, many ideas emerged. Since 2013 this trend is growing exponentially. Internet of Things The Internet of Things (IoT) for short has recently gained much traction due to its potential for transforming business applications and everyday life. IoT can be defined as a network of computationally intelligent physical objects (any object such as cars, fridges, industrial sensors, and so on) that are capable of connecting to the internet, sensing real-world events or environments, reacting to those events, collecting relevant data, and communicating it over the internet.

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PacktPub.com Contributors About the author About the reviewer Packt is searching for authors like you Preface Who this book is for What this book covers To get the most out of this book Download the example code files Download the color images Conventions used Get in touch Reviews Blockchain 101 The growth of blockchain technology Distributed systems The history of blockchain and Bitcoin Electronic cash Blockchain Blockchain defined Peer-to-peer Distributed ledger Cryptographically-secure Append-only Updateable via consensus Generic elements of a blockchain How blockchain works How blockchain accumulates blocks Benefits and limitations of blockchain Tiers of blockchain technology Features of a blockchain Types of blockchain Distributed ledgers Distributed Ledger Technology Public blockchains Private blockchains Semiprivate blockchains Sidechains Permissioned ledger Shared ledger Fully private and proprietary blockchains Tokenized blockchains Tokenless blockchains Consensus Consensus mechanism Types of consensus mechanisms Consensus in blockchain CAP theorem and blockchain Summary Decentralization Decentralization using blockchain Methods of decentralization Disintermediation Contest-driven decentralization Routes to decentralization How to decentralize The decentralization framework example Blockchain and full ecosystem decentralization Storage Communication Computing power and decentralization Smart contracts Decentralized Organizations Decentralized Autonomous Organizations Decentralized Autonomous Corporations Decentralized Autonomous Societies Decentralized Applications (DApps) Requirements of a Decentralized Application Operations of a DApp DApp examples KYC-Chain OpenBazaar Lazooz Platforms for decentralization Ethereum MaidSafe Lisk Summary Symmetric Cryptography Working with the OpenSSL command line Introduction Mathematics Set Group Field A finite field Order An abelian group Prime fields Ring A cyclic group Modular arithmetic Cryptography Confidentiality Integrity Authentication Entity authentication Data origin authentication Non-repudiation Accountability Cryptographic primitives Symmetric cryptography Stream ciphers Block ciphers Block encryption mode Electronic Code Book Cipher Block Chaining Counter mode Keystream generation mode Message authentication mode Cryptographic hash mode Data Encryption Standard Advanced Encryption Standard How AES works Summary Public Key Cryptography Asymmetric cryptography Integer factorization Discrete logarithm Elliptic curves Public and private keys RSA Encryption and decryption using RSA Elliptic Curve Cryptography Mathematics behind ECC Point addition Point doubling Discrete logarithm problem in ECC RSA using OpenSSL RSA public and private key pair Private key Public key Exploring the public key Encryption and decryption Encryption Decryption ECC using OpenSSL ECC private and public key pair Private key Private key generation Hash functions Compression of arbitrary messages into fixed-length digest Easy to compute Preimage resistance Second preimage resistance Collision resistance Message Digest Secure Hash Algorithms Design of Secure Hash Algorithms Design of SHA-256 Design of SHA-3 (Keccak) OpenSSL example of hash functions Message Authentication Codes MACs using block ciphers Hash-based MACs Merkle trees Patricia trees Distributed Hash Tables Digital signatures RSA digital signature algorithm Sign then encrypt Encrypt then sign Elliptic Curve Digital Signature Algorithm How to generate a digital signature using OpenSSL ECDSA using OpenSSL Homomorphic encryption Signcryption Zero-Knowledge Proofs Blind signatures Encoding schemes Financial markets and trading Trading Exchanges Orders and order properties Order management and routing systems Components of a trade The underlying instrument General attributes Economics Sales Counterparty Trade life cycle Order anticipators Market manipulation Summary Introducing Bitcoin Bitcoin Bitcoin definition Bitcoin&#xA0;&#x2013; a bird's-eye view Sending a payment to someone Digital keys and addresses Private keys in Bitcoin Public keys in Bitcoin Addresses in Bitcoin Base58Check encoding Vanity addresses Multisignature addresses Transactions The transaction life cycle Transaction fee Transaction pools The transaction data structure Metadata Inputs Outputs Verification The script language Commonly used opcodes Types of transactions Coinbase transactions Contracts Transaction veri&#xFB01;cation Transaction malleability Blockchain The structure of a block The structure of a block header The genesis block Mining Tasks of the miners Mining rewards Proof of Work (PoW) The mining algorithm The hash rate Mining systems CPU GPU FPGA ASICs Mining pools Summary Bitcoin Network and Payments The Bitcoin network Wallets Non-deterministic wallets Deterministic wallets Hierarchical Deterministic wallets Brain wallets Paper wallets Hardware wallets Online wallets Mobile wallets Bitcoin payments Innovation in Bitcoin Bitcoin Improvement Proposals (BIPs) Advanced protocols Segregated Witness (SegWit) Bitcoin Cash Bitcoin Unlimited Bitcoin Gold Bitcoin investment and buying and selling bitcoins Summary Bitcoin Clients and APIs Bitcoin installation Types of Bitcoin Core clients Bitcoind Bitcoin-cli Bitcoin-qt Setting up a Bitcoin node Setting up the source code Setting up bitcoin.conf Starting up a node in testnet Starting up a node in regtest Experimenting with Bitcoin-cli Bitcoin programming and the command-line interface Summary Alternative Coins Theoretical foundations Alternatives to Proof of Work Proof of Storage Proof of Stake (PoS) Various stake types Proof of coinage Proof of Deposit (PoD) Proof of Burn Proof of Activity (PoA) Nonoutsourceable puzzles Difficulty adjustment and retargeting algorithms Kimoto Gravity Well Dark Gravity Wave DigiShield MIDAS Bitcoin limitations Privacy and anonymity Mixing protocols Third-party mixing protocols Inherent anonymity Extended protocols on top of Bitcoin Colored coins Counterparty Development of altcoins Consensus algorithms Hashing algorithms Difficulty adjustment algorithms Inter-block time Block rewards Reward halving rate Block size and transaction size Interest rate Coinage Total supply of coins Namecoin Trading Namecoins Obtaining Namecoins Generating Namecoin records Litecoin Primecoin Trading Primecoin Mining guide Zcash Trading Zcash Mining guide Address generation GPU mining Downloading and compiling nheqminer Initial Coin Offerings (ICOs) ERC20 tokens Summary Smart Contracts History Definition Ricardian contracts Smart contract templates Oracles Smart Oracles Deploying smart contracts on a blockchain The DAO Summary Ethereum 101 Introduction The yellow paper Useful mathematical symbols Ethereum blockchain Ethereum &#x2013; bird's eye view The Ethereum network Mainnet Testnet Private net Components of the Ethereum ecosystem Keys and addresses Accounts Types of accounts Transactions and messages Contract creation transaction Message call transaction Messages Calls Transaction validation and execution The transaction substate State storage in the Ethereum blockchain The world state The account state Transaction receipts Ether cryptocurrency / tokens (ETC and ETH) The Ethereum Virtual Machine (EVM) Execution environment Machine state The iterator function Smart contracts Native contracts Summary Further Ethereum Programming languages Runtime bytecode Opcodes and their meaning Arithmetic operations Logical operations Cryptographic operations Environmental information Block information Stack, memory, storage, and &#xFB02;ow operations Push operations Duplication operations Exchange operations Logging operations System operations Blocks and blockchain The genesis block The block validation mechanism Block finalization Block difficulty Gas Fee schedule Forks in the blockchain Nodes and miners The consensus mechanism Ethash CPU mining GPU mining Benchmarking Mining rigs Mining pools Wallets and client software Geth Eth Pyethapp Parity Light clients Installation Eth installation Mist browser Geth The geth console Funding the account with bitcoin Parity installation Creating accounts using the parity command line APIs, tools, and DApps Applications (DApps and DAOs) developed on Ethereum Tools Supporting protocols Whisper Swarm Scalability, security, and other challenges Trading and investment Summary Ethereum Development Environment Test networks Setting up a private net Network ID The genesis file Data directory Flags and their meaning Static nodes Starting up the private network Running Mist on private net Deploying contracts using Mist Block explorer for private net / local Ethereum block explorer Summary Development Tools and Frameworks Languages Compilers Solidity compiler (solc) Installation on Linux Installation on macOS Integrated Development Environments (IDEs) Remix Tools and libraries Node version 7 EthereumJS Ganache MetaMask Truffle Installation Contract development and deployment Writing Testing Solidity language Types Value types Boolean Integers Address Literals Integer literals String literals Hexadecimal literals Enums Function types Internal functions External functions Reference types Arrays Structs Data location Mappings Global variables Control structures Events&#xA0; Inheritance Libraries Functions Layout of a Solidity source code &#xFB01;le Version pragma Import Comments Summary Introducing Web3 Web3 Contract deployment POST requests The HTML and JavaScript frontend Installing web3.js Example Creating a web3 object Checking availability by calling any web3 method Contract functions Development frameworks Truffle Initializing Truffle Interaction with the contract Another example An example project&#xA0;&#x2013; Proof of Idea Oracles Deployment on decentralized storage using IPFS Installing IPFS Distributed ledgers Summary Hyperledger Projects under Hyperledger Fabric Sawtooth Lake Iroha Burrow Indy Explorer Cello Composer Quilt Hyperledger as a protocol The reference architecture Requirements and design goals of Hyperledger Fabric The modular approach Privacy and confidentiality Scalability Deterministic transactions Identity Auditability Interoperability Portability Rich data queries Fabric Hyperledger Fabric Membership services Blockchain services Consensus services Distributed ledger The peer to peer protocol Ledger storage Chaincode services Components of the fabric Peers Orderer nodes Clients Channels World state database Transactions Membership Service Provider (MSP) Smart contracts Crypto service provider Applications on blockchain Chaincode implementation The application model Consensus in Hyperledger Fabric The transaction life cycle in Hyperledger Fabric Sawtooth Lake PoET Transaction families Consensus in Sawtooth The development environment&#xA0;&#x2013; Sawtooth Lake Corda Architecture State objects Transactions Consensus Flows Components Nodes The permissioning service Network map service Notary service Oracle service Transactions Vaults CorDapp The development environment&#xA0;&#x2013; Corda Summary Alternative Blockchains Blockchains Kadena Ripple Transactions Payments related Order related Account and security-related Interledger Application layer Transport layer Interledger layer Ledger layer Stellar Rootstock Sidechain Drivechain Quorum Transaction manager Crypto Enclave QuorumChain Network manager Tezos Storj MaidSafe BigchainDB MultiChain Tendermint Tendermint Core Tendermint Socket Protocol (TMSP) Platforms and frameworks Eris Summary Blockchain &#x2013; Outside of Currencies Internet of Things Physical object layer Device layer Network layer Management layer Application layer IoT blockchain experiment First node setup Raspberry Pi node setup Installing Node.js Circuit Government Border control Voting Citizen identification (ID cards) Miscellaneous Health Finance Insurance Post-trade settlement Financial crime prevention Media Summary Scalability and Other Challenges Scalability Network plane Consensus plane Storage plane View plane Block size increase Block interval reduction Invertible Bloom Lookup Tables Sharding State channels Private blockchain Proof of Stake Sidechains Subchains Tree chains (trees) Block propagation Bitcoin-NG Plasma Privacy Indistinguishability Obfuscation Homomorphic encryption Zero-Knowledge Proofs State channels Secure multiparty computation Usage of hardware to provide confidentiality CoinJoin Confidential transactions MimbleWimble Security Smart contract security Formal verification and analysis Oyente tool Summary Current Landscape and What&#x27;s Next Emerging trends Application-specific blockchains (ASBCs) Enterprise-grade blockchains Private blockchains Start-ups Strong research interest Standardization Enhancements Real-world implementations Consortia Answers to technical challenges Convergence Education of blockchain technology Employment Cryptoeconomics Research in cryptography New programming languages Hardware research and development Research in formal methods and security Alternatives to blockchains Interoperability efforts Blockchain as a Service Efforts to reduce electricity consumption Other challenges Regulation Dark side Blockchain research Smart contracts Centralization issues Limitations in cryptographic functions Consensus algorithms Scalability Code obfuscation Notable projects Zcash on Ethereum CollCo Cello Qtum Bitcoin-NG Solidus Hawk Town-Crier SETLCoin TEEChan Falcon Bletchley Casper Miscellaneous tools Solidity extension for Microsoft Visual Studio MetaMask Stratis Embark DAPPLE Meteor uPort INFURA Convergence with other industries Future Summary Another Book You May Enjoy Leave a review&#xA0;&#x2013; let other readers know what you think Preface This book has one goal, to introduce theoretical and practical aspects of the blockchain technology.

Surviving AI: The Promise and Peril of Artificial Intelligence
by Calum Chace
Published 28 Jul 2015

Happy in the full possession of her vegetables, she drove home, humming along to Joni Mitchell. 2.2 – Converting information into knowledge – at different speeds The science fiction writer William Gibson is reported as saying that “The future is already here – it’s just not evenly distributed.” (13) Most of the things mentioned in the short story above are already available in prototype and early incarnations, and the rest is firmly under development – some of it as part of the so-called “internet of things”. It could take anywhere from five to fifteen years for you to have working versions of all of them. Some people will think the life described above is frightening, perhaps de-humanised. It is likely that more people will welcome the assistance, and of course generations to come will simply take it for granted.

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As Douglas Adams said, anything invented after you’re thirty-five is against the natural order of things, anything invented between when you’re fifteen and thirty-five is new and exciting, and anything that is in the world when you’re born is just a natural part of the way the world works. (14) Of course there is no guarantee that the future will work out this way – in fact the details are bound to be different. For example we don’t yet know whether the myriad devices connecting up to the Internet of Things will communicate with us directly, or via personal digital assistants like Hermione. Will you be reminded to take your pill in the morning because its bottle starts glowing, or will Hermione alert you? No doubt the outcome will seem obvious in hindsight. It has been said that all industries are now part of the information industry – or heading that way.

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But serious consideration of exponential growth makes very hard problems seem more tractable. Buckminster Fuller estimated that at the start of the twentieth century the sum of human knowledge was doubling every century, and that by the end of the second world war that had reduced to twenty-five years. (40) Now it takes 13 months and in 2006 IBM estimated that when the internet of things becomes a reality the rate would be every 12 hours. (41) The football stadium thought experiment illustrates how progress at exponential rate can take you by surprise – even when you are looking for it. Many sensible people become suspicious when they hear the phrase exponential growth: they fear it used as a cover for wishful (or so-called “magical”) thinking.

Thank You for Being Late: An Optimist's Guide to Thriving in the Age of Accelerations
by Thomas L. Friedman
Published 22 Nov 2016

GE, thanks in large part to its accelerating ability to put sensors all over its industrial equipment, is becoming more of a software company, with a big base now in Silicon Valley. Forget about washing machines—think intelligent machines. GE’s ability to install sensors everywhere is helping to make possible the “industrial Internet,” also known as the “Internet of Things” (IoT), by enabling every “thing” to carry a sensor that broadcasts how it is feeling at any moment, thus allowing its performance to be immediately adjusted or predicted in response. This Internet of Things, Ruh explained, “is creating a nervous system that will allow humans to keep up with the pace of change, make the information load more usable,” and basically “make every thing intelligent.”

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IBM ice sheets; shrinking of identity, proof of IEDs (improvised explosive devices) IEX illiteracy Ilulissat, Greenland Immelt, Jeff immigrants, immigration; diversity and; as entrepreneurs; into Europe; integration of; policy reform for imperialism, fading of inclusion, ethos of India; connectivity in Indian Institute of Technology Indonesia Industrial Revolution; Second inflection points; age of accelerations; year 2000; year 2007 information technology revolution infrastructure; in weak states innovation: in geopolitics; global flows and; in India; lag between consequences and; in Mexico; in post–post–Cold War geopolitics; as response to change; in social technologies; supernova and; see also education, innovation in; ethics, innovation in; politics, innovation in; software innovation; technological change; workforce, innovation in Institute for the Future integrated circuits; Moore’s law and Intel intelligent algorithms intelligent assistance; AT&T and; skill sets and intelligent assistants; education and; job seekers and; workforce and interdependence; in ecosystems; in financial flow; in geopolitics; healthy vs. unhealthy; of natural systems International Commission on Stratigraphy International Institute for Strategic Studies International Journal of Business, Humanities, and Technology International Organization for Migration International Rescue Committee Internet; cloud and, see supernova (cloud computing); digital divide and; GDP and; government policy on; mobile phones and; weak states and Internet of Things Internet of Things Foundry intuition, and detection of weak signals Invictus (film) Iorio, Luana iOS iPhones; AT&T’s gamble on Iran Iraq Iraq War Isbin, Sharon Islam Islamic State (ISIS); videos by Islamists Islamist terrorism isolation, as disease Israel Israeli-Palestinian War (1982) Istanbul Itasca Project Ixigo.com Jabr, Jumana Jacklin, Tony Jackson, Wes Jacobs, Irwin Jacobs, Jeff Jacobs, Lawrence Jacobs, Paul Japan Japanese Americans Jennings, Ken Jennings, Peter Jeopardy!

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While media, music, books, and games represent the first wave of digital trade, 3-D printing could eventually expand digital commerce to many more product categories. And forget the fact that so many “friends” are connecting on Facebook. How about all the “things” getting to know one another? You want to see flows—wait until the “Internet of Things” gets to scale and machines start talking to machines everywhere and always! “Only 0.6 percent of things are connected today,” Plamen Nedeltchev, distinguished IT engineer at Cisco, wrote on Cisco.com in an essay entitled “It is inevitable. It is here. Are we ready?” on September 29, 2015. “There were 1,000 Internet-connected devices in 1984,” said the article, a million in 1992, and ten billion in 2008.

Only Humans Need Apply: Winners and Losers in the Age of Smart Machines
by Thomas H. Davenport and Julia Kirby
Published 23 May 2016

By now, heavy use of embedded analytics, or operational analytics, has given rise to what Tom has elsewhere called “Analytics 3.0,” a new era in which data drive the workings of organizations at dramatically greater speed and scale.5 Gartner, the IT market research firm, recognized “advanced, pervasive, and invisible analytics” as one of its “ten strategic technologies for 2015.”6 Bill Franks, the chief analytics officer of Teradata, is referring to the same transformation in his book on operational analytics called The Analytics Revolution. If you are already weary of the buzzwords “big data” and “the Internet of things,” this is why; both represent fire hoses of data that become extremely valuable when the computing power is in place to find patterns and make decisions to capitalize on them. Already today, the Internet connects more smart objects than people (and has thus become an Internet of things); by 2020, Cisco estimates, the number of devices connected to the Internet will rise to 50 billion.7 As they transmit data in near-real time, fast computers are able to make frequent decisions based on continuous analysis.

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The most sophisticated underwriting systems generate literally millions of different pricing cells and do so easily, because it is only a matter of following logical rules and equations. Computer systems gain an even greater advantage as devices with sensors—cars, trucks, boilers, and other types of equipment—start reporting regularly on their own performance and usage. With such massive amounts of data to consider, humans are truly out of their league. Dealing with the “Internet of things” is something computers are capable of. Humans, not so much. Yet that doesn’t have to be the end of the story. Underwriters who can learn to focus on other strengths they bring to the job can survive this capture of its core, and even come out better for it—perhaps never regretting that forgone career as pro baseball player, ballerina, or astronaut.

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Most scientists would struggle to identify one such protein in a year; Watson took only a few weeks to find six (although, to be fair, it took several years to prepare Watson to do this).6 Other organizations are using similar technologies to glean insights from natural-language content that exists in enormous volume. Or think about the “Internet of things”—the ability to place small sensors on objects in the physical world and have them communicate readings in real time. The rise of this technology has been governed by the rise of computers with the processing power to deal with the immense amounts of data produced; unaided humans could not conceivably monitor and control the vast sensor networks used to, for example, detect if a tsunami is brewing far offshore.

The Big Nine: How the Tech Titans and Their Thinking Machines Could Warp Humanity
by Amy Webb
Published 5 Mar 2019

The Robot’s Dilemma: The Frame Problem in Artificial Intelligence. Norwood, NJ: Ablex, 1987. Riedl, M. O. “The Lovelace 2.0 Test of Artificial Creativity and Intelligence.” https://arxiv.org/pdf/1410.6142.pdf. Schneier, B. “The Internet of Things Is Wildly Insecure—and Often Unpatchable.” Wired, January 6, 2014. https://www.wired.com/2014/01/theres-no-good-way-to-patch-the-Internet-of-things-and-thats-a-huge-problem/. Shannon, C., and W. Weaver. The Mathematical Theory of Communication. Urbana: University of Illinois Press, 1963. Singer, P. Wired for War: The Robotics Revolution and Conflict in the 21st Century.

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This, in turn, has a compounding effect on the future of many other technologies adjacent to or directly intersecting with AI: autonomous vehicles, CRISPR and genomic editing, precision medicine, home robotics, automated medical diagnoses, green- and geoengineering technologies, space travel, cryptocurrencies and blockchain, smart farms and agricultural technologies, the Internet of Things, autonomous factories, stock-trading algorithms, search engines, facial and voice recognition, banking technologies, fraud and risk detection, policing and judicial technologies… I could make a list that spans dozens of pages. There isn’t a facet of your personal or professional life that won’t be impacted by AI.

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Tech journalists attribute the glitches to the “spooky ways” in which “AI acts weird sometimes.” At first, the attacks seem novel and random. So we all blame Google, Apple, and Amazon for faulty products and crappy customer service. Then cybersecurity experts are gobsmacked to discover all the glitches are actually linked. It is a new kind of “Internet of Things” attack originating in China and enabled by machine learning. The Chinese have a name for it: , or bèi kùn, which translates to “trapped.” The hackers, backed by the Chinese government, thought it was clever to launch “bacon” attacks during breakfast hours in America and to effectively trap our food, drinks, and eating utensils in our AI-powered appliances.

Makers
by Chris Anderson
Published 1 Oct 2012

This construction—“atoms” versus “bits”—originated with the work of a number of thinkers from the MIT Media Lab, starting with its founder, Nicholas Negroponte, and today most prominently exemplified by Neal Gershenfeld and the MIT Center for Bits and Atoms. It is shorthand for the distinction between software and hardware, or information technology and Everything Else. Today the two are increasingly blurring as more everyday objects contain electronics and are connected to other objects, the so-called Internet of Things. That’s part of what we’ll be talking about here. But even more, we’ll look at how it’s changing manufacturing, otherwise known as the flippin’ Engine of the World Economy. The idea of a “factory” is, in a word, changing. Just as the Web democratized innovation in bits, a new class of “rapid prototyping” technologies, from 3-D printers to laser cutters, is democratizing innovation in atoms.

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Practically every electronic device in your home works this way, from your thermostat to your alarm clocks, stereos, microwave oven, and portable music players. Your car has dozens of embedded computers. The difference is that they are all closed and proprietary, while Arduino is designed to be easy for anyone to use and modify. Much of the emerging “Internet of Things” movement is built on Arduino-based devices connected to the Web, from coffeemakers that tweet their status to pet feeders you can control from your phone, wherever you are. So, because I knew it best, I decided to base the sprinkler controller on Arduino. That meant it could tap into a huge community of people who are using Arduino for all sorts of other purposes, and who had already solved most of the problems of connecting it to the Internet and any sensor you can imagine.

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So the team, emboldened by its flood of orders, went looking for the right 4.0 modules and were able to source them, giving the watch better battery life and making it more future-proof. Finally, other Kickstarter projects joined the parade and announced that they would be writing apps to run on Pebble, including Twine, an “Internet of Things” device that could let Pebble do things like tell you when someone’s knocking at your door. As of this writing, Pebble has not yet shipped its watches (they’re due in September 2012), and perhaps production glitches will mar or delay the launch. But even before that, it’s not hard to see in Pebble a superior model: a small team using crowdfunding to move more quickly in all ways—R&D, finance, and marketing—than a lumbering electronics giant.

Good Profit: How Creating Value for Others Built One of the World's Most Successful Companies
by Charles de Ganahl Koch
Published 14 Sep 2015

Our investment focus will be on those opportunities that—by utilizing existing or adding new capabilities—provide the greatest value creation, the highest returns and contribution, and new growth platforms. This is Koch’s Vision. But every company, no matter what size or type, should strive to develop and clearly communicate a unique vision of its own. FROM PAPER TOWELS TO THE INTERNET OF THINGS Several steps are necessary when developing a vision. The first is creating a view of how the organization can create superior value for its customers and society and capture a share of it. The vision is a description of exactly how the organization plans to create that value. Some of the capabilities that are critical to Koch’s ability to create superior value are commercial excellence, operations excellence, talent, innovation, a trading mentality, and public sector effectiveness.

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An example of the unpredictability of our future directions—and the role of vision in guiding them—is our acquisition of Georgia-Pacific, which set in motion an evolutionary path that started with paper towels and led to exploration of how limitless and low-cost connectivity between objects, machines, and people (the Internet of Things) can create value in both manufacturing plants and offices. It’s not too hard to envision a “washroom of the future,” with sensors monitoring peak usage times, hygiene patterns, and mold potential while automatically reordering bathroom tissue, towels, and soap. This technology could improve sanitation, reduce costs, and streamline communications between GP and commercial building owners.

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Although growth in paper sales from the original enMotion dispenser has slowed since 2009, additional innovations have enabled sales for the entire business to grow more than ten times faster than the market. Electronic touchless systems now have more than a 15 percent share of the away-from-home hand-drying market, with GP owning well over half of that. The Internet of Things is also of obvious interest to Molex, Koch’s second-largest acquisition. An innovative electronics manufacturer, Molex is exploring “digital ceilings” in commercial buildings. These incorporate LED lighting with integrated sensor arrays, networked using standard Cat5 Ethernet cable, which reduces installation costs for consumers, conserves energy by optimizing voltage according to detected usage, and can be customized to satisfy the local needs of employees.

The Dark Cloud: How the Digital World Is Costing the Earth
by Guillaume Pitron
Published 14 Jun 2023

But by measuring emissions alone, other forms of pollution, such as the impact of chemical waste on water quality, tend to be overlooked. Moreover, only considering the carbon dioxide emissions of consumer products is ‘a very reductionist approach’, says Karine Samuel, professor of business sciences and an expert in the internet of things (IoT).4 Since the 1990s, the MIPS method offers an entirely new approach: instead of taking into consideration the environmental damage as a result of what an object produces (such as carbon dioxide emissions), the MIPS approach factors in what goes into producing an object. Looking at what goes into an object as opposed to what comes out of it delivers a radical change of perspective.

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Failing that, corporations will continue to talk — schizophrenically — in the same breath about the explosion of data, environmental responsibility, processing power, and carbon-free electricity from rivers. Let’s not fool ourselves: very few people are ready to change. Not least because the way we use digital is about to evolve as we brace ourselves for the vertiginous world of the internet of things. After the internet of the eyes and ears, the internet of the body. The internet of everything, even. Such an entirely connected future could completely sterilise the energy gains made at the Earth’s poles. CHAPTER SEVEN Expansion of the digital universe IN 2017, STEVE CASE, THE FOUNDER OF AMERICA ONLINE (AOL), one of the first global internet service providers, left a deep impression with his book The Third Wave: an entrepreneur’s vision of the future.1 During the internet’s first wave, he explained, web companies, including AOL, IBM, and Microsoft, built the infrastructure that allowed computers to connect to one another.

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He calls it ‘the Internet of Everything’.2 Around the same time, fellow technoprophet Kevin Kelly, founder of the American magazine Wired, made a similar prediction in his book on the ‘12 technological forces that will shape our future’.3 In the future, he wrote, every surface will have become a screen, personalised e-services will anticipate our every whim, there will be complete surveillance of consumers and citizens, and we will have constructed ‘a planetary system connecting all humans and machines into a global matrix’ — a superorganism called holos. In 2025, humans will not be ‘on’ or ‘in’ holos; they will be holos itself. This generalised connection to the internet ‘has already begun’, said Kelly.4 Today it is called the Internet of Things (IoT) — a term coined in 1999 by researchers at the Massachusetts Institute of Technology (MIT) in reference to the ability of objects, of things, to transmit and receive information via an RFID chip, for instance.5 From mobile phones, tablets, thermostats, watches, lighting, to air conditioning, IoT has grown so fast that there are some 20 billion connected objects in the world today.

The Patient Will See You Now: The Future of Medicine Is in Your Hands
by Eric Topol
Published 6 Jan 2015

When patients with like conditions can connect with and learn from each other, without the constraints of time or place as they would have with a doctor’s visit, yet another critical dimension of democratized medicine is discernible. FIGURE 1.3: The rise in connected devices on the Internet of Things from 2003 to 2020, projected. Source: D. Evans, “The Internet of Things: How the Next Evolution of the Internet is Changing Everything,” Cisco Internet Business Solutions Group, April 2011, http://www.cisco.com/web/about/ac79/docs/innov/IoT_IBSG_0411FINAL.pdf. Courtesy of Cisco Systems, Inc. Unauthorized use not permitted. August 1, 2014. The marked connectivity is taken further when one considers the Internet of Things (IoT). That is the unbridled growth of not only people but also devices that are wirelessly connected via the Internet.

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The prospect here would not be possible without exquisite tracking of individuals by themselves—recall the double entendre of the term “individualized medicine.”6 Picking up a signal long before there are any symptoms relies on one’s GIS, not an annual visit with the doctor. With the little wireless devices that we carry and the Internet of Things, we’re developing the capability of continuous, critical, real-time surveillance of our bodies. When that gets fully developed, as it ultimately will, The Economist’s predictions for the next thirty years in medicine don’t seem as far-fetched. FIGURE 13.1: Increase in life expectancy and projection for most diseases “cured.”

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The Framing of Physical Activity Biases Subsequent Snacking,” Marketing Letters, May 27, 2014, http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2442383. 60a. E. Topol, The Creative Destruction of Medicine (New York, NY: Basic Books, 2012), 126–127. 60b. N. Gohring, “This Company Saved $300k on Insurance by Giving Employees Fitbits,” Cite World, July 7, 2014: http://www.citeworld.com/article/2450823/internet-of-things/appirio-fitbit-experiment.html. 60c. P. Olson, “Wearable Tech Is Plugging into Health Insurance,” Forbes, June 19, 2014, http://www.forbes.com/sites/parmyolson/2014/06/19/wearable-tech-health-insurance/. 61. S. Lohr, “Salesforce Takes Its Cloud Model to Health Care,” New York Times, June 26, 2014, http://bits.blogs.nytimes.com/2014/06/26/salesforce-takes-its-cloud-model-to-health-care/. 62.

Postcapitalism: A Guide to Our Future
by Paul Mason
Published 29 Jul 2015

The aggregated data of our lives – which will soon include our driving speed, our weekly diet, our body mass and heart rate – could be a hugely powerful ‘social technology’ in itself. Once the Internet of Things is rolled out, we are at the real takeoff point of the information economy. From then on, the key principle is to create democratic social control over aggregated information, and to prevent its monopolization or misuse by states and corporations. The Internet of Things will complete a vast social ‘machine’. Its analytical power alone could optimize resources on a scale that significantly reduces the use of carbon, raw materials and labour.

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Between 2006 and 2012 humanity’s annual information output grew tenfold.25 It’s hard to tell exactly where you are in a tech revolution but my hunch is the simultaneous arrival of tablets, streaming video and music and the takeoff of social media between 2009 and 2014 will be seen as the key moment of synergy. The rollout of billions of machine-to-machine connections, known as the ‘Internet of Things’, in the next ten years will then populate the global information network with more intelligent devices than there are people on earth. To live through all this was exhilarating enough. Even more exhilarating now is to watch a kid get their first smartphone and find it all – Bluetooth, GPS, 3G, wifi, streaming video, hi-res photography and heart-rate monitor – as if it had always been there.

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Jeremy Rifkin, an influential management consultant, came closest to describing current reality in his 2014 book The Zero Marginal Cost Society.53 Rifkin argues that peer-production and capitalism are two different systems; currently they coexist and even gain energy from each other, but ultimately peer-production will reduce the capitalist sector of the economy to a few niches. Rifkin’s most radical insight was to understand the potential of the Internet of Things. The most enthusiastic consultancies – for example McKinsey – have valued the impact of this process as up to $6 trillion a year, mainly in healthcare and manufacturing. But the vast majority of that $6 trillion is in reduced cost and increased efficiency: that is, it contributes to reducing the marginal cost of physical goods and services in the same way as copy and paste reduces the cost of information goods.

Trees on Mars: Our Obsession With the Future
by Hal Niedzviecki
Published 15 Mar 2015

IT is what allows, as the Race Against the Machine authors put it, “digital technologies” to execute “mental tasks that had been the exclusive domain of humans in the past.”37 An MIT Sloan School of Management study revealed that companies that had invested in some form of “data-driven decision-making” had their productivity go up by as much as 6 percent compared to similar firms.38 The obvious conclusion was that more and more companies would be putting in place these systems. They have and continue to do so. This can be seen particularly in what has come to be called “the Internet of things,” essentially the move to connect everyday devices from toothbrushes to thermostats to ovens to the Internet. Writes social theorist Jeremy Rifkin: “Cisco forecasts that by 2022, private-sector productivity gains wrought by the Internet of Things will exceed $14 trillion. A General Electric study estimates that productivity advances from the Internet of Things could affect half the global economy by 2025.”39 The systems are working. Productivity is skyrocketing. Efficiency is impressive.

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Thompson cites the ever-increasing trail of data we leave behind while we go about our lives as the great shift that makes all this possible: “Cell phone, CRM [customer relationship management] systems, point of sale,” but also, he notes, “check in on places like Foursquare or searches on Yelp, some of it is social media, tweets, some of it is really in the stream—all these devices are monitored in real time so your presence and location can be captured.” The much-touted and vaunted move to mobile technologies—primarily phones that operate via cellular signal but also an evolving array of eyeglasses, watches, and other objects gradually joining the Internet of things—are used to triangulate position (collectively or individually). With the position of just about everybody moving through a metropolis now reliably charted for the first time in human history, patterns and trends can be discerned. “Geography delivers context and understanding,” says Thompson.

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Though we’re inarguably now living in a state of permanent anxiety and stress, we are still gamely trying to make it work, trying to bring the imperative of change at all costs into our lives, trying to make our hearts beat in time to the relentless thrum of the future. If anything, as the levels of technological adoption of everything from the techniques of factory farming to smart phones to the Internet of things suggests, we have proven ourselves all too amenable to change. We eagerly bring new technologies into our lives with very little consideration for how each highly hyped, supposed innovation is going to alter our day-to-day. From traffic jams to TV dinners in front of the TV to drunk texting, the story of technology is littered with unintended consequences that we do our best to just shrug off.

Stakeholder Capitalism: A Global Economy That Works for Progress, People and Planet
by Klaus Schwab
Published 7 Jan 2021

In its path, commodity producers from Latin America to the Middle East and Africa benefitted as well, as did Western consumers. Meanwhile, on the ruins of the dot-com crash, surviving and new technology firms started to lay the beginnings of a Fourth Industrial Revolution. Technologies such as the Internet of Things came to the forefront, and machine learning—now dubbed “artificial intelligence”—had a revival and rapidly gained traction. Trade and technology, in other words, were once more back as twin engines of global economic growth. By 2007, globalization and global GDP had reached new peaks. But it was globalization's last hurrah.

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Back in 2000, Asia still only counted for one third of global output. Today, at the dawn of the Fourth Industrial Revolution, Asia is reconquering the dominant position it held for millennia. And, going by the advances in China, it may well outperform the rest of the world on everything from the Internet of Things to artificial intelligence, locking in its advantage for decades. Figure 3.3 By Some Measures, the Asian Century Has Already Begun Source: Redrawn from The Financial Times, Valentina Romei, International Monetary Fund. The rise of China—and of other emerging markets in its slipstream—does represent an incredible milestone.

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The subsequent second and third waves of industrialization—which brought the world the internal combustion engine, cars, planes, and computers—made the human footprint on the environment only worse, even as it increased the quality of life for billions of people. The Fourth Industrial Revolution, which started recently, and brought us innovations such as the Internet of Things, 5G, artificial intelligence, and cryptocurrencies, is so far adding to the ever-expanding human footprint on the environment. Electricity required to produce Bitcoin, one of the most popular cryptocurrencies, leads to annual carbon emissions of 22 to 23 megatons of CO2, scientists calculated.37 That figure is comparable to the emissions of countries such as Jordan or Sri Lanka.

Stakeholder Capitalism: A Global Economy That Works for Progress, People and Planet
by Klaus Schwab and Peter Vanham
Published 27 Jan 2021

In its path, commodity producers from Latin America to the Middle East and Africa benefitted as well, as did Western consumers. Meanwhile, on the ruins of the dot-com crash, surviving and new technology firms started to lay the beginnings of a Fourth Industrial Revolution. Technologies such as the Internet of Things came to the forefront, and machine learning—now dubbed “artificial intelligence”—had a revival and rapidly gained traction. Trade and technology, in other words, were once more back as twin engines of global economic growth. By 2007, globalization and global GDP had reached new peaks. But it was globalization's last hurrah.

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Back in 2000, Asia still only counted for one third of global output. Today, at the dawn of the Fourth Industrial Revolution, Asia is reconquering the dominant position it held for millennia. And, going by the advances in China, it may well outperform the rest of the world on everything from the Internet of Things to artificial intelligence, locking in its advantage for decades. Figure 3.3 By Some Measures, the Asian Century Has Already Begun Source: Redrawn from The Financial Times, Valentina Romei, International Monetary Fund. The rise of China—and of other emerging markets in its slipstream—does represent an incredible milestone.

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The subsequent second and third waves of industrialization—which brought the world the internal combustion engine, cars, planes, and computers—made the human footprint on the environment only worse, even as it increased the quality of life for billions of people. The Fourth Industrial Revolution, which started recently, and brought us innovations such as the Internet of Things, 5G, artificial intelligence, and cryptocurrencies, is so far adding to the ever-expanding human footprint on the environment. Electricity required to produce Bitcoin, one of the most popular cryptocurrencies, leads to annual carbon emissions of 22 to 23 megatons of CO2, scientists calculated.37 That figure is comparable to the emissions of countries such as Jordan or Sri Lanka.

The People vs Tech: How the Internet Is Killing Democracy (And How We Save It)
by Jamie Bartlett
Published 4 Apr 2018

And of course it matters that data is harvested and used legally and ethically. But in one sense this is a distraction. The bigger picture is the way that companies like Cambridge Analytica understand our inner thoughts, rather than a distinct technique.* After all, just imagine what personality targeting will be possible with ‘the internet of things’. There are lots of stories these days about how internet-enabled devices present a security risk – like your fridge or baby monitor getting hacked. But think about what the explosion of everyday life data will do for political campaigns. Consider it: within a decade your fridge data will know what time you eat, your car will know where you’ve been, and your home assistant device will work out your approximate anger levels by the tone of your voice.

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Being able to instantly send money to anywhere in the world with no fees, charges or banks will be especially liberating for people in countries with an over-leveraged banking sector run by corrupt politicians. It might even provide a secure digital payment option for the millions who are still excluded from the formal banking system. These are not trivial benefits. The economic boon of blockchain is potentially staggering – especially if twinned with the internet of things. Imagine a bridge with embedded sensors which could detect minor faults and necessary repairs. It could also track which vehicles have used it. Once a threshold of faults is reached, a smart contract could be automatically initiated, with every user charged immediately proportionate to their use.

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The reason this is so important is because I suspect future technology will increase further the ability of small groups of individuals to do great harm, which means the authorities will need greater power, not less. For reasons still not entirely clear to me, humanity is currently embarked on a quixotic quest to connect everything to everything else. Within a decade, your TV, dog, house, car, fridge and clothing, will be part of the invisible internet of things network, all chipped and communicating with each other. Sometimes they will be lifesaving: a smart fire alarm might immediately turn on your phone alarm, unlock your door and contact the fire brigade. But they will also be vulnerable, because the security standards for these ‘IoT’ devices are notoriously bad.

Stake Hodler Capitalism: Blockchain and DeFi
by Amr Hazem Wahba Metwaly
Published 21 Mar 2021

The food can be trailed back through each stop to its source if a food is contaminated. Furthermore, these companies can also witness other things they may have come in contact with, identifying the problem that is likely to happen far sooner, thereby saving lives. We will cover more of Blockchain and IoT applications in part 3 of "Stake Hodler Capitalism: Blockchain and IoT (Internet of Things)." This is an instance of the blockchains in practice, but there are many other blockchain implementation forms. Let's delve deeper into more applications of Blockchain. Banking and finance Maybe no industry stands a chance to gain from integrating blockchain into its business operations more than banking.

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DeFi's money market can undoubtedly help create a more open and accessible financial system that anyone with an internet connection can access, which will bridge the gap between our lives on earth and rely on the human factor to potentially reach living on Mars. At the same time, we generate passive income from yield farming on earth-based securities. In future parts of the “Stake Hodler Capitalism” book series we intend to cover many applications of blockchain in depth including, smart contracts, Internet of Things, Retail, Agriculture, and Manufacturing.

City 2.0: The Habitat of the Future and How to Get There
by Ted Books
Published 20 Feb 2013

Ideally, we’ll get to choose how much data we share, with whom we share it, and how it will be used. Ultimately, it won’t be just our phones collecting and interfacing with the information around us. Data sensors will be embedded in streets, buildings, and even in cars and transit vehicles. Building such a network could bring about what many have called the Internet of Things — a concept that foresees the ability of physical objects and people to communicate and share information. A water pipe could tell a central computer that it’s about to fracture. A road could communicate with a streetlight to tell it that, after hours of sitting dark, it will need to illuminate for a car heading in its direction.

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It won’t be practical to invest millions of dollars implanting sensors across a city if they’re incompatible with new systems that will emerge a few years later. This is why it’s incredibly useful to have sensors embedded within the tools and objects we swap out frequently, like our phones and cars. In fact, much of the progress being made toward the Internet of Things has occurred in transportation. In the very near future, cars will communicate with other cars to improve the safety and flow of traffic. Google’s self-driving car is one high-profile example, but another project, run by the U.S. National Highway Traffic Safety Administration (NHTSA), is a more likely predictor of where this concept could go.

Fancy Bear Goes Phishing: The Dark History of the Information Age, in Five Extraordinary Hacks
by Scott J. Shapiro

C2, see Command and Control server CAPTCHA Carroll, Lewis celebrity hacks; see also Hilton, Paris cell phone hacks; see also Hilton, Paris cellular automaton central processing units (CPUs) CFAA, see Computer Fraud and Abuse Act China Chong, Jane Chua, Yi Ting CIA Citizenfour (movie) Clark, Jim Clarke, Richard Clear Web click fraud Clinton, Hillary, campaign (2016): Assange and; DNC hacks and; Fancy Bear phishing and; Guccifer 2.0 hacks and; Putin and; Russian relations and; WikiLeaks and clockwork dolls cloud servers code; Achilles and the Tortoise and; binary strings conversion from; data difference from; duality principle and; instruction pointers and; see also duality principle; metacode; Turing, Alan Coelho, Robert Cohen, Fred Command and Control server (C2) Commander Tosh, see Todorov, Todor Compatible Time-Sharing System (CTSS) CompuServe computer evolution; see also Turing, Alan Computer Fraud and Abuse Act (CFAA); DDoS attacks under; introduction of; Morris, R., Jr., case and; requirements and punishments under consumer product hacking, see Internet of Things Cook, Philip Corbató, Fernando “Corby” Cornell University corporate accountability Corsi, Jerome Cozy Bear/the Dukes CPUs, see central processing units CrowdStrike cryptocurrency, see Bitcoin cryptography crypt program CTSS, see Compatible Time-Sharing System cybercrime: “aging out” of; Bitcoin as payment for; as business; corporate data breaches relating to; cyber-enabled and cyber-dependent; early legislation on; empowerment for protection from; extradition for; FBI Kill Chain for; feelings of helplessness about; financial loss due to; global cooperation on; ignorance to threat of; interoperability issues and; moral duality and; Morris Worm debates on; motivations for; pay-per-install malware and; prevention approaches to; profile and psychology; property crime move to; prosecution and penalties; Secret Service investigations of; solutions and interventions for; traditional crime moving into; war paradigm with; youth as feature of cyberespionage: cybercrime compared to; cyberwarfare role of; DNC hacks and; domestic; economic; international law on; NSA tactics for; SolarWinds cybersecurity: black hat hackers transition to; CTSS and; at DCCC and DNC; after DNC hacks; early World Wide Web and; human behavior as threat to; IoT legislation on; Kill Chain model and; in late 1980s; limits of; metacode limits and; Microsoft efforts in; military; moral duality and; Morris Worm lessons of; Multics and; NSA history and approach to; pre-internet; professionals and job market; Reagan executive order on; scientific internet beginnings and; SEC regulation on; social inequality and; solutionism in; terminology; T-Mobile; UNIX and; upcode role in; upcode solutions for; see also specific topics Cybersecurity and Infrastructure Security Agency cyberwarfare: Clarke book on; costs and impacts of; cyber clubs and; cyberespionage relation to; defining; DNC hacks relation to; election tampering and; hyperspecialized weapons and; laws on; Russian; sensationalizing; by United States; upcode for; war history and future of Dark Avenger: Bontchev and; Eddie virus of; Gordon and; identity of; maliciousness of viruses by; Mutation Engine creation by; Nomenklatura virus of; psychology of; remorse; virus writers’ anger at Dark Web data: breach accountability; code difference from; deep packet inspection of; duality principle and; see also duality principle; metacode DCCC, see Democratic Congressional Campaign Committee Delavan, Charles Democratic Congressional Campaign Committee (DCCC) Democratic National Committee (DNC) hacks: CIA and; Clinton 2016 campaign and; Cozy Bear; cyberespionage and; cybersecurity after; cybersecurity prior to; cyberwarfare relation to; delayed response to; Fancy Bear; FBI investigation of; Guccifer 2.0 and; prosecution for; Putin and; Trump and; WikiLeaks’ publishing of demon Denial of Service attacks, see Distributed Denial of Service attacks Descartes, René DigiNotar Dimov, Peter Distributed Denial of Service (DDoS) attacks; Akamai and; CFAA on; DNS records and; on Estonia by Russia; FBI investigations of; financial motivation in; Krebs as target of; MafiaBoy arrest for; Minecraft and Jha beginning with; Mirai versions and imitators of; Rutgers University; solutions for; 2016 increase of; VDoS gang and; see also botnets; Mirai botnet and gang DNA DNC hacks, see Democratic National Committee hacks DNS records DOS operating system downcode: Achilles and the Tortoise logic and; definition of; of UNIX; upcode interplay with; varieties of drivers duality principle Dukes, the, see Cozy Bear/the Dukes EDVAC election tampering; see also Democratic National Committee hacks; presidential election email: basic principles behind; botnets use in spam; characteristics of fake; early providers of; legitimate Google security alert; spoofing; viruses exploiting; worm in; see also phishing EMPACT ENIAC Equifax espionage; see also cyberespionage Estonia ethical hacking, see white-hat hacking “evil maid” attack Facebook famines Fancy Bear: Bitcoin use by; Bitly use by; DNC hack by; Google accounts phishing by; GRU origins of; hacking mistakes of; heuristics exploited by; Lukashev role in; mudges from; name origins; phishing by; Podesta phishing by; state election infrastructure probes by; typosquatting use by; website security certificates and; X-Agent tool of; Yermakov reconnaissance for Federal Bureau of Investigation (FBI): DNC hack investigations by; evidence-gathering ability of; hybrid duties of; Kill Chain model; LaCroix raid by; Mirai investigation by; Morris Worm and; NSA surveillance role of; search warrants; surveillance of citizens Federal Sentencing Guidelines FidoNet File Transport Protocol (FTP) Finger: function and principles; Markoff revealing Morris, R., Jr., using; Morris Worm attack on firewalls FISA, see Foreign Intelligence Surveillance Act FISC, see Foreign Intelligence Surveillance Court Five Eyes Flood, Warren Foreign Intelligence Surveillance Act (FISA): about; citizen surveillance and; reform; transparency about Foreign Intelligence Surveillance Court (FISC) Forys, Jeff FOSS (free and open-source software) Franceschi-Bicchierai, Lorenzo Frank, Robert FSB (Russian security service) FTP, see File Transport Protocol Gates, Bill: internet products development under; Sinofsky work for; on trustworthy computing and security; Windows creation and; see also Microsoft company gender disparity genetics Genovese, Will German Enigma code Gerrold, David Glickman, Dan Glueck, Sheldon and Eleanor Gödel, Kurt Google: Jha and Mirai attacks on; legitimate security alert from; phishing targeting accounts on Gordon, Sarah: antivirus industry opinions on; background; on Dark Avenger identity; Dark Avenger relationship with; Dedicated virus and; malware report by; virus writers study by government surveillance Graham, Paul: Morris, R., Jr., and; on Morris Worm programming Greenwald, Glenn GRU (Russian military intelligence): Fancy Bear origins in; Guccifer 2.0 identity tied to; hacking department; Mueller indictment on hacking by; poisonings by; scouting and recruitment by Guccifer and Guccifer 2.0 Guidoboni, Thomas hackers: Anonymous hactivist; Bitly used by; bulletin boards; childhood origins of; code and data difference exploited by; conventions and methods of; cyber-enabled and cyber-dependent; definition of; dumpster-diving approach of; global differences of; intervention approaches; Kill Chain model used by; learning from one another; mentors and role models for; misapprehensions about; money laundering and cashing out for; motivation for; 1980s cultural view of; physicality principle exploited by; profile and psychology of; proxies use by; reputation; side-channel attacks used by; upcode; virus writing and; vulnerability announcements exploited by; WarGames influence on; women; see also specific events and individuals hacking; alarmism about; Bluetooth technology; business of; consumer “smart” products; escalation of; “evil maid”; financial damages from; history; IP addresses and; kernels; lingo; for public interest; Russian history and education in; social stigma around; solutionism and; speculative execution; SQL injections and; white-hat/ethical; see also specific topics HAL (fictional computer) Harvard University Hawkins, Adrian heuristics: Affect; Availability; dual-process theories and; Loss Aversion; nudging and; operating systems use of; physicality principle and; Representativeness; survival role of Hilton, Paris: cell phone hack; password weakness and; sex tape; in The Simple Life HIV/AIDS Hupp, Jon Hutchings, Alice IBM computers Imitation Game, The (movie) inequities, social instruction pointers international law internet: attacks and outages; basic function and principles of; browsers design and market for; Clear Web; cybersecurity prior to; Dark Web; deep packet inspection of data over; first graphical browsers for; first major viruses exploiting; FTP and; government agencies and; ignorance of workings of; introduction to public; Microsoft product development for; military; Morris Worm impact on; scientific; solutions for safer; speed of evolution; TCP/IP protocols and; vulnerabilities; website security and; World Wide Web beginnings on; worms and design of Internet Explorer Internet of Things (IoT): botnets hacking; security legislation about; security patches internet service providers (ISPs) IoT, see Internet of Things IP addresses; Dark Web and; definition and forms of; DNS servers and; hacks and; see also TCP/IP protocols Iran ISPs, see internet service providers Jacobsen, Nicholas Jha, Paras: background; click fraud of; cybercrime war and; DDoS attack beginnings; evasion techniques; false-flag operation; financial motivations of; Google attacks by; Minecraft obsession of; as Mirai botnet founder; Mirai code dump; on Peterson influence; Poodle Corp botnet of; ProTraf Solutions launch; Rutgers University DDoS attacks by Jobs, Steve juvenile delinquency Kahneman, Daniel kernel building and hacking Kill Chain Klyushin, Vladislav Kozachek, Nikolay Krafft, Dean Krebs, Brian: DDoS attacks targeting; LaCroix and LaCroix, Cameron: arrests and jail sentences for; background of; FBI raid on; Hilton hack by; Krebs and; parole; post-jail hacks of; psychology and motivation of; Sidekick cell phones of; skill of Laub, John Lavigne, Avril laws and legislation; cryptocurrency; cybercrime punishments; cyberespionage; cyberwarfare; data breaches and; DDoS attacks and; disclosure to public; early cybercrime; government metadata collection; international cybercrime; on IoT security; on NSA data collection; Patriot Ac; on search warrants; software vulnerabilities and; upcode and; on warfare; see also Computer Fraud and Abuse Act; Foreign Intelligence Surveillance Act Lehel, Marcel Lazăr Linux Lohan, Lindsay Loss Aversion Heuristics Lukashenko, Alexander Lukashev, Aleksey Lusthaus, Jonathan macro viruses MafiaBoy malware; Beast; classification and types; coining of term; coordination of computers with; cross-platform; evolution of; Gordon reporting on; hyperspecialization of; Microsoft Word; as national security threat; pay-per-install; selling and acquisition of; viruses contrasted with; X-Agent Russian; see also botnets; viruses; vorms; wiruses; worms Markoff, John Marquardt, David Massachusetts Institute of Technology (MIT): Corbató CTSS invention at; IBM early computers and; Morris as professor at; worm released at Mateev, Lubomir Matrix, The (movie) McGill, Andrew Merkel, Angela metacode metadata: breaches of; government collection of; surveillance capitalism and Microsoft (company); antivirus protection; browsers; copyright suits and; cybersecurity; driver vulnerability approach of; early mission and growth of; internet product development at; legal action against; Minecraft success for; Sinofsky work for; Slivka role at; Winner Take All market and Microsoft Windows Microsoft Word military: cybersecurity; internet; Multics application for; WarGames movie and; see also GRU Milnet Minecraft: about; DDoS attacks and; Jha obsession with Mirai botnet and gang; code dump; DDoS attacks versions and imitators; FBI investigation of; Google attacks by; IoT devices patch and; IoT hacking by; Jha founder of MIT, see Massachusetts Institute of Technology morality Morgachev, Sergey Morozov, Evgeny Morris, Robert, Jr.: background and character of; CFAA and case against; Cornell University attendance and; criminal case against; father’s response to worm of; Graham friendship with; jurors in trial against; lawyer defending; post-trial career of; remorse of; trial testimony of; worm creation motivation of; see also Morris Worm Morris, Robert, Sr.; on Morris Worm creation; NSA job of; UNIX developments by Morris Worm; attack vectors; Bulgarian media on; computer community debates over; cybercrime debates and; cybersecurity actions after; duality principle exploitation with; FBI investigation of; Finger attack by; flaw in code; impact; lessons and increased security from; media coverage on; Melissa virus compared with; motivation for creating; origins; password discovery by; patch for and eradication of; programming of; reinfection rate of; SENDMAIL attack by; Sudduth warning email about Mosaic browser movies and television: artificial intelligence portrayal; Citizenfour (movie); cybersecurity early portrayals in; cyberwar themes in; The Imitation Game; The Matrix; Mr.

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These devices are impressive for what they are, but they are not about to become self-conscious. In many ways, they are quite stupid. They cannot tell the difference between a human being and a bread toaster—as we will soon see. Just as digital networks were hard to predict in 1968, the so-called Internet of Things (IoT) was difficult to imagine in 1984. Even when internet- enabled consumer appliances emerged in the last decade, the computer industry, and the legal system, have been slow to recognize the dangers of this new technology. They failed to predict IoT botnets: giant networks of embedded devices infected with malicious software and remotely controlled as a group without the owners’ knowledge.

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Cyber-dependent war, by contrast, doesn’t use computers to control weapons—computers are the weapons. Cyber-dependent war has worried analysts because “cyber-physical” systems—systems that use computers to control physical devices so as to maximize efficiency, reliability, and convenience—have become commonplace. The Internet of Things that Mirai exploited is a cyber-physical internetwork, as are industrial control systems used in power plants, chemical processing, and manufacturing, which were exploited by Stuxnet. By hacking into computer networks, attackers can now cause physical destruction and disruption using only streams of zeros and ones.

This Is How They Tell Me the World Ends: The Cyberweapons Arms Race
by Nicole Perlroth
Published 9 Feb 2021

An excellent analysis of Russia’s weaponization of the vaccination “debates” appeared in the October 2018 edition of the American Journal of Public Health, “Weaponized Health Communication: Twitter Bots and Russian Trolls Amplify the Vaccine Debate,” authored by David A. Broniatowski, Amelia M. Jamison, SiHua Qi, Lulwah AlKulaib, Tao Chen, Adrian Benton, Sandra C. Quinn, and Mark Dredze. Statistics regarding the rapid adoption of the “Internet of Things” were sourced from a 2017 McKinsey report, “What’s New with the Internet of Things?” The ratio of one hundred cyberwarriors working on offense to only one working on defense was taken from a remark made by Ed Giorgio, who spent thirty years at the NSA, on a 2015 panel at the RSA Conference. Giorgio said that when he was NSA’s chief codemaker, he led a group of seventeen cryptographers, and when he led the NSA’s codebreakers, he led a group of seventeen hundred cryptanalysts.

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Kramer and Nicole Perlroth, “Expert Issues a Cyberwar Warning,” New York Times, June 3, 2012. For Russian anxieties about cyber escalation, see Timothy Thomas, “Three Faces of the Cyber Dragon: Cyber Peace Activist, Spook, Attacker,” Foreign Military Studies Office, 2012. For details of Russia’s Internet of Things—or IoT—market, see MarketWatch, “Russia Internet of Things (IoT) Market Is Expected to Reach $74 Billion By 2023,” October 17, 2019. For statistics on Russia’s GDP, purchasing power parity, and population growth, I relied on the CIA World Factbook, the World Bank GDP Ranking, and the Wilson Center studies on Russia’s demographic trends.

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I have done my best, but to this day, so much about the cyberarms trade remains impenetrable that it would be folly to claim that I have gotten everything right. Any errors are, of course, my own. My hope is that my work will help shine even a glimmer of light on the highly secretive and largely invisible cyberweapons industry so that we, a society on the cusp of this digital tsunami called the Internet of Things, may have some of the necessary conversations now, before it is too late. —Nicole Perlroth November 2020 PROLOGUE Kyiv, Ukraine By the time my plane touched down in Kyiv—in the dead of winter 2019—nobody could be sure the attack was over, or if it was just a glimpse of what was to come.

The Internet Is Not What You Think It Is: A History, a Philosophy, a Warning
by Justin E. H. Smith
Published 22 Mar 2022

We typically suppose, namely, that what is erroneously called “3D printing,” or what is somewhat less erroneously called “the internet of things”—in short, wherever we have an information-processing machine directing its information in a way that brings about a change in the physical world, a plastic gun appearing from a mold, say, or a stove caused from a distance to commence its self-cleaning—is only the very latest development in a much longer history of computer science. 3D printing is thought to come after paper printing; the internet of things is supposed to follow the internet of screens. In fact, however, as we have been seeing, a prime candidate for the distinction of “first computer” had as its sole purpose what we may rightly call the 3D printing of brocaded stuffs.

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We find, much more, that the history of looms and the history of computers is at certain moments literally one and the same history, as we shall now see.5 Algebraic Weaving In 1808, the French inventor Joseph Marie Jacquard introduced to the world his automated loom, capable of transferring a design onto silk that had been “programmed” into a sequence of punched cards.6 At first glance it might not seem that the punched-card weaving machine deserves a place in the history of computer science, alongside other technologies more narrowly focused on data processing rather than on the manufacture of a product. Yet consider: today some of the most remarkable innovations in computing are taking place in 3D printing and in the development of the so-called internet of things, or of physical objects networked together by sensors and software, “smart homes,” smart energy grids, remote health monitoring, shipping, and so on, all of which trace their origins back at least as much to the manufacturing machines of the industrial era as they do to the reckoning machines and analytical engines of the same period.

Digital Transformation at Scale: Why the Strategy Is Delivery
by Andrew Greenway,Ben Terrett,Mike Bracken,Tom Loosemore
Published 18 Jun 2018

It becomes even easier for a large business or government administration to ignore hard yet necessary tasks if they can find something else that has the characteristics of work, while being much more comfortable to sink time into. Fortunately, the technology hype cycle is ready to provide a stream of distractions. All too often, the word digital is conflated with whatever technology fad has made it into the colour supplements this month. Blockchain. Artificial intelligence. The Internet of Things and connected devices. Robotic Process Automation. The captains of industry, ministers and senior officials who read colour supplements during their brief periods of down time see these exciting things and commission policy papers to unpick their potential effect on the organisations they run.

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You’ve annoyed people on the way of course – that’s a pity – so they think that perhaps now is the moment to consolidate the success and slow things down. If the digital team is all too tired to keep going, those keen to go back to an easy life will push back. Resisting the hype Every business strategy presentation for the last two years (and the next three) will have a slide that says something like: ‘AI, blockchain, Internet of Things – what should we do?’ For most organisations, this discussion is a little premature. Even allowing for the fact these technology breakthroughs are near the top of their hype cycle at the time of writing, we are not saying that they are unimportant for large organisations, public or private.

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We have partnered with organisations in more than 20 countries, and worked in collaboration with several multilateral organisations, including the European Union and the ­Inter-American Development Bank. Andrew Greenway worked in five government departments, including the Government Digital Service, where he led the team that delivered the UK’s digital service standard. He also led a government review into applications of the Internet of Things, commissioned from Government’s Chief Scientific Advisor by the UK Prime Minister in 2014. He now writes for several UK and international publications on government and institutional reform. Ben Terrett was Director of Design at the Government Digital Service, where he led the multidisciplinary design team for GOV.UK which won the Design of the Year award in 2013.

Capitalism Without Capital: The Rise of the Intangible Economy
by Jonathan Haskel and Stian Westlake
Published 7 Nov 2017

Define two kinds of data: raw records and transformed data. Raw records are raw data not yet cleaned up, formatted, or transformed—not ready for analysis. They can include, for instance, data scraped from the web, data generated by transactions between agents, data generated by sensors embedded in machines or equipment (the “Internet of Things”), or data generated as a by-product of some other business operation or process. Transformed data has been cleaned up, formatted, combined, and/or structured such that it is suitable for some form of data analytics. Turning to information, we can think of information as synonymous with transformed data: for example, analyzable data on, say, sales of hurricane lamps and weather, constitutes information.

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But we can also think of things that help it along. Prizes, like the eighteenth-century Longitude Prize or the twenty-first-century Ansari-X Prize for private spaceflight, can help crowd investment into a neglected area. No doubt, part of the reason the technology press hypes new technologies, like the Internet of Things or solar energy, is not only because it makes for more exciting stories, but because it also has a functional role of drawing attention to up-and-coming areas and encouraging coordinated investment. Perhaps the hype is misplaced; but the role of encouraging coordination is important nevertheless.

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There are any number of firms experimenting with new ways of Internet-enabled collaboration, in fields from healthcare research (such as Patientslikeme or 23andMe) to brokering intellectual property among companies (such as Nathan Myhrvold’s Intellectual Ventures) to data analytics (such as Kaggle, recently acquired by Google). It is easy to laugh when technology advocates make predictions that don’t come to pass. Where is the paperless office? Where is the Internet of Things? But the fact that widespread effective teleworking has not seriously reduced the importance of face-to-face communication may be a sign not that it will never happen, but rather that it is a complicated type of change and takes time. So telecoms infrastructure will matter more in an intangible economy as a way to build connections and make the most of spillovers.

Green Swans: The Coming Boom in Regenerative Capitalism
by John Elkington
Published 6 Apr 2020

But both Gray and Black Swan challenges will erupt, including shocking job losses, scandalously unethical use of data, and pernicious embedded blind spots reflecting blind spots in the minds and experience of those designing such systems. We already see growing concerns about wrongful exclusion and arrest, privacy, cars that are happy to run over black people, and the surveillance state. •The Internet of Things, Robotics, and Smart Cities: There are now more connected devices in the world than there are people. Collectively dubbed the Internet of Things (IoT), they range “from smart building technologies that monitor and manage energy usage, to connected vehicles that help anticipate and avoid potential collision.” The number of IoT devices is projected to exceed 20 billion by 2022, owing both to “continued technological advances and the plummeting costs of computing, storage and connectivity.”

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Fascinating, essential work, but the task is expanding all the time as new technologies crowd into the public arena. Among next-generation technologies we have been watching are 3-D printing, AI, big (and little) data, drones, autonomous and electric cars, food technology (including synthetic meat and fish), fusion power, genomic medicine, geoengineering, the Internet of Things, mini-satellites, nanotechnology, new materials and sensors, smart buildings and infrastructures, soil carbon capture, synthetic biology, and vertical farming. As the OTA would have been quick to point out, such technologies will cross-pollinate in unexpected ways. Rather than simply observing such trends from the sidelines, the system change community must now proactively shape the next round of industrial revolutions.

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See also change process stages; Future-Fit change approach; technology allowing to flourish, 166 Anthropocenic route, taking, 230–234 antibiotics through lens of, 108 assets with characteristics of, 73 be a leader, not an algorithm, 223–230 Black Swans starting off as, 182 business models with characteristics of, 53 calories through lens of, 102 capitalism with characteristics of, 202–208 carbon economy through lens of, 111 defined, 9, 22, 167 democracy with characteristics of, 208–213 different thinking, need for, 23–27 early sharing of content about, 199–201 exponential leaders, 236–242 future, differing views of, 190–193 global grand challenges approach, 186–187 governance with characteristics of, 71 gradual, then sudden evolution of, 76 growth with characteristics of, 57–58 historical, 43 impact with characteristics of, 64 liability with characteristics of, 68 losing control, risk of, 193–197 materiality with characteristics of, 69–70 overview, 1–3, 22–23, 219–223 as parallel reality with Black Swans, 9–10 plastics through lens of, 97 profitability with characteristics of, 55 purpose with characteristics of, 50 push and pull in evolution of, 189–190 recent examples, 42 reinventing everything, 197–199 space junk through eyes of, 116 spotting, 254–256 sustainability with characteristics of, 213–218 systemic change overview, 201–202 three horizons, two scenarios, 2000-2100, 38–39 and triple bottom line concept, 12–13 U-bend, unclogging, 234–236 value with characteristics of, 61 Green Swans (film), 9–10, 248 Green Transition Scoreboard, 233 growth, 47, 55–58 The Guardian (newspaper), 96 H Haan, Nick, 200, 222 Hamid, Mohsin, 109, 110 Harvard Business Review (HBR), 32, 155–158 Haut, Sonja, 253 Hawken, Paul, 141, 142, 232 Hemingway, Ernest, 79 Hichens, Robert, 198–199 Hill-Landolt, Julian, 229–230 Hippocratic Oath, 108 Hoffman, Donald, 27 Hofstetter, Dominic, 220 Holocene epoch, 86 Honda, 135 horseshoe crabs, 231–232 How Adam Smith Can Change Your Life (Roberts), 80 The Human Planet (Lewis and Maslin), 29 Humanitarians, exponential leaders as, 238 humor, 120–121 Hunter, Sarah, 240 Hurd, Nick, 212–213 Hutton, Will, 196 Hwang Sang-ki, 126 Hyatt, John Wesley, 94 hydropower, 201 Hype Cycle, Gartner, 173–175 I Ibbitson, John, 222–223 Ignatius, Adi, 155–156 illusion of control, 44 impact, 47, 61–64, 256 impact investing, 63, 64 Impossible Foods, 233 incremental change, 34, 35f, 57, 233–234 India, 82 Indonesia, 220–221 industrial revolutions, 175–176 industry federations and associations, 132 inflated expectations, in Gartner Hype Cycle, 174 influencing activities, 145–146 information, role in economy, 190, 192 Innovation Trigger stage, Gartner Hype Cycle, 174 innovations, in three horizons framework, 38–39 Innovators, exponential leaders as, 238 Institute for Energy Economics and Financial Analysis, 242 Institute for Transformative Technologies (ITT), 184–185 insulin, 156–157 insurance industry, 136–137 intangible assets, 72 integrated business models, 52 Interface, 142 intergenerational transfer of wealth, 201 International Accounting Standards Board, 58 International Finance Corporation, 119 international OTA, need for, 183, 185 International Renewable Energy Agency (IRENA), 133 International Space Station (ISS), 111 internet, 173, 175, 192 Internet of Things (IoT), 177 investors/investing, 63, 64, 162, 205–208, 242–244 invisible hand, 18, 25, 85 Ipsos, 219 Israel, 171 Ive, Jony, 213–214 Iversen, Torben, 212 ivory, 93 J Jackson, Clive, 245–246 Jackson, Tim, 56 Jakarta, Indonesia, 220–221 Japan, 135 Johnson, Nicholas, 113 Johnson & Johnson, 13 Johnson County, Indiana, 126–127 joint and several liability, 66 Jørgensen, Lars Fruergaard, 158–159 journalists, 227 JPMorgan, 142–143 Just, Inc., 233 JWT, 140–141 K Kahneman, Daniel, 204 Kaiser Permanente, 255 Kelly, Kevin, 36 Kelly, Marjorie, 205 Kendall, Geoff, 159–164 Kerr, Andrew, 201 Kessler, Donald, 112 Kingston, Phil, 189 Klee, Louis, 109 Klimenko, Svetlana, 207–208 Kondratiev, Nikolai, 203 Kramer, Mark, 59 Kuhn, Thomas, 41, 121–122, 123, 191, 230 L Langer, Ellen, 44 Lawrence Berkeley National Lab, 184–185 Layton, David, 190–191 leaded gasoline, 172 leaders, 223–230, 236–242.

Machines of Loving Grace: The Quest for Common Ground Between Humans and Robots
by John Markoff
Published 24 Aug 2015

Although few people encountered the hulking mainframe computers of the 1950s and 1960s, there was a prevailing sense that these machines exerted some sinister measure of control over their lives. Then in the 1970s personal computing arrived and the computer became something much friendlier—because people could touch these computers, they began to feel that they were now in control. Today, an “Internet of Things” is emerging and computers have once again started to “disappear,” this time blending into everyday objects that have as a result acquired seemingly magical powers—our smoke detectors speak and listen to us. Our phones, music players, and tablets have more computing power than the supercomputers of just a few decades ago.

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Every footstep and every utterance is now tracked and collected, if not by Big Brother then by a growing array of commercial “Little Brothers.” The Internet has become an intimate technology that touches every facet of our culture. Today our smartphones, laptops, and desktop computers listen to us, supposedly at our command, and cameras gaze from their screens as well, perhaps benignly. The impending Internet of Things is now introducing unobtrusive, always-on, and supposedly helpful countertop robots, like the Amazon Echo and Cynthia Breazeal’s Jibo, to homes across the country. Will a world that is watched over by what sixties poet Richard Brautigan described as “machines of loving grace” be a free world?

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Years later Shneiderman would acknowledge that there were some cases in which using speech and voice recognition might be appropriate. He did, however, remain a staunch critic of the basic idea of software agents, and pointed out that aircraft cockpit designers had for decades tried and failed to use speech recognition to control airplanes. When Siri was introduced in 2010, the “Internet of Things” was approaching the peak in the hype cycle. This had originally been Xerox PARC’s next big idea after personal computing. In the late 1980s PARC computer scientist Mark Weiser had predicted that as microprocessor cost, size, and power collapsed, it would be possible to discreetly integrate computer intelligence into everyday objects.

Dawn of the Code War: America's Battle Against Russia, China, and the Rising Global Cyber Threat
by John P. Carlin and Garrett M. Graff
Published 15 Oct 2018

See Human Rights Watch Huang Zhenyu, 265 Huawei Technologies, 249n Hughes, Patrick, 100 Hughes, Seamus, 371–372 Hughes, William, 90 Huisui Zhang, 259, 262 Human Rights Watch (HRW), 377 Hupp, Jon, 82 Hussain, Junaid, 1–3, 14, 19–21, 23–25, 27, 29, 401 Hussein, Saddam, 100, 331 “I Love You” virus, 111 IBM, 38 Ibragimov, Ruslan, 120 iDefense, 181 identity theft, 115, 116 Identity Theft Resource Center, 365 Ignatius, David, 215 illegal file-sharing, 65, 66 Immigration and Refugee Board of Canada, 195–196 individuals, nation-state difference from, 58–59 information: inter-department sharing of, 188; weaponization of, 60–61 information warfare, 149, 164 Inglis, Chris, 199 Innocence of Muslims (video), 223, 224 innovation, as economic advantage, 37–38 Inspire (magazine), 18 Institute of Global Prosperity, 75, 109 intellectual property, 65, 133, 134 intellectual property theft, 37, 131 intelligence analysis, confidence levels in, 330–332 intelligence reforms, 127–128 International Republican Institute, 196 internet: ARPANET development, 79–82; borders blurred by, 60; censorship of, 184–185; China use base, 41; commercial use of, 96; decentralization of, 85; hacking and ethos of, 77; individual-nation-state difference blurred by, 58–59; Morris Worm and, 91–95; network protocols, 84; openness of early, 79–82; physical-virtual difference blurred by, 59–60; public-private difference blurred by, 58; security in early era of, 40; war-peace difference blurred by, 58. See also specific sites and topics Internet of Things (IoT), 40, 56, 396 Internet Relay Chat, 102 Internet Research Agency, 57, 401 Interpol, 304 The Interview (film), 309, 311–313, 322, 334–336, 339 Inventing the Internet (Abbate), 81 IoT. See Internet of Things IRA, 8 Iran, 401, 402; Cafe Milano assassination plot, 211–216; cyber activities, 52, 54, 224; cyber espionage by, 231–233; democracy movements in, 217–219; financial sector DDoS attack and, 224, 224n, 229, 230; industrial sabotage by, 219; internal cyberactivities, 217–219; Sands Casino attack and, 235, 238; United States approach to confronting, 216–217 Iranian Cyber Army, 217 Iran-Iraq War, 212 Iraq, 100, 102 Iraq war, 331 Irhabi 007, 6, 7 ISIL.

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Few of the original creators of the internet understood just how integral it would become to modern life—that the decisions they made in setting up a primitive network among a small group of trusted and known colleagues would lead, down the road, to a technological transformation that would become ubiquitous in daily life, with first hundreds of millions and then billions of users. The rise of the “internet of things” will only accelerate these connections: by 2020, there may be as many as 20 billion devices connected to the internet.9 During the early era of the internet, security often remained an afterthought and authentication procedures were almost unheard of. The early internet connected a small community of like-minded engineers and scientists who intrinsically trusted each other.

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In the financial world, we’ve seen sophisticated hackers hit a one-two punch of their own: stealing millions in cash through fraud-focused malware and then hitting the bank’s servers with a DDoS attack that distracts bank officials until after the money is safely gone. We know, too, where the next threats will come: adversaries are already beginning to target so-called Internet of Things devices. Moreover, as we’re beginning to see, cyberattacks target a particularly nefarious vulnerability in our society. While we’ve spent the last decade primarily thinking about cybersecurity as the theft or leaking of data, increasingly the threat comes from the alteration and destruction of data.

The Grid: The Fraying Wires Between Americans and Our Energy Future
by Gretchen Bakke
Published 25 Jul 2016

Whatever they thought about the SmartGridCity, what made them really angry was the utility’s unwillingness to integrate more wind power. Val, however, has different concerns. She wants her house to manage itself. She wants it to make electricity, store it, and use it without her having to do much more than punch into her smart phone, DISHES WASHED BY 5 P.M. and MAKE SURE THE CAR IS CHARGED BY 7. The coming “Internet of Things,” of which smart phones, smart appliances, smart meters, and electric cars are all integral parts (it is coming, by the way, it just hasn’t quite arrived yet), is in many ways the continuation of an emancipation project that began in the 1930s to free women from the drudgery of household work by electrifying common appliances.

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In fact, the further we proceed into the age of information the more electricity becomes the base for all that we do, from banking, to reading, to collaborative thinking. The future promises an even more thorough integration of electricity into our lives, more data (which is after all, just electricity), more “smart” things (coming to populate the Internet of Things), and the elimination of fuel from cars, necessary if we’d like to stop global warming before it exceeds the 2-degrees-Celsius disaster line. Most important, we’d like this means of “being electric” to come from nothing, to be transmitted by nothing, to cause no damage, and to work always and wherever.

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All the visions of ubiquitous technology, sentient cities, chips everywhere could well take their alpha form in the electric grid. It is, after all, as Nicola Tesla pointed out, not only a system for powering the world but also essential to the lines of communication that weave our economies, our labor, and our imaginations together. If we are going to bring the Internet of Things into our daily lives, then why not start with the biggest thing of all? The grid, tick-bright and aglow with promise. Afterword Contemplating Death in the Afternoon As I write this, the power is out. It’s below freezing outside, though it’s midafternoon on a sunny day in early spring.

Heart of the Machine: Our Future in a World of Artificial Emotional Intelligence
by Richard Yonck
Published 7 Mar 2017

The entire interchange has occurred in the space of a few dozen paces. Despite her best efforts, the woman’s expression briefly indicates she’s very interested in the jacket. Moments later, she steps into the store and the transaction is quickly made. ———— The increasing use of sensors in our daily environment, commonly known as the Internet of Things, combined with the predictive power of big data analytics is altering our relationship with the world, not all of it in good ways. Issues of privacy, autonomy, and even self-determination have been raised when discussing these intrusive technologies. As disturbing as all this sounds, it becomes vastly more so when combined with the ability to rapidly read, interpret, and react to our emotional responses.

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The next twenty to thirty years are going to see a veritable explosion of technologies and capabilities. As computer processing continues to grow in power, even as it shrinks in size, we’ll see more and more aspects of our world acquire new ways of interacting with us through digital means. Sensors in the environment, smart appliances, programmable matter—the world of the Internet of Things, or IoT, is becoming a reality, and as it does it will totally change the way we interact with our world and with each other. Similarly and closely interlinked, big data (the accumulation of vast data sets from which patterns and insights can be mined using powerful computing, sophisticated analytics tools, and visualization methods) is becoming increasingly prevalent.

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It’s a question that will no doubt keep ethicists, lawyers, and legal analysts arguing for a very long time. This is the emotionally connected world we might expect to find ourselves in twenty or thirty years from now. Sensors scattered throughout the urban and natural environment, in what has come to be called the Internet of Things, will be able to readily and accurately detect our emotional states at every turn. Depending on the choices we make about the access we give to our emotional lives, we may find ourselves dealing with all kinds of strange and challenging situations. It may sound odd to contemplate, but that’s often the case when viewing the future from a vantage point of the past.

Lessons from the Titans: What Companies in the New Economy Can Learn from the Great Industrial Giants to Drive Sustainable Success
by Scott Davis , Carter Copeland and Rob Wertheimer
Published 13 Jul 2020

The turnaround was multidimensional and required time and patience: fix the factories, seed a culture of continuous improvement, creatively address a portfolio that was stale and well past its expiration date, aggressively manage liabilities, and play offense by rolling out cost-advantaged new products. Honeywell pivoted and reenergized around big themes: energy efficiency, productivity, and connectivity—more popularly known as the industrial internet of things (IIoT), with the high-margin software businesses that have accompanied the strategy. Today, it has become the envy of much of the industrial world. The modern Honeywell story began on February 19, 2002, the start date of an unlikely corporate savior: CEO Dave Cote (pronounced “Cody”). He led a turnaround against tremendous odds—a broken company trying to survive an economic downturn with an unproven, unconventional CEO.

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POSTMORTEM Cote’s final act at Honeywell, on the eve of his retirement in 2017, was to convince the board to promote Darius Adamczyk as his successor. Rather than a Cote clone, Adamczyk is a far different breed. Where Cote had less interest in (or time for) advanced technology, the kind usually reserved for West Coast high-flyers, the new CEO has invested heavily in the industrial internet of things. It’s an important pivot for the company. Adamczyk’s vision of software, digital connectivity, and emerging technologies like quantum computing is edgier than that of his predecessor, but it’s better suited to Honeywell’s strong current position. The company can afford to play offense now. That’s how a company sustains growth over time: Combine a clear vision with systems to keep people focused on delivering value every day.

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SBD is also working to bring advanced manufacturing into its factories, thereby lowering costs, localizing production, and accelerating product development. The industrial world has an ongoing movement called Industry 4.0, which aims to bring manufacturing into the digital age. There are 30 or so advanced technologies in that broad concept, and SBD is aggressively pursuing several. The first phase is deploying the industrial internet of things at scale. There’s been lots of talk around IIOT for the past few years, and it’s not always clear what it means or who benefits. Put simply, added sensors and technology on production lines bring an increase in visibility and the ability to respond to changes in volume or what’s being made.

Robots Will Steal Your Job, But That's OK: How to Survive the Economic Collapse and Be Happy
by Pistono, Federico
Published 14 Oct 2012

http://yro.slashdot.org/story/12/04/27/0029256/will-ibm-watson-be-your-next-mayor 89 Computers to Acquire Control of the Physical World, P. Magrassi, A. Panarella, N. Deighton, G. Johnson, 2001. Gartner research report. T-14-0301. 90 A World of Smart Objects, P. Magrassi, T. Berg, 2002. Gartner research report. R-17-2243. http://www.gartner.com/DisplayDocument?id=366151 91 The Internet of Things. Wikipedia. http://en.wikipedia.org/wiki/Internet_of_Things 92 Study: Intelligent Cars Could Boost Highway Capacity by 273%, 2012. Institute of Electrical and Electronics Engineers. http://spectrum.ieee.org/automaton/robotics/artificial-intelligence/intelligent-cars-could-boost-highway-capacity-by-273 93 INTERNET USAGE STATISTICS.

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Watson could be fully capable of performing this task if there was ever the intention of doing so, and even then we would be using only a tiny fraction of its immense power. This is just the beginning. Watson-like technologies could be used for virtually anything: legal advice, city planning (IBM and Cisco are already working on smart cities),87 and why not policy-making?88 The Internet of Things is coming, and we had better be ready. Technology is becoming so cheap and so powerful it will be integrated into everyday objects, which will help us make better decisions. With all objects in the world equipped with minuscule identifying devices, daily life on Earth would undergo a transformation.89 Companies would not run out of stock or waste products, as involved parties would know which products are required and consumed.90 Mislaid and stolen items would be easily tracked and located, as would the people who use them.

Big Data at Work: Dispelling the Myths, Uncovering the Opportunities
by Thomas H. Davenport
Published 4 Feb 2014

The data generated by them and the insights they reveal about their authors, however, are not going away. In general, however, sensor data is here to stay. The number of networked devices overtook the global population of humans in 2011. ­Analysts estimate that fifty billion sensors will be connected to the ­internet by 2025 (“the Internet of Things”), and each one can ­produce a passel of data. While early prognostications suggested that internet-connected sensors would primarily be used in consumer ­ devices, there has been only limited progress in that regard. Our ­refrigerators may not be connected to the internet anytime soon (if they were, they could, for example, automatically order fresh milk to be delivered when we run low), but our TVs, security systems, and thermostats are increasingly networked.

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HunchWorks is also described as “a mechanism to make the membranes between silos of knowledge both inside and outside of the UN more permeable.” An important aspect of big data is that it is Chapter_01.indd 20 03/12/13 3:24 AM Why Big Data Is Important to You and Your Organization   21 often external to the organization using it. Whether one is addressing internet data, human genome data, social media data, the Internet of Things, or some other source, chances are good that it doesn’t come from your company’s internal transaction systems. The exceptions to this pattern—which I’ll describe in chapter 2—are most likely to be in the telecommunications and financial services industries, which are blessed with massive amounts of internally generated data to analyze.

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See also Apache Impala industrial products and services, 13, 16, 25–26, 65, 75, 185, 197 industrial products firms, 42t, 43, 47, 83 informatics, 66, 156 information technology impact of big data on, 55–56 See also architecture; technology; and specific processes and products 03/12/13 2:04 PM Index  223 Ingenix, 155–156 in-memory analytics, 114t, 116, 124, 199 innovation, focus on, 147 Insight Data Science Fellows Program, 104 insurance industry, 34, 42, 42t, 67, 77, 137, 142, 162, 202 integration, 126–128, 127f, 199–200 Intel, 47 Intel Hadoop, 115 intellectual property (IP), 161 Intermountain Healthcare, 156. See also Home Warner Center for Informatics Research International Institute for Analytics, 135 Internet of Things, 11, 21 internship programs, 103 Intuit, 141–142 iPod, 12 J.R. Simplot, 11 Java language, 89, 123 Jimenez, Joe, 66 job growth for data scientists, 111, 111f, 184–185 John Deere, 47 Johnson & Johnson, 54 Kaplan Inc., 16, 41, 66 Karu, Zoher, 143 Keeping Up with the Quants (Davenport and Kim), 93 Klamka, Jake, 104 Kyruus, 161, 162, 168 large companies action plan for Analytics 3.0 for ­managers in, 204 automating existing processes in, 190–193 big data objectives in, 178–180 big data’s value proposition in, 187 big data used in, 175–176 chief analytics officer role in, 202 company case studies in, 178, 181, 183, 186–187, 187–188, 192, 196, 198 data scientists and teams in, 201 historical context for analytics and big data in, 194–197 Index.indd 223 integrated and embedded models in, 199–200 hybrid technology models in, 200–201 integrating organizational structures and skills in, 182–185 managers’ views of big data in, 176–177 multiple data types in, 197–199 prescriptive analytics used in, 202–203 return on investment in, 188–189, 190f speed of technologies and methods in, 199 leadership, 139–143, 151 Library of Congress, 1 life-cycle management, 129 LinkedIn, 16, 65, 82, 83, 92, 104, 127, 146, 148, 153, 155, 157, 158–159, 160–161, 164, 165 People You May Know (PYMK) ­feature of, 23–24, 140–141, 148, 158 Lockheed Martin, 78 Louisiana State University, 102 machine learning, 4t, 29, 88, 96, 102, 110–111, 113, 114t, 118, 124, 183, 199 Macy’s, 63–64, 179, 183 Macys.com, 63, 182, 183 management big data technology perspective of, 15–18 big data usage and changes in, 27–28 leadership in big data initiatives and, 139–143, 151 new roles in, 141–143 managers action plans for, 30, 57, 84, 112, 134, 151–152, 173, 204 big data skills for, 106–110 in large companies, 176–177 retraining of, 112 visual analytics and, 109 manufacturing, 8t, 52–53, 56, 77, 193, 197 MapReduce framework, 29, 89, 114t, 116, 122, 123, 127f, 132, 148, 157, 199 marketing automated narrative for, 126 banking and, 44, 49, 55, 109 big data strategy and, 5, 8t, 66, 69, 71, 193 B2B firms and, 45–46 Caesars Entertainment and, 179 03/12/13 2:04 PM 224 Index marketing (continued) data-based products and services for, 75, 79, 92, 163, 171, 182 LinkedIn’s use of, 158–159 managerial roles for, 141–142 organizational structure and, 15, 18 retail and, 37–38, 63, 71, 183, 192 sources of data for, 50–51 targeting offers to, 27, 55, 63–64, 65, 67, 72, 79, 107, 108–109, 128, 142, 144, 179, 180, 197 Massachusetts Institute of Technology (MIT), 102, 142, 202, 206 massively parallel processing (MPP), 189, 195, 208 Matters Corp, 69 Mayer, Marissa, 166 Mayo Clinic, 181 McAfee, Andy, 27, 206 McGraw-Hill, 143 McKinsey, 185 media and entertainment firms, 5, 42, 44, 48–49, 54, 179–180 medical record systems, 9, 43, 44–45, 72, 121–122, 156, 181 MetaScale, 192 Me-trics, 13 Microsoft, 14, 37, 163 Microsoft Hadoop, 115 Microsoft Windows Azure, 163.

Move Fast and Break Things: How Facebook, Google, and Amazon Cornered Culture and Undermined Democracy
by Jonathan Taplin
Published 17 Apr 2017

This emphasis on speed and subversion is embraced by much of the technology community. The problem, of course, especially as we move to the Internet of Things—the 6.4 billion Internet-connected sensors and devices—is that security is often an afterthought in building the latest shiny new object of our desire. In a preview of our future, the massive Internet outages experienced in October of 2016 were triggered by (possibly Russian) hackers using the unsecured Internet of Things. As the New York Times reported, “in a troubling development, the attack appears to have relied on hundreds of thousands of internet-connected devices like cameras, baby monitors and home routers that have been infected—without their owners’ knowledge—with software that allows hackers to command them to flood a target with overwhelming traffic.”

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As the New York Times reported, “in a troubling development, the attack appears to have relied on hundreds of thousands of internet-connected devices like cameras, baby monitors and home routers that have been infected—without their owners’ knowledge—with software that allows hackers to command them to flood a target with overwhelming traffic.” As the Sun Microsystems CEO, Scott McNealy, said more than fifteen years ago, “You have zero privacy anyway. Get over it.” Beyond the feelings of paranoia brought on by Russian hackers, the Internet of Things will bring new privacy and security worries. In 2015 more than 40 percent of the thermostats sold in the United States will be “smart thermostats,” many of them sold by Google’s subsidiary Nest. Most buyers of a Nest device don’t realize it’s capable of more than just lowering the temperature when you leave the house.

Work in the Future The Automation Revolution-Palgrave MacMillan (2019)
by Robert Skidelsky Nan Craig
Published 15 Mar 2020

This is not only a quantitative increase in a particular type of data (say, geolocation or financial data), but also a qualitative increase in the kinds of data that are being collected. Partly because of this, what we see is all of these companies starting to expand out from their core business to other places. Amazon is no longer just an ecommerce company; it is getting involved in cloud computing, media content, logistics and the consumer internet of things, to name just a few endeavours. Likewise, with Google and, to a lesser degree, with Facebook. Both are investing and buying up companies all across the tech space in areas that offer new data extraction possibilities. The old monopolies were based on vertical or horizontal integration—but today there is a more rhizomatic integration based upon data as a resource.

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Another major channel of job creation is the rise of new capital, software and robotics industries. The same process innovations that displace workers in the user industries create demand for workers in the producer industries. The new robots and smart machines need to be developed, designed, built, maintained and repaired. Additionally, the Internet of Things, Industry 4.0, digital Taylorism, driverless cars, big data and artificial intelligence require high investment in new infrastructure such as broadband, transport equipment and IT equipment, as well as increasingly complex software. As a result, process innovations and compensation effects destroy and create jobs, however, they tend to create fewer jobs than they destroyed.

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Strong, 99 Artisans, 12, 29, 38, 74, 93, 94 Attitudes to work, 1, 4, 53–62, 73, 75 Aubrey, 184 Austria, 68, 196 Authenticity, 116 Authority, 120, 165 Automation restrictions on, 95 speed of, 21, 137 task automation vs job automation, 92, 93, 110, 141 Autonomous cars, 114, 115, 118 Autor, David, 59, 126 Autor Levy Murnane (ALM) hypothesis, 126–128, 131 B Bailey, Olivia, 180 Bairoch, Paul, 44, 46 Banking (automation of ), 87, 147 Bargaining, 68, 70, 177, 181, 182, 184, 185 Bastani, Aaron, 179n2 Beckert, Sven, 44 Berger, Thor, 95 Bessen, James, 4 Blumenbach, Wenzel, 41 Bosch, Gerhard, 179 Bostrom, Nick, 112, 113 Bourgeois household, 39 Brain and AI, 113 analagous to computer, 100, 103, 104, 115 Brown, William, 185 Bullshit jobs psychological effects, 162 Bureaucracy, 169 C Capitalism, 12, 17, 28, 53, 57, 58, 61, 75, 135, 159 Capper, Phillip, 127, 128 Care work, 3, 48, 75, 117, 178 Carlyle, Thomas, 28 Catholic, 74 Central Europe, 38, 40 Centralisation, 69, 175, 176 Chalmers, David, 103 Chatbots, 91 Chen, Chinchih, 95 Chess (and AI), 112 China, 95, 135 Christian (view of work), 74, 75, 161, 166 Clark, A, 60 Class, 13–15, 17, 30, 39, 43, 46, 47, 118, 159, 160, 162, 165, 172 Classical economics, 54, 55 Climate change, 30, 198 Cloud computing, 139, 140 Coase, Ronald, 70 Coats, David, 184, 185 Collective bargaining, 68, 181, 182, 185 Communism, 13, 57, 58, 61 Competition, 12, 16–18, 39, 91, 94, 112, 115, 119, 139, 140, 152, 199 Index Computational Creativity, 109, 115, 120, 121 Computer aided design (CAD), 34, 35 Computer programming, 100, 116 Computer revolution, 90, 94, 95, 99 Computers, 20, 34, 84, 86, 90, 92–94, 99–107, 110, 111, 115, 116, 120, 131, 134, 146, 147, 151, 197 Consciousness of AI, 110–111 the hard problem, 103 of humans, 105 objective vs. subjective, 102, 103 Consumerism/consumer society, 30, 74, 161, 194 Consumption, 3, 5, 12, 13, 16, 19, 38, 41, 56, 59, 61, 62, 66, 85, 88, 166, 176, 192, 194, 197, 199 Contested concepts, 120 Cooperatives, 40, 61, 69 Craftsmanship, 3, 11, 35, 36, 39, 194 Craig, Nan, 4, 179 Creative work, 3, 48, 74 Creativity, 3, 5, 57, 91, 105–107, 110, 120, 121, 193–195 D D’Arcy, Conor, 177 Data, 2, 84, 92, 107, 129, 130, 137–140, 146, 149, 150, 153, 178, 191, 197, 198 Davies, W.H., 31 De Spiegelaere, Stan, 181, 183 205 Deep Blue, 91, 112, 129, 130 Dekker, Fabian, 180 Deliveroo, 136 Demand effects on automation, 4, 21, 86 elasticity, 86 of work, 4, 13, 15, 16, 76, 158, 164, 180, 199 Democracy, 28 Denmark, 68, 177, 180 Dennett, Daniel, 100, 102, 103 Developing countries, 145 Digital economy, 5, 19, 125–132, 140 Digital revolution, 70 Division of labour, 11, 35, 38, 43, 44, 55 Donkin, Richard, 3 Dosi, Giovanni, 192, 195 Do what you love, 73, 74, 76 Dreyfus, Herbert, 100 E Economics, 1, 4, 5, 7, 10, 12, 14, 15, 18, 29, 30, 53–62 Economic view of work, 53–62 Education, 41, 42, 48, 67–69, 126, 131, 169, 171, 196, 197 Efficiency, 5, 16, 75, 159, 168, 184 Empathy, 106, 107 Employment law, 68 rates, 67, 68, 70 English East India Company, 44 Entrepreneurs, 29, 70, 77, 190, 192, 197, 199 Environment, 25, 31, 56, 70, 87, 91, 109, 111, 113, 120, 178, 198 206 Index Equality of opportunity, 69 of outcome, 69 social, 163 Ethics of AI, 6, 110, 119, 145–153, 197 stagnation of, 151–152 of work, 28 Exit, 69 Experience, 36, 61, 85, 90, 94, 99–105, 116, 119, 189, 190 F Facebook, 136–141, 161 Factory system, 29–30 Families, 3, 26, 29, 37–48, 75, 76, 138, 159, 162, 178, 196 Feminist (arguments about work), 79 Finance, 48, 87, 170, 197 Fire, harnessing/discovery of, 29 Firestone, Shulamith, 159 Firms, 16, 17, 68, 70, 85, 87, 133, 148, 149, 151, 152, 168, 169, 172, 190 Flexicurity, 68 Ford, Henry, 30 Ford, Martin, 2, 59, 106 France, 4, 6, 66–70, 177, 181, 182 Franklin, Benjamin, 28 Freeman, Chris, 192 French Revolution, 43 Frey, Carl Benedikt, 4, 180 Friedman, Milton, 171 Fuzzy matching, 148, 149 G Galbraith, JK, 66 GDP, 19, 178 Gender, 38, 43, 44, 48, 151, 178 Gendered division of labour, 38, 43, 44 Germany, 6, 177, 180–182, 196 Gig economy, 27, 184 Globalisation, 20, 30, 90, 95 Google Google Cloud, 140 Google Home, 140 Google Maps, 35 Google Translate, 106 Google DeepMind, 112, 119 Gorz, A., 59 Graeber, David, 6, 76, 157, 161, 168 Greek ideas of work, 74 Growth, 2, 6, 7, 12, 25, 27, 30, 31, 55, 69, 75, 85, 86, 88, 110, 126, 128, 130, 135, 169, 176, 180, 183, 185, 190, 192, 198, 200 H Happiness, 5, 62, 195 Harrop, Andrew, 180 Hassabis, Demis, 119 Hayden, Anders, 182, 183 Healthcare, 3, 87, 94, 117, 165, 197 Heterodox economics, 54, 56, 62 Hierarchy, 46, 48, 55, 69, 170 High-skilled jobs, 128, 134 Homejoy, 135 Homo economicus, 56, 57 Homo laborans, 3 Homo ludens, 3 Household economy, 4, 38–40, 45, 47 Housewives, 42, 43, 46, 47 Housework, 39, 40, 42, 44, 47 Hunter-gatherers, 11, 26, 27, 30 Index I Idleness, 54 India, 44–47 Industrial Revolution, 2, 4, 14, 29, 37, 75, 93, 94, 175, 177, 190, 191 Inequality, 67–69, 86, 87, 192, 193, 199, 200 Informal economy, 47 Information technology, 86, 161 Infrastructure digital, 140 physical, 103 Innovation, 6, 10, 14, 16, 18, 34, 67, 69, 189–199 process innovation vs. product innovation, 16, 18, 190–191, 195 International Labour Organisation (ILO), 193 Internet of Things, 139, 191 Investment in capital, 114 in skills, 70 J Japan, 117 Jensen, C, 55 Job guarantee, 172 Jobs, Steve, 73 Journalism automation of, 118 clickbait, 118 Juries, algorithmic selection of, 150, 153 K Karstgen, Jack, 196 Kasparov, Garry, 91, 112, 129, 130 207 Katz, Lawrence, 198 Kennedy, John F., 160 Keune, Maarten, 180 Keynes, John Maynard, 6, 9, 11, 27, 60, 61, 160, 161, 176 King, Martin Luther, 171 Knowledge (tacit vs. explicit), 127 Komlosy, Andrea, 4, 75 Kubrick, Stanley, 26 Kurzweil, Raymond, 101, 103, 104 Kuznets, Simon, 190 L Labour, 3, 10, 11, 13–16, 18–21, 29, 34–36, 38, 43–46, 55, 59, 65–70, 73–76, 85–87, 89, 90, 93, 94, 96, 114, 125, 126, 128, 130, 131, 141, 158, 165, 176–180, 183–184, 189, 190, 192–196, 199–200 Labour market polarisation, 67, 70, 126 Labour markets, 67, 68, 70, 87, 90, 96, 125, 126, 128, 130, 131, 141, 178, 183–184, 189, 192, 193, 195, 196, 199–200 Labour-saving effect, 86 Lall, Sanjaya, 193 Language translation, 105, 106 Latent Damage Act 1986, 127 Law automation of, 145, 152, 153 ethics, 145–153 Lawrence, Mathew, 177 Layton, E., 58 Le Bon, Gustave, 101 Lee, Richard, 26 Legal search/legal discovery, 148–150 208 Index Leisure, 3, 10, 11, 19, 27, 48, 55, 56, 59–62, 65, 77, 79, 117, 118, 159, 161, 178, 180, 182, 184, 191, 195 Levy, Frank, 126 List, Friedrich, 193 Love, 55, 74, 76, 99, 103, 106, 112, 118 Low-income jobs, 96 Loyalty, 69 Luddites, 2, 14, 18, 35, 59, 94, 96 Lyft, 136 M Machine learning, 59, 84, 90, 91, 96, 138, 139 Machines, 2, 5, 10, 12–15, 17, 19, 20, 35, 36, 38, 59, 84–87, 90–96, 99–103, 105–107, 109–121, 127–131, 138, 139, 145, 147, 148, 160, 168, 191 Machine vision, 120 Malthusian, 19 Man, Henrik de, 79 Management, 27, 30, 41, 69, 70 management theory/ organisational theory (see also Scientific management) Mann, Michael, 46 Manual work, 1 Manufacturing, 86, 87, 90, 94, 95, 176, 184, 198 Markets/market forces, 5, 6, 21, 38, 44–46, 67, 68, 70, 79, 85–88, 90, 96, 120, 125, 126, 128, 130, 131, 140, 141, 150, 152, 159, 164, 165, 171, 178, 183, 189–193, 195, 196, 198–200 Marx, Karl, 17, 18, 27, 56–59, 61, 62, 78 Matrimonial relationships, 37 McCormack, Win, 159 Meaning, 4, 9, 10, 19, 25, 54, 57, 58, 66, 73, 76, 78, 79, 84, 106, 116, 176, 180 Mechanisation, 15, 17, 19, 20, 192 Meckling, W., 55 Méda, Dominique, 183 Medical diagnosis (automation of ), 128, 129 Menger, Pierre-Michel, 4 Mental labour, 3 Meritocracy, 28 Middle-income jobs, 90, 93, 94 Migration, 40, 47 Minimum wage, 67, 69 Mining, 26, 38, 197 Mokyr, J., 59 Monopolies, 6, 136, 138–140 Morals/morality, 48, 77, 159, 160, 162, 164, 166, 167 Moravec’s paradox, 131 Murnane, Richard, 126 N Nagel, Thomas, 100, 102 National Living wage, 184 Needs vs.

Belt and Road: A Chinese World Order
by Bruno Maçães
Published 1 Feb 2019

“We both agree that Chinese companies should be united and must not be provoked by outsiders,” he added, before speaking about his company’s efforts over the past thirty years and expressing zero tolerance for any questioning of the loyalty of the “national brand.” The dispute highlights how much national champions are expected to benefit from the definition of which technologies will be used to power the coming revolution in autonomous cars and the internet of things. * * * Because Germany’s top firms have become so dependent on the Chinese market, the government in Berlin has avoided confronting China head-on.16 The United States took longer to react, but when it finally did the response was considerably more aggressive. The ongoing dispute was initially centered around the country’s trade deficit with China but quickly turned to Made in China 2025.

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What the Belt and Road does is increase China’s control over the way value chains are organized and grant it the power to reorganize them on better terms. To give the most obvious example, the Chinese economy still has to rely on a steady supply of foreign-made semiconductors, the heartbeat of the internet of things and the industrial factories of the future, a fragility made evident when the giant electronics company ZTE was taken to the brink of extinction after the Trump administration temporarily banned it from buying US-made components such as chips. In a speech two years earlier, Xi Jinping had berated China’s dependence on foreign suppliers for critical components and key technologies.

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INDEX Abbasi, Zafar Mahmood, 126 Abe, Shinzo, 118, 137 Addis Ababa, Ethiopia, 68 Aden Gulf, 72 Adil, Umer, 60 Advancing the Development of the One Belt, One Road Leading Group, 39 aerospace, 88, 103 Afghanistan, 53, 107, 127, 128, 129, 135, 172 Africa, 3, 8, 25, 44, 124, 163 Djibouti, 4, 12, 46, 63, 67–8, 101, 117 Ethiopia, 46, 68, 154, 170, 186 manufacturing, 68, 77 Maritime Silk Road, 23, 26, 45, 62 oil, 64 Partnership for Quality Infrastructure, 138 piracy, 72 telecommunications, 101, 170–71 aging population, 75 Agricultural Bank of China, 48 agriculture, 11, 61, 76, 99–100, 103 Ahmedabad, Gujarat, 138 aircraft, 81, 91, 103 Akto, Xinjiang, 60 Aktogay, East Kazakhstan, 103 Alibaba, 44 Allison, Graham, 7–8 Alps, 189 aluminum, 17, 20, 88 Andalusia, Spain, 189 Andijan, Uzbekistan, 54 anti-dumping, 92, 113 Antwerp, Flanders, 65 Apollo program, 9 aquaculture, 71 Arabian Sea, 72, 106 Arctic, 4, 62, 66, 188 artificial intelligence (AI), 44, 75, 88 Arunachal Pradesh, India, 111 Asian Development Bank, 45, 137 Asian Financial Forum, 49 Asian Infrastructure Investment Bank, 48 Association of Southeast Asian Nations (ASEAN), 122 Astana International Exchange, 56 Astana, Kazakhstan, 25–6, 39, 56, 58 asteroids, 187 Athens, 8 Atlantic Ocean, 3, 115, 119, 138, 139 Atushi, Xinjiang, 60 Australia, 5, 12, 25, 119, 121, 122, 132–3, 135 automated vehicles, 88, 90, 186, 187, 190 automobile industry, 74, 81, 86, 90–91, 97, 104 Autor, David, 177 aviation, 81, 91, 103 Azad Jammu and Kashmir (AJK), 60 Azerbaijan, 186 Badakhshan, Afghanistan, 128 Baidu, 188 Baldwin, Richard, 74, 80 Balkans, 8, 12, 140 Balochistan, Pakistan, 60, 105 Gwadar port, 46, 59, 61–2, 63, 64, 99–100, 101, 105–7, 117 separatism and terrorism, 106, 127, 128 Baltic Sea, 51 Bangkok, Thailand, 65, 136–7 Bangladesh, 48, 53, 64, 109, 134, 136, 138, 150, 189 Bangladesh-China-India-Myanmar Economic Corridor (BCIM-EC), 52, 62 Bank of China, 48 banking, 46–51 bargaining theory, 152–3 Bay of Bengal, 22, 64, 72, 119 Beijing, China, 20, 28, 48, 126, 165 Beijing University, 183, 188 Belgium, 56, 65 Belgrade, Serbia, 143 Belt, see Silk Road Economic Belt Belt and Road Advancing the Development of the One Belt, One Road Leading Group, 39 backlash against, 12, 108, 121–4, 130–46, 155 bridges, 40, 54, 156, 173, 186 Buddhism, 112 cities, 11, 43, 44, 48, 149–52, 187–8 ‘community of shared destiny’, 26–9, 33, 36, 43, 45, 170 connectivity (wu tong), 42, 43, 52–3, 127, 158, 167 currency integration, 26 data, 44 debt, 12, 46, 47, 108, 109, 124, 126, 130, 132, 153–62 digital infrastructure, 43–4, 59, 86 e-commerce, 44, 59 economic corridors, 2, 11, 51–4, 55, 62 economic policy coordination, 28 energy, 11, 17, 19, 20–23, 40, 46, 48, 49, 52, 61, 64, 86, 92, 188 financing, 11, 36, 46–51, 54, 108–9, 124, 126, 130, 132, 138, 141, 153–64 Forum for International Cooperation (2017), 12, 108, 143, 152 impatience, 152–3 inauguration (2013), 11, 17, 23 industrial capacity cooperation, 85–8 industrial parks, 10, 43, 55, 61, 67, 99, 102 infrastructure, see infrastructure internal discontent, 163 international court, 28, 190 loans, 11, 36, 46–7, 54, 108–9, 124, 126, 130, 132, 138, 141, 153–62, 163 maps, 2–6, 24, 41, 64, 69 Maritime Silk Road, 24, 26, 28, 39, 41 market integration, 41 military bases, 12, 67, 71, 72, 101, 117, 126–7 overcapacity, 19 ports, see ports railways, 9–10, 11, 12, 18, 43, 46, 52, 53–4, 68, 86, 122, 130 roads, 9, 19, 40, 43, 52, 54 security, 127–9 Silk Road, 2, 9–10, 23–6, 45, 82, 138 Silk Road Economic Belt, 24, 25–6, 28, 39, 51–62, 83 success, definition of, 164, 174 telecommunications, 43–4, 52, 86, 101, 170–71 timeline, 10 TIR Convention, 55 transnational industrial policy, 81, 84 transport infrastructure, 9–10, 11, 18, 19, 25, 26, 40, 48, 49, 53–4, 83 urban development, 11, 43, 44, 48, 149–52 Vision and Actions document (2015), 40, 41, 45, 49, 50, 52, 62, 67, 78 Vision for Maritime Cooperation (2017), 62 Bering Strait, 66 Bharatiya Janata Party (BJP), 110 Bhat, Vinayak, 107 Bhutan, 107–8 big data, 44 Bishkek, Kyrgyzstan, 127 Blackwater, 128 blue economic passage, 62 Boao Forum for Asia (2015), 27, 32 Brahmaputra river, 136 Brazil, 174 Brewster, David, 63 BRIC (Brazil, Russia, India and China), 19, 174 bridges, 40, 54, 156, 173, 186 British Broadcasting Corporation (BBC), 188 Budapest, Hungary, 143 Buddhism, 111–12 Bush, George Walker, 169 California, United States, 64 Cambodia, 52, 54, 70, 129, 132, 155 Cameroon, 68, 187 Canada, 136 car industry, see automobile industry Caribbean, 25 Carr, Robert ‘Bob’, 122 Cartagena, Spain, 92 Caspian Sea, 186 Caucasus, 20, 129 CDMA (code-division multiple access), 89 cement, 17, 49–50, 83 Center for Strategic and International Studies, 19, 123 center of gravity, 115 Central African Republic, 186 Central Asia, 9, 20, 25, 51, 52, 82–3, 188 energy, 22, 106 Eurasian Economic Union (EEU), 57–9 India, trade with, 107 industrial capacity cooperation, 104 Islamism, 127 Russia, relations with, 57–9, 129, 133 steel industry, 82–3 terrorism, 127 textile industry, 101 transport infrastructure, 9, 54 Central Huijin Investment, 49, 50 Central Military Commission, 166 century of humiliation (1839–1949), 165, 186 Chabahar, Sistan-Baluchistan, 106–7 Chalay Thay Saath, 60 Chao Phraya River, 65 ChemChina, 48 Chengdu Economic Daily, 129 China Abbasi’s visit (2018), 126 Academy of Information and Communications Technology, 44 aging population, 75 Banking and Insurance Regulatory Commission, 50 Bishkek Embassy bombing (2016), 127 Boao Forum for Asia (2015), 27, 32 Buddhism, 111–12 century of humiliation (1839–1949), 165, 186 Doklam plateau dispute, 107–8, 113 energy, see energy EU-China summit (2015), 138 five-year plan (2016–20), 41 Food and Drug Administration, 114 Foreign Policy Center of the Central Party School, 7 Gants Mod crossing closure (2016), 36 General Navigation Office, 69 ‘Going Out’ strategy, 86 Guangxi Nonferrous Metals Group bankruptcy (2016), 16 Guiding Opinion on Promoting International Industrial Capacity (2015), 86 Guiding Opinion on Standardizing the Direction of Overseas Investment (2017), 86 incremental approach, 7 Indian Dilemma, 21 Institute of International Studies, 92 International Trust and Investment Corporation, 132 Investment Corporation, 48 keeping a low profile (tao guang yang hui), 15, 18, 32 labour shortages, 75 Macron’s visit (2018), 146–7 Made in China 2025 strategy, 85, 87, 90–92, 93 Malacca Dilemma, 21–2, 64, 131 Merchants, 68–9 middle-income trap, 75–7, 85 migrant workers, 75 military, 12, 13, 59, 67, 71, 72, 101, 117, 126–7 minimum wage, 75 Ministry of Commerce, 21, 40, 93 Ministry of Communications, 69 Ministry of Finance, 49 Ministry of Foreign Affairs, 40 Ministry of Industry and Information Technology, 19 Ministry of Transportation, 14 Modi–Xi summit (2018), 135 National Bureau of Statistics, 75 National Congress, 28, 29, 44, 165, 181 National Cybersecurity Work Conference (2018), 84 National Development and Reform Commission, 40, 98 National Health Commission, 114 Opium War, First (1839–1842), 165 overcapacity, 16, 19–20, 88 Overseas Chinese Affairs Office, 19 Overseas Investment Industrial Guiding Policy, 86 People’s Navigation Company, 69 Ports-Park-City model, 67 presidential term limits repeal (2018), 164, 174 real estate market, 16, 75 reform and opening up, 13–15, 73 renminbi, 22–3, 159 responsible stakeholder, 169 shipbuilding, 14, 17 soft power, 111, 170 Soviet Union, relations with, 13, 14, 15 State Administration of Foreign Exchange, 48 State Council, 19, 39, 40, 49, 66, 86 state-owned companies, 42, 153, 160–61, 189 steel industry, 16–17, 18, 20, 82–4, 86, 88 striving for achievement, 18 Swaraj’s visit (2018), 135 Taiwan, relations with, 14, 26, 142 technology transfers, 85–92, 97, 177–8 Thucydides’ trap, 8 Tianxia, 26–7, 29, 31–5, 78, 79, 192–3 TIR Convention, 55 Trump’s visit (2017), 124 ‘two heads abroad’ (liangtou zai haiwai), 17 United States, relations with, see Sino–US relations Working Conference on Neighborhood Policy (2013), 17–18 China Construction Bank, 48 China Development Bank, 16, 48, 49, 97, 98, 99, 103, 160 China Export & Credit Insurance Corp, 104 China Export-Import Bank, 46, 47, 48, 49, 103, 154 China Fantasy, The (Mann), 177 China Global Television Network, 188 China Nonferrous Metals Industry Group, 103 China Three Gorges Corp, 48 China-Indian Ocean-Africa-Mediterranean Sea Blue Economic Passage, 62 China-Indochina Peninsula Economic Corridor, 51, 52, 54, 62 China-Oceania-South Pacific, 62 China-Pakistan Economic Corridor (CPEC), 52, 59, 60, 62, 105–7, 108 Chinese Communist Party Advancing the Development of the One Belt, One Road Leading Group, 39 and Australia, 133 Constitution, 41, 164 founding of (1921), 165 National Congress, 18th (2012), 28 National Congress, 19th (2017), 29, 44, 165, 181 and New Zealand, 132 Politburo, 39, 40, 165 reform and opening up, 13–15 and steel industry, 16 Third Plenum of the 18th Party Central Committee (2013), 39 Chongyang Institute for Financial Studies, 106 Christianity, 128 Churchill, Winston, 183 cities, 11, 43, 44, 48, 149–52, 187–8 climate change, 4, 66, 85, 171 Clinton, William ‘Bill’, 177 cloud computing, 44 CloudWalk Technology, 44 Club Med, 189 CNN, 188 cobalt, 81, 104 Cold War, 2, 14, 21–2, 36, 40, 125, 171 Colombo, Sri Lanka, 156, 162 colonialism, 120, 162 ‘community of shared destiny’, 26–9, 33, 36, 43, 45, 135, 170 Confucianism, 31, 34 Congo, Democratic Republic of, 81, 104 connectivity, 42, 43, 52–3, 109, 122, 127, 146, 158, 167 Connectivity Platform, 139 construction, 18, 75, 86, 98 convergence, 4, 14, 166, 167, 169, 174, 177 copper, 103, 104 corridors, see economic corridors corruption, 133, 155–6, 158, 187 cosmopolitan neighborhoods, 4 Country Garden, 151 Cowboys and Indians, 188 cultural exchanges, 42, 43, 56–7 currency, 22–3, 26, 159–60 customs cooperation, 55, 57, 59, 63 Cyprus, 140 Dalai Lama, 36, 112 Dalian, Liaoning, 55, 93 Daming Palace, Xi’an, 147 Dangal, 111 data, 44 Davidson, Phillip, 125–6 Davos, Switzerland, 168 Dawn of Eurasia, The (Maçães), 185, 191 Dawood, Abdul Razak, 158 debt, 12, 16, 46, 47, 108, 109, 124, 126, 130, 132, 153–62 democracy, 125, 133, 166, 171, 172, 174, 175, 176, 181–3 Democratic Republic of Congo, 81, 104 Deng Xiaoping, 13–15, 18, 31, 32, 69, 73, 183 Diaoyu Islands, 187 digital infrastructure, 43–4 division of labor, 53, 78, 79, 80 Djibouti, 4, 12, 46, 63, 67–8, 101, 117, 186 Doklam plateau, 107–8, 113 Doraleh, Djibouti, 63, 67–8 DP World, 68 dry ports, 57 Dubai, UAE, 62, 68, 160 Dudher Zinc project, 127 Duterte, Rodrigo, 156 DVD (digital versatile disc), 89 e-commerce, 44, 59 East China Sea, 118 economic corridors, 2, 11, 51–4, 55 economic nationalism, 102 economic policy coordination, 28 Economist, The, 190 Egypt, 101 electric cars, 81, 104 electricity, 40, 46, 49, 52, 61, 98, 156, 188 end of history, 36 energy, 4, 11, 17, 19, 20–23, 48, 49, 82, 86, 92, 188 electricity, 40, 46, 49, 52, 61, 98, 156, 188 gas, 21, 22, 40, 52, 64, 72, 106 hydropower, 48 oil, 21, 22, 23, 40, 52, 64, 72, 106 renewable, 21, 187, 188 English language, 111, 188 Enhanced Mobile Broadband coding scheme, 89 Enlightenment, 193 environmental sustainability, 75 Erenhot, Inner Mongolia, 55 Ethiopia, 46, 68, 154, 170, 186 Eurasia, 1–5, 11, 20, 26, 45, 52, 57, 63, 120, 121, 138 Eurasian Economic Union (EEU), 57–9 Eurasian Resources Group, 103 European Commission, 143, 145 European empires, 120–21 European Union (EU), 5, 29, 57, 58, 138–47, 159, 176, 179 and Belt and Road, 10, 12, 30, 138–47 Connecting Europe and Asia strategy (2018), 145–6 and Djibouti, 67 economic policy coordination, 28 5G mobile networks, 43 immigration, 187 steel industry, 17 tariffs, 83 technology transfers, 87–8, 178 transnational framework, 81 Turkey, relations with, 4 Export-Import Bank of China, 46, 47, 48, 49, 103, 154 exports, 15, 17, 19, 79 Facebook, 188 facial recognition, 44, 190 fashion industry, 101 fate, 34 Fergana Valley, 54 fertilizers, 19 fibre-optic connectivity, 101 fifth generation (5G) mobile networks, 43–4, 89 finance, 11, 36, 46–51, 54, 126, 138, 141, 153–64 Financial Times, 10, 63, 143, 154, 157, 158, 159 five-year plan (2016–20), 41 Folding Beijing (Hao), 150 food imports, 76 foreign direct investment, 46, 144–6 foreign exchange, 16, 94, 153 Forest City, Johor, 149–51, 155 France, 11, 96, 129, 141, 144, 146–7, 189 free and open order, 125 free-trade zones, 11, 42, 55–6, 71 freedoms of speech, 172, 189 French Foreign Legion, 129 French, Howard, 13 Frontier Services Group, 128–9 Fu Chen, 129 Fu Ying, 140 Fukuyama, Francis, 184–5 Gabon, 96 Gabriel, Sigmar, 142 Gang of Four, 14 Gants Mod crossing closure (2016), 36 gas, 21, 22, 40, 52, 64, 72, 106 General Navigation Office, 69 generic drugs, 114 Genghis Khan, 2, 25 Georgia, 58 Germany, 11, 65, 80, 87–8, 90, 100, 141–2, 144, 189 ghost ships, 186 Gibraltar, 92 Gilgit-Baltistan, Pakistan, 54, 60, 108 Gland Pharma, 113 glass, 17, 83 Global Energy Interconnection, 188 global financial crisis (2008), 16–17, 85, 161, 178 Global Infrastructure Center, 190 Global Times, 67, 109, 131 global value chain revolution, 74 global warming, 4, 66, 85 globalization, 19, 28, 66, 78, 102, 124, 144, 168, 174, 192 ‘Going Out’ strategy, 86 good governance, 183–4 Google, 152, 188 Goubet, Djibouti, 67 government procurement, 12, 59 Grand Palace, Bangkok, 65 Grand Trunk Road, 53 Greece, 30, 31, 65, 140, 141, 142 GSM (Global System for Mobile communications), 89 Guangdong, China, 28, 75, 151 Guangxi Beibu Gulf International Port Group, 67 Guangxi Nonferrous Metals Group, 16 Guiding Opinion on Promoting International Industrial Capacity (2015), 86 Guiding Opinion on Standardizing the Direction of Overseas Investment (2017), 86 Guo Chu, 33 Gwadar, Balochistan, 46, 59, 61–2, 63, 64, 99–100, 101, 105–7, 117 Hainan, China, 71 Hambantota, Sri Lanka, 46–7, 63, 64, 68, 117, 162 Hamburg, Germany, 65 Hamilton, Clive, 133 Han Empire (206 BC–220 AD), 25 Hao Jingfang, 150 ‘harmonious world’, 33, 36 Havelian, Khyber Pakhtunkhwa, 54 He Yafei, 19, 168 heavy industry, 75, 82 Hebei, China, 83 Heilongjiang, China, 55 Hesteel, 83 high-speed railways, 18, 53–4, 83, 89, 98, 122, 130, 137, 138, 143, 186–7 highways, see roads Hillman, Jonathan, 8 Hobbes, Thomas, 27 Holslag, Jonathan, 189 Hong Kong, 49, 103 Hongshi Holding Group, 49 Horgos, Xinjiang, 55, 55–6, 57 Horn of Africa, 3 Hu Huaibang, 49, 97 Hu Jintao, 21, 33, 70 Hu Xiaolian, 154 Huang Libin, 19 Huangyan Island, 187 Huawei, 89–90, 101, 171 Hub, Balochistan, 127 hukou (household registration), 76 human rights, 141–2, 170, 171, 189 Hun Sen, 155 Hungary, 30, 140, 141, 142, 143, 144 Huntington, Samuel, 184 Hussain, Chaudhry Fawad, 157 hydropower, 48 Ibrahim Ismail, Sultan of Johor, 151 immigration, 187 impatience, 152–3 imports, 17, 19, 22, 79–84 India and the Indian Ocean (Panikkar), 118 India, 3, 5, 64, 105–25, 134–6, 174, 179 Bangladesh Liberation War (1971), 109 and Belt and Road, 11, 12, 52, 72, 105–15, 130, 133 Belt and Road Forum for International Cooperation (2017), 12, 108 British Raj (1858–1947), 107 Buddhism, 111–12 cosmopolitan neighborhoods, 4 cultural mission to China (1952), 113 Doklam plateau dispute, 107–8, 113 economic autarchy, 110, 117 free and open order, 125 Grand Trunk Road, 53 imports, 113–14 and Indian Ocean, 3, 116–19 Indo-Pacific, 116–23, 125 Japan, relations with, 118 Kashmir dispute, 108–9, 117 Malabar naval exercises (2018), 135 and maritime hegemony, 72 migrant workers, 150 military bases, 3, 131 Modi–Xi summit (2018), 135 Mumbai-Ahmedabad high-speed railway, 138 nuclear tests (1998), 109 Pakistan, relations with, 105–7, 108–9, 117, 134 pharmaceuticals, 113, 114 Quadrilateral Security Dialogue, 121–2 Research and Analysis Wing (R&AW), 105–6 and Sabang Island, 131 Siliguri Corridor, 107–8 Southeast Asia, 113, 117–18 Swaraj’s visit to China (2018), 135 Tibet, relations with, 111–12, 117, 136 United States, relations with, 119, 121–2, 134, 135 Indian Dilemma, 21 Indian Ocean, 3, 8, 9, 26, 51, 62, 63, 66, 68, 71–2, 116–19 Indo-Pacific, 116–23, 125, 126 and Japan, 4 Kra Isthmus canal proposal, 65, 186 meticulous selection, 72 Myanmar oil and gas pipeline, 64, 72 oil, 21, 64 and Pakistan, 59, 61, 64 individualism, 27, 189 Indo-Pacific, 116–23, 125, 126 Indo-Pacific Business Forum, 122 Indo-Pacific Command, US, 126 Indochina, 51, 52, 54, 62 Indonesia, 2, 5, 18, 26, 39, 48, 83, 117, 131 Industrial and Commercial Bank of China, 48, 49, 103 industrial capacity cooperation, 85–8, 98, 102–4 industrial internet, 44 industrial parks, 10, 43, 55, 61, 67, 99, 102 Industrial Revolution, 84 information technology, 43–4, 74, 81, 86, 90, 94, 111, 170–71, 190 infrastructure, 3, 23, 26, 30, 40–45, 48, 50, 55, 58, 63, 86, 88, 124, 139, 141, 162, 167, 186 Afghanistan, 135 communications, 81, 118 digital, 43–4 European Union, 10, 141, 145 India, 64, 118, 135 Japan, 4, 136–8 Maritime Silk Road, 66, 67 Mediterranean, 65 Pakistan, 54, 62, 99, 105 Quadrilateral Security Dialogue, 121–2 Southeast Asia, 18–19, 70, 117, 130, 132 steel industry, 18 transportation, see transportation value chains, 96 Xinjiang, 20, 54 Inner Mongolia, China, 55 innovation, 76 Institute for International Finance, 153 intellectual property, 59, 88–9, 91, 97, 180, 190 international courts, 28, 190 international industrial capacity cooperation, 85–8, 98, 102–4 International Monetary Fund (IMF), 15, 156–7, 158–9, 172 Internet, 43–4, 86, 170–71 internet of things, 90, 94 Iran, 4, 22, 105–6 Iraq, 24 Irkeshtam, Xinjiang, 55 iron, 17 Islamabad, Pakistan, 60, 99, 101, 127, 157 Islamic State, 128 Islamism, 127–9 Istanbul, Turkey, 4, 24, 65 Italy, 48, 65, 140, 189 Izumi, Hiroto, 137 Jadhav, Kulbhushan, 105–6 Jakarta, Indonesia, 2, 5, 26, 39 Japan, 1, 5, 22, 123, 133, 136–8, 145, 165, 166, 169, 189 Buddhism, 111 Cold War, 21–2 India, relations with, 118 Indian Ocean, 4 infrastructure development, 4, 136–8 Quadrilateral Security Dialogue, 121–2 Second World War (1937–45), 119, 165 Javaid, Nadeem, 46 Jiang Qing, 14 Jiang Shigong, 183–4 Jiang Zemin, 15 Jiangsu Delong, 83 Jin Qi, 98 Jinnah Town, Quetta, 128 Johor, Malaysia, 149–51 Joint Statement on Cooperation on EEU and Silk Road Projects (2015), 57–8 joint ventures, 97 Journey to the West, 186, 188 Juncker, Jean-Claude, 138 Kaeser, Joe 170 Karachi, Sindh, 59, 100, 105, 106, 127 Karakoram Highway, 54, 60, 64 Kashgar, Xinjiang, 54, 59, 60, 64, 101 Kashmir, 60, 108–9, 117 Katanga, Democratic Republic of Congo, 104 Kaz Minerals 103 Kazakhstan, 8, 55–9, 129, 189 Astana International Exchange, 56 China–EEU free-trade agreement signing (2018), 58 and Eurasian Economic Union, 57 gateway to Europe, 56 Horgos International Cooperation Center, 55–6 industrial capacity cooperation, 103–4 railways, 54 Xi’s speech (2013), 23, 25–6, 39 Kazakhstan Aluminum, 103 keeping a low profile (tao guang yang hui), 15, 18, 32 Kenya, 101, 138, 171 Khan, Imran, 157–8 Khawar, Hasaan, 53 Khunjerab Pass, 101 Khyber Pakhtunkhwa, Pakistan, 54, 60, 100 Kizilsu Kirghiz, Xinjiang, 60 knowledge, 74, 76, 87 Kolkata, West Bengal, 64 Kortunov, Andrey, 135 kowtow, 35 Kra Isthmus, Thailand, 65, 186 Kuala Linggi Port, Malacca, 63 Kuala Lumpur, Malaysia, 130 Kuantan, Pahang, 63, 67 Kudaibergen, Dimash, 57 Kunming, Yunnan, 188 Kyaukpyu, Rakhine, 63, 64, 132, 154 Kyrgyzstan, 53, 54, 55, 103, 127 labor costs, 74, 83, 85, 99 labor shortages, 75 Lagarde, Christine, 158–9 Lahore, Punjab, 100, 157 Laos, 50, 52, 54, 129, 132 Latin America, 25, 187, 188 Leifeld Metal Spinning AG, 88 Lenin, Vladimir, 6, 78 Lenovo, 89–90 Li Hongzhang, 69 Li Keqiang, 44 Li Ruogu, 47 Liaoning, China, 55 liberal values, 123, 125, 133, 170 liberal world order, 141, 144, 167–86, 190, 192 Lighthizer, Robert, 91 lignite, 61 liquefied natural gas (LNG), 48, 66 Lisbon, Portugal, 2, 5 lithium-ion batteries, 81 Liu Chuanzhi, 89–90 Liu He, 92 loans, 11, 36, 46–7, 54, 108–9, 124, 126, 130, 132, 138, 141, 153–63, 190 London, England, 65, 160 Lord of the Rings, The (Tolkien), 1 Lou Jiwei, 76 Luo Jianbo, 7 Machiavelli, Niccolò, 31–4 machinery, 81, 90, 98, 156 Mackinder, Halford, 120 Macron, Emmanuel, 146–7 Made in China 2025 strategy, 85, 87, 90–92, 93 Mahan, Alfred, 120 Mahathir Mohamad, 130–31, 151, 155 Malabar naval exercises (2018), 135 Malacca, Malaysia, 3, 63 Malacca Strait, 21–2, 64, 65, 72, 117, 131 Malay Mail, 155 Malaysia, 3, 70, 117, 130–31, 154 debt, 154 Forest City, 149–51, 155 high-speed railways, 54, 130 Mahathir government (2018–), 130–31, 151, 155 1Malaysia Development Berhad scandal (2015–), 155 ports, 63, 67 Maldives, 134, 155 Mali, 129 Malik, Ashok, 109 Malta, 140 Mandarin, 107, 149, 188 Manila, Philippines, 122 Mann, James, 177 manufacturing, 11, 19, 68, 77, 85, 99 outsourcing, 68, 99 value chains, 3, 43, 64, 73–4, 79–82, 84–5, 94–104, 141 Manzhouli, Inner Mongolia, 55 Mao Zedong, 13–14, 31, 183 maps, 2–6, 24, 41, 64, 69 Maritime Silk Road, 24, 26, 28, 39, 41, 53, 62–72, 117 market integration, 41 Mars, 187 Marshall Plan, 40 Marx, Karl, 6 Marxism, 78 Massachusetts Institute of Technology (MIT), 177 Matarbari port, Bangladesh, 138 matchmaking services, 11 Mattis, James, 124 McMahon Line, 111 Mediterranean Sea, 4, 51, 62, 65, 119 Mei Xinyu, 21 Mekong Delta, 8 mergers and acquisitions, 42 Merkel, Angela, 88, 141, 144 meticulous selection, 72 Middle East, 4, 6, 22, 64, 120, 129, 163, 171 middle-income trap, 75–7, 85 migrant workers, 75 Milanovic, Branko, 173 military, 3, 12, 67, 71, 72, 101, 117, 126–7 Ming Empire (1368–1644), 163 Ming Hao, 30 minimum wage, 75 Ministry of Commerce, 21, 40, 93 Ministry of Communications, 69 Ministry of Finance, 49 Ministry of Foreign Affairs, 40 Ministry of Industry and Information Technology, 19 Ministry of Transportation, 14 Minmetals International Trust Co, 16 mobile payments, 193 Modi, Narendra, 106, 135–6 Mohan, Raja, 3, 121 Mombasa, Kenya, 138 Mongol Empire (1206–1368), 2, 25 Mongolia, 8, 36, 52, 55, 111 Moon, 187 Moraes, Frank, 112–13 Moscow, Russia, 4 Most Favored Nation status, 15 Mozambique, 138 multinationals, 74, 88–9 multipolar world system, 179 Mumbai, Maharashtra, 4, 105, 138 Myanmar, 52, 54, 63, 64, 72, 129, 132, 138, 154 Nacala, Nampula, 138 narcotics trade, 127 Nathan, Andrew, 159 National Aeronautics and Space Administration (NASA), 9 National Bureau of Statistics, 75 National Congress 18th (2012), 28 19th (2017), 29 National Cybersecurity Work Conference (2018), 84 National Development and Reform Commission, 40, 98 National Health Commission, 114 National League for Democracy, Myanmar, 132 National Museum of China, Beijing, 165, 166 National Party of New Zealand, 132 National People’s Congress, 44 National Rescue Party of Cambodia, 155 Nazarbayev University, 25–6 Nehru, Jawaharlal, 113 Nepal, 134, 135, 150 Netherlands, 56, 65 New Zealand, 132–3 Nigeria, 68 Ning Jizhe, 137 Nordin, Astrid, 42 Northern Sea Route, 66 Northwest Passage, 66 NPK fertilizer, 99 nuclear power/weapons, 21, 83, 88, 109, 166, 187 oil, 21, 22, 23, 40, 52, 64, 72, 106 1Malaysia Development Berhad scandal (2015–), 155 One China policy, 142 Open Times, 183 Opium War, First (1839–1842), 165 Organisation for Economic Co-operation and Development (OECD), 79 Osh, Kyrgyzstan, 54 overcapacity, 16, 19–20, 88 Overseas Chinese Affairs Office, 19 Overseas Investment Industrial Guiding Policy, 86 Pacific Command, US, 125–6 Pacific Journal, 71 Pacific Ocean, 3, 5, 9, 26, 45, 62, 116, 117, 125–6, 139 Indo-Pacific, 116–23, 125, 126 Pakistan, 12, 20, 46, 48, 52, 59–62, 64, 98–102, 105, 126–9, 133–4, 155, 156–8 Abbasi’s Beijing visit (2018), 126 agriculture, 99–100 balance of payments crisis, 156–8 Economic Corridor, 52, 59, 60, 62, 105, 108, 156–8 electricity production, 61 fibre-optic connectivity, 101 gateway to the Indian Ocean, 59 Grand Trunk Road, 53 Gwadar port, 46, 59, 61–2, 63, 64, 99–100, 101, 105–7, 117 hydropower, 48 IMF loans, 156–7 India, relations with, 105–7, 108–9, 117, 134 investment, 48 Jadhav arrest (2016), 105–6 Karakoram Highway, 54, 60, 64 Kashmir dispute, 108–9 loans, 46, 54, 156–8 manufacturing, 99 safe city project, 101 Tehreek-e-Insaf, 157–8 television, 101 terrorism, 106, 127–8, 135 textiles, 100 Thar desert, 61 value chains, 98–102 Pakistan-East Africa Cable Express, 101 Pandjaitan, Luhut, 131 Panikkar, Kavalam Madhava, 118 Pantucci, Raffaello, 134 Partnership for Quality Infrastructure, 137–8 patents, 88–9, 190 Pavlodar, Kazakhstan, 103 Peak Pegasus, 93 Pearl River Delta, China, 152 Peking University, 183, 188 Penang, Malaysia, 63 People’s Daily, 57 People’s Liberation Army (PLA), 32, 169 People’s Navigation Company, 69 Pericles, 8 Persian Gulf, 51, 64, 72 Peshawar, Khyber Pakhtunkhwa, 100 petcoke, 103 Petrochina, 103 pharmaceuticals, 113, 114 Phaya Thai Station, Bangkok, 137 Philippines, 19, 70, 117, 122, 156 philosophy, 40, 183 Phnom Penh, Cambodia, 70 phosphate, 19 piracy, 72 Piraeus, Greece, 65 Pirelli, 48 Plato, 150 Poland, 58, 140 Polar Silk Road, 66 Politburo, 39, 40, 165 political correctness, 182 Polo, Marco, 2, 10 Polonnaruwa, Sri Lanka, 156 Pompeo, Michael, 122–3, 157 ports, 9, 10, 12, 19, 36, 40, 46–7, 57, 63–5, 67–9, 96 Chabahar, Iran, 106–7 Doraleh, Djibouti, 63, 67–8 Gwadar, Pakistan, 46, 59, 61–2, 63, 64, 99–100, 101, 117 Hambantota, Sri Lanka, 46–7, 63, 64, 68, 117, 162 Kuala Linggi, Malaysia, 63 Kuantan, Malaysia, 63, 67 Kyaukpyu, Myanmar, 63, 64, 132, 154 Mediterranean, 65 Mombasa, Kenya, 138 Nacala, Mozambique, 138 Penang, Malaysia, 63 Ports-Park-City model, 67 Portugal, 2, 3, 5, 140, 163 power, see energy Prince, Erik, 128–9 property bubbles, 75 protectionism, 102, 114 public procurement, 12, 59 Punjab, Pakistan, 60, 99, 100, 157 Putin, Vladimir, 3, 57 Pyrenees, 189 Qing Empire (1636–1912), 107, 178 Quadrilateral Security Dialogue, 121–2 Qualcomm, 89 Quetta, Balochistan, 128 Raikot, Gilgit-Baltistan, 54 railways, 9–10, 11, 12, 18, 43, 52, 53–4, 57, 68, 83, 86, 89, 98, 100, 135 Addis Ababa–Djibouti, 46, 68 Bangkok–Chiang Mai, 137 Belgrade–Budapest, 143 Djibouti–Yaoundé, 68, 186–7 Islamabad–Gwadar, 60 Kashgar–Andijan, 54 Kuala Lumpur–Singapore, 130 Lahore overhead, 157 Mumbai–Ahmedabad, 138 United States, 122 Yunnan–Southeast Asia, 54 Rawat, Bipin, 108 RB Eden, 92 real estate market, 16, 75 reciprocity, 178–80 Red Sea, 72 reform and opening up, 13–15, 73 Ren Zhengfei, 90 Renaissance, 7 renewable energy, 21, 187, 188 Renmin University, 106 renminbi, 22–3, 159 Rennie, David, 190 Republic (Plato), 150 Research and Analysis Wing (R&AW), 105–6 responsible stakeholder, 169 Rio Tinto, 36 Road Towards Renewal exhibition (2012), 165 Road, see Maritime Silk Road roads, 9, 19, 40, 43, 52, 54, 55, 57, 67, 107–8 robotics, 75, 88, 90 Rogin, Josh, 122 Rolland, Nadège, 188, 190 Ross, Wilbur, 92 Rotterdam, South Holland, 65 Ruan Zongze, 92 rule of law, 28, 109, 111, 183–4 Russia, 5, 51, 52, 55, 133, 134, 139, 174, 175–6, 180, 181 and Central Asia, 57–9, 129, 133 energy, 22, 23 Eurasian Economic Union, 57–9 Eurasianism, 3–4 Joint Statement on Cooperation on EEU and Silk Road Projects (2015), 57–8 Pacific Fleet, 118 and renminbi internationalization, 23 Soviet era, see under Soviet Union steel industry, 82 Ukraine crisis (2013–), 176 Western values, rejection of, 175, 180, 181 Yamal LNG project, 48, 66 Sabang, Indonesia, 131 safe cities, 101, 171 salt, 67, 71 San Francisco, California, 151–2 Saravan, Sistan-Baluchistan, 105 Sargsyan, Tigran, 59 Sassanian Empire (224–651), 4 satellites, 187 second unbundling, 74 Second World War (1939–45), 165 self-driving vehicles, 88, 90, 186, 187, 190 Serbia, 83, 143 Set Aung, U, 132 Shandong University, 163 Shanghai, China, 2, 20, 92 Shanghai Cooperation Organization, 136 Shanghai Fosun Pharmaceutical, 113 Shanghai Pudong Development Bank, 16 Shanghai Stock Exchange, 50, 56, 103 Sharif, Nawaz, 133–4 sharp power, 170 sheet glass, 17, 83 Shenwan Hongyuan Securities, 16 Shenzhen, Guangdong, 28, 151 shipbuilding, 14, 17, 81, 186 Sichuan, China, 149 Siemens, 170 silicon dioxide, 103 Siliguri Corridor, India, 107–8 Silk Road, 2, 9–10, 23–6, 45, 53, 82, 138 Silk Road Economic Belt, 24, 25–6, 28, 39, 51–62, 83 Silk Road Fund, 48, 56, 98 silk, 23–4 Sindh, Pakistan, 59, 60, 99, 100, 101, 105, 106, 127 Singapore, 54, 77, 92, 119, 130, 150, 151, 160 Sino–Myanmar oil and gas pipeline, 64, 72 Sino–US relations, 116, 119, 121–6, 136, 179–80 and Belt and Road, 5–6, 11, 12, 15, 72, 121–4, 130, 136, 168 and Cold War, 14 and foreign exchange reserves, 16 and Indo-Pacific, 116, 119, 121–3, 125, 126 and Kra Isthmus canal proposal, 65 and Malacca Dilemma, 21–2, 64 and maritime hegemony, 70, 72 and Most Favored Nation status, 15 and Pakistan, 157 and reciprocity, 179–80 and reform and opening up, 14–15 and renminbi internationalization, 23 and South China Sea, 70 and steel, 17 Strategic and Economic Dialogue, 39 and Taiwan, 14 and tariffs, 83, 90–94 and technology transfers, 90–92, 178 Thucydides’ trap, 8 and trade deficit, 90, 92 trade war, 92–4, 173 war, potential for, 5, 8, 13, 14 Yangtze River patrols (1854–1937), 165 and ZTE, 94 Sirisena, Maithripala, 155–6 Sistan-Baluchistan, Iran, 105, 106–7 SLJ900/32, 54 Small, Andrew, 59, 158 smart cities, 44, 151–2 Smederevo, Serbia, 83 soft power, 111, 170 solar power, 187, 188 Somalia, 72 Somersault Cloud, 186 sorghum, 92 South Africa, 101 South America, 25, 187, 188 South China Sea, 21, 62, 65, 69–71, 118, 142, 170, 179 South Korea, 1, 77, 96, 97, 128 South Sudan, 186 Southeast Asia, 6, 8, 12, 18, 100, 131–2, 189 Buddhism, 111 China-Indochina Peninsula Economic Corridor, 51, 52, 54, 62 Indo–Chinese relations, 113, 117–18 Kra Isthmus canal proposal, 65, 186 Maritime Silk Road, 26 phosphate market, 19 South China Sea dispute, 21, 69–71, 142, 170, 179 textile industry, 100 Soviet Union, 1, 13, 14, 15, 21–2, 57, 104 soybeans, 90, 93 space travel, 187 Spain, 92, 140, 189 Sparta, 8 Sri Lanka, 12, 46–7, 63, 64, 68, 89, 117, 134, 155–6, 162 Hambantota port, 46–7, 63, 64, 68, 117, 162 Sirisena’s grant announcement (2018), 156 standards, 89–90 State Administration of Foreign Exchange, 48 State Council, 19, 39, 40, 49, 66, 86 state-owned companies, 42, 153, 160–61, 189 steamships, 69 steel industry, 16–17, 18, 20, 67, 82–4, 86, 88 striving for achievement, 18 Stuenkel, Oliver, 167 subprime mortgage crisis (2007–10), 153 Suez Canal, 3, 66, 68, 72, 119 Suifenhe Port, Heilongjiang, 55 Sukkur, Sindh, 99, 101 Sulawesi, Indonesia, 83 Sumatra, Indonesia, 3 Sun Pharmaceuticals, 114 Sun Wenguang, 163 Surkov, Vladislav, 3–4 surveillance, 44, 101, 171, 187, 190 Suvarnabhumi Airport, Bangkok, 137 Swamy, Subramanian, 110 Swaraj, Sushma, 135 Switzerland, 160, 168 Syria, 24 Tadjoura gulf, Djibouti, 67 taikonauts, 187 Taiwan, 14, 142 Tajikistan, 48, 127 Tanjung Pelepas Johor, 150 Tanzania, 138 tao guang yang hui, 15, 18, 32 Taoism, 11, 51 tariffs, 17, 56, 58, 79, 82, 83, 179 Tawang Monastery, Arunachal Pradesh, 111 tax holidays, 61 TBM Slurry, 54 technology transfers, 85–92, 97, 118, 177–8 Tehreek-e-Insaf, 157–8 telecommunications, 43–4, 52, 86, 89–90, 98, 101, 170–71 television, 101 terrorism, 106, 127–9, 135, 171 Texas, United States, 92 textiles, 86, 100–101 Thailand, 18, 54, 65, 83, 89, 129, 132, 136–7, 186 Thakot, Khyber Pakhtunkhwa, 54 Thar desert, 61 Thein Sein, 132 Thilawa special economic zone, Myanmar, 138 throw-money diplomacy, 163 Thucydides’ trap, 8 Tianjin, China, 129 Tianxia, 26–7, 29, 31–5, 78, 79, 192–3 Tibet, 36, 111–12, 117, 136, 189 Tibetan Academy of Buddhism, 112 Tillerson, Rex, 11, 123, 125 timber, 96 Times of India, 109 Tinbergen, Jan, 20 TIR (Transports Internationaux Routiers) Convention, 55 titanium dioxide, 103 Tokyo, Japan, 137 Tolkien, John Ronald Reuel, 1 tourism, 10, 11, 61, 71 trade wars, 92–4, 113–14, 173 trains, 9–10, 11, 12, 18, 43, 46 Trans-Siberian railway, 10 Transatlantic trade, 3, 139 transnational industrial policy, 81, 84 Transpacific trade, 3, 139 transparency, 12, 28, 109, 143, 144, 146, 157, 173, 193 Transpolar Route, 66 transportation, 9–10, 19, 25–6, 48–9, 52–4, 63–4, 81–3, 99, 103, 104, 118, 143, 162, 186 maritime, 63 railways, see railways roads, 9, 19, 40, 43, 52, 54, 55, 57, 67, 107–8 tributary system, 34–5 Trieste, Italy, 65 Trump, Donald, 83, 91, 93, 122, 124, 167, 179 Tsinghua University, 76, 163 Tsingshan Group Holdings, 83 Tumshuq, Xinjiang, 60 Turkey, 4, 24, 65, 82 Turkmenistan, 186 Twitter, 188 ‘two heads abroad’ (liangtou zai haiwai), 17 Ukraine, 11, 82, 176 United Arab Emirates, 62, 68, 160 United Kingdom, 2, 3, 17, 43, 65, 107, 112, 160, 165, 189, 193 United Nations, 29, 55, 72, 142, 172 United States, 1–2, 5–7, 8, 11, 12, 121–6, 161, 166–9, 176, 185–6 Apollo program, 9 Bush administration (2001–9), 169 Camp Lemonnier Djibouti, 68 China, relations with, see Sino–US relations Clinton administration (1992–2001), 177 Cold War, 14 immigration, 187 India, relations with, 119, 121–2, 134, 135 industrial output per person, 193 and International Monetary Fund (IMF), 157 Marshall Plan, 40 midterm elections (2018), 12–13 National Defense Strategy (2018), 116 National Security Strategy (2017), 179–80 Pacific Command, 125–6 Quadrilateral Security Dialogue, 121–2 Senate Armed Services Committee, 124 State Department, 123–4 steel industry, 17 subprime mortgage crisis (2007–10), 153 Taiwan, relations with, 14 Trump administration (2017–), 83, 90–94, 122–4, 167, 179 universal values, 175, 181, 184 Urdu, 128 Urumqi, Xinjiang, 20, 101, 188 Uyghurs, 20 Uzbekistan, 53, 54, 129 value chains, 3, 43, 64, 73–4, 79–82, 84–5, 94–104, 141 vanadium pentoxide, 103 Venice, Veneto, 65 Vietnam, 19, 54, 70, 100, 117, 132 Vision and Actions document (2015), 40, 41, 45, 49, 50, 52, 67, 78 Vision for Maritime Cooperation (2017), 62 Vladivostok, Primorsky Krai, 118 Wakhan corridor, Afghanistan, 128 Wallerstein, Immanuel, 78 Wang Changyu, 112 Wang Huning, 40 Wang Jisi, 31, 76 Wang Yang, 39 Wang Yi, 40, 60, 123 Wang Yingyao, 50–51 Wang Yiwei, 26 Wang Zhaoxing, 50 Warsaw, Poland, 140 Washington Post, 122 Wei Fenghe, 126 Weibo, 188 Weissmann, Mikael, 42 Wenzhou, Zhejiang, 83 West Asia corridor, 51, 52 West Germany (1949–90), 22, 166 Western world, 5, 30, 31, 165–86, 190–93 Asia-Pacific region, 13 Cold War, 1, 2 cultural imperialism, 28 democracy, 125, 133, 166, 171, 172, 174, 176, 181–3 end of history, 36 global financial crisis (2008), 16–17, 161 individualism, 27, 189 liberal world order, 141, 144, 167–86, 190, 192 Machiavellianism, 31–4 market economies, 16 Marxism, 78 polis, 31 rule of law, 183 rules-based order, 11, 35, 179 separation of powers, 182 soft power, 111 standards, 89 technology, 15, 87, 177–8 telecommunications, 101 and Tianxia, 30–34, 78, 192 value chains, 95, 96, 100, 104 values, 123, 125, 133, 167, 175, 177–8 white elephants, 51 Wickremesinghe, Ranil, 47 win-win, 27–8, 33, 37 wind power, 188 Witness to an Era (Moraes), 113 Working Conference on Neighborhood Policy (2013), 17–18 World Bank, 15, 172 World Economic Forum, 168 World Trade Organization, 170, 177 world-systems theory, 78 Wright, Thomas, 174 Xi Jinping, 11, 183 Astana speech (2013), 23, 25–6, 39 Belt and Road Forum for International Cooperation (2017), 152 Boao Forum for Asia speech (2015), 27, 32 and Constitution, 164 Davos speech (2017), 168 Duterte, relationship with, 156 Jakarta speech (2013), 23, 26, 39 Joint Statement on Cooperation on EEU and Silk Road Projects (2015), 57 London visit (2015), 43 Mahathir’s letter (2018), 130–31 Modi, summit with (2018), 135 National Congress, 19th (2017), 29, 181 National Cybersecurity Work Conference (2018), 84 presidential term limits repeal (2018), 164, 174 Road Towards Renewal exhibition (2012), 165 Sirisena, grant to (2018), 156 and state-owned companies, 42, 153 Sun Wenguang’s letter (2018), 163 telecommunications, 43 Trump’s visit (2017), 124 and value chains, 94 Wang Huning, relationship with, 40 and Western democracy, 166, 181 Working Conference on Neighborhood Policy (2013), 17–18 Xi’an, Shaanxi, 24, 28, 147, 188 Xinhua, 24, 41, 64 Xinjiang, 20, 54, 55, 56, 59, 60, 100–101, 128–9, 188, 189 Xiong Guangkai, 32 Xu Jin, 33 Xu Zhangrun, 163–4 Yamal LNG project, 48, 66 Yang Jian, 132 Yang Jiechi, 39, 171 Yang Jing, 40 Yangtze River, 165 Yao Yunzhu, 169–70 Ye Peijian, 187 yuan, see renminbi Yunnan, China, 54, 129, 149, 188 Zeng Jinghan, 181 zero-sum, 27 Zhang Gaoli, 39 Zhang Qian, 25 Zhang Weiwei, 182–3, 184 Zhao Tingyang, 27 Zheng He, 162–3 Zhi Zhenfeng, 84 Zimbabwe, 12, 44 Zoellick, Robert, 169 ZTE, 94, 170–71 Zurich, Switzerland, 160 First published in the United Kingdom in 2018 by C.

Internet for the People: The Fight for Our Digital Future
by Ben Tarnoff
Published 13 Jun 2022

Over the course of the 2010s, the internet went mobile. In 2011, only 35 percent of Americans had a smartphone; by 2019, that number was 81 percent. At the same time, more objects acquired a network connection, from cars to thermostats to security cameras to industrial equipment. According to Cisco, this “Internet of Things” will account for fully half of all networked devices globally by 2023. In the 1980s, the internet went from being a protocol to a place. In the 1990s and 2000s, that place grew massively. In the 2010s, it became a different kind of place altogether. It cut its tether, losing its anchorage in a fixed point.

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“Smartness” always belongs in quotes because it rarely works exactly as promised; for one, it’s impossible to determine someone’s gender by scanning their face. Still, technologies don’t have to work exactly as promised for the companies producing them to profit. And there is no doubt that the smartphone and the Internet of Things have opened new frontiers for profit-making. The major online malls have been central participants in, and beneficiaries of, the diffusion of the internet. Smartphones’ geolocation data lets Google and Facebook promise more precise forms of targeting to advertisers. The Echo “smart speaker” lets Amazon learn more about its customers by placing listening devices in their living rooms.

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Online malls emerged to mediate a range of digital interactions while making data about them, and this data was monetized in a variety of ways. The rise of the cloud and the evolution of “big data” techniques like machine learning contributed valuable tools for organizing and analyzing data, while the diffusion of the internet through the smartphone and the Internet of Things opened new sites for manufacturing it. This was the triumphant phase, in which the internet was remade for the purpose of profit maximization and became the basis for some extraordinarily successful companies. These companies were so successful, in fact, that investors became willing to plow unprecedented sums of money into startups in the hopes of finding the next one.

The Human Age: The World Shaped by Us
by Diane Ackerman
Published 9 Sep 2014

Toiling invisibly in the background, the council of computers can organize massive subway repairs, or send you a personal cell phone alert if your bus is running late. It’s a little odd thinking of computers taking meetings on the fly and gabbing together in an alien argot. But naming it the Internet of Things domesticates an idea that might otherwise frighten us. We know and enjoy the Internet, already older than many of its users, and familiar now as a pet. In an age where even orangutans Skype on iPads, what could be more humdrum than the all-purpose, nondescript word “things”? The Internet of Things reassures us that this isn’t a revolutionary idea—though, in truth, it is—just an everyday technology linked to something vague and harmless sounding.

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In some high-tech enclaves, smart locks are now opened by virtual keys on iPhones, and family members wear a computer tracking chip that stores their preferences. As they move through each room, lights turn on ahead of them and fade away behind, a thermostat adjusts itself, the song or TV show or movie they were enjoying greets them, favorite food and drink are proffered. The house’s nervous system is what’s known as the “Internet of Things.” In 1999, the technology pioneer Kevin Ashton coined the term for a cognitive web that unites a mob of physical and virtual digital devices—furnace, lights, water, computers, garage door, oven, etc.—with the physical world, much as cells in the body communicate to coordinate actions. As they cabal among themselves, synchronizing their energy use and activities, they can also share data with the neighborhood, city, and wired world.

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As they cabal among themselves, synchronizing their energy use and activities, they can also share data with the neighborhood, city, and wired world. Combining animal, vegetable, mineral, and machine, his idea is playing out in the avant-garde new city of Songdo, South Korea, where the Internet of Things is nearly ubiquitous. Smart homes, shops, and office buildings stream data continuously to a cadre of computers that sense, scrutinize, and make decisions, monitoring and piloting the whole synchronous city, mainly without human help. They’re able to analyze picayune details and make sure all the infrastructure hums smoothly, changing traffic flow during rush hour as needed, watering parks and market gardens, or promptly removing garbage (which is sucked down through subterranean warrens to a processing center where it’s sorted, deodorized, and recycled).

Python Data Analytics: With Pandas, NumPy, and Matplotlib
by Fabio Nelli
Published 27 Sep 2018

Data Availability : Open Data Source, Internet of Things, and Big Data Another very important factor affecting the development of deep learning is the huge amount of data that can be accessed. In fact, the data are the fundamental ingredient for the functioning of neural networks, both for the learning phase and for their verification phase. Thanks to the spread of the Internet all over the world, now everyone can access and produce data. While a few years ago only a few organizations were providing data for analysis, today, thanks to the IoT (Internet of Things ), many sensors and devices acquire data and make them available on networks.

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“Science leads us forward in knowledge, but only analysis makes us more aware” This book is dedicated to all those who are constantly looking for awareness Table of Contents Chapter 1:​ An Introduction to Data Analysis Data Analysis Knowledge Domains of the Data Analyst Computer Science Mathematics and Statistics Machine Learning and Artificial Intelligence Professional Fields of Application Understanding the Nature of the Data When the Data Become Information When the Information Becomes Knowledge Types of Data The Data Analysis Process Problem Definition Data Extraction Data Preparation Data Exploration/​Visualization Predictive Modeling Model Validation Deployment Quantitative and Qualitative Data Analysis Open Data Python and Data Analysis Conclusions Chapter 2:​ Introduction to the Python World Python—The Programming Language Python—The Interpreter Python 2 and Python 3 Installing Python Python Distributions Using Python Writing Python Code IPython PyPI—The Python Package Index The IDEs for Python SciPy NumPy Pandas matplotlib Conclusions Chapter 3:​ The NumPy Library NumPy:​ A Little History The NumPy Installation Ndarray:​ The Heart of the Library Create an Array Types of Data The dtype Option Intrinsic Creation of an Array Basic Operations Arithmetic Operators The Matrix Product Increment and Decrement Operators Universal Functions (ufunc) Aggregate Functions Indexing, Slicing, and Iterating Indexing Slicing Iterating an Array Conditions and Boolean Arrays Shape Manipulation Array Manipulation Joining Arrays Splitting Arrays General Concepts Copies or Views of Objects Vectorization Broadcasting Structured Arrays Reading and Writing Array Data on Files Loading and Saving Data in Binary Files Reading Files with Tabular Data Conclusions Chapter 4:​ The pandas Library—An Introduction pandas:​ The Python Data Analysis Library Installation of pandas Installation from Anaconda Installation from PyPI Installation on Linux Installation from Source A Module Repository for Windows Testing Your pandas Installation Getting Started with pandas Introduction to pandas Data Structures The Series The DataFrame The Index Objects Other Functionalities on Indexes Reindexing Dropping Arithmetic and Data Alignment Operations Between Data Structures Flexible Arithmetic Methods Operations Between DataFrame and Series Function Application and Mapping Functions by Element Functions by Row or Column Statistics Functions Sorting and Ranking Correlation and Covariance “Not a Number” Data Assigning a NaN Value Filtering Out NaN Values Filling in NaN Occurrences Hierarchical Indexing and Leveling Reordering and Sorting Levels Summary Statistic by Level Conclusions Chapter 5:​ pandas:​ Reading and Writing Data I/​O API Tools CSV and Textual Files Reading Data in CSV or Text Files Using RegExp to Parse TXT Files Reading TXT Files Into Parts Writing Data in CSV Reading and Writing HTML Files Writing Data in HTML Reading Data from an HTML File Reading Data from XML Reading and Writing Data on Microsoft Excel Files JSON Data The Format HDF5 Pickle—Python Object Serialization Serialize a Python Object with cPickle Pickling with pandas Interacting with Databases Loading and Writing Data with SQLite3 Loading and Writing Data with PostgreSQL Reading and Writing Data with a NoSQL Database:​ MongoDB Conclusions Chapter 6:​ pandas in Depth:​ Data Manipulation Data Preparation Merging Concatenating Combining Pivoting Removing Data Transformation Removing Duplicates Mapping Discretization and Binning Detecting and Filtering Outliers Permutation Random Sampling String Manipulation Built-in Methods for String Manipulation Regular Expressions Data Aggregation GroupBy A Practical Example Hierarchical Grouping Group Iteration Chain of Transformations Functions on Groups Advanced Data Aggregation Conclusions Chapter 7:​ Data Visualization with matplotlib The matplotlib Library Installation The IPython and IPython QtConsole The matplotlib Architecture Backend Layer Artist Layer Scripting Layer (pyplot) pylab and pyplot pyplot A Simple Interactive Chart The Plotting Window Set the Properties of the Plot matplotlib and NumPy Using the kwargs Working with Multiple Figures and Axes Adding Elements to the Chart Adding Text Adding a Grid Adding a Legend Saving Your Charts Saving the Code Converting Your Session to an HTML File Saving Your Chart Directly as an Image Handling Date Values Chart Typology Line Charts Line Charts with pandas Histograms Bar Charts Horizontal Bar Charts Multiserial Bar Charts Multiseries Bar Charts with pandas Dataframe Multiseries Stacked Bar Charts Stacked Bar Charts with a pandas Dataframe Other Bar Chart Representations Pie Charts Pie Charts with a pandas Dataframe Advanced Charts Contour Plots Polar Charts The mplot3d Toolkit 3D Surfaces Scatter Plots in 3D Bar Charts in 3D Multi-Panel Plots Display Subplots Within Other Subplots Grids of Subplots Conclusions Chapter 8:​ Machine Learning with scikit-learn The scikit-learn Library Machine Learning Supervised and Unsupervised Learning Training Set and Testing Set Supervised Learning with scikit-learn The Iris Flower Dataset The PCA Decomposition K-Nearest Neighbors Classifier Diabetes Dataset Linear Regression:​ The Least Square Regression Support Vector Machines (SVMs) Support Vector Classification (SVC) Nonlinear SVC Plotting Different SVM Classifiers Using the Iris Dataset Support Vector Regression (SVR) Conclusions Chapter 9: Deep Learning with TensorFlow Artificial Intelligence, Machine Learning, and Deep Learning Artificial intelligence Machine Learning Is a Branch of Artificial Intelligence Deep Learning Is a Branch of Machine Learning The Relationship Between Artificial Intelligence, Machine Learning, and Deep Learning Deep Learning Neural Networks and GPUs Data Availability:​ Open Data Source, Internet of Things, and Big Data Python Deep Learning Python Frameworks Artificial Neural Networks How Artificial Neural Networks Are Structured Single Layer Perceptron (SLP) Multi Layer Perceptron (MLP) Correspondence Between Artificial and Biological Neural Networks TensorFlow TensorFlow:​ Google’s Framework TensorFlow:​ Data Flow Graph Start Programming with TensorFlow Installing TensorFlow Programming with the IPython QtConsole The Model and Sessions in TensorFlow Tensors Operation on Tensors Single Layer Perceptron with TensorFlow Before Starting Data To Be Analyzed The SLP Model Definition Learning Phase Test Phase and Accuracy Calculation Multi Layer Perceptron (with One Hidden Layer) with TensorFlow The MLP Model Definition Learning Phase Test Phase and Accuracy Calculation Multi Layer Perceptron (with Two Hidden Layers) with TensorFlow Test Phase and Accuracy Calculation Evaluation of Experimental Data Conclusions Chapter 10:​ An Example— Meteorological Data A Hypothesis to Be Tested:​ The Influence of the Proximity of the Sea The System in the Study:​ The Adriatic Sea and the Po Valley Finding the Data Source Data Analysis on Jupyter Notebook Analysis of Processed Meteorological Data The RoseWind Calculating the Mean Distribution of the Wind Speed Conclusions Chapter 11:​ Embedding the JavaScript D3 Library in the IPython Notebook The Open Data Source for Demographics The JavaScript D3 Library Drawing a Clustered Bar Chart The Choropleth Maps The Choropleth Map of the U.​S.​

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Index A Accents, LaTeX Advanced Data aggregation apply() functions transform() function Anaconda Anderson Iris Dataset, see Iris flower dataset Array manipulation joining arrays column_stack() and row_stack() hstack() function vstack() function splitting arrays hsplit() function split() function vsplit() function Artificial intelligence schematization of Artificial neural networks biological networks edges hidden layer input and output layer multi layer perceptron nodes schematization of SLP ( see Single layer perceptron (SLP)) weight B Bar chart 3D error bars horizontal matplotlib multiserial multiseries stacked bar pandas DataFrame representations stacked bar charts x-axis xticks() function Bayesian methods Big Data Bigrams Biological neural networks Blending operation C Caffe2 Chart typology Choropleth maps D3 library geographical representations HTML() function jinja2 JSON and TSV JSON TopoJSON require.config() results US population data source census.gov file TSV, codes HTML() function jinja2.Template pop2014_by_county dataframe population.csv render() function SUMLEV values Classification and regression trees Classification models Climatic data Clustered bar chart IPython Notebook jinja2 render() function Clustering models Collocations Computer vision Concatenation arrays combining concat() function dataframe keys option pivoting hierarchical indexing long to wide format stack() function unstack() function removing Correlation Covariance Cross-validation Cython D Data aggregation apply() functions GroupBy groupby() function operations output of SPLIT-APPLY-COMBINE hierarchical grouping merge() numeric and string values price1 column transform() function Data analysis charts data visualization definition deployment phase information knowledge knowledge domains computer science disciplines fields of application machine learning and artificial intelligence mathematics and statistics problems of open data predictive model process data sources deployment exploration/visualization extraction model validation planning phase predictive modeling preparation problem definition stages purpose of Python and quantitative and qualitative types categorical data numerical data DataFrame pandas definition nested dict operations structure transposition structure Data manipulation aggregation ( see Data aggregation) concatenation discretization and binning group iteration permutation phases of preparation ( see Data preparation) string ( see String manipulation) transformation Data preparation DataFrame merging operation pandas.concat() pandas.DataFrame.combine_first() pandas.merge() procedures of Data structures, operations DataFrame and series flexible arithmetic methods Data transformation drop_duplicates() function mapping adding values axes dict objects replacing values remove duplicates Data visualization adding text axis labels informative label mathematical expression modified of text() function bar chart ( see Bar chart) chart typology contour plot/map data analysis 3D surfaces grid grids, subplots handling date values histogram installation IPython and IPython QtConsole kwargs figures and axes horizontal subplots linewidth plot() function vertical subplots legend chart of legend() function multiseries chart upper-right corner line chart ( see Line chart) matplotlib architecture and NumPy matplotlib library ( see matplotlib library) mplot3d multi-panel plots grids, subplots subplots pie charts axis() function modified chart pandas Dataframe pie() function shadow kwarg plotting window buttons of commands matplotlib and NumPy plt.plot() function properties QtConsole polar chart pyplot module saving, charts HTML file image file source code scatter plot, 3D Decision trees Deep learning artificial ( see Artificial neural networks) artificial intelligence data availability machine learning neural networks and GPUs Python frameworks programming language schematization of TensorFlow ( see TensorFlow) Digits dataset definition digits.images array digit.targets array handwritten digits handwritten number images matplotlib library scikit-learn library Discretization and binning any() function categorical type cut() function describe() function detecting and filtering outliers qcut() std() function value_counts() function Django Dropping E Eclipse (pyDev) Element-wise computation Expression-oriented programming F Financial data Flexible arithmetic methods Fonts, LaTeX G Gradient theory Graphics Processing Unit (GPU) Grouping Group iteration chain of transformations functions on groups mark() function quantiles() function GroupBy object H Handwriting recognition digits dataset handwritten digits, matplotlib library learning and predicting OCR software scikit-learn svc estimator TensorFlow validation set, six digits Health data Hierarchical indexing arrays DataFrame reordering and sorting levels stack() function statistic levels structure two-dimensional structure I IDEs, see Interactive development environments (IDEs) Image analysis concept of convolutions definition edge detection blackandwhite.jpg image black and white system filters function gradients.jpg image gray gradients Laplacian and Sobel filters results source code face detection gradient theory OpenCV ( see Open Source Computer Vision (OpenCV)) operations representation of Indexing functionalities arithmetic and data alignment dropping reindexing Integration Interactive development environments (IDEs) Eclipse (pyDev) Komodo Liclipse NinjaIDE Spyder Sublime Interactive programming language Interfaced programming language Internet of Things (IoT) Interpreted programming language Interpreter characterization Cython Jython PVM PyPy tokenization IPython and IPython QtConsole Jupyter project logo Notebook DataFrames QtConsole shell tools of Iris flower dataset Anderson Iris Dataset IPython QtConsole Iris setosa features length and width, petal matplotlib library PCA decomposition target attribute types of analysis variables J JavaScript D3 Library bar chart CSS definitions data-driven documents HTML importing library IPython Notebooks Jinja2 library pandas dataframe render() function require.config() method web chart creation Jinja2 library Jython K K-nearest neighbors classification decision boundaries 2D scatterplot, sepals predict() function random.permutation() training and testing set L LaTeX accents fonts fractions, binomials, and stacked numbers with IPython Notebook in Markdown Cell in Python 2 Cell with matplotlib radicals subscripts and superscripts symbols arrow symbols big symbols binary operation and relation symbols Delimiters Hebrew lowercase Greek miscellaneous symbols standard function names uppercase Greek Learning phase Liclipse Linear regression Line chart annotate() arrowprops kwarg Cartesian axes color codes data points different series gca() function Greek characters LaTeX expression line and color styles mathematical expressions mathematical function pandas plot() function set_position() function xticks() and yticks() functions Linux distribution LOD cloud diagram Logistic regression M Machine learning (ML) algorithm development process deep learning diabetes dataset features/attributes Iris flower dataset learning problem linear/least square regression coef_ attribute fit() function linear correlation parameters physiological factors and progression of diabetes single physiological factor schematization of supervised learning SVM ( see Support vector machines (SVMs)) training and testing set unsupervised learning Mapping adding values inplace option rename() function renaming, axes replacing values Mathematical expressions with LaTeX, see LaTeX MATLAB matplotlib matplotlib library architecture artist layer backend layer functions and tools layers pylab and pyplot scripting layer (pyplot) artist layer graphical representation hierarchical structure primitive and composite graphical representation LaTeX NumPy Matrix product Merging operation DataFrame dataframe objects index join() function JOIN operation left_index/right_index options left join, right join and outer join left_on and right_on merge() function Meteorological data Adriatic Sea and Po Valley cities Comacchio image of mountainous areas reference standards TheTimeNow website climate data source JSON file Weather Map site IPython Notebook chart representation CSV files DataFrames humidity function linear regression matplotlib library Milan read_csv() function result shape() function SVR method temperature Jupyter Notebook access internal data command line dataframe extraction procedures Ferrara JSON file json.load() function parameters prepare() function RoseWind ( see RoseWind) wind speed Microsoft excel files dataframe data.xls internal module xlrd read_excel() function MongoDB Multi Layer Perceptron (MLP) artificial networks evaluation of experimental data hidden layers IPython session learning phase model definition test phase and accuracy calculation Musical data N Natural Language Toolkit (NLTK) bigrams and collocations common_contexts() function concordance() function corpora downloader tool fileids() function HTML pages, text len() function library macbeth variable Python library request() function selecting words sentimental analysis sents() function similar() function text, network word frequency macbeth variable most_common() function nltk.download() function nltk.FreqDist() function stopwords string() function word search Ndarray array() function data, types dtype (data-type) intrinsic creation type() function NOSE MODULE “Not a Number” data filling, NaN occurrences filtering out NaN values NaN value NumPy library array manipulation ( see Array manipulation) basic operations aggregate functions arithmetic operators increment and decrement operators matrix product ufunc broadcasting compatibility complex cases operator/function BSD conditions and Boolean arrays copies/views of objects data analysis indexing bidimensional array monodimensional ndarray negative index value installation iterating an array ndarray ( see Ndarray) Numarray python language reading and writing array data shape manipulation slicing structured arrays vectorization O Object-oriented programming language OCR, see Optical Character Recognition (OCR) software Open data Open data sources climatic data demographics IPython Notebook matplotlib pandas dataframes pop2014_by_state dataframe pop2014 dataframe United States Census Bureau financial data health data miscellaneous and public data sets musical data political and government data publications, newspapers, and books social data sports data Open Source Computer Vision (OpenCV) deep learning image processing and analysis add() function blackish image blending destroyWindow() method elementary operations imread() method imshow() method load and display merge() method NumPy matrices saving option waitKey() method working process installation MATLAB packages start programming Open-source programming language Optical Character Recognition (OCR) software order() function P Pandas dataframes Pandas data structures DataFrame assigning values deleting column element selection filtering membership value nested dict transposition evaluating values index objects duplicate labels methods NaN values NumPy arrays and existing series operations operations and mathematical functions series assigning values declaration dictionaries filtering values index internal elements, selection operations Pandas library correlation and covariance data structures ( see Pandas data structures) function application and mapping element row/column statistics getting started hierarchical indexing and leveling indexes ( see Indexing functionalities) installation Anaconda development phases Linux module repository, Windows PyPI source testing “Not a Number” data python data analysis sorting and ranking Permutation new_order array np.random.randint() function numpy.random.permutation() function random sampling DataFrame take() function Pickle—python object serialization cPickle frame.pkl pandas library stream of bytes Political and government data pop2014_by_county dataframe pop2014_by_state dataframe pop2014 dataframe Portable programming language PostgreSQL Principal component analysis (PCA) Public data sets PVM, see Python virtual machine (PVM) pyplot module interactive chart Line2D object plotting window show() function PyPy interpreter Python data analysis library deep learning frameworks module OpenCV Python Package Index (PyPI) Python’s world code implementation distributions Anaconda Enthought Canopy Python(x,y) IDEs ( see Interactive development environments (IDEs)) installation interact interpreter ( see Interpreter) IPython ( see IPython) programming language PyPI Python 2 Python 3 running, entire program code SciPy libraries matplotlib NumPy pandas shell source code data structure dictionaries and lists functional programming Hello World index libraries and functions map() function mathematical operations print() function writing python code, indentation Python virtual machine (PVM) PyTorch Q Qualitative analysis Quantitative analysis R R Radial Basis Function (RBF) Radicals, LaTeX Ranking Reading and writing array binary files tabular data Reading and writing data CSV and textual files header option index_col option myCSV_01.csv myCSV_03.csv names option read_csv() function read_table() function .txt extension databases create_engine() function dataframe pandas.io.sql module pgAdmin III PostgreSQL read_sql() function read_sql_query() function read_sql_table() function sqlalchemy sqlite3 DataFrame objects functionalities HDF5 library data structures HDFStore hierarchical data format mydata.h5 HTML files data structures read_html () web_frames web pages web scraping I/O API Tools JSON data books.json frame.json json_normalize() function JSONViewer normalization read_json() and to_json() read_json() function Microsoft excel files NoSQL database insert() function MongoDB pickle—python object serialization RegExp metacharacters read_table() skiprows TXT files nrows and skiprows options portion by portion writing ( see Writing data) XML ( see XML) Regression models Reindexing RoseWind DataFrame hist array polar chart scatter plot representation showRoseWind() function S Scikit-learn library data analysis k-nearest neighbors classification PCA Python module sklearn.svm.SVC supervised learning svm module SciPy libraries matplotlib NumPy pandas Sentimental analysis document_features() function documents list() function movie_reviews negative/positive opinion opinion mining Shape manipulation reshape() function shape attribute transpose() function Single layer perceptron (SLP) accuracy activation function architecture cost optimization data analysis evaluation phase learning phase model definition explicitly implicitly learning phase placeholders tf.add() function tf.nn.softmax() function modules representation testing set test phase and accuracy calculation training sets Social data sort_index() function Sports data SQLite3 stack() function String manipulation built-in methods count() function error message index() and find() join() function replace() function split() function strip() function regular expressions findall() function match() function re.compile() function regex re.split() function split() function Structured arrays dtype option structs/records Subjective interpretations Subscripts and superscripts, LaTeX Supervised learning machine learning scikit-learn Support vector classification (SVC) decision area effect, decision boundary nonlinear number of points, C parameter predict() function regularization support_vectors array training set, decision space Support vector machines (SVMs) decisional space decision boundary Iris Dataset decision boundaries linear decision boundaries polynomial decision boundaries polynomial kernel RBF kernel training set SVC ( see Support vector classification (SVC)) SVR ( see Support vector regression (SVR)) Support vector regression (SVR) curves diabetes dataset linear predictive model test set, data swaplevel() function T TensorFlow data flow graph Google’s framework installation IPython QtConsole MLP ( see Multi Layer Perceptron (MLP)) model and sessions SLP ( see Single layer perceptron (SLP)) tensors operation parameters print() function representations of tf.convert_to_tensor() function tf.ones() method tf.random_normal() function tf.random_uniform() function tf.zeros() method Text analysis techniques definition NLTK ( see Natural Language Toolkit (NLTK)) techniques Theano trigrams() function U, V United States Census Bureau Universal functions (ufunc) Unsupervised learning W Web Scraping Wind speed polar chart representation RoseWind_Speed() function ShowRoseWind() function ShowRoseWind_Speed() function to_csv () function Writing data HTML files myFrame.html to_html() function na_rep option to_csv() function X, Y, Z XML books.xml getchildren() getroot() function lxml.etree tree structure lxml library objectify parse() function tag attribute text attribute

Reset
by Ronald J. Deibert
Published 14 Aug 2020

Social media do not stand alone. They are embedded in a vast technological ecosystem. In order to participate in social media, you need some kind of networked device: a smartphone, tablet, laptop, or PC. (The number of networked devices is expanding quickly with 5G networks and the so-called Internet of Things, and now includes internet-enabled fridges, home security systems, dishwashers, and automobiles.) Those devices send electronic signals through radio waves or cables that are transmitted through a physical infrastructure of routers, servers, cell towers, and data farms, in some cases spread throughout multiple countries.

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After a year of back and forth with the parent company, Alibaba, most of the problems were fixed. But the case was an interesting illustration of how poor software engineering combined with overzealous data harvesting can lead to significant risks for a sizable population of unwitting users — and yet another fishing hole for signals intelligence gathering agencies. The “Internet of Things” towards which surveillance capitalism is now directed will turn the average home into a showroom for these split-personality higher/lower-level functionalities. Your dishwasher cleans the dishes, but it may also monitor your morning routine. Your fridge keeps food cool, but also tracks what you eat.

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Just look around your own house and count the number of electronic appliances you plug in. How many of them are always on? Some estimates say Americans waste up to $19 billion annually in electricity costs through “vampire appliances,” digital devices that draw power even when they are turned off.343 Those numbers are set to rise substantially as the Internet of Things rolls out worldwide: fridges, microwaves, home security systems, baby monitors, and more, all pulsating with continuous data streams, networked through our handheld devices. And that is just the beginning. Over half of the world’s population is not yet connected to the internet, but that is changing fast.

Robot Rules: Regulating Artificial Intelligence
by Jacob Turner
Published 29 Oct 2018

The power could be remote controlled, allowing a user to determine how darkened the toast should be, http://​www.​livinginternet.​com/​i/​ia_​myths_​toast.​htm, accessed 1 June 2018. 84David Schatsky, Navya Kumar, and Sourabh Bumb, “Intelligent IoT: Bringing the Power of AI to the Internet of Things”, Deloitte, 12 December 2017, https://​www2.​deloitte.​com/​insights/​us/​en/​focus/​signals-for-strategists/​intelligent-iot-internet-of-things-artificial-intelligence.​html, accessed 1 June 2018. 85Aatif Sulleyman, “Durham Police to Use AI to Predict Future Crimes of Suspects, Despite Racial Bias Concerns”, Independent, 12 May 2017, http://​www.​independent.​co.​uk/​life-style/​gadgets-and-tech/​news/​durham-police-ai-predict-crimes-artificial-intelligence-future-suspects-racial-bias-minority-report-a7732641.​html, accessed 1 June 2018.

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In the light of this uncertainty, the European Commission (one of the three law-making bodies within the EU ’s governing institutions) promulgated an Evaluation Project of the Products Liability Directive, which was completed in July 2017. The Evaluation’s aims included “[to]…assess if the Directive is fit-for-purpose vis-à-vis the new technological developments such as the Internet of Things and autonomous systems”.59 It investigated matters including “whether apps and non-embedded software or the Internet of things based products are considered as ‘products’ for the purpose of the Directive”; and “whether an unintended, autonomous behaviour of an advanced robot could be considered a ‘defect’ according to the Directive”. Respondents included consumers, producers, public authorities, law firms, academics and professional associations.60 The results were published in May 2017.61 In response to the question “According to your experience, are there products for which the application of the Directive on liability of defective products is or might become uncertain and/or problematic?”

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The complex algorithms behind search engines improve themselves based on our searches and reaction to the results. Every time we use a search engine, that search engine is using us.82 Virtual Personal Assistants including Apple’s Siri, the Google Assistant, Amazon’s Alexa and Microsoft’s Cortana are now commonplace. This trend is connected to the growth of the “Internet of Things”, where household devices are connected to the Internet.83 Whether it is a fridge which learns when you need eggs and orders them for you, or a hoover which can tell which parts of your floor need the most cleaning, AI is coming to fulfil the roles once played by domestic servants.84 The uses of AI as an aid to or even as a replacement for human judgement and decision-making can go from the immaterial—selection of which song to play next—to the highly consequential.

A World of Three Zeros: The New Economics of Zero Poverty, Zero Unemployment, and Zero Carbon Emissions
by Muhammad Yunus
Published 25 Sep 2017

He was making a good salary and steadily earning increased responsibility and power when one day the company CEO called him into his office to explain the next project he wanted Vanizette to undertake. He told Vanizette he would be spending the next several months working for a client, figuring out how to connect refrigerators to an electronic communications network—part of the growing digital phenomenon known as the Internet of Things. Vanizette found that he was troubled. He knew that this would be an interesting and challenging job from a technological standpoint. But he wondered about the practical social benefit it would generate. The more he thought about it, the less meaningful it seemed. “There has to be a better way for me to use my abilities than teaching refrigerators how to talk to one another,” he decided.

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See International Monetary Fund Impact Hub, 161, 162 Impact Water, 113–114 Industrial Revolution, 98 inequality addressing old ways of, 18 capitalism and, 6–10 inadequate use of, 49 political polarization and, 9 social unrest and, 9 sustainability and, 126 unemployment and, 68 wealth concentration and, 4 information and communication technology (ICT), 174 elections and, 203 Grameen Bank and, 182 Grameen Phone and, 175 health care and, 196 MakeSense and, 187 poverty and, 193 power of, 181–189 wealthy people and, 176 infrastructure, 128 economic growth and, 214 good governance and, 210 government and, 247 investments in, 210 kickbacks from, 211 public-private partnerships and, 211 social business and, 213 transformation in, 225 See also human infrastructure An Inquiry into the Nature and Causes of the Wealth of Nations (Smith), 259 insurance, 192, 193 See also microinsurance International Food Waste Coalition, 139 International Labour Organization (ILO), 68 International Monetary Fund (IMF), 24, 121 Internet of Things, 156 investments banks and, 263 Danone Communities and, 251–252 in infrastructure, 210 for privet sector, 211 social business and, 246 social business funds and, 248 Izumo, Mitsuru, 52 Jackley, Jessica, 183 Jao, Jezze, 159 Jean-Baptiste, Stéphane, 108 job market, 70, 150 Jorgensen, Vidar, 85 Jung, Andrea, 86 Kendzior, Sarah, 146 Kenya, 192 Kilimo Salama, 192 Kiva, 183, 184, 185 Kreyol Essence, 108 Krishna, Suresh, 253 labor, 94 legal systems, 229 financial systems and, 231–236 help from, 236–242 lending, 23–24 livestock, 45 living standards, 8 loans, 88 London School of Economics and Political Science (LSE), 56 low-income people, 4 See also poor people LSE.

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George Valley Organic Farm, 135 startups, 30, 161 Sundarbans, 99 sustainability climate change and, 125 consumption and, 128 deforestation and, 126 development with, 125 fishing and, 126 human society and, 127 meaning of, 125 sustainable development, 124–132 Sustainable Development Goals (SDGs) breakthrough of, 124 development of, 124 goals of, 127–129 new civilization and, 178 new economy and, 132–141 NGOs and, 131 targets of, 129 United Nations and, 131 See also SDG Advocates Syngenta Foundation for Sustainable Agriculture, 191 Tachibana, Taro, 73 tax laws, 240, 241 technology, 148 ACRE and, 192 agriculture and, 190 Dalio, M., and, 179 Doctor in a Box and, 197 elections and, 202 entrepreneurs and, 40 environmental problems and, 96 globalization and, 176 good governance and, 212 Grameen Phone and, 175 human creativity and, 266 Internet of Things and, 156 marketplace gap of, 177 Mrittikā and, 194 new civilization and, 173 poor people and, 177, 190–198 privet sector and, 212 renewable energy sources and, 99 social change and, 174, 190 social direction for, 178 voting and, 203 See also automation; data usage terrestrial ecosystem, 129 The Theory of Moral Sentiments (Smith), 260–261, 262 TI.

Applied Artificial Intelligence: A Handbook for Business Leaders
by Mariya Yao , Adelyn Zhou and Marlene Jia
Published 1 Jun 2018

These attacks may even target and hijack supposedly secure AI systems by exploiting their vulnerabilities. One grim possibility is the deployment of autonomous weapons systems, such as a drone, using facial recognition technology to identify and attack individuals in a crowd. Through wearables, standard computing devices, and the burgeoning Internet of Things (IoT), AI will inevitably permeate every corner of our existence. This means that our physical security, digital security, and even political security will be at risk of attack. While we spend much of our productive hours tethered to digital devices and roaming cyberspace, we still inhabit physical bodies and live in a material world.

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In this chapter, we highlight many of the organizational and political issues that routinely block technical innovation and give you strategies for overcoming them. Be Honest About Your Readiness Despite many public claims to innovation, many corporations are still playing catch up on existing technologies such as big data, mobile, and the Internet of Things (IoT). Many brands have built up their social media presence and now offer mobile-friendly apps and websites, but these are merely digital consumer endpoints, not the basis for an enterprise-wide technological transformation. Other companies have accumulated big piles of data, but aren’t actively transforming their information assets into improved business practices.

The Digital Doctor: Hope, Hype, and Harm at the Dawn of Medicine’s Computer Age
by Robert Wachter
Published 7 Apr 2015

Interviews of Peter Pronovost and Mark Romig by the author, July 22, 2014, and S. Rice, “Ambitious Checklist App Comes as Hospitals Struggle with Basic Checklists,” Modern Healthcare, June 21, 2014. 262 There is no doubt, however, that the “Internet of Things” See S. Ferber, “How the Internet of Things Changes Everything,” HBR Blog Network, May 7, 2013, available at http://blogs.hbr.org/2013/05/how-the-internet-of-things-cha/. 265 Instead, most will be of outcomes Harvard’s Michael Porter has been promoting this argument, such as here: M. E. Porter, “What Is Value in Health Care?” New England Journal of Medicine 363:2477–2481 (2010).

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They will ultimately “know” what behavioral prompts work for each patient (or, in that Amazonlike way, “for patients like you”). I’ll leave it to your imagination to decide whether, in the case of the recalcitrant patient, the computer system will ultimately be granted the authority to lock down the salt shaker or the refrigerator. There is no doubt, however, that the “Internet of Things” will give the system the wherewithal to do so, as well as the capacity to offer rewards, of a sort, for good works. For the patient with an acute medical issue, the capacity for home care will be greatly enhanced through new devices and telemedicine. The mom with a child who has an earache will, in fact, be able to look in the child’s ear and beam the image to a nursepractitioner or a physician, who will diagnose it and prescribe a treatment (at some point, a computer will be able to make simple diagnoses based on visual images).

The Metaverse: And How It Will Revolutionize Everything
by Matthew Ball
Published 18 Jul 2022

Arguing that immersive VR is a requirement for the Metaverse is similar to arguing that the mobile internet can only be accessed via apps, thereby excluding mobile browsers. In truth, we don’t even need a screen to access mobile data networks and mobile content, as is often the case with vehicular tracking devices, select headphones, and countless machine-to-machine and internet of things (IoT) devices and sensors. (The Metaverse won’t require screens either, by the way—more on this in Chapter 9.) Real-Time Rendered Rendering is the process of generating a 2D or 3D object or environment using a computer program. The goal of this program is to “solve” an equation made up of many different inputs, data, and rules that determine what should be rendered (that is, visualized) and when, and by using various computing resources, such as a graphics processing unit (or GPU) and central processing unit (CPU).

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Hyper-detailed projection cameras will also play a part, enabling virtual objects, worlds, and avatars to be transplanted into the real world and in realistic detail. Key to these projections are various sensors that enable the cameras to scan and understand the non-flat, non-perpendicular landscapes they will project against, and alter their projection accordingly so that it appears undistorted to the viewer. Technologists have long imagined an internet-of-things future where sensors and wireless chips are as ubiquitous as electrical outlets, albeit more diverse, thereby enabling us to light up any number of experiences wherever we go. Imagine a construction site with drones overhead, each filled with cameras, sensors, and wireless chips, and with workers below them wearing AR headsets or glasses.

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All of the negotiation and contracting between parties is handled within seconds by RNDR’s protocol, neither side knows the identity or specifics of the task being performed, and all transactions occur using RNDR cryptocurrency tokens. Another example is Helium, which the New York Times has described as “a decentralized wireless network for ‘internet of things’ devices, powered by cryptocurrency.”5 Helium works through the use of $500 hot-spot devices which allow their owner to securely rebroadcast their home internet connection—and up to 200 times faster than a traditional home Wi-Fi device. This internet service can be used by anyone, from consumers (say, to check Facebook) to infrastructure (e.g., a parking meter processing a credit card transaction).

The People's Platform: Taking Back Power and Culture in the Digital Age
by Astra Taylor
Published 4 Mar 2014

In 2013, Google showed further signs of weakening its resolve on the issue when it began to offer fiber broadband with advantageous terms of service that many observers found violate the spirit of Net neutrality.40 Given the steady shift to mobile computing, including smart phones and tablets, and the emerging Internet-of-things (the fact that more and more objects, from buildings to cars to clothing, will be networked in coming years), the FCC’s 2010 ruling was alarmingly insufficient even when it was made. Nevertheless, telecommunications companies went on offense, with Verizon successfully challenging the FCC’s authority to regulate Internet access in federal appeals court in early 2014.

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Google, for example, is already able to build a “three-dimensional profile” of each of us: first, “the knowledge person”—who we are based on search queries and click-stream data; second, “the social person”—who we are based on whom we communicate to and connect with through e-mail and other social tools; and third, “the embodied person”—namely, our whereabouts as revealed by the physical position of our computer or mobile device. With the Internet-of-things on the horizon, opportunities for data collection will increase as more everyday objects go online. Soon our ovens and automobiles may deliver personalized sales pitches. In theory, Pariser argues, algorithms could be fairer than fallible humans, introducing us to wider range of material than we may otherwise seek out, expanding our exposure to diversity by being less conscious of race, gender, and class or things like political orientation.

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Craig Aaron, “Google Reserves the Right to Be Evil,” Huffington Post, July 31, 2013, http://www.huffingtonpost.com/craig-aaron/google-reserves-the-right_b_3685306.html. 41. Marvin Ammori, “The Next Big Battle in Internet Policy,” Slate.com, October 2, 2012, http://www.slate.com/articles/technology/future_tense/2012/10/network_neutrality_the_fcc_and_the_internet_of_things_html. 2: FOR LOVE OR MONEY 1. Quotes from an interview with the author except for this one, which is from Justin Cox, “Documenting a Bin Laden Ex-Confidante: Q&A with Filmmaker Laura Poitras,” TheHill.com, July 13, 2010, http://thehill.com/capital-living/cover-stories/108553-documenting-a-bin-laden-ex-confidante-qaa-with-filmmaker-laura-poitras#ixzz2YfhpMdXu. 2.

Big Business: A Love Letter to an American Anti-Hero
by Tyler Cowen
Published 8 Apr 2019

There is no particular reason to think Google will dominate those new dimensions, and in fact Google’s success may stop it from seeing the new paradigms when they come along. I don’t pretend I am the one who can name those new dimensions of competition, but what about search through virtual or augmented reality? Search through the Internet of Things? Search through the offline “real world” in some manner? Search through an assemblage of AI capabilities, or perhaps in some longer-run brain implants or genetic information? I genuinely don’t know. What I do know is that new dimensions of product quality arise all the time, and supposed natural monopolies find out their monopolies are not so natural after all.

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It helps that many states have made such non-consensual recordings against the law; still, anonymously recorded conversations uploaded and tweeted anonymously can ruin careers, and I expect we will see more of this. We should toughen the relevant laws here and make them consistent across the states—and, in general, be careful what we say. Politeness was underrated to begin with, and all the more so in this new world to come. Even your home spaces may not be fully secure as we move to an Internet of Things in the daily household. It will be wonderful to talk to your garage door, stereo, and television and have them respond immediately to your commands. Of course, that means they are listening all the time too—to your family arguments, to how you scold your kids, to how you talk about your colleagues at work, and to what you do in bed and how loud your orgasm is (or isn’t).

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Immelt, Jeffrey immigration inclusion income corporate distribution guaranteed annual income income taxes income inequality per capita wealth and work and See also taxes India Influence (Cialdini) information technology infrastructure initial public offerings (IPOs) Instagram intellectual property (IP) laws See also copyright Internal Revenue Service (IRS) See also taxes International Monetary Fund Internet of Things Iraq War James, LeBron Johnson, Earvin “Magic” Jonze, Spike Jordan, Michael JPMorgan Chase Juicero Kahneman, Daniel Kashkari, Neel Keynes, John Maynard Kling, Arnold Knack, Stephen Koenig, Thomas Konczal, Mike Kravis, Henry R. Krueger, Alan Landier, Augustin Lee, Esther LeFevre, Judith LinkedIn List, John A.

The Wires of War: Technology and the Global Struggle for Power
by Jacob Helberg
Published 11 Oct 2021

Tom Wheeler, a former chairman of the Federal Communications Commission, believes that 5G’s speed “will change the very nature of the internet.”119 One Silicon Valley venture capitalist speculated to me that 5G could “massively open up” a new frontier of virtual reality and augmented reality, allowing us to superimpose data onto the surface of futuristic lenses. Additionally, 5G could turbocharge the rise of the long-heralded Internet of Things, in which virtually all our devices will be connected. All told, 5G could inject $12 trillion into the global economy by 2035 and add 22 million jobs just in the United States.120 With good reason, 5G has been hailed as “the central nervous system of the 21st-century economy.”121 Whoever presides over that central nervous system could have unprecedented control over virtually everything in our lives—who reads our emails and texts, how our homes operate, where our autonomous vehicles are going.

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Meanwhile, the Trump administration withdrew official U.S. participation from 3GPP—and companies concerned about running afoul of U.S. sanctions on Huawei by attending such standards-setting discussions have likewise removed themselves127—effectively ceding the forums to Beijing.128 Huawei’s 5G dominance is not just an economic challenge. It poses a direct threat to American—and global—security. And the problem isn’t simply the tremendous data collection capabilities 5G networks would offer foreign intelligence services. The more items are connected to the Internet of Things via 5G, the more those items could potentially be weaponized against us. What if China directed a fleet of self-driving cars to mow down pedestrians? What if your Internet-enabled pacemaker stopped working? What if your thermostat was cranked up to 120 degrees in the heat of summer? What if China were able to pinpoint the exact geographic cellular location of Indian soldiers along its disputed border?

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(Indeed, Russian hackers apparently penetrated voting systems in several states in 2016, although there is no evidence that they altered any votes.)86 Any one of these moves could upend an election. And because this manipulation would take place on the back-end—through an undersea cable or with an implant stealthily inserted into a voting machine—it might prove far harder to identify than any fake news on the front-end. All that’s before we even get to the Internet of Things. In 2020, there were an estimated 30 billion Internet-connected devices, from Roombas to medical hardware.87 SoftBank predicts that by 2025 there might be as many as 1 trillion such devices—about 100 for every person on the planet.88 These devices will control some of the most sensitive and vital aspects of our daily lives.

The Business Blockchain: Promise, Practice, and Application of the Next Internet Technology
by William Mougayar
Published 25 Apr 2016

Even in the Ethereum implementation, smart contracts run as quasi-Turing complete programs. This means there is finality in their execution, and they do not risk looping infinitely. 9. Smart contract have a wide range of applications. Like HTML, the applications are limited by whoever writes them. Smart contracts are ideal for interacting with real-world assets, smart property, Internet of Things (IoT), and financial services instruments. They are not limited to money movements. They apply to almost anything that changes its state over time, and could have a value attached to it. Developers with smart contracts expertise will be in demand. Learning smart contracts allows one to get into blockchains, without the burden of getting directly under the hood of blockchains.

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The trend for decentralized computing architectures requires a new mindset for planning and writing applications that is different than the traditional Web architectures. Finally, each time you download a software client that sits on your personal computer or smartphone and it “listens” to the network, you are potentially opening security risks, unless it is well implemented. We also need to be aware that Internet of Things devices also are subject to potential security breaches, because potential vulnerabilities are being pushed from the centers to the edges, wherever there is some computing resources at the edge. Luckily, some solutions are in the works, such as private blockchains, zero-knowledge proofs and ring signatures, but we will not enter this technical territory within the scope of this book.

Slowdown: The End of the Great Acceleration―and Why It’s Good for the Planet, the Economy, and Our Lives
by Danny Dorling and Kirsten McClure
Published 18 May 2020

Currently there is enormous duplication. By 2020, it is estimated that 1.7 megabytes of data will be created every second for every person on Earth. In 2018, according to Forbes magazine, there were “2.5 quintillion bytes of data created each day at our current pace, but that pace is only accelerating with the growth of the Internet of Things (IoT).”3 A quintillion is 1018, and a byte is a very small piece of information that can take up to 256 forms, rather like a letter in a word. Each letter you read here is stored as a byte on the computer I am typing this on. Given all the estimates floating around, we may believe we are collectively writing a very long story equivalent to 8 billion letters in length for everyone living on the planet every month of the year.

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Today even our washing machines can talk to one another. But why would they? Will data be transmitted between washing machines using 5G technology—or will it always go from one washing machine to some central control hub? If our washing machines are connected to one another in the brave new Internet of Things—then can we expect them to revolt?!19 The washing machine itself was a great leap forward. Getting washing machines to talk to one another is not. But of course there are still leaps to be made. The total quantity of data being amassed by space telescopes may still be accelerating, but is ever-greater resolution quite the same as producing the first lens that allowed detail on the moon to be viewed, or hearing the first buzz from the first radio telescope?

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Unsurprisingly, there is more than you might ever want to read about Moore’s law ever so easily available: see Wikipedia, accessed 2 September 2019, https://en.wikipedia.org/wiki/Moore%27s_law. 18. Wgsimon, “Microprocessor Transistor Counts 1971–2011 & Moore’s Law,” Wikimedia Commons, 13 May 2011, https://commons.wikimedia.org/wiki/File:Transistor_Count_and_Moore%27s_Law_-_2011.svg. 19. The Internet of Things is a term that is itself going through a rapid slowdown in use and usefulness. It may mean nothing to you, depending on when you read this book. So much that we hyped as new and amazing at the start of the twenty-first century was, in hindsight, simply hype. CHAPTER 5. Climate Epigraph: Jacob Jarvis, “Greta Thunberg Speech: Activist Tells Extinction Rebellion London Protesters ‘We Will Make People in Power Act on Climate Change,’” London Evening Standard, 21 April 2019, https://www.standard.co.uk/news/london/greta-thunberg-tells-extinction-rebellion-protesters-we-will-make-people-in-power-act-on-climate-a4122926.html. 1.

No Ordinary Disruption: The Four Global Forces Breaking All the Trends
by Richard Dobbs and James Manyika
Published 12 May 2015

The manufacturing process will “democratize” as consumers and entrepreneurs start to print their own products. 4.IT and how we use it. We may think of the mobile Internet as a familiar technology, but with over one billion people already using smart phones or tablets, it is dramatically changing the way we perceive and interact with the world around us. Consider the rapid growth of the Internet of Things—embedded sensors and actuators in machines and other physical objects that are being adopted for data collection, remote monitoring, decision making, and process optimization in everything from manufacturing to infrastructure to health care. Sensors in limekilns can tell operators how to optimize temperature settings; in consumer goods, they can inform manufacturers about how products are being used; and in bridges, they can warn city administrators about maintenance needs.

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More than 90 percent of eBay commercial sellers export to other countries, compared with an average of less than 25 percent of traditional small businesses.28 12 The Disruptive Dozen Twelve technologies have massive potential for disruption in the coming decade CHANGING THE BUILDING BLOCKS OF EVERYTHING 1.Next-generation genomics Fast, low-cost gene sequencing, advanced big data analytics, and synthetic biology (“writing” DNA) 2.Advanced materials Materials designed to have superior characteristics (e.g., strength, weight, conductivity) or functionality RETHINKING ENERGY COMES OF AGE 3.Energy storage Devices or systems that store energy for later use, including batteries 4.Advanced oil and gas exploration and recovery Exploration and recovery techniques that make extraction of unconventional oil and gas economical 5.Renewable energy Generation of electricity from renewable sources with reduced harmful climate impact MACHINES WORKING FOR US 6.Advanced robotics Increasingly capable robots with enhanced senses, dexterity, and intelligence used to automate tasks or augment humans 7.Autonomous and near-autonomous vehicles Vehicles that can navigate and operate with reduced or no human intervention 8.3-D printing Additive manufacturing techniques to create objects by printing layers of material based on digital models IT AND HOW WE USE IT 9.Mobile Internet Increasingly inexpensive and capable mobile computing devices and Internet connectivity 10.Internet of things Networks of low-cost sensors and actuators for data collection, monitoring, decision making, and process optimization 11.Cloud technology Use of computer hardware and software resources delivered over a network or the Internet, often as a service 12.Automation of knowledge work Intelligent software systems that can perform knowledge work tasks involving unstructured commands and subtle judgments The data avalanche is set to become more powerful only because of a movement toward “open data,” in which data are freely shared beyond their originating organizations—including governments and businesses—in a machine-readable format at low cost.

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foreign direct investments from, 76 goods and services customization for, 103, 104–105 new competitor entry of, 170 new consuming class of, 96, 97–98 operational complexity management in, 29 public-private partnerships in, 198–199 renewable energy plan of, 126 smart city technology in, 41 space exploration by, 2–3 urbanization speed and scale in, 20 (fig.), 131–132 Indian cities Bangalore, 29, 105 Mumbai, 29, 131–132 Pune, 41 Surat, 25 Industrial Revolution, 16, 18, 33, 202 Infrastructure data and productivity gains, 196–197 deficit, 131–132, 133, 135–136 energy storage and, 36 financing partnerships, 29–30 policy and new business opportunity, 197–199 private partnerships for, 27–28, 29–30, 198–199 public spending, 188–189, 191–193 repurposed services for, 28 smart city technology and, 28, 41, 198 urban innovation in, 23, 28 See also Transportation Inner Mongolia, China, 135 Innovation acceleration of, 33–34, 35 (fig.), 42 ripple effect of, 202 time and betting on, 50–51 urbanization and, 23, 25–28 Instacart, 25 Insurance industry, 46 (fig.), 50, 173 Interconnection. See Globalization Interest rates decrease, 133, 134 (fig.), 138–140 increase, 141, 144–145 Internet. See Mobile Internet Internet of Things, 38, 40 (fig.) Intuition reset, 8–12, 204–207 See also Adaptation; Trend break Investing agility in, 145–146 aging consumer services in, 69 capital cost decrease and, 10, 138–140 capital cost increase and, 135–138 capital productivity optimization and, 140–142 cosmopolitan capitalism, 144 crowd-sourced funding, 47, 85, 143–144 digital capital, 50–51, 52 foreign direct, 76, 80 global rate of, 132, 135, 137 (fig.)

Peers Inc: How People and Platforms Are Inventing the Collaborative Economy and Reinventing Capitalism
by Robin Chase
Published 14 May 2015

A $69 million financing in November 2013, including $30 million from GE, allowed Quirky to spin off Wink, a wholly owned subsidiary.27 Wink provides a technology ecosystem (a platform) that makes it simple to bring together connected-home devices with smartphones, giving GE a way to participate in both the Internet of Things and crowd-sourced innovation. (Chapter 8 will delve into the ways in which large mainstream companies are adapting to the new organizational paradigm.) The first GE + Quirky–branded product was the Aros air conditioner, which lets you change the room temperature setting from a distance when you are away, and which automatically instructs your Aros to begin cooling the room to a predetermined temperature when your smartphone is within a certain proximity of home.

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People who are watching their weight, improving their sleep and exercise regimens, and following doctor’s orders will contribute their personal data via sophisticated and user-friendly devices, as is already happening today. Some of that will be anonymized and become part of large population databases that will transform public health and the delivery of health care. Smart health, smart cities, and big data (together becoming the Internet of Things) are in fact all grounded in Peers Inc: repurposed data that is collected by all kinds of peers and organized and analyzed by various Incs, empowering individuals and cities to make better decisions, learn faster, evolve more quickly, and have a different relationship with our environment. We can see the transition happening today.

…

I’ve loved mesh networking for a very long time now, championing it not just for its potential for emergency services but also for its promise to provide very low-cost wireless connectivity to people around the globe. Mesh networks are a terrific solution for wireless in congested or remote places, helping connect up the Internet of Things. They also give us the possibility of individual real-time billing for road and electricity use, key to creating the right incentives to stop dirty energy consumption. A mesh works like this: Instead of your phone call being routed through a satellite or a cell tower and then back to your friend across town (or just across the street), it could go directly, perhaps hopping once over a friendly neighbor’s phone or server to get to its destination.

AI Superpowers: China, Silicon Valley, and the New World Order
by Kai-Fu Lee
Published 14 Sep 2018

They pioneered online-to-offline services that stitched the internet deep into the fabric of the Chinese economy. They turned Chinese cities into the first cashless environments since the days of the barter economy. And they revolutionized urban transportation with intelligent bike-sharing applications that created the world’s largest internet-of-things network. Adding fuel to this fire was an unprecedented wave of government support for innovation. Guo’s mission to build the Avenue of the Entrepreneurs was just the first trickle of what in 2014 turned into a tidal wave of official policies pushing technology entrepreneurship. Under the banner of “Mass Innovation and Mass Entrepreneurship,” Chinese mayors flooded their cities with new innovation zones, incubators, and government-backed venture-capital funds, many of them modeled on Guo’s work with the Avenue of the Entrepreneurs.

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That is four times the number of global rides Uber was giving each day in 2016, the last time it announced its totals. In the spring of 2018, Mobike was acquired by Wang Xing’s Meituan Dianping for $2.7 billion, just three years after the bike-sharing company’s founding. Something new was emerging from all those rides: perhaps the world’s largest and most useful internet-of-things (IoT) networks. The IoT refers to collections of real-world, internet-connected devices that can convey data from the world around them to other devices in the network. Most Mobikes are equipped with solar-powered GPS, accelerators, Bluetooth, and near-field communications capabilities that can be activated by a smartphone.

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See global economic inequality; wealth and class inequality information and communication technologies (ICT), 150–51, 152, 154 insurance industry, 10, 110 Intel, 96 intelligent superhighways, 133 international research, 11–13 internet, monopolization of, 170 internet AI, 105–6, 107–10, 136 Internet Explorer, 41 internet-of-things (IoT) networks, 54, 78, 79 iPhone, 32, 57 iPhone X, 117 iron rice bowl, 67 Italy, 85, 191–92 J Japan, 20, 229 Jesuits, 29 JingChi, 135 Jinri Toutiao. See Toutiao (news platform) job displacement by automation, 160, 162, 204. See also under economy and AI Jobs, Steve, 26, 32, 33, 226 jobs, threat to.

The Pirate's Dilemma: How Youth Culture Is Reinventing Capitalism
by Matt Mason

“For the better part of a century,” writes Howard Rheingold in Smart Mobs, “people have lived among invisible electric motors and thought nothing of it. The time has come to consider the consequences of computers disappearing into the background the way motors did.” The consequences are what some people are referring to as “the Internet of things.” This is a world where objects are connected via tiny but widely distributed computers, such as radio-frequency identification chips (RFID), which cost less than 5 cents each and are already being used in products by Wal-Mart, Target, and Tesco to track goods. They are being used in passports, money, car keys, credit and travel cards, and are being embedded in livestock and even people.

…

But think how useful a real-world version of this would be at 3:00 a.m. on a Saturday morning, virtually embedded in your beer goggles? The idea of graffiti is central to ubiquitous computing. “Tags” are already used in search engines: you get the results you want by searching for tags or keywords. Tags are attached to sites to give information visibility and make it accessible, and they will be even more important in the Internet of things. When people begin virtually annotating real space, the nature of privacy, the public domain, and the role of graffiti suddenly changes. Who will and who will not have the right to tag the virtual environment? How will advertising work when we can block out billboards from our vision, like human TiVos?

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Ji Lee, interview by author, June 29, 2006 (other quotes from Lee that appear throughout this chapter are taken from the same interview). Ji Lee, Talk Back: The Bubble Project (New York: Mark Batty Publisher, 2006). Howard Rheingold, Smart Mobs (New York: Basic Books, 2006), p. 88. Elizabeth Biddlecombe, “UN predicts ‘Internet of things’,” BBC News, November 17, 2005. http://news.bbc.co.uk/2/hi/technology/4440334.stm. Elliott Malkin, “Cemetery 2.0,” We Make Money Not Art, November 17, 2006. http://www.we-make-money-not-art.com/archives/009130.php. CHAPTER 5 BOUNDARIES: Disco Nuns, the Death of the Record Industry, and Our Open-Source Future Sister Alicia Donohoe, interview by author, January 10, 2007.

Net Zero: How We Stop Causing Climate Change
by Dieter Helm
Published 2 Sep 2020

It is government failure and poor regulation of private utilities. The centrality of infrastructure networks Imagine what a net zero economy would look like. There would be lots of decentralised renewables generation, possibly some nuclear power stations (both large and small), smart meters, smart devices, interconnected homes and the internet-of-things, autonomous electric cars and perhaps hydrogen-powered vehicles and electric trains. Travel, especially by air, would be much reduced, and holidays would be much more local, as would quite a lot of food production. There would probably be more remote working, including from home using video links, as many people had to do during the Covid-19 lockdowns.

…

Digitalisation is not confined to robots, data and AI, but also includes the things that these will enable. 3D printing ushers in a much greater degree of customisation, as products are printed from digital images and instructions. The food chain itself has already been digitalised, but there is more to come here too, with blockchain and the internet-of-things allowing precision identification and tracking of each item and payment. Agricultural activities are increasingly being integrated with food processing and logistics supply chains. Food itself is being broken down by genetics. Where once there were cows and sheep, and then specific sorts of cows and sheep, it is increasingly possible to regard each as bunches of genes.

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acid rain 25, 194 Africa xiv, xv, 2, 25, 30, 38, 44, 45, 47, 48, 51, 137, 229 agriculture 2, 6, 12, 13, 14, 23, 35–6, 43, 44–5, 70, 76, 86, 87–8, 95, 100, 102, 109, 116, 146–7, 149, 159, 163–80, 181, 183, 192, 197, 198, 206, 220 baseline, the 164–8 biodiversity loss and 2, 5, 100, 164, 165, 168, 169, 171, 172, 174, 180 biofuels and 197–8 carbon emissions and 2, 12, 13, 35–6, 76–7, 146–7, 163–80 carbon price and 167–70, 171, 172, 173, 180 China and 28–9, 35, 45, 180 economics of 76, 165, 166–7, 171, 174 electricity and 13, 166, 168, 174, 178, 180 fertiliser use see fertiliser lobby 14, 110, 164, 165, 169, 170, 197 methane emissions 23, 84, 177, 178, 179 net gain and 172–4 net value of UK 76, 166 new technologies/indoor farming 87–8, 174–9, 180, 213 peat bogs and 2, 179 pesticide use see pesticides petrochemicals and 166 polluter-pays principle and 76, 168–70, 172, 173 pollution 36, 86, 163, 165–6, 168–70, 172, 173, 177–8, 230 public goods, agricultural 170–4, 180 sequestering carbon and 12, 95, 163, 166, 168, 169, 170, 171, 172, 173–4, 177, 179, 180 soils and 2, 146, 163, 164, 165, 166, 168, 169, 171, 172, 175, 179 subsidies 14, 76, 102, 109, 116, 164, 165, 166, 167, 169, 170, 172, 180, 228 25 Year Plan and 179–80 Agriculture Bill (2018), UK 170 air conditioning 135–6, 224, 233 air quality xiii, 13, 25, 46, 52, 61, 70, 135, 153, 177, 180, 201, 216, 230, 232 air transport 3–4, 6, 11, 13, 22, 50, 53, 73, 87, 88, 92, 107, 125, 128, 129, 132, 133, 134, 149, 156–7, 186, 195, 201, 203–5 aluminium 7, 117 Amazon rainforest 2, 34, 35, 95, 145, 149–50, 151, 155, 229, 230 ammonia 35, 137, 191 anaerobic digesters 35, 165, 230 animal welfare 167, 177 antibiotics 93, 165, 174 Arctic 26, 46, 114, 178 artificial intelligence (AI) 32, 175, 220, 231 autonomous vehicles 13, 129, 132, 175, 189–90, 231 Balkans 137–8 Bank of England 121 batteries 6, 31, 131, 135, 141, 183, 184, 185–90, 191, 199, 204, 213, 214, 219, 220, 221, 225, 231 beef 5, 95, 116, 117, 167, 230 Berlin, Isaiah 104 big 5 polluter products 117–18, 120 bin Salman, Mohammad 27 biocrops 36 biodiversity xiv, 2, 5, 12, 13, 28, 35, 51, 76, 94, 100, 148, 149, 152, 153, 158, 159, 164, 165, 168, 169–70, 171, 172, 174, 180, 227, 233 bioenergy 31, 34–5, 36 biofuels 21, 35, 49, 50, 67, 70, 95, 135, 183, 184, 197–8, 210, 230 biomass 32, 34, 49, 50, 67, 69, 109, 146, 147, 151, 210, 217 bonds, government 220 BP 27, 149, 187, 199 Deepwater Horizon disaster, Gulf of Mexico (2010) 147 Brazil 2, 35, 38, 44–5, 47, 95, 145, 149–50, 155, 198 Brexit 42, 47, 56, 117, 165 British Gas 102, 139 British Steel x, 194 broadband networks 6, 11, 90, 92, 125, 126, 127–8, 130–1, 132–3, 135, 140–1, 199, 201, 202, 205, 211, 214, 231, 232 Brundtland Commission 45 BT 127–8, 141 Openreach 214 Burn Out (Helm) ix, xiv Bush, George W. 36, 48, 53, 103 business rates 76, 165 Canada 52, 191, 193 capitalist model 26, 42, 99, 227 carbon border tax/carbon border adjustment xii, 11, 13, 60, 80, 115–20, 194–6, 204 carbon capture and storage (CCS) xiv, 12, 75–6, 95, 109, 146, 147–8, 149, 154, 159, 203–4, 207, 209, 222, 223 Carbon Crunch, The (Helm) ix, xiv, 221 carbon diary 4–5, 8, 10, 11, 64–6, 83, 86, 116, 143, 144, 155, 156, 167, 180, 181, 185, 203, 205 carbon emissions: agriculture and see agriculture by country (2015) 30 during ice ages and warm periods for the past 800,000 years 21 economy and 81–159 electricity and see electricity global annual mean concentration of CO2 (ppm) 19 global average long-term concentration of CO2 (ppm) 20 measuring 43–6 since 1990 1–14, 17–37 transport and see individual method of transport 2020, position in 36–7 UN treaties and 38–57 unilateralism and 58–80 see also unilateralism carbon offsetting xiii–xiv, 4, 5, 12, 34, 45, 72, 74, 79, 94–6, 97, 105, 143–59, 192, 201, 203, 207, 214, 222, 223, 234 for companies 148–50 for countries 151–5 for individuals 155–7 markets 71–2, 110–13, 117, 144, 157–9, 208 travel and 156, 201–3 see also sequestration carbon permits 71–2, 79, 110–13, 117, 144, 208 carbon price/tax xii, xiii, xv, 8, 11, 12, 13, 26, 60, 61, 71, 72, 77, 79, 80, 84, 85–6, 102–3, 105, 106–24, 134, 143, 146, 147, 150, 151–4, 157, 159, 192, 197, 198, 199, 203, 227–30, 232, 234 agriculture and 167, 168, 169–70, 171, 173, 180 domain of the tax/carbon border adjustment xii, 11, 13, 60, 80, 115–20, 121, 124, 192, 194–6, 197, 204, 227 electric pollution and 216–18 ethics of 107–10 floor price 115, 117, 208 for imports 11, 13 prices or quantities/EU ETS versus carbon taxes 110–13 setting 113–15 transport and 192–9 what to do with the money 121–4 where to levy the tax 119–20 who fixes the price 120–1 carbon sinks 2, 5, 166, 169, 203 carboniferous age 34 cars 1, 3, 4, 7, 20, 22, 36, 44, 70, 73, 114, 129, 181, 182, 183, 184–5, 190, 191, 193, 196, 197, 198, 199 see also electric vehicles cartels 39, 40, 43, 45, 46, 47, 56 cattle farming 35, 36, 95, 150, 166, 167, 173, 177, 198 Central Electricity Generating Board (CEGB) 102, 139, 218 cement 6, 7, 26, 29, 34, 87, 117, 171 charging networks, electric vehicle 91, 129–30, 141–2, 184, 185–90, 199, 200, 202, 219 Chernobyl 78 China xi, xv, 1–2, 5, 8, 18, 42, 46, 47, 48, 64, 66, 74, 101, 180, 229 Belt and Road Initiative 28, 45 coal use 1–2, 8, 23–4, 24, 28, 31, 38, 117, 154, 206, 208 Communist Party 2, 27, 42, 46 demand for fossil fuels/carbon emissions 1–2, 8, 18, 20, 22, 23–4, 24, 25, 27–31, 36, 38, 51, 73, 117, 154, 206, 208 export market x–xi, 5, 9, 64, 66, 117, 155, 194 fertiliser use 35 GDP xv, 27, 29 nationalism and 42 petrochemical demand 22 renewables companies 9, 32, 73, 74, 77, 79 Tiananmen Square 42 unilateralism and 58, 59 UN treaties and 46, 47, 48, 53, 54, 55, 58, 59 US trade war 56, 118 Churchill, Winston 183 citizen assemblies 99–101 climate change: carbon emissions and see carbon emissions 1.5° target 38, 57 2° target 1, 10, 22–3, 28, 30, 38, 39, 45, 47, 54, 55, 57, 108, 122, 155, 206 see also individual area of climate change Climate Change Act (2008) 66, 74–7 Clinton, Bill 40, 48 Club of Rome 98 coal 1–2, 5, 8, 13, 20, 23–5, 28, 29, 30, 31, 32, 34, 36, 38, 50, 52, 53, 60–1, 67, 72, 77, 78–9, 101, 109, 112, 116, 117, 119, 134, 136, 145, 147, 148, 151, 154, 155, 182, 183, 194, 196, 206–9, 210, 212, 214, 216, 217, 218, 229, 230 coastal marshes 146, 159 colonialism 45 Committee on Climate Change (CCC), UK x–xi, 7, 74–5, 120, 164, 166, 169, 217, 235 ‘Net Zero: The UK’s Contribution to Stopping Global Warming’ report x–xi conference/video calls 6, 129, 156, 202, 205 Conference of the Parties (COP) xii, 10, 48, 50, 53–4, 55, 59, 205 congestion charges 198 Copenhagen Accord 48, 53–4, 59 Coronavirus see Covid-19 cost-benefit analysis (CBA) 71, 108, 110, 114, 138 cost of living 116 Covid-19 x, xi–xii, 1, 3, 6, 9, 18, 19, 22, 25, 27, 30, 37, 44, 46, 50, 57, 65, 69, 80, 89, 93, 129, 135, 148, 171, 201, 202, 204, 232 CRISPR 176 crop yields 172, 177 dams 2, 36, 52–3, 179 DDT (Dichlorodiphenyltrichloroethane) 100 deforestation 2, 5, 34, 35, 36, 38, 43, 44, 47, 55, 87, 95, 145, 146, 149–50, 155, 172–3, 179, 197–8, 229 Defra (Department for Environment, Food and Rural Affairs) 170 deindustrialisation x, 29, 46, 52, 54, 59, 72–4, 218 Deng Xiaoping 27 Denmark 69–70, 136–7 desalination 135–6, 179 diesel 4, 20–1, 70, 76, 86, 109, 119, 121, 129, 132, 164, 165, 166, 174, 175, 178, 179, 181, 182, 185, 186, 191, 192, 196–7, 208, 217, 230 ‘dieselgate’ scandal 196–7 digitalisation 1, 8, 11, 13, 33, 92, 117, 136, 174, 175, 180, 206, 211, 215, 221, 228–9, 231 DONG 69 Drax 147, 151, 154, 218 economy, net zero 10–12, 81–159 delivering a 96–103 intergenerational equity and 96–7 markets and 103–5 net environmental gain see net environmental gain political ideologies and 98–101 polluter-pays principle see polluter-pays principle public goods, provision of see public goods, provision of technological change and 98 EDF 139, 218 Ehrlich, Paul 98 electricity 1–2, 4, 6, 11, 12, 13, 23, 31, 32, 49, 53, 61, 65, 66, 68, 70, 73, 77, 78, 79, 91, 92, 101, 102, 109, 117, 125, 127, 128, 129–30, 131–2, 134, 135, 136, 137, 139, 140, 141, 149, 158, 166, 168, 174, 178, 180, 182, 183, 228, 229, 231, 232, 234, 235 coal, getting out of 206–7 electric pollution and the carbon price 216–18 electric vehicles 4, 6, 13, 20, 23, 49, 61, 91, 92, 94, 121, 125, 128, 129–30, 131–2, 134, 141, 183–92, 193, 194, 197, 200, 201, 202, 206, 219, 228 equivalent firm power auctions and system operators 210–16 future of 206–25 gas, how to get out of 207–9 infrastructure, electric 185–90, 218–20 low-carbon options post-coal and gas 209–10 net gain and our consumption 222–5 R&D and next-generation renewables 220–2 renewable see renewables Energy Market Reform (EMR) 219 equivalent firm power (EFP) 212–16, 217, 220 ethanol 35, 71, 95, 197 eucalyptus trees xiv, 152 European Commission 60, 71, 72, 112 European Union (EU) xiv, 2, 7, 8, 9, 37, 42, 44, 46, 47, 117, 137, 165, 166, 197; baseline of 1990 and 51–2 Common Agricultural Policy (CAP) 76, 165 competition regime and customs union 56 deindustrialisation and 46, 52, 54, 59, 72–4 directives for 2030 66 Emissions Trading System (EU ETS) 71–2, 73, 79, 110–13, 117, 144, 208 importing carbon emissions 59 Internal Energy Market (IEM) 68, 71 Kyoto and 9, 51, 59, 66–7 Mercosur Agreement 44, 95 net zero target for 2050 66, 115, 143, 155, 167, 180 Paris and 54 Renewable Energy Directive 68–71, 73, 109 2020 targets signed into law 66 2020–20–20 targets 67, 69, 74 unilateralism and 59, 66–71, 80 Eurostar 133 externalities 104, 170, 180, 196 Extinction Rebellion 6 farmers 14, 26, 35, 36, 43, 71, 76, 86, 95, 102, 109, 110, 146–7, 164, 165, 166, 169, 170, 174, 175, 196, 197, 198 fertiliser 4, 6, 7, 26, 29, 35, 61, 73, 86, 87, 116, 117, 119, 163, 165, 169, 174, 175, 178, 179, 191, 194, 197 fibre/broadband networks 6, 11, 90, 92, 125, 126, 127–8, 130–1, 132–3, 135, 140–1, 201, 202, 205, 211, 214, 231, 232 financial crisis (2007/8) 1, 19, 69 first-mover advantage 75 First Utility 199 flooding 13, 77, 149, 152, 153, 159, 170, 233 food miles 167 food security 170–1 food waste 178, 180, 231 Forestry Commission xiv Formula One 186, 196 fossil fuels, golden age of 20–5 see also individual fossil fuel France 46, 47, 52, 56, 73, 78, 101, 113, 130, 136, 138 free-rider problem 39–40, 43, 62–4, 106, 119 fuel duty 121, 195–6 fuel efficiency 197 fuel prices 26, 112–13, 209 fuel use declaration 195 Fukushima Daiichi nuclear disaster (2011) 52, 78 Fukuyama, Francis: The End of History and the Last Man 40–1 gardens 6, 43, 143, 156 gas, natural ix, 2, 5, 8, 20, 23, 24, 25, 26, 29, 31, 32, 36, 50, 52, 68, 69, 79, 102, 109, 117, 119, 129, 136, 137, 146, 147–8, 149, 183, 190, 193, 194, 207–9, 210, 211, 214, 216–17 G8 47 gene editing 172, 176, 231 general election (2019) 121 genetics 98, 172, 174–6, 231 geoengineering 177 geothermal power 137, 178 Germany 9, 30, 47, 52, 59, 60, 62, 66, 67, 69, 70, 71, 72, 73, 75, 77–80, 83, 91, 101, 112, 136, 137, 138, 144, 206, 208, 209 Energiewende (planned transition to a low-carbon, nuclear-free economy) 59, 69, 77–80, 112, 144, 208 Gilets Jaunes 101, 113 GMOs (genetically modified organisms) 176, 177 Great Northern Forest, Britain 151 Green and Prosperous Land (Helm) xiii, xiv, 165, 169, 234 greenbelt 173 greenhouse effect 17 green new deal 90, 102, 234 green parties/green votes 69, 77, 78 green QE (quantitative easing) 102–3 green walls 153, 231 greenwash 156 gross domestic product (GDP) xii, xv, 1, 25, 27, 29, 41, 57, 59, 73, 76, 83, 93, 98, 103, 133, 165, 207, 227, 229, 233 growth nodes 133 G7 47 G20 47 Haber-Bosch process 35, 163 Hamilton, Lewis 186 ‘hands-free’ fields 175 Harry, Prince 6 Heathrow 133, 134 hedgerow 76, 166, 167, 172 Helm Review (‘The Cost of Energy Review’) (2017) ix, 120, 141, 200, 210, 212, 215, 217, 220, 238 herbicide 163 home insulation 102 House of Lords 170 housing 101, 223–4 HS2 92, 125, 132–4, 138, 202 Hume, David 49 hydrogen 13, 49, 92, 125, 128, 135, 137, 183, 184, 190–2, 199, 200, 204, 206, 213, 228 hydro power 31, 35, 36, 50, 52–3, 70, 136, 137, 191 Iceland 137, 178 imports x–xi, xiii, 5, 8, 10, 11, 12, 13, 62, 68, 70, 117–18, 155, 167, 178, 173, 180, 196, 227 income effect 72, 111 income tax 121, 122, 232 India xiv, xv, 25, 30, 31, 38, 43, 44, 47, 48, 51, 54, 55, 57, 154, 229 individuals, net zero for 155–7 Indonesia 2, 35 indoor farming 87–8, 177–8, 180, 213 indoor pollutants 223, 232 Industrial Revolution 1, 18, 19, 25, 47, 116, 145 INEOS Grangemouth petrochemical plant xi information and communications technology (ICT) 117, 202, 231 infrastructures, low-carbon xiii, xiv, 11–12, 14, 28, 60, 62, 65, 66, 90, 91–4, 96, 105, 109, 123, 125–42, 143, 147, 151, 154, 159, 171, 184, 186, 187, 190, 199–200, 214, 218–20, 228, 230, 231–2, 234–5 centrality of infrastructure networks 128–30 electric 125–41, 218–20 making it happen 141–2 net zero national infrastructure plan 130–6 private markets and 125–8, 141–2 regional and global infrastructure plan 136–7 state intervention and 126, 127–8, 141–2 system operators and implementing the plans 138–41 inheritance tax 76, 165 insects 164, 177, 231 insulation 102, 224 Integrated Assessment Models 114 intellectual property (IP) 75 Intergovernmental Panel on Climate Change (IPCC) 17–18, 47, 55, 57, 108, 172 internal combustion engine 13, 22, 181–2, 183, 184, 200, 221, 228 Internal Energy Market (IEM) 68, 71, 138 International Energy Agency (IEA) 25, 207 International Monetary Fund (IMF) 51 internet banking 131, 213 internet-of-things 128, 175 Iran 27, 42, 113, 137 Iraq 56, 192 Ireland 43, 157 Italy 137, 182 Japan 27, 28, 30, 52, 73, 78, 101, 185 Jevons Paradox 224 Johnson, Boris 89–90 Kant, Immanuel 104 Keynes, John Maynard 89, 102, 103, 105 Kyoto Protocol (1997) xii, 2, 7, 9, 13, 17–18, 37, 38, 39, 40–1, 47–8, 49, 51, 52–3, 59, 66–7, 119 laissez-faire 104, 138, 188 land use 35, 61, 95, 172, 237 LED (light-emitting diode) lighting 87, 178, 179, 180, 213 liquefied natural gas (LNG) 136, 183 lithium-ion battery 185 lobbying 10, 14, 33, 69, 71, 109, 110, 111–12, 115, 121, 157, 169, 170, 187, 197, 209, 223, 227, 228 location-specific taxes 194 maize 35, 165, 197 Malaysia 2, 229 Malthus, Thomas 98 Mao, Chairman 27, 42 meat xi, 65, 164, 177, 180, 232 Mekong River 2, 28, 179, 229 Mercosur Agreement 44, 95 Merkel, Angela 78 methane 4, 23, 84, 177, 178, 179, 216 microplastics 22 miracle solution 49–50, 55, 209 mobile phone 5, 125, 185 National Farmers’ Union (NFU) 110, 164, 165, 169, 170, 171 National Grid 139, 141, 189, 200, 211, 214, 219 nationalisations 101–2, 126–7 nationalism 41, 43, 55, 56, 138 nationally determined contributions (NDCs) 54–5 natural capital xiii, 14, 33–6, 51, 85, 86, 88, 90, 94, 97, 154, 158, 168, 171, 173–4, 236 Nature Fund 123, 169, 234 net environmental gain principle xiii, xiv, 10, 12, 62, 84, 94–6, 105, 143–59, 169, 172–4, 192, 201–3, 222–5 agriculture and 169, 172–4 carbon offsetting and see carbon offsetting electricity and 222–5 principle of 94–6, 143–4 sequestration and see sequestration transport and 192, 201–3 Netherlands 138 Network Rail 214 net zero agriculture and see agriculture defined x–xv, 3–14 economy 10–12, 81–159 see also economy, net zero electricity and see electricity transport and see individual method of transport 2025 or 2030 target 89 2050 target x, xi, 5, 59, 66, 74, 75, 115, 120, 135, 143, 155, 167, 169, 180, 184, 216, 217, 222, 226, 230, 231, 232 unilateralism and see unilateralism NHS 65 non-excludable 91, 93, 126, 170 non-rivalry 91, 93, 126, 170 North Korea 42 North Sea oil/gas 9, 40, 75, 97, 102, 137, 139, 147, 148, 193 Norway 130, 137, 191 nuclear power 5, 9, 12, 18, 23, 52, 60, 73, 77–9, 109, 125, 128, 129, 136, 140, 178, 194, 199, 206, 207, 208, 209–10, 212, 214, 216, 218, 219, 222, 228 Obama, Barack 48, 53, 54, 59 oceans 2, 14, 22, 33, 85, 86, 88, 148, 163, 231 offsetting see carbon offsetting offshore wind power 31, 69, 75–6, 208, 212, 219, 221 Ofgem 220 oil ix, 2, 20, 22–3, 25, 26, 27, 31, 32, 33, 36, 39, 40, 50, 67, 69, 86, 97, 117, 119, 129, 136, 137, 146, 147, 148–9, 150–1, 152, 181–3, 184, 185, 187, 189, 190, 192–4, 196, 197, 199, 206, 209, 210, 216–17, 229 OPEC 39, 40, 193 Orbán, Viktor 41, 42 organic food 61, 87, 178 Ørsted 70 palm oil 2, 5, 6, 35, 36, 66, 71, 167, 173, 197–8, 230 pandemic see Covid-19 Paris Climate Change Agreement (2015) xii, 2, 10, 13, 18, 30, 37, 38, 39, 48, 49, 54–5, 56, 57, 58, 66, 80, 105, 106, 118, 119, 227 peat bogs xiv, 2, 13, 14, 33, 35, 36, 43, 109, 146, 169, 179 pesticides 4, 26, 61, 163, 165, 169, 174, 178, 231 petrochemicals xi, 7, 8, 20, 22–3, 29, 73, 80, 86, 117, 166, 182 petrol 4, 86, 119, 121, 129, 185, 186, 187, 191, 192, 199 photosynthesis 34, 197 plastics 1, 22, 28, 35, 43, 66, 86, 87, 119, 143, 166, 184, 231 polluter-pays principle xiii, xv, 84–90 agriculture and 76, 168–70, 172, 173 carbon price and see carbon price/tax generalised across all sources of pollution 86 identifying polluters that should pay 86 importance of 10–11, 13, 61, 62, 65 intergenerational balance and 96–7 net environmental gain and 94 sequestration and see sequestration, carbon sustainable economy and 96–7, 105, 106 transport and 192–5, 198–9 see also individual type of pollution population growth 93, 97, 177, 178, 179, 232 privatisation 127, 140, 218–19, 220 property developers 94 public goods, provision of xiii, 10, 11–12, 62, 75, 84, 90–4, 96, 104, 105, 109, 122, 123, 126, 128, 141, 147, 151, 153, 159, 164, 168, 173–4, 180, 192, 199–200, 202, 218, 229, 230 agricultural 170–4, 180 low-carbon infrastructures see infrastructures, low-carbon research and development (R&D) see research and development (R&D) Putin, Vladimir 27, 41, 42, 89 railways 11, 13, 13, 87, 91, 92, 94, 125, 128, 129, 130, 131, 132–3, 138, 139, 156, 182, 183, 187, 202, 212, 214, 232 rainforest 2, 5, 34, 35, 36, 38, 44, 47, 55, 87, 95, 145, 149, 155, 173, 179–80, 197, 229 rationalism 40–1 Reagan, Ronald 103 red diesel 76, 109, 164, 165, 196 regulated asset base (RAB) 127, 141, 215, 220 remote working 128, 156, 201–2, 205 renewables ix, 6, 8, 9–10, 18, 19, 21, 26, 31–5, 36, 49, 50, 55, 61, 67, 72, 77, 79, 85, 86, 109, 110, 112, 123, 125, 128, 131, 135, 138, 140, 144, 149, 178, 188, 191, 194, 197, 199, 207, 209–10, 211, 212, 213, 214, 215, 216, 217, 219, 220–2, 224, 228 Chinese domination of market 9, 32, 73, 74, 77, 79 cost-competitiveness of 9–10, 49, 51, 61, 68 failure of, 1990-now 19, 31–3, 36 modern global renewable energy consumption measured in TWh per year 32 miracle solution and 49–51 Renewable Energy Directive 68–71, 73, 109 subsidies ix, 9, 10, 50, 68–9, 71, 79, 80 see also individual renewable energy source Renewables UK 110 research and development (R&D) xiv, 12, 13, 14, 62, 65, 66, 90, 93–4, 104, 109, 123, 165, 172, 192, 200, 218, 220–2, 223, 228, 234 reshoring businesses 8, 204 rivers 2, 22, 28, 86, 128, 152, 165, 169, 179, 214, 230 roads 11, 28, 45, 91, 92, 125, 129, 131–2, 140, 165, 182, 189, 194, 198, 202, 232 robotics 32, 175, 204, 206, 231 Rosneft 26 Royal Navy 183 Russia 26, 27, 30, 40, 42, 44, 45, 46, 47, 48, 50, 52, 55, 56, 192, 193 RWE 139, 218 Ryanair 156–7 rye grass 35 salmon 169, 177 Saudi Arabia 26, 33, 40, 42, 50, 137, 192, 193 Saudi Aramco 26, 50 seashells 34 sequestration, carbon xi, xiv, 12, 61, 66, 85, 90, 95, 143–59, 228, 229, 231, 232 agriculture and 12, 163, 166, 168, 169, 170, 171, 172, 173, 176–7, 179, 180 baseline definition and 146–7 biofuels and 35, 146, 217 carbon capture and storage (CCS) xiv, 12, 75–6, 95, 109, 146, 147–8, 149, 154, 159, 203–4, 207, 209, 222, 223 companies, net zero for 148–51 countries, offsetting for 151–5 electricity and 222, 223 gas and 207 individuals, net zero for xi, xiv, 155–7 markets, offsetting 157–9 natural capital destruction and 2, 19, 33–6, 44, 45, 51 natural sequestration xi, xiii, 2, 7, 12, 14, 33–6, 37, 45, 52, 66, 85, 90, 94–6, 105, 143–59, 163, 168, 171, 173, 176–7, 179, 180, 203, 206, 207, 222, 223 net gain principle and 143–4, 146, 149–50 offsetting principle and 143–5 peat bogs and see peat bogs principle of xi, xiii, 2, 7, 12–13 soils and see soils transport and 185, 190, 203 tree planting and see trees, planting/sequestration and types of 145–8 wetlands/coastal marshes and 146, 159, 233 shale gas 8, 208 Shell 27, 149, 199 shipping 8, 13, 22, 28, 36, 49, 114, 125, 137, 181, 182–3, 191, 194–5, 203–5, 217 Siberia 2, 46 smart appliances 128, 129, 132 smart charging 11, 13, 128, 129, 130, 139, 214, 219 soils xiii, 2, 5, 7, 12, 14, 33, 35, 36, 43, 55, 76, 109, 146, 149, 152, 156, 159, 163, 164, 165, 166, 168, 169, 171, 172, 175, 179, 203, 228 solar panels/solar photovoltaics (PV) 5, 6, 9, 12, 13, 21, 31, 32, 33, 49, 53, 68, 69, 71, 74, 79, 87, 91, 135, 136, 137, 178, 179, 188, 204, 207, 208, 209, 210, 211, 213, 214, 216, 217, 221, 222, 223, 224–5 Sony 185 Soviet Union 18, 40, 52, 67–8, 89 soya 95 Spain 69, 130, 137 sport utility vehicles (SUVs) 106, 121, 192 spruce xiv, 152, 170 standard of living xv, 1, 5, 8, 10, 11, 14, 229, 233 staycations 201 steel x–xi, 6, 7, 8, 26, 28, 29, 53, 66, 73, 80, 87, 116, 117, 118, 119, 171, 184, 194–5 Stern, Nicholas: The Economics of Climate Change 41, 63 subsidies ix, 9, 10, 14, 32, 50, 51, 52, 53, 69, 71, 76, 79, 80, 89, 102, 109, 110, 113, 116, 123, 140, 154, 164, 165, 166, 167, 169, 170, 172, 180, 193, 196, 198, 209, 215, 221, 222, 228, 230 sugar cane 35, 71, 95, 197, 198 sulphur pollution 22, 25, 28, 78, 191, 194, 197, 230 sustainable economic growth xv, 10, 12, 14, 61, 83, 92, 94, 97, 98, 105, 227, 233 Taiwan 42 taxation xii, 11, 62, 71, 72, 76, 80, 87, 89, 90, 91, 92, 97, 101, 102, 103, 106–24, 126, 127, 130, 133, 147, 150, 151–2, 153–4, 157, 159, 165, 169, 170, 192–6, 197, 198, 199, 203, 232, 234 technological change 98, 127, 141, 174–5, 221 Thatcher, Margaret 17 Thompson, Emma 6 3D printing 175, 204 Thunberg, Greta 6, 205 tidal shocks 159 top-down treaty frameworks 13, 38–57, 80, 110, 119 tourism/holidays 6, 22, 36, 88, 94, 107, 114, 128, 156, 201, 204–5 transport, reinventing 181–205 aviation 195, 201, 203–5 see also air transport batteries and charging networks 185–90 biofuels 196–8 electric alternative 183–5 hydrogen and fuel cells 190–2 innovation, R&D and new infrastructures 199–200 internal combustion engine 181–2 net gain and offsets (reducing travel versus buying out your pollution) 201–3 oil 183–4 polluter pays/carbon tax 192–6 shipping 203–5 urban regulation and planning 198–9 vehicle standards 196–8 see also individual type of transport Treasury, UK 120–2 trees, planting/sequestration and xi, xiii, xiv, 2, 7, 13, 14, 33, 34, 45, 76, 85, 94–6, 146, 148, 149–51, 152–3, 155, 156, 157, 158, 159, 168, 169, 172, 179, 203, 231 trophy project syndrome 133 Trump, Donald 2, 8, 41, 42, 48, 89, 99, 103, 121 25 Year Environment Plan xiii, 153, 170, 179–80 UK 47, 69 agriculture and 164, 166, 167, 173 carbon emissions (2015) 30 carbon price and 115, 120 Climate Change Act (2008) 66, 74–7 coal, phasing out of 24–5, 60–1, 77, 208 Committee on Climate Change (CCC) x–xi, 7, 74–6, 120, 164, 166, 169, 217, 235 deindustrialisation and 72–4 80 per cent carbon reduction target by 2050 74 electricity and 206, 208, 218, 219, 224 Helm Review (‘The Cost of Energy Review’) (2017) ix, 120, 141, 200, 210, 212, 215, 217, 220, 238 infrastructure 125, 132–3, 134, 137, 139–40 net zero passed into law (2019) 66 sequestration and 145, 150, 153, 154, 155, 156 transport and 195–6, 197, 198 unilateralism and 58–9, 60–1, 65, 66, 69, 72–7, 80 unilateralism xi, 8, 10, 11, 25, 58–80, 83, 105, 106, 119, 125, 143, 144, 155, 164, 167, 197, 203, 227 in Europe 66–80 incentive problem and 58–60 morality and 62–6 no regrets exemplars and/showcase examples of how decarbonisation can be achieved 60–2 place for 80 way forward and 80, 83 United Nations xi, xii, 6, 10, 17, 37, 38, 118 carbon cartel, ambition to create a 39–40, 43, 45, 46–7, 56 climate treaty processes xi, 6, 10, 13, 17–18, 36, 37, 38–57, 59, 80, 110, 118, 119, 204–5 see also individual treaty name Framework Convention on Climate Change (UNFCCC) 17–18, 36, 38, 59 miracle solution and 50–1 origins and philosophy of 41 Security Council 46, 47, 57 United States 8, 74, 139, 206 agriculture in 175, 176, 197 carbon emissions 8, 29, 30 China and 27–8, 42, 118 coal and 2, 24, 28, 29, 208 economic imperialism 45 energy independence 50 gas and 8, 20, 23, 24, 29, 50, 208 oil production 40, 50, 193 pollution since 1990 29 unilateralism and 58, 59, 74 UN climate treaty process and 38, 40–1, 44, 45, 46, 47, 48, 53, 54, 56 universal service obligations (USOs) 92, 126, 131, 202 utilitarianism 41, 63–4, 108, 110 VAT 117, 119–20, 121, 122, 232 Vesta 69 Volkswagen 196–7 water companies 76, 214, 230 water pollution/quality xiv, 12, 22, 61, 76, 152, 153, 165, 169, 170, 171, 172, 175, 177, 178, 179, 180, 232 Wen Jiabao 53, 59 wetlands 159, 233 wildflower meadow 164, 184 wind power 5, 9, 12, 21, 31, 32, 33, 49, 53, 68, 69–70, 71, 74, 75, 76, 78, 79, 91, 135, 136, 137, 138, 139, 178, 188, 191, 207, 208, 209, 210, 211, 212, 213, 214–15, 216, 217, 219, 221, 222 wood pellets 67, 217, 230 Woodland Trust 156, 158 World Bank 51 World Trade Organization (WTO) 52, 56, 118 World War I 183 World War II (1939–45) 78, 90, 92, 101, 106, 171 Xi Jinping 27, 41, 42 ACKNOWLEDGEMENTS So much is now discussed, written and published about climate change that it is impossible to keep track of all the ideas and conversations that have influenced my understanding of the subject.

On the Future: Prospects for Humanity
by Martin J. Rees
Published 14 Oct 2018

Some scientists fear that computers may develop ‘minds of their own’ and pursue goals hostile to humanity. Would a powerful futuristic AI remain docile, or ‘go rogue’? Would it understand human goals and motives and align with them? Would it learn enough ethics and common sense so that it ‘knew’ when these should override its other motives? If it could infiltrate the internet of things, it could manipulate the rest of the world. Its goals may be contrary to human wishes, or it may even treat humans as encumbrances. AI must have a ‘goal’, but what really is difficult to instil is ‘common sense’. AI should not pursue its goal obsessively and should be prepared to desist from its efforts rather than violating ethical norms.

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See also inorganic intelligences Intergovernmental Panel on Climate Change (IPCC), 39, 40, 58 International Atomic Energy Agency, 218 international institutions, 10, 32, 218–19 International Space Station, 140, 146 international tensions, 100 International Thermonuclear Experimental Reactor (ITER), 54 internet: leveling global education and health, 83–84, 220–21; national and religious divisions on, 100; security on, 220. See also information technology (IT); social media internet of things, 104 interstellar travel, 8, 79, 154 invasive species, 74 in vitro fertilisation (IVF), 67, 68 Iranian nuclear weapons programme, 20 iris recognition, 84–85 James Webb Space Telescope, 137 jobs: declining wages and security, 91; disrupted by technology, 5; in personal services, 96–97; resurgence of arts and crafts, 98; shortened working week, 97–98; taken over by machines, 91–94 Juncker, Jean-Claude, 28 Kardashev, Nikolai, 156 Kasparov, Garry, 86, 87–88 Keeling, Charles, 38 Keeling, Ralph, 38 Kennedy, John F., 17 Kepler, Johannes, 131 Kepler project, 131–32 Khrushchev, Nikita, 17 kidneys sold for transplant, 71 killer robots, 101–2 The Knowledge: How to Rebuild Our World from Scratch (Dartnell), 217 Kolmogorov, Andrey, 172 Kolmogorov complexity, 172, 174, 193 Kuhn, Thomas, 205 Kurzweil, Ray, 81, 108 Large Hadron Collider, 206–7 Lee Sedol, 88 Lehrer, Tom, 17 Leonov, Alexey, 138 life: Earth as only known home of, 121; habitable planets and, 125, 126–27, 133, 135–36; origin of, 128–29, 135–36; universe fine-tuned for, 186, 197–98.

Data for the Public Good
by Alex Howard
Published 21 Feb 2012

When combined, those factors mean that we now see earthquake tweets spread faster than the seismic waves themselves. Networked publics can now share the effects of disasters in real time, providing officials with unprecedented insight into what’s happening. Citizens act as sensors in the midst of the storm, creating an ad hoc system of networked accountability through data. The growth of an Internet of Things is an important evolution. What we saw during Hurricane Irene in 2011 was the increasing importance of an Internet of people, where citizens act as sensors during an emergency. Emergency management practitioners and first responders have woken up to the potential of using social data for enhanced situational awareness and resource allocation.

Data and the City
by Rob Kitchin,Tracey P. Lauriault,Gavin McArdle
Published 2 Aug 2017

This is particularly the case with urban operational data wherein traditional city infrastructure, such as transportation (e.g. roads, rail lines, bus routes, plus the vehicles/carriages) and utilities (e.g. energy, water, lighting), have become digitally networked, with grids of embedded sensors, actuators, scanners, transponders, cameras, meters and GPS (constituting what has been called the Internet of Things) producing a continuous flow of data about infrastructure conditions and usage. Many of these systems are generating data at the individual level, tracking travel passes, vehicle number plates, mobile phone identifiers, faces and gaits, buses/trains/taxis, meter readings, etc. (Dodge and Kitchin 2005).

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The case of the Flemish city monitor for livable and sustainable urban development’, Applied Research Quality Life 5: 341–352. 10 Sharing and analysing data in smart cities Pouria Amirian and Anahid Basiri Introduction Nowadays, the successful and efficient management of a city depends on how data are collected, shared and transferred within and between various organizations in a city and how data analytics are used to extract actionable insights for decision-making. Such data include public administrative records, operational management information, as well as that produced by sensors, transponders and cameras that make up the internet of things, smartphones, wearables, social media, loyalty cards and commercial sources. In many cases, cities are turning to big data technologies and their novel distributed computational infrastructure for the reliable and fault tolerant storage, analysis and dissemination of data from various sources.

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Index abstraction 17, 90, 91, 93, 114, 117, 128, 142, 143, 160, 165, 166, 173, 179 accountability 48, 62, 64, 67, 68, 113, 120, 122, 144, 220 Actor Network Theory (ANT) 22, 86, 94, 161 Acxiom 75, 76 agency 95, 153, 165, 197, 198, 201, 205, 217 algorithm 1, 19, 20, 27, 31, 51, 53, 54, 102, 117, 143, 159, 160, 161, 172, 177, 179, 203, 205, 214, 216 API 7, 46, 99, 113, 116, 130, 135, 157 apps 23, 48, 130, 138 assemblage 19, 20, 22, 22, 78, 85, 158–60, 162, 163, 164, 192, 193, 213, 216 assemblage theory 7, 157 automation 9, 26, 31, 31, 34, 175, 184, 197, 218 auto-spatialization 162 Batty, M. 1, 2, 3–4, 10, 17, 19, 20, 26, 31, 33, 34, 47, 75, 114, 158, 190 bias 63, 65, 73, 74, 76, 77, 78, 79, 87, 92, 98, 99, 100, 102, 103, 104, 117, 118, 195, 196, 197, 198 biopolitics 174 bitcoin, 7, 141–3, 148, 149, 150, 151, 153, 154 black-boxed 11, 22, 24, 45, 51, 53, 75, 76, 119, 156, 214 blockchain, 2, 7, 141–57 Borgmann, A. 214, 215, 216, 221–22 boundary object 86, 88, 93 calibration 1, 35, 50, 77, 102, 116, 117, 142, 147 cameras 17, 46, 52, 53, 113, 127, 213, 214, 219, 220, 221 capital 9, 77, 114, 151, 196 capta 60, 68 Castells, M. 25, 26, 156 census 31, 32, 33, 45, 99, 113, 120, 173 Chicago school 18, 24 CitiStat 121 citizen science 2, 9, 11, 21, 46, 209, 213, 216–19, 222 citizenship 2, 9, 11, 203, 207 city operating systems 1, 46, 47, 48 Clery Act 67, 68 code/space 22, 76–77, 159 Cohen, J. 204, 205 communities of practice 86, 88 community 9, 21, 22, 25, 26, 27, 63–4, 91, 100, 123, 145, 204, 216, 217, 220, 221, 222 context 5, 6, 8, 21, 22, 23, 50, 51, 60, 63, 67, 68, 72, 73, 79, 80, 92, 98, 99, 100, 101, 103–05, 115, 122, 163, 166, 179, 191–7 contingency 49, 51, 52, 76, 86, 87, 90, 92, 122, 160, 181 control 10, 11, 44, 48, 51, 54, 60, 62, 76, 122, 166, 202, 203, 214, 217 control rooms 1, 7, 17, 46, 47, 111 counter-narrative 6, 9, 68, 120, 122 crime 5, 53, 59–69, 141, 202 critical data studies 72, 75, 80, 81, 91, 173, 179 crowdsourcing 46, 68, 69, 105, 113, 222 culture 18, 24–7, 49, 60, 64, 68, 95, 100, 104, 114, 153, 159, 191, 193 Customer Relationship Management 153 cybernetics 19, 48, 92, 165, 202, 207 cyberspace 158, 160, 201–07 dashboard 2, 6, 7, 17, 47, 111–124, 135, 137, 138, 139, 141, 154, 190, 197, 198 data: access 11, 17, 23, 45, 50, 51, 60, 61, 64, 68, 99, 105, 113, 115–17, 122, 226 Index 123, 131, 138, 139, 158, 174, 190, 205, 207, 208; administration 2, 45, 46, 47, 78, 88, 111, 113, 127; analytics 1, 19, 45, 47, 48, 53, 59, 60, 74, 75, 127, 196, 197; assemblage 4, 6, 11, 50, 51, 53, 86, 181; big 1, 3, 4, 6, 9, 32, 33–39, 41, 42, 44, 45–9, 50, 51, 52, 72, 75, 78, 104, 113, 115, 116, 118, 122, 127, 135, 138, 139, 159, 175, 184, 189–91, 213, 216; brokers 1, 9, 10, 46, 53; citizens 9, 10, 11, 201–10; control 4, 45, 51; coverage 4, 45, 50, 103; crime 4, 59–69; cube 4, 32–33; culture 1, 2, 8–9, 10, 11, 189–198; derived 5, 44, 53, 113; determinism 53; encounter 5, 73, 79–81; financial 76, 77; framework 51, 116, 184; friction 2, 6, 93, 99, 101, 103; governance 8, 189, 196; indexical 2; infrastructure 1, 2, 5, 7, 8, 77, 78, 85, 156–66, 172, 173, 180, 182, 193; integrity 4, 45, 52, 66, 67, 68, 73, 144; journey 5, 85, 93; lineage 73, 80, 116, 117; linked 184; management 59, 174, 177; minimization 52; mining 31, 38, 42, 47, 114; model 8, 45, 102, 171, 173, 175–77, 178, 179, 180–82; open 1, 4, 10, 47, 48, 59, 60, 61, 64, 66, 67, 69, 77, 99, 113, 116, 118, 120, 122, 124, 193, 196, 198; ownership 4, 45, 51, 80, 202, 208; politics 10, 49–51; portal 7, 99, 123, 139, 193; power 8, 11, 189–98; practice 2, 8, 10, 75, 76, 91, 92, 93, 94, 189, 191, 193–7, 198; protection 4, 45, 64, 207, 208; provenance 2, 5, 72–81, 85, 95, 180, 184, 202; proxies 6, 98, 99, 115, 138, 153; quality 2, 5, 45, 50, 61, 65, 89, 90, 103, 116–18, 194; re-use 66, 116, 128, 134, 190, 193; science 4, 10, 32, 47, 139; security 4, 45, 52; sharing 4, 7, 63, 78, 116, 127, 128, 134, 139; small 4, 32, 33, 41, 46; spatial 32, 72, 73, 79, 80, 117, 118, 131, 175; statistical 47, 61, 64, 113; sticky 6, 98–105; threads 5, 11, 85–95 database 7, 8, 19, 23, 45, 80, 81, 87, 87, 141, 145, 145, 153, 159, 171, 172, 173, 178, 179, 181, 184 data-driven urbanism 2, 3, 4, 11, 12, 44, 48, 113 dataveillance 4, 10, 45, 49, 52, 197 democracy 2, 9, 11, 122, 190, 222 demographic 60, 63, 100, 101, 104, 192, 196 device paradigm 9, 214–16, 217, 218 Dodge, M. 21, 22, 46, 48, 49, 52, 76, 79, 157, 159, 175, 204, 205, 214 Dublin Dashboard 6, 113, 117, 118, 120, 122 dynamic nominalism 8, 11, 181, 182–85 efficiency 11, 19, 48, 72, 75, 123, 127, 190, 196, 197, 213, 222 embodiment 92, 159, 203, 209, 210 empowerment 19, 48, 147 epistemology 2, 3, 6, 7, 10–11, 17, 18–21, 22, 27, 59, 77, 78, 85, 113–15, 121, 123, 160, 162, 163, 194, 214 error 5, 88, 89, 99, 117, 118, 120, 154, 180, 217 essentialism 4, 75, 114, 115, 158 ethics 2, 10, 27, 45, 49, 52–3, 95, 122–123, 190, 198 Euclidean space 7, 161, 162, 164 Evans, L. 23 fab lab 219 Facebook 76, 130, 153, 156, 214, 215 feminism 94, 202 focal practice 9, 214–16, 217, 218, 219–221, 222 Foucault, M. 50, 51, 78, 174, 178, 179, 181, 203, 205 Foursquare 46, 77, 78, 156 Fuchs, C. 206, 207 gender 63, 68, 196 geodemographic 19, 53, 76, 78, 123 geoservices 7, 128, 131–4, 135, 136 geosurveillance 49, 52 Github, 164 Google 73, 76, 104, 130, 135, 160, 210, 216, 217, 220 governance 1, 2, 8, 9, 44, 45, 48, 49, 72, 78, 85, 95, 104, 115, 121, 122, 123, 191, 192, 193, 197, 202; algorithmic 48, 54; anticipatory 4, 45, 49, 53; technocratic 9, 49, 78, 121 government, 9, 10, 19, 21, 22, 48, 59, 64, 79, 88, 111, 113, 116, 141, 174, 189, 190, 192, 193, 205, 208, 222 GPS 46, 53, 80, 149, 152, 160, 165, 217, 219 gravity model 36 hacking 4, 45, 48, 49, 52, 105, 219, 221 Hacking, I. 8, 172, 178, 179, 181–83 Hägerstrand, T. 141, 144, 145, 153 HarassMap 68 heterogeneity 19, 27, 39, 50, 160, 179 Index 227 IBM 19, 47, 189, 190, 218 ideology 5, 20, 21, 49, 86, 95, 114, 115, 205, 206, 214 immutable mobile 86, 162, 163 indicators 5, 51, 85, 88, 89, 90, 91, 113, 115, 116, 117, 122, 138, 139, 194 inequality 49, 62, 100, 101, 146, 158 infant mortality 86–9 infrastructure 1, 3, 22, 40, 44, 45, 48, 49, 50, 52, 54, 66, 72, 77, 85, 86, 91, 92, 111, 127, 136, 157, 158, 159, 162, 172, 179, 184, 184, 189, 192, 195, 205, 214 innovation 48 institution 1, 5, 9, 10, 19, 20, 22, 50, 60, 61–2, 68, 78, 98, 99, 116, 174, 179, 182, 183, 210 instrumentally 2, 5, 6, 9, 11, 12, 23, 54, 72, 121, 122, 123, 222 intelligent transport systems 2, 48 interface 22, 25, 26, 51, 93, 114, 130, 131, 133, 134, 158, 166 internet of things 46, 127 interoperability 6–7, 45, 127, 129, 130, 131, 134, 135, 136, 139, 184 interpretation 5, 33, 34, 60, 68, 79, 80, 87, 117, 119, 150, 194, 209, 210, 214 ISO 37, 120 51, 89, 90, 91 Judgement 5, 10, 60 Kitchin, R. 1, 4, 6, 8, 11, 17, 20, 21, 22, 27, 34, 44, 46, 47, 48, 49, 52, 53, 60, 74, 75, 76, 78, 85, 113, 114, 115, 116, 121, 122, 157, 159, 171, 178, 179, 180, 189, 190, 190, 191, 192, 194, 196, 197, 204, 205, 214 labour 10, 100, 153, 190 Latour, B. 22, 159, 160, 161, 162, 164, 165, 166, 208 ledgers 141–57 Lefebvre, H. 165, 206, 209 Lessig, L. 204, 205 licensing 45, 53, 85, 95, 116, 123 lightings 101–03 literacy 113, 119–20, 123, 158, 194 living labs 21, 148 loose coupling 7, 128, 129, 137 machine learning 3, 19, 42, 47, 145 management 3, 4, 6, 10, 17, 25, 34, 45, 47, 48, 50, 54, 61, 62, 95, 111, 113, 115, 120, 121, 122, 127, 134, 135, 144, 153, 190, 193, 197, 214 Map Knitter 220 mapping 2, 10, 45, 46, 64–66, 116, 119, 135, 172, 174, 210, 216, 217, 220, 222 Marx, K. 141, 142 materiality 5, 7, 8, 85, 91, 93, 95, 157, 158, 159, 163–65 media 22, 26, 31, 44, 45, 66, 100, 116, 156, 159, 160 metadata 5, 6, 7, 72, 73, 74, 75, 76, 77, 79, 80, 81, 90, 98, 99, 116, 118, 123, 134, 135 metaphysics 27, 94, 114 Microsoft 47, 130, 135, 191 misinterpretation 74 mobile; devices 39, 52, 99, 184; phone 23, 46, 51, 76, 158, 159, 160, 210 model 4, 17, 19, 35–9, 78, 104, 119, 145, 147, 172, 173, 180, 184 modelling 19, 35–9, 40, 47, 100, 114, 118, 120, 132, 172, 180, 181, 184, 213 Modifiable Areal Unit Problem 119 money 39, 40, 141–4, 148, 153, 190, 196 Mumford, L. 24–5 MySpace 26 NASA 102, 103 neoliberalism 63, 65, 78, 196 network 1, 7, 19, 22, 23, 40, 45, 46, 52, 53, 86, 93, 129, 135, 142, 143, 150, 158, 161, 162, 163, 164, 165, 173, 193, 203, 205; society 26, 156; topology 161 networked; locality 22; urbanism 44, 48, 52, 54 neutrality 4, 5, 8, 10, 22, 48, 49, 51, 54, 60, 78, 79, 86, 87, 114, 115, 134, 135, 166, 179, 202, 214 normative 2, 3, 4, 11–2, 18, 24, 26, 27, 28, 54, 115 objectivity 5, 8, 10, 18, 49, 51, 54, 60, 61, 69, 78, 79, 81, 86, 87, 103, 114, 115, 123, 144, 172, 179, 181, 194, 195, 206, 209 object-oriented model 8, 179 ontology 3, 4, 8, 23, 72, 79, 80, 81, 86, 113, 162, 165, 171, 174, 175, 177, 182, 183, 184, 194, 209, 214 Open Street Map 46, 217 Ordnance Survey Ireland 8, 173, 174–8, 183–5 organizational service layer 136–8 participation 2, 9, 10, 48, 48, 68, 122, 203, 214, 216–19, 221, 222 pavement management system 173 228 Index performance 26, 62, 86, 120, 121, 141, 164; metrics 74, 88–91, 122, 197 performativity, 23, 206, 207, 209 phenomenology 23 planning 18, 34, 47, 54, 60, 72, 74, 78, 89, 90, 184 platform 6, 27, 49, 51, 100, 101, 104, 127, 129, 130, 142, 143, 148, 150, 154, 173, 174, 175, 179, 198, 208, 209, 210, 215; independency 7, 127, 128, 134, 135, 137; society 26 policy 5, 6, 12, 44, 45, 47, 49, 50, 64, 74, 89, 90, 115, 118, 120, 121, 122, 190, 192, 196 political economy 8, 49, 114, 173, 181, 185, 197 politics 2, 4, 8, 9, 10, 11, 12, 21, 27, 49, 50, 52, 54, 78, 86, 91, 95, 99, 102, 114, 115, 120, 124, 157, 193, 196, 201, 202, 203, 208, 209, 213 post-human 164–5 post-political 12 power 5, 7, 8, 9, 10, 20, 21, 22, 24, 25, 27, 49, 73, 143, 144, 160, 161, 166, 189–98, 201–208, 210, 222 power/knowledge 8, 51, 179, 180, 185 prediction 7, 35–7, 47, 52, 53, 76, 100, 102, 104, 114, 139, 153 predictive policing 2, 5, 53, 104 privacy 4, 9, 45, 52, 65, 68, 72, 105, 122, 201, 202, 207, 208 privatization 51, 63, 68, 116 profiling 2, 19, 77 protocols 1, 7, 76, 81, 93, 130, 133, 135, 157, 163, 205 public; good 8, 49, 77, 78; space 26, 27, 100, 152, 205 Public Lab 219, 222 race 63, 64, 67, 196 realism 21, 22, 50, 79, 103, 114, 121, 146, 158, 172, 194, 197, 215, 216, 217, 222 real-time 1, 3, 4, 17, 18, 19, 23, 34, 39–41, 44, 46, 47, 48, 54, 72, 75, 113 regulation 9, 10, 11, 22, 50, 50, 51, 52, 54, 142, 192, 203, 205, 206, 207 relational space 79, 94, 161 relationality 5, 7, 17, 51, 85, 86, 93, 122, 156, 173 representation 23, 25, 42, 59, 60, 68, 72, 75, 76, 89, 92, 92, 93, 98, 105, 114, 117, 141, 142, 143, 145, 149, 153, 158, 184, 195, 197, 206, 216, 217 Research Data Alliance 191 resistance 10, 183, 197 resource allocation 66 RESTful service 7, 128, 129–36, 138 RFID 39, 53 sampling 2, 4, 36, 45, 50, 89, 99, 113, 116, 118 scalable 7, 128, 130, 135, 139 scale 11, 37, 38, 76, 80, 81, 86, 95, 99, 103, 119, 158, 171, 173, 175, 184 science and technology studies 157, 159 security 2, 5, 9, 46, 48, 63, 64, 67, 72, 130, 134, 135, 164, 202, 213 Senseable City Lab 19 sensors 2, 19, 34, 46, 49, 50, 52, 53, 60, 62–3, 102, 103, 113, 116, 127, 213, 214, 216, 219, 221, 222 service orientation principles 7, 128–9 sharing economy 48 simulation 2, 47, 48, 114 smart: card 34, 39, 40, 53, 116; cities, 1, 2, 3, 4, 7, 9, 10, 18, 19, 20, 21, 42, 44, 45–9, 52, 54, 68, 75, 78, 80, 113, 122, 123, 127–139, 148, 190, 197, 202, 213–222; smartphone 52, 53, 54, 80, 116, 127, 215, 218, 219, 221 social: media 42, 46, 48, 49, 76, 99, 100, 101, 104, 113, 206; network 17, 26, 35, 100, 215; sorting 2, 4, 45, 52, 123, 172 socio-technical: assemblage 4, 7, 8, 10, 49, 49, 50, 161, 172, 178, 179, 184, 185, 202; practices 80 space 25, 52, 65, 79, 92, 93, 94, 100, 115, 144, 145, 153, 157, 158, 162, 163, 165, 183, 191, 201, 202, 204, 205, 206; production of 3, 8, 21, 22, 25, 206 space of flows, 7, 156 spacetime 93, 94, 165 space-time compression, 7, 156 spacetimematter 94, 165 spatial: imaginaries 86, 92–4; interaction 32–3, 42; media 2; sorting 49, 123; structure 95; urban 18, 21–4, 68, 156, 157, 173, 196, 209, 213; video 46 spatiality 7, 24, 85, 91, 92, 93, 94, 143, 161, 163 standards 1, 5, 6, 46, 51, 62, 64, 73, 74, 75, 80, 85, 87, 89, 90, 91, 116, 117, 118, 123, 129, 130, 134, 173, 184, 195, 195 statistical analysis 47 statistics 33, 44, 45, 59, 61, 62, 64, 65, 67, 85, 87, 88, 89, 115, 118, 184, 213 subjective 5, 22, 49, 60, 166, 196, 198, 201, 207, 210 Index 229 subjectivity 2, 10, 60, 165, 202, 203, 208 surveillance 2, 9, 10, 46, 52, 78, 100, 102, 144, 202, 210 survey 2, 45, 49, 61, 64, 66, 74, 89, 89, 89, 90, 99, 113, 210 sustainability 11, 48, 122, 123, 127, 148, 197 truth 49, 77, 78, 114, 123, 174, 184 Twitter, 6, 46, 49, 99–101, 103, 113, 218 TCP/IP 93 technicity 159 territory 7, 174 Thrift, N. 144, 158 time-spaces 7, 163, 164, 164–65 topology 7, 11, 157, 160, 161–4, 165, 166, 173, 175, 176, 181, 183, 184 Toyota 147, 154 transduction 8, 22, 159, 162, 171, 173 transparency 20, 48, 60, 64, 66–8, 113, 120, 122, 164, 181, 193, 216, 221 transponder 46, 53, 113, 127 transport 4, 20, 33, 34–41, 45, 46, 48, 51, 53, 76, 90, 184, 215 trust 5, 7, 24, 25–6, 27, 52, 62, 73, 74, 85, 103, 111, 117, 118, 148, 150, 151, 154, 195 values 8, 9, 10, 33, 78, 88, 91, 98, 142, 143, 146, 191, 192, 193, 195–7, 202, 213 veracity 5, 6, 33, 45, 76, 95, 113, 117–19, 120, 123 virtual 142, 157, 158, 159, 201, 204 visualization 4, 6, 33, 34, 35, 38, 42, 44, 47, 59, 60, 61, 64, 65, 66, 68, 69, 103, 111, 113, 114, 120, 154, 213, 216 volunteer computing 218 volunteered geographic information 1, 46 Uber 26, 76, 156 urban: entrepreneurship 48; informatics 18, 47, 48, 114, 123; modelling 2, 33; science 4, 10, 18, 48, 47, 114 Web Services 7, 128, 129–131, 134, 135, 136, 138 wicked problems 20, 49, 122 wifi 53 World Council on City Data 90–1 

RDF Database Systems: Triples Storage and SPARQL Query Processing
by Olivier Cure and Guillaume Blin
Published 10 Dec 2014

See Hyper text transfer protocol (HTTP) Huffman algorithm, 88 Huffman tree, 82 Hu–Tucker front coding, 88 HypergraphDB system, 142 Hypertable, 34 Hyper text markup language (HTML), 4 Hyper text preprocessor (PHP), 4 library, 146 Hyper text transfer protocol (HTTP), 3 I IBM, 6, 105 IDB. See Intentional database (IDB) id-to-string, 81, 82, 84, 97 Infinite Graph system, 35 Infobright system, 19 Information technology (IT), 1 INGRES/Star system, 20 INSERT statement sequence, 163 Intentional database (IDB), 201 238 Index Internet Archive, 99 Internet of things (IoT), 3 Internet protocols, 5 IoT. See Internet of things (IoT) IRI, 41, 44 iStore system, 113 IT. See Information technology (IT) Iterated front coding, 89 J Java, 79 Java database connectivity (JDBC), 149 JavaScript, 4 JavaScript Object Notation (JSON), 2 document, 29 JDBC. See Java database connectivity (JDBC) Jena, 143 SDB, 120 TDB system, 120 Jena framework, 78 Jena SDB system, 126 Jena TDB system, 99, 158 Job tracker, 175 JSON.

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Among the most stunning recent values, we can highlight that Facebook announced that, by the beginning of 2014, it is recording 600 terabytes of data each day in its 300 petabytes data warehouse and an average of around 6,000 tweets are stored at Twitter per second with a record of 143,199 tweets on August 3, 2013. Introduction The Internet of Things (IoT) is another contributor to the Big Data ecosystem, which is just in its infancy but will certainly become a major data provider.This Internet branch is mainly concerned with machine-to-machine (M2M) communications that are evolving on a Web environment using Web standards such as Uniform Resource Identifiers (URIs), HyperText Transfer Protocol (HTTP), and representational state transfer (REST).

Amazon: How the World’s Most Relentless Retailer Will Continue to Revolutionize Commerce
by Natalie Berg and Miya Knights
Published 28 Jan 2019

Amazon partnered with department store retailer Kohl’s in late 2017 for an instore returns programme while, perhaps a less well-known example, Swiss giant Migros and e-tailer brack.ch have formed a similar click & collect arrangement in the name of better serving the customer, creating a more unified physical and digital retail experience. Pervasive computing: shopping without stores or screens Finally, we can’t talk about a blended online and offline shopping experience without mentioning the Internet of Things. When we think about the shopping experience blending into the background of consumers’ homes, we can already see the impact that AR and VR, as well as voice and simplified replenishment solutions, are having. They are supercharging consumers’ already high expectations, set by online, for speed, convenience, value and personalization.

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Dash Wand: handheld device that allows for barcode scanning and voice-activated reordering. Dash Virtual Buttons: as the name implies, one-click reordering buttons available on Amazon’s app and site. In a bid to stay on top of these new competitive threats, in 2017 Walmart filed a patent to integrate IoT (Internet of Things) into its actual products. Like Amazon’s Dash replenishment scheme, this would allow for automatic re-ordering of items without any input from the customer. The difference, of course, is that Walmart’s patent is for product-driven and not device-driven replenishment, which would generate more widespread usage and accelerate the trend.

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(and) 242–45 basic principles for retailers’ co-existence with Amazon 244 regulation and legislation 243 Connected Home 46 Connell, B (CEO, Target) 226 Co-op 209 see also Italy and Deliveroo delivery service 102 Costco 46, 181, 217 Cummins, M (CEO, Pointy) 172 Darvall, M (director of marketing and communications,Whistl) 215–16 Debenhams 81, 193, 194 definition(s) of showrooming 174 webrooming 168 Dhaliwal, T (MD, Iceland) 116 Diewald, G (head of Ikea US food operations) 189 digital automation and customer experience 165–85 see also ROBO and ZMOT the digital customer experience 176–83 see also subject entry location as a proxy for relevance 170–73, 173 research online, buy offline 167–70 the store as a showroom 174–76 see also definition(s) the digital customer experience (and) 176–83 see also robots digital points of purchase 179–80 the human touch, importance of 180–81 intelligent space 177–79 from self-checkout to no checkout 182–83 Dixons Carphone (Currys PC World) 188 membership scheme for use of washing machines, etc 201 drones 238 see also JD.com Prime Air 151 Dunn, A (CEO, Bonobos, 2016) 75 East, M (former M&S executive) 116 eBay 36, 216–17 and Shutl 217 e-commerce, growth of 48 Edison, T: quoted on failure 11 end of pure-play e-commerce: Amazon’s transition to bricks and mortar retailing 62–86 Amazon makes it move 77, 80–82, 78–79, 80 clicks chasing bricks – the end of online shopping 71–77 O2O: incentives for getting physical 72–75 cost of customer acquisition 74–75 shipping costs 73–74 O2O: who and how 75–77 key drivers of convergence of physical and digital retail (and) 66–71 click, collect and return 69–70 pervasive computing: shopping without stores or screens 70–71 role of mobile: frictionless, personalized experience 67–69 role of mobile: knowledge is power 66–67 next-generation retail: quest for omnichannel 63–66 electronic shelf labels (ESLs) 177–79 The Everything Store 6, 29 see also Stone, B Facebook 45, 76 Marketplace 213 Messenger purchasing bot 179 Payments 213 Fear of Missing Out (FOMO) 55 FedEx 224–25, 229 figures Amazon operating margin by segment 19 Amazon opened first checkout-free store, Amazon Go (2018) 109 Amazon’s first-ever bricks and mortar retail concept, Amazon Books, 2015 80 the flywheel: the key to Amazon’s success 7 growing complexity of fulfilling e-commerce customer orders 211 growing importance of services: Amazon net sales by business segment 18 Market Capitalization: Select US Retailers (7 June 2018) 6 new fulfilment options driving heightened complexity in retail supply chains 210 online-only is no longer enough: Amazon acquired Whole Foods Market (2017) 108 playing the long games: Amazon sales vs profits 12 top reasons why US consumers begin their product searches on Amazon 173 France (and) 2, 113 see also Auchan and Carrefour Amazon and Fauchon and Monoprix 103 ‘click and drive’ 208 Monoprix 236 frugality 9, 122 at Amazon, Mercadona and Walmart 9 Furphy, T 94 Galloway, S (NYU professor) 14 Generation Z 54 Germany (and) 2, 35, 209, 232 Amazon and Feneberg 103 H&M ‘Take Care’ service 49 Metro 191 retailer HIT Sütterlin 180 Rossman drugstore chain 236 unions call for strikes over Amazon workers’ pay rates (2013) 229 Gilboa, D (co-founder Warby Parker) 75 Gimeno, D (Chairman, El Corte Ingles) 52 Glass, D (CEO, Walmart) 50 global shipping market, worth of 230 Goldman Sachs 13 and independent factors correlating to online grocery adoption & profitability 88 Google 1, 14, 19, 45, 66, 76, 115, 154, 179 Assistant 157, 160 Checkout 213 DeepMind 159 Express 157, 160, 217 Home 153, 157 Knowledge Panel 171, 172 Maps 172, 177 Nest heating thermostat controller 155 Play 213 Search 157 See What’s In Store (SWIS) 171–72 Shopping Actions 157 What Amazon Can’t Do (WACD) 171 and ‘zero moment of truth’ (ZMOT) 171, 172 Great Recession 48, 122 Gurr, D (Amazon UK Country Manager UK, (2018) 21, 29, 44, 64 Ham, P 94 Hamleys: Moscow store mini-theme park 196 Han, L (General Manager of International Supply Chain, JD Logistics) 235 Harkaway, N 222 Herbrich, R (Amazon, director of machine learning) 150 Herrington, D 94 Home Depot 2, 157, 172 online returns instore 70 Huang, C (founder and CEO of Boxed, 2018) 71 Ikea (and) 71 acquires TaskRabbit (2018) 202 mobile AR 175 Place app 175 India 31, 116 see also Prime Video and Walmart Amazon Stella Flex service tested in 232 Instacart 89, 112–13, 119, 157, 216, 219, 224, 236 Sprouts teamed with 103 Intel and RealSense technology for ESLs (2018) 178 intelligence software: trialled by The Hershey Company, Pepsi and Walmart 178 Internet of Things (IoT) 70, 96 Italy 16, 209 see also Carrefour Co-op’s ‘store of the future’ in 191 James, S (Boots CEO) 55 Japan (and) 2, 35 Prime Video 31 Tokyo 102 Uniqlo 175–76 JD.com (and) 182–83, 230 7fresh 112, 183 BingoBox 182 Europe–China freight train (2018) 235 Logistics 235 online retail: opening 1000 stores a day in China 63 use of drones 238 John Lewis (and) see also Nickolds, P co-working space 193 customers staying overnight 187 ‘discovery room’ 200 Jones, G (CEO, Borders) 47 Kaness, M (CEO, Modcloth) 76 Kenney, M 190 Khan, L 242, 243 Kiva Systems 94, 151, 223 see also robots Kohl’s 2, 70, 81, 193, 233 Kopalle, Professor P 151 Kroger 2, 19, 46, 114–15, 208 see also case studies HomeChef 116 ‘Scan, Bag, Go’ 214–15 smart shelf solution 178 Kwon, E (former executive Amazon fashion) 127 Ladd, B 13, 115, 219 see also case studies Landry, S (VP, Amazon Prime Now) 218 the last-mile infrastructure 222–41 see also Amazon Amazon as a carrier 231–32 fulfilment by Amazon 232–33 growing IT infrastructure 226–29 last-mile labour 223–26 race for the last mile 233–36 real estate demand 229–31 remote innovation 236–38 Leahy, Sir T 62 Lebow, V 54, 122 see also articles/papers legislation (US) and calls for legislation to be rewritten and regulation of tech giants 243 Tax Act (2017) 16 Lego 195 allows building in-store 196–97 AR kiosks in stores (2010) and X app 175 Leung, L (Prime Director) 29 Levy, H P 147 Lidl 33, 51, 122, 209 Limp, D (Amazon Digital Devices SVP) 153 Liu, R (JD.com founder/chief executive) 182 lockers/collection lockers 74, 90, 112, 209–10, 233 emmasbox (Germany) 209 Lore, M (co-founder of Quidsi; CEO Walmart domestic e-commerce operations) 76–77, 97, 224, 235, 236 loyalty schemes 32–33 Ma, J (founder, Alibaba) 63 McAllister, I (Director of Alexa International) 10, 19 McBride, B (ASOS Chairman, former Amazon UK boss) 9 Mackey, J (Whole Foods Market CEO and Co-Founder) 107, 110 McDonalds McDelivery 218 in Walmart stores 189 McMillon, D (CEO, Walmart, 2017) 87, 89, 107 Macy’s 52, 69, 71, 172, 177, 193 New York store as ‘World’s Largest Store’ 50 Mahaney, M (RBC Capital Managing Director/analyst) 14, 111 Mansell, K (Chairman, President and CEO of Kohl) 233 Marks & Spencer (M&S) 49, 81, 193, 196 delivery service partnership with Gophr 102 Marseglia, M (Director, Amazon Prime) 101 Mastandrea, M 94 Mathrani, S (CEO of GGP) 49 Mehta, A (CEO, Instacart) 113 MercadoLibre as Latin America’s answer to eBay 36 Metrick, M (president, Saks Fifth Avenue) 190 Microsoft 19, 115 Bing 173 checkout-less store concept 182 Millennials 122, 144, 157 Miller, B (Miller Value Partners) 13 Millerberg, S (managing partner, One Click Retail) 158 Misener, P (Amazon VP for Global Innovation) 10 Mochet, J P (CEO of convenience banners, Casino Group, 2018) 192 Morrisons 102, 209, 217, 236 Mothercare 195, 196 Motley Fool 15 see also Bowman, J Mountz, M 94 Mulligan, J (chief operating officer, Target) 225–26 Musk, E 194 near-field communications (NFC) technology 178–79 Newemann, A (CEO WeWork) 192 Next 188 and pizza and prosecco bars instore 190 Nickolds, P (MD, John Lewis, 2017) 64 Nike 103 selling on Amazon 127 Nordstrom, E (Co-President, Nordstrom, 2017) 45 Nordstrom 135, 193 Local (launched 2017) 199 Ocado 19, 112–15, 135 see also Clarke, P and Steiner, T and Alexa 157 deal with Casino Groupe (2017) 113 Smart Platform 113 Olsavsky, B (Amazon CFO, 2018) 124 One Click Retail 90, 123, 129, 155, 158 online to offline (O2O) 63 capabilities 216 incentives for getting physical 72–75 who and how 75–77 Ovide, S (Bloomberg) 47, 119, 154 Park, D (co-founder, Tuft & Needle) 81 PayPal 45, 137, 213–14 Peapod 87 see also Bienkowski, C and ‘Ask Peapod’ skill for Alexa 156–58 Penner, G (Walmart Chairman, 2017) 77 Perrine, A (Amazon General Manager, 2018) 29 polls see reports Price, Lord M (former Waitrose MD) 51 Prime (and) 11, 14, 20, 92, 112, 121, 137, 153, 174, 210, 215, 217, 218, 222, 227 see also Prime 2.0; Prime Air; Prime ecosystem and Prime Now AmazonFresh 34 AmazonFresh Pickup 37 Day 32, 136, 147 Fresh Add-on 237 members 2 Pantry 34, 100–101, 226, 227 Video 30–31 Wardrobe 128, 226 Prime 2.0 (and) 38–40 ‘Invent and Simplify’ Leadership Principle 143 looking to new demographics for growth 39 more bells and whistles 38 more fee hikes 40 Prime Wardrobe (2017) 38–39 Prime Air 151 development centres U~S, Austria, France, Israel 238 first autonomous drone delivery 238 Prime ecosystem: redefining loyalty for today’s modern shopper (and) 28–40 advantages for Amazon 33–35 going global 35–36, 35–36 integrating Prime at point of sale 38 Prime 2.0 38–40 see also subject entry Prime as loyalty programme?

The Scandal of Money
by George Gilder
Published 23 Feb 2016

With transactional overhead dominated by offline financial infrastructure, micropayments are uneconomic, and the Internet fills with mendacious free goods, bogus contracts, and pop-up hustles. Some 36 percent of web pages are spurious, emitted by bots to snare information from unwary surfers.3 At the same time, Silicon Valley moves toward an “internet of things,” sensors and devices—from heart monitors and “smart grid” gauges to automated cars and heating systems—linked across the net and needing secure automated transactions without offline intermediaries. Reform of world money is less a far-fetched dream than a rising imperative. Gold and digital currencies converge to provide a new solution to the enigma of money.

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See Hayek money hedge funds, 4, 11, 102, 104, 129, 170 Heritage Foundation, xiv Hezbollah, 46 high-powered money, 32, 36, 53 Holdren, John, 3 Homo economicus, xvii–xx Hong Kong, 30, 36, 42, 49, 106 Huawei, 158 hypertrophy of finance, 13, 57, 87–89, 95, 97–111, 125, 132, 167–68, 170–71 I Iceland, 94, 117 Iger, Robert, 122 Immelt, Jeffrey, 130 income taxes, 91, 152 index funds, 131, 171 India, 45, 106, 118, 158 Indonesia, 110 Industrial Revolution, xx, 100, 158 second industrial revolution, 8 inequality, 3–6, 11, 23, 43, 53, 59, 87–92, 95–96, 106, 125 inflation, xi, 36–37, 62, 78–80, 85, 92, 138, 142, 146–47, 149, 153, 155–57, 159, 162 China and, 29–30 correctives to, 81–82, 92, 152, 157 debtors and, 80, 116 departure from gold standard and, 11 gold and, 22, 111–12, 158 Japan and, 108 monetarism and, 30, 33, 35, 53, 61, 79 recovery from Great Recession and, xvi, 56, 58 Volcker and, xv, 110 information age, 106 information economy, 1, 9, 93, 100, 149, 172 information theory, xviii, 20, 24, 31, 59, 62, 64, 71–72, 77, 84, 93, 95, 133, 138, 140, 143–44, 163, 168–69, 171, 174 initial public offerings (IPOs), 49–50, 115, 119–22, 154 injustice, 25–26 Institute for New Economic Thinking (INET), 88–89 Intel, 8, 119, 121, 169 interest rates, 19–20, 22, 35, 66, 109 Fed control of, 15, 24, 80, 114, 123, 132 financial recoveries and, xv high rates, xi, xv–xvi immorality of, 141–42 index of time, 14, 21, 24, 61, 113, 124–25, 141–42, 170 instability of, 11, 13–14, 155 low rates, xvii, 155 zero interest rates, 15, 24, 26, 54, 61, 92–94, 108, 110, 114, 123–25, 132, 142 Internal Revenue Service, 6 International System of Units (SI), 144–46 Internet, the, xviii, 13, 50, 53, 63–64, 73, 78, 80–81, 103, 113, 139, 144, 153, 170, 174 China and, 46–48 digital currencies and, 44, 67, 69–70, 72, 158–61, 163, 172 economy of, 13, 160–61 “internet of things,” 70 Internet Protocols, 8, 71 regulation of, xxii, 5 software stack, xxi, 170 spam, 70 iPhones, 18, 84, 153 Iran, 46 Ireland, 55, 57 Islam, 47, 141 Ivy League, xiv, 26 J Jackson Hole, WY, xvii Janszen, Eric, 107 Japan, 30, 36, 40, 88, 94, 99, 108 Jiang Mianheng, 43 Jiang Zemin, 42–43, 48–49, 51 JPMorgan Chase, 127 K Kahneman, Daniel, xx Keynes, John Maynard, 10, 49, 98 Keynesians, Keynesianism, xviii, xx, 34, 59, 147, 150, 154, 171–72 Kling, Arnold, 150 knowledge, xvii, 17, 93, 129, 131 learning and, 9 source of growth, 9, 14 suppression of, 15 wealth as knowledge, 17–18, 23–24, 31, 64, 100, 106, 108, 117, 124, 132–34, 140, 161, 163, 167–69, 172 Knowledge and Power (Gilder), 17 Koch brothers, xiv Krugman, Paul, xiv–xvii, 35, 54–56, 89, 98–99, 150 Kurzweil, Ray, 18, 169 Kwarteng, Kwasi, 10, 66 L Las Vegas, NV, xiv Laughlin, Robert, 133–34 learning, 127, 129 crucial to knowledge and wealth, 1, 9, 17–18, 23, 32, 64, 108, 124, 145, 163, 169 source of growth, 17, 22, 24, 26, 84–85, 100, 108, 117, 131, 134, 138, 140, 158, 169, 172 suppression of, 21, 24, 62, 67, 128–29, 131, 133, 144, 155 learning curves, xix, 18–19, 22, 37, 63, 75, 84–85, 95, 130, 155–56, 160–61, 169 Left, the, xx, 4, 86–87, 89 Lehrman, Lewis, 37 Lenovo, 158 libertarians, xiv–xvii, 30–31, 51, 53, 81 Likud Party, 153 Linear Technology, 119 Lipsky, Seth, 13 London, 8, 105, 134–35 M Mach, Ernst, 144 Main Street, xvii, xxii, 56, 87, 113–25, 127–29, 132, 149, 164, 170–71 Malpass, David, 58 Malthusianism, 3–4 Maoism, 30 Mao Zedong, 42, 47, 154 Marxism, Marxists, xviii, 3–4, 6 Mauldin, John, 40 McAdam, Lowell, 122 McKinnon, Ronald, 31, 97 McTigue, Maurice, 152 Medicaid, 15, 123 Medicare, 15 mergers and acquisitions (M&As), 121 Microsoft, 8, 119–20, 122, 160 middle class, the, 4, 118 anxieties in, xiii Chinese members of, 116 economic well-being of, xxii, 115, 120 harmed by economic policies, xxii, 15, 58–59, 66, 91–92, 95, 113–14, 119–20, 128 Mises, Ludwig von, xx, 102, 139–40 monetarism, monetarists Chinese rejection of, 38, 43–44, 51, 154 Milton Friedman and, 29, 32 tenets of theory, 32–38, 59, 92, 156, 171–72 versus information theory of money, 31, 61, 155 Monetary History of the United States (Friedman), 99 money connection to time, 9, 14, 17, 21–24, 61–68, 70–72, 74, 85–86, 93–96, 113, 134, 137–47, 150, 154–55, 157–58, 160–61, 163, 170–73 as measuring stick, 9, 21, 24, 49, 59, 63–64, 67, 73, 75, 78, 96, 108–9, 144–46, 157, 160, 173 money supply, 20, 29–37, 58, 80, 92–93, 98–99, 158 monopoly money, xxii, 1, 34, 53, 58, 81, 92, 124, 149, 155, 157, 163–64, 167, 170–72 Moore, Gordon, 169 Moore, Steve, xiv–xv, xvii Moore’s Law, 18, 64, 160, 169 Morgan Stanley, 127 Mundell, Robert, 43–44, 51 Mundell International University of Entrepreneurship, 44 mutual funds, 130 N Nadella, Satya, 122 NASDAQ exchange, 49, 121 National Bureau of Economic Research, 56 National Interstate and Defense Highways Act, xxii National Review, 35 Netflix, 122 Netscape, 115 Nevada, 55 New Deal, 87 New Hampshire, 2, 41 Newton, Isaac, xx, 63, 136 New York, NY, 74, 95 New York Times, xiv, 54, 122 New Zealand, 152–53 Nixon, Richard, 9–10, 12, 41, 98–99 Nobel Prize, xx, 29, 31, 36, 43, 89, 133, 150 Nordhaus, William, 19 North America, 118 North Dakota, 55 North Korea, 46 O Obama, Barack, xi–xii, xv–xvi, xxi, 4 economy under, xi–xiii, xvi presidential administration of, 3, 40, 99 Obamacare, 6 Office of Price Administration, 150 oil, 11–12, 81, 83, 116, 158, 168 One Percent, xvii, 3 Oracle, 119 Organisation for Economic Co-operation and Development, 82 P Page, Larry, 122 Palantir, 121 Palo Alto, CA, 118, 171 Parks, Larry, 157 People’s Bank of China, 48 People’s Liberation Army, 42 Piketty, Thomas, 3–6, 9, 15, 19, 87–96, 110, 141–42, 150 polymerase chain reaction (PCR), xxi, 8 Ponnuru, Ramesh, 35 Popper, Karl, 89, 169 Popular Economics (Tamny), 11 Portugal, 55 principal trading funds (PTFs), 102, 104 producer price index (PPI), 81 Progress and Poverty (George), 90 Q Qualcomm, 115, 119, 160 quantitative easing, 14, 54, 80, 114, 123, 147, 157 R Rand, Ayn, xviii, 137 Reagan, Ronald, xi, xiv–xvii, 12, 40–41, 51, 115 real estate, xvi–xvii, 11, 13, 26, 37, 55, 66, 88, 90, 92–95, 117, 168, 173 Reason, 63 regulation, xiii–xiv, xvi, xix, xxii, 6, 26, 29, 31, 35, 39, 50, 57, 80, 88, 91, 93–94, 96, 122, 128, 130, 151, 154, 167 Reich, Robert, xxi Republican Party, Republicans, xii–xiv, xvii, 25, 35, 150 Right, the, 40, 89 Robinson, Arthur, 48 Romer, Christina, 99–100 Rueff, Jacques, 36–37 S Samsung, 158 Samuelson, Paul, 10, 150 Sanders, Bernie, xxii San Francisco, CA, 95 Sarbanes-Oxley, 49, 122 Satoshi Nakamoto, 64, 72–73, 172 Schacht, Hjalmar, 110 Schiff, Peter, 53 Schumpeter, Joseph, 83 science of information, 17 second industrial revolution, 8 second law of thermodynamics, 62, 142 secular stagnation, 3, 26, 56, 89, 91–92, 150–51.

Team Human
by Douglas Rushkoff
Published 22 Jan 2019

This is how products you may have looked at on one website magically show up as advertisements on the next. And that’s just a primitive, obvious example of what’s going on behind the scenes. The AIs are in constant communication, sharing with one another what they have learned by interacting with us. They are networked and learning. The internet of things, or IOT as its proponents like to call it, is a name for the physical objects in this tremendous network of chips and algorithms seeking to understand and manipulate us. While a networked thermostat or baby monitor may have certain advantages for the consumer, its primary value is for the network to learn about our behaviors or simply extract data from them in order to place us in ever more granular statistical categories.

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They then share this information with the other bots on the network for them to try on other humans. Each one of us is not just up against whichever algorithm is attempting to control us, but up against them all. If plants bind energy, animals bind space, and humans bind time, then what do networked algorithms bind? They bind us. On the internet of things, we the people are the things. Human ideals such as autonomy, social contact, and learning are again written out of the equation, as the algorithms’ programming steers everyone and everything toward instrumental ends. While human beings in a digital environment become more like machines, entities composed of digital materials—the algorithms—become more like living entities.

Small Wars, Big Data: The Information Revolution in Modern Conflict
by Eli Berman , Joseph H. Felter , Jacob N. Shapiro and Vestal Mcintyre
Published 12 May 2018

Caverly, “The Myth of Military Myopia: Democracy, Small Wars, and Vietnam,” International Security 34, no. 3 (2010): 119–57. 4. World Bank, World Development Report 2011: Conflict, Security and Development (Washington, DC: World Bank, 2011). 5. For a review of the possibilities, see U.K. Government Office for Science, “The Internet of Things: Making the Most of the Second Digital Revolution,” https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/409774/14-1230-internet-of-things-review.pdf, accessed 23 February 2016. 6. Xin Lu, Linus Bengtsson, and Petter Holme, “Predictability of Population Displacement after the 2010 Haiti Earthquake,” Proceedings of the National Academy of Sciences 109, no. 29 (2012): 11576–81. 7.

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BIG DATA The first trend motivating our book is that small wars and their tragic costs are here to stay; the second is that society is increasingly using data to understand our world. Talk of “big data” is ubiquitous, but what professionals mean by the term is not so much that there’s more data available—which of course there is—but that we have a growing set of computational and analytical tools to learn from it. The currently proliferating Internet of Things, for example, is already sending data from previously unconnected objects, like watches, toys, thermostats, pacemakers, and pet collars, back for analysis, informing decisions by doctors, government, manufacturers, and service providers. That should target products to suit our tastes and habits, save energy, and make us safer.

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See also intrastate wars Integrity Watch Afghanistan, 287 intelligence, effect of, on casualties, 272–73 intent, harm judged by, 188 International Humanitarian Law, 109, 150 International Military Intervention (IMI) Dataset, xii, 11, 295 International Security Assistance Force (ISAF), 13–14, 23–24, 31–32, 36, 86, 89, 185, 186, 196–98, 208, 211, 219–21, 305–6, 357n35 Internet of Things, 12 interstate wars, x intrastate wars: character of, x; consequences of, ix, xiii–xiv; economic conditions underlying, 226–27; fatalities in, ix, x, xi; global effects of, xiii–xiv; rise of, x–xiii, xi. See also insurgents and insurgency investment, violence in relation to, 257–58 Iran, xiv, 100, 302 Iraq, ix, x, xiv; asymmetric warfare in, 8–9; cellular coverage and insurgent violence in, 87–91, 101, 106–8; civilian casualties in, 200–202; combat incidents in, 35, 36; counterinsurgency failure in, 306–7; development assistance in, 109–13, 123–28, 146–48; effects of the surge on, 45–46; employment-violence relationship in, 242–43; insurgency tactics in, 5–6; Islamic State in, 106–8; oil pipeline reconstruction in, 163–64; part-time insurgents in, 235; service provision in, 79; solatia in, 191–92; surge strategy in, 167, 170–77, 274, 306–7, 353n46, 353n50; U.S. military in, 2–7 Iraq Body Count (IBC), 45, 171, 200, 205, 353n42, 358n45, 358n47 Iraq Reconstruction Management System (IRMS) database, 127 Iraq Relief and Reconstruction Fund, 109, 113, 115 Iraqna, 264 Irish Republican Army, 79 IS.

Army of None: Autonomous Weapons and the Future of War
by Paul Scharre
Published 23 Apr 2018

Next year, TJ will offer a course in computer vision that will cover convolutional neural networks. Maybe it’s a cliché to say that the projects students were working on are mind-blowing, but I was floored by the things I saw TJ students doing. One student was disassembling a Keurig machine and turning it into a net-enabled coffeemaker so it could join the Internet of Things. Wires snaked through it as though the internet was physically infiltrating the coffeemaker, like Star Trek’s Borg. Another student was tinkering with something that looked like a cross between a 1980s Nintendo Power Glove and an Apple smartwatch. He explained it was a “gauntlet,” like that used by Iron Man.

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Brumley’s aim with Mayhem isn’t to beat the best human hackers, though. He has something far more practical—and transformative—in mind. He wants to fundamentally change computer security. As the internet colonizes physical objects all around us—bringing toasters, watches, cars, thermostats and other household objects online in the Internet of Things (IoT), this digitization and connectivity also bring vulnerabilities. In October 2016, a botnet called Mirai hijacked everyday networked devices such as printers, routers, DVR machines, and security cameras and leveraged them for a massive DDoS attack. Brumley said most IoT devices are “ridiculously vulnerable.”

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Abbreviations AAA antiaircraft artillery ABM Anti-Ballistic Missile ACTUV Anti-submarine warfare Continuous Trail Unmanned Vessel AGI artificial general intelligence AGM air-to-ground missile AI artificial intelligence AMRAAM Advanced Medium-Range Air-to-Air Missile ARPA Advanced Research Projects Agency ASI artificial superintelligence ASW anti-submarine warfare ATR automatic target recognition BDA battle damage assessment BWC Biological Weapons Convention CCW Convention on Certain Conventional Weapons C&D Command and Decision CIC combat information center CIWS Close-In Weapon System CODE Collaborative Operations in Denied Environments DARPA Defense Advanced Research Projects Agency DDoS distributed denial of service DIY do-it-yourself DMZ demilitarized zone DoD Department of Defense FAA Federal Aviation Administration FIAC fast inshore attack craft FIS Fire Inhibit Switch FLA Fast Lightweight Autonomy GGE Group of Governmental Experts GPS global positioning system ICRAC International Committee for Robot Arms Control ICRC International Committee of the Red Cross IEEE Institute of Electrical and Electronics Engineers IFF identification friend or foe IHL international humanitarian law IMU inertial measurement unit INF Intermediate-Range Nuclear Forces IoT Internet of Things J-UCAS Joint Unmanned Combat Air Systems LIDAR light detection and ranging LOCAAS Low Cost Autonomous Attack System LRASM Long-Range Anti-Ship Missile MAD mutual assured destruction MARS Mobile Autonomous Robotic System MMW millimeter-wave NASA National Aeronautics and Space Administration NGO nongovernmental organization NORAD North American Aerospace Defense Command ONR Office of Naval Research OODA observe, orient, decide, act OPM Office of Personnel Management PGM precision-guided munition PLC programmable logic controllers RAS IEEE Robotics and Automation Society R&D research and development ROE rules of engagement SAG surface action group SAR synthetic aperture radar SAW Squad Automatic Weapon SEC Securities and Exchange Commission SFW Sensor Fuzed Weapon SORT Strategic Offensive Reductions Treaty START Strategic Arms Reduction Treaty SUBSAFE Submarine Safety TASM Tomahawk Anti-Ship Missile TBM tactical ballistic missile TJ Thomas Jefferson High School TLAM Tomahawk Land Attack Missile TRACE Target Recognition and Adaption in Contested Environments TTO Tactical Technology Office TTP tactics, techniques, and procedures UAV uninhabited aerial vehicle UCAV uninhabited combat aerial vehicle UK United Kingdom UN United Nations UNIDIR UN Institute for Disarmament Research U.S.

Spies, Lies, and Algorithms: The History and Future of American Intelligence
by Amy B. Zegart
Published 6 Nov 2021

National Security Commission on Artificial Intelligence, “Interim Report,” November 2019, https://science.house.gov/imo/media/doc/Schmidt%20Testimony%20Attachment.pdf (accessed September 26, 2020), 6. 9. Fredrik Dahlqvist, Mark Patel, Alexander Rajko, and Jonathan Shulman, “Growing Opportunities in the Internet of Things,” McKinsey & Company, July 22, 2019, https://www.mckinsey.com/industries/private-equity-and-principal-investors/our-insights/growing-opportunities-in-the-internet-of-things# (accessed September 24, 2020). 10. Cade Metz, “Google Claims a Quantum Breakthrough That Could Change Computing,” New York Times, October 23, 2019, https://www.nytimes.com/2019/10/23/technology/quantum-computing-google.html (accessed September 24, 2020). 11.

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Internet connectivity is supercharging politics, fueling protest movements like the Arab Spring and Hong Kong’s Umbrella Movement, repressive crackdowns like China’s persecution of the Uighurs, and Russian information warfare campaigns that reach deep into the societies of other nations. The so-called Internet of Things (everyday devices with Internet connections) is spreading to billions of toys, cars, appliances, and more—and bringing cyber vulnerabilities with it.9 Facebook algorithms are deciding what news we read and influencing how we think, enabling the manipulation of populations at scale. There is greater upheaval still to come.

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See also coordination of U.S. intelligence; employees of IC Intelligence Reform and Terrorism Prevention Act of 2004, 70–71 intentions, predicting: difficulty of, 81–82; and fundamental attribution error, 123–24; by projection, 124–26 Internet: and breech of CIA computer system, 167–68; challenges created by, 2–3; and conspiracy theories, 37–38; and public access to open-source information, 234–35; and vulnerability to attack, 8. See also social media Internet of Things, 2–3, 271 Internet Research Agency (IRA), 251 interrogation. See counterterrorism interrogation methods Iran: bin Laden’s son under arrest in, 102–3; covert action in, 63, 174, 176, 183, 184; cyberattacks by, 261, 263, 266–67; hostage crisis, 178; hostage rescue efforts, 64, 106, 178, 180; Internet influence campaigns, 243, 266–67; Iran-Contra affair, 182–83, 344n105, 346–47n151; monitoring of, 72, 79, 89, 121, 225–26, 239–40, 248; nuclear weapons program, 112–13, 225–26, 229–30, 231, 239–40, 263, 264; offensive cyber operations against, 226, 263, 264, 269; Shah, U.S. support for, 183; spying on U.S., 146–47; use of drones, 282n19 Iraq: covert action in, 174, 175, 176, 192; drone strikes in, 175; and Gulf War, 157, 272; Iraq Survey Group, 120; monitoring of, 124; Saddam bomb hoax, 244–45; use of drones, 282n19; and U.S.

Four Battlegrounds
by Paul Scharre
Published 18 Jan 2023

Yet as information technology proliferated, it leveled the playing field among nations, allowing other countries to procure their own precision-guided weapons and build their own balkanized, censored portions of the internet. Even as information technology diffuses around the globe, the information revolution continues to mature. Big data, Internet of Things (IoT) devices, high-speed wireless networking, autonomous systems, and machine learning are just some of the digital systems transforming industries and society today. The current wave of digital innovation is highly globalized, with centers of gravity in the United States, China, and Europe.

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ABBREVIATIONS ABC American Broadcasting Company ACE Air Combat Evolution ACLU American Civil Liberties Union AFWERX Air Force Works AGI artificial general intelligence AI artificial intelligence AIDS acquired immunodeficiency syndrome ALS amyotrophic lateral sclerosis (also known as Lou Gehrig’s disease) ASIC application-specific integrated circuit AU African Union AWACS airborne warning and control system AWCFT Algorithmic Warfare Cross-Functional Team BAAI Beijing Academy of Artificial Intelligence BBC British Broadcasting Corporation BERT Bidirectional Encoder Representations from Transformers BCE before common era C4ISR Command, Control, Communication, Cloud, Intelligence, Surveillance, and Reconnaissance CBC Canadian Broadcasting Corporation CBP Customs and Border Patrol CCP Chinese Communist Party CEIEC China National Electronics Import and Export Corporation CEO chief executive officer CFIUS Committee on Foreign Investment in the United States CIA Central Intelligence Agency CLIP Contrastive Language–Image Pretraining CMU Carnegie Mellon University COBOL common business-oriented language COVID coronavirus disease CPU central processing unit CSAIL Computer Science and Artificial Intelligence Laboratory DARPA Defense Advanced Research Projects Agency DC District of Columbia DDS Defense Digital Service DEA Drug Enforcement Administration DIU Defense Innovation Unit DIUx Defense Innovation Unit—Experimental DNA deoxyribonucleic acid DoD Department of Defense EOD explosive ordnance disposal EPA Environmental Protection Agency ERDCWERX Engineer Research and Development Center Works EU European Union EUV extreme ultraviolet FBI Federal Bureau of Investigation FedRAMP Federal Risk and Authorization Management Program FEMA Federal Emergency Management Agency FOUO For Official Use Only FPGA field-programmable gate arrays GAN generative adversarial network GAO Government Accountability Office GB gigabytes GDP gross domestic product GDPR General Data Protection Regulation GIF graphics interchange format GNP gross national product GPS global positioning system GPU graphics processing unit HA/DR humanitarian assistance / disaster relief HUD head-up display IARPA Intelligence Advanced Research Projects Activity ICE Immigration and Customs Enforcement IEC International Electrotechnical Commission IED improvised explosive device IEEE Institute for Electrical and Electronics Engineers IJOP Integrated Joint Operations Platform IoT Internet of Things IP intellectual property IP internet protocol ISIS Islamic State of Iraq and Syria ISO International Organization for Standardization ISR intelligence, surveillance, and reconnaissance ITU International Telecommunication Union JAIC Joint Artificial Intelligence Center JEDI Joint Enterprise Defense Infrastructure KGB Komitet Gosudarstvennoy Bezopasnosti (Комитет государственной безопасности) MAGA Make America Great Again MAVLab Micro Air Vehicle Lab MIRI Machine Intelligence Research Institute MIT Massachusetts Institute of Technology MPS Ministry of Public Service MRAP mine-resistant ambush protected NASA National Aeronautics and Space Administration NATO North Atlantic Treaty Organization NBC National Broadcasting Company NGA National Geospatial-Intelligence Agency NLG Natural Language Generation nm nanometer NOAA National Oceanic and Atmosphere Administration NREC National Robotics Engineering Center NSIC National Security Innovation Capital NSIN National Security Innovation Network NUDT National University of Defense Technology OTA other transaction authority PhD doctor of philosophy PLA People’s Liberation Army QR code quick response code R&D research and development RFP request for proposals RYaN Raketno Yadernoye Napadenie (Ракетно ядерное нападение) [nuclear missile attack] SEAL sea, air, land SMIC Semiconductor Manufacturing International Corporation SOFWERX Special Operations Forces Works SpaceWERX Space Force Works STEM science, technology, engineering, and mathematics TEVV test and evaluation, verification and validation TPU Tensor Processing Unit TRACE Target Recognition and Adaptation in Contested Environments TSA Transportation Security Administration TSMC Taiwan Semiconductor Manufacturing Company TTC Trade and Technology Council UAV unmanned aerial vehicle UK United Kingdom UN United Nations U.S.

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INTRODUCTION 1air combat maneuvering: Naval Air Training Command, Flight Training Instruction: Basic Fighter Maneuvering (BFM) Advanced NFO T-45C/VMTS (Corpus Christie, TX: Department of the Navy, 2018), https://www.cnatra.navy.mil/local/docs/pat-pubs/P-826.pdf. 1one of the most challenging skills: Colin “Farva” Price, “Navy F/A-18 Squadron Commander’s Take on AI Repeatedly Beating Real Pilot In Dogfight,” The Drive, August 24, 2020, https://www.thedrive.com/the-war-zone/35947/navy-f-a-18-squadron-commanders-take-on-ai-repeatedly-beating-real-pilot-in-dogfight. 1air combat has evolved: John Stillion, Trends in Air-to-Air Combat: Implications for Future Air Superiority (Center for Strategic and Budgetary Assessments, 2015), https://csbaonline.org/uploads/documents/Air-to-Air-Report-.pdf. 1“department of mad scientists”: Michael Belfiore, The Department of Mad Scientists: How DARPA Is Remaking Our World, from the Internet to Artificial Limbs (New York: HarperCollins, 2010), https://www.amazon.com/Department-Mad-Scientists-Remaking-Artificial/dp/0062000659. 2ACE program: “Training AI to Win a Dogfight,” Defense Advanced Research Projects Agency, May 8, 2019, https://www.darpa.mil/news-events/2019-05-08. 2flew with superhuman precision: Price, “Navy F/A-18 Squadron Commander’s Take.” 2forward-quarter gunshots: Naval Air Training Command, Flight Training Instruction. 2maneuvering to avoid a collision: Price, “Navy F/A-18 Squadron Commander’s Take.” 2“a gunshot that is almost impossible”: Joseph Trevithick, “AI Claims ‘Flawless Victory’ Going Undefeated In Digital Dogfight With Human Fighter Pilot,” The Drive, August 20, 2020, https://www.thedrive.com/the-war-zone/35888/ai-claims-flawless-victory-going-undefeated-in-digital-dogfight-with-human-fighter-pilot. 3level of g-forces: Trevithick, “AI Claims ‘Flawless Victory.’” 3“The human will always be the limiting factor”: Price, “Navy F/A-18 Squadron Commander’s Take.” 3live flight demonstration: “Collaborative Air Combat Autonomy Program Makes Strides,” Defense Advanced Research Projects Agency, March 18, 2021, https://www.darpa.mil/news-events/2021-03-18a. 3“We envision a future”: “Training AI to Win a Dogfight.” 3another industrial revolution: Klaus Schwab, “The Fourth Industrial Revolution: What It Means, How to Respond,” World Economic Forum, January 14, 2016, https://www.weforum.org/agenda/2016/01/the-fourth-industrial-revolution-what-it-means-and-how-to-respond/; Robot Revolution—Global Robot & AI Primer (Bank of America Merrill Lynch, December 16, 2015), https://www.bofaml.com/content/dam/boamlimages/documents/PDFs/robotics_and_ai_condensed_primer.pdf (page discontinued); Rob Thomas, “How AI Is Driving the New Industrial Revolution,” Forbes, March 4, 2020, https://www.forbes.com/sites/ibm/2020/03/04/how-ai-is-driving-the-new-industrial-revolution/#7225c870131a; Sean Gallagher, “The Fourth Industrial Revolution Emerges from AI and the Internet of Things,” Ars Technica, June 18, 2019, https://arstechnica.com/information-technology/2019/06/the-revolution-will-be-roboticized-how-ai-is-driving-industry-4-0/. 4nearly half of all tasks: James Manyika et al., Harnessing Automation for a Future That Works (McKinsey Global Institute, January 2017), https://www.mckinsey.com/featured-insights/digital-disruption/harnessing-automation-for-a-future-that-works. 4“[AI] will enliven inert objects”: Kevin Kelly, “The Three Breakthroughs That Have Finally Unleashed AI on the World,” Wired, October 27, 2014, https://www.wired.com/2014/10/future-of-artificial-intelligence/. 4AI has applications: For some examples of security-related applications of artificial intelligence, see Miles Brundage et al., The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation (February 2018), https://maliciousaireport.com/; and Michael C.

The Second Curve: Thoughts on Reinventing Society
by Charles Handy
Published 12 Mar 2015

Carelessly handing my credit card to a waiter to take away to pay the bill, I later found that I had apparently bought an expensive flat-screen television the next day. None of these experiences is now uncommon. Identity theft was a term unknown to my parents, but then so were computers. Delighted by the new ‘internet of things’, we snap up the app that will park your car for you or find your lost key; the internet-enabled home even has a device that allows your phone to unlock your door without your having to take it out of your pocket. It is gadget heaven, until someone else gets hold of that phone. Every Second Curve brings its own learning curve, until we eventually work out how to live with its consequences.

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Much of our lives will be organised by algorithms and computer-controlled systems. It will be, some say, a world where humans service the machines rather than the other way round, science fiction become fact. The new servants, better termed technicians, will need to be highly skilled, but, and here’s the rub, few in number. I am unconvinced. Computers and the internet of things may remove some of the drudgery of life, but we humans will not lightly surrender our lives to machines, particularly when those machines may one day be able to think for themselves. People will always congregate to create things, to gain power or influence, to make money or to help and care for others, things machines cannot do.

Designing Reactive Systems: The Role of Actors in Distributed Architecture
by Hugh McKee
Published 5 Sep 2016

Actors may be cluster aware and designed to be notified when nodes join or leave the cluster. This can be used to react to the cluster changes. Chapter 5. Actors in an IoT Application In this final chapter, let’s work through a more realistic example of using actors to implement features in a real-life system. In this example, we are responsible for building an Internet of Things (IoT) application, in which we currently have hundreds of thousands of devices that are monitored continuously (with the expectation of this to grow over time into the millions). Each device periodically feeds status data back to the application over the Internet. We decide that we want to represent each device with an actor that maintains the state of the device in our system.

An Optimist's Tour of the Future
by Mark Stevenson
Published 4 Dec 2010

If you wish, your mobile will remember where you have been and will keep track of … objects such as your briefcase, car keys and glasses. ‘Where are my glasses?’ you will ask. ‘You were last within … reach of them while in the living room,’ your mobile or laptop will say. ‘There’s the “Internet of things” in addition to the Internet of people and ideas,’ says Vint. As computing technology continues to get smaller, almost every object has the potential to become a node on the Internet. It’s a world where if you’ve lost your keys you’ll Google them, where your fire alarm will call you to let you know it’s been activated, and your toothpaste orders more of itself as you reach the end of the tube.

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Well, there are hostile actions going on every day all the time and they’re capable of rendering parts of the ’Net inoperable but I don’t think the machine would stop in and of itself.’ Technology’s story is our story. Burke’s ‘warm blanket of technology’ isn’t separate from us, we’re woven into the fabric of it and vice versa. And in the next chapter of the Internet’s story, intertwined with ‘the Internet of things’ is something called ‘augmented reality,’ a phrase that strikes the same fear into my heart as those thin yellow burger slices that are ‘cheese flavoured’ and not actual cheese. What’s wrong with real reality then? A man walks into a shop and picks up a packet of paper towels. As he does so, an image appears on the packet telling him how much bleach was used in its manufacture.

…

Dan argued that concentrating on energy efficiency is the best thing we can do for the planet in the short term. ‘I apologise that this is not the exciting stuff and therefore may not make it into your book, but the low-hanging fruit is doing more with less; energy efficiency across the entire economy.’ Part of that solution will be the ‘Internet of things’ I talked about with Vint Cerf. ‘We’re starting to see smart appliances entering the market,’ Dan said. ‘The more they can talk to each other and your electricity supply about how much they’re consuming, the more they’ll be able to coordinate to use less electricity. Do I really care whether my dishwasher runs at six o’clock when it’s a hundred degrees out and the electricity system is browning out because everyone’s got their air-conditioning on, or whether it runs at three in the morning at half the cost with a lot less impact?

The Deep Learning Revolution (The MIT Press)
by Terrence J. Sejnowski
Published 27 Sep 2018

Cade Metz, “Uncle Sam Wants Your Deep Neural Networks,” New York Times, June 22, 2017, https://www.nytimes .com/2017/06/22/technology/homeland-security-artificial-intelligence-neural -network.html. Notes 307 2. For a video of my lecture “Cognitive Computing: Past and Present,” see https:// www.youtube.com/watch?v=0BDMQuphd-Q. 3. See Jen Clark, “The Countdown to IBM’s IoT, Munich,” IBM Internet of Things (blog), posted February 8, 2017. https://www.ibm.com/blogs/internet-of-things/ countdown-ibms-iot-hq-munich/. 4. The BRAIN report made recommendations and set priorities for innovative technologies to help advance our understanding of neural circuits and behavior. BRAIN Working Group, BRAIN 2025: A Scientific Vision, Report to the Advisory Committee to the Director, NIH (Bethesda, MD: National Institutes of Health, June 5, 2014), https://www.braininitiative.nih.gov/pdf/BRAIN2025_508C.pdf. 5.

…

Watson can answer questions and make recommendations that are based on more data than any human could possibly know, although, of course, as with other machine learning programs, it still takes humans to ask the questions and choose among the recommendations made. 172 Chapter 12 IBM had long since parted with its hardware division, and its computer services division was no longer competitive. By banking on Watson, IBM was counting on its software division to help replace a $70 billion revenue stream. The company has invested $200 million in a new global headquarters for its Watson Internet of Things business in Munich,3 one of IBM’s largest investments ever in Europe in response to growing demand from more than 6,000 customers who want to transform their operations with artificial intelligence—and only part of the company’s global plan to invest $3 billion in cognitive computing. But many other companies are also making major investments into AI and it is too early to say which bets will be winners, and who will be the losers.

Talk to Me: How Voice Computing Will Transform the Way We Live, Work, and Think
by James Vlahos
Published 1 Mar 2019

Moments later, the system, having scoured the internet for listings, called back. “The following new advertisements meet your search criteria,” a robotic voice intoned. Cheyer continued his experiments with natural-language interfaces, developing prototypes of technologies that were to proliferate years later with the rise of the Internet of Things. He and his colleagues created a speech-controlled refrigerator that could answer whether it held any ice cream and an automotive navigation system that could provide directions to restaurants and gas stations. But the technological prehistory of Siri still had its most important chapter yet to come, and this one involved a crucial new player: the U.S. military.

…

xiii The city is hosting the annual Consumer Electronics Show: Details from multiple press accounts, including Jared Newman, “How Amazon and Google’s AI Assistant War Made CES Relevant Again,” Fast Company, January 17, 2018, https://goo.gl/tsY8Jb; Brian Heater, “Google Assistant had a good CES,” TechCrunch, January 13, 2018, https://goo.gl/wvRmCj; and Will Oremus, “The Internet of Things That Won’t Shut Up,” Slate, January 7, 2018, https://goo.gl/4AS6t5. xiii Amid the cacophony: Details about voice applications from “What can I do for you,” Google Assistant website, https://goo.gl/2TQPPu, and Amazon listing of available Alexa skills, https://goo.gl/qagcGL. xvi “We’re living in that future”: Patrick Seitz, “Amazon Seeks ‘Star Trek’ Level Conversations For Alexa Assistant,” Investor’s Business Daily, January 10, 2018, https://goo.gl/KdTfFT. 1.

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A., 68 Hollingshead, John, 68 Holocaust survivors, 272–74 holograms, 273 Holtzman, Ari, 163–64 HomePod, 218, 225, 280 homonyms, 112 homophones, 97 Horsley, Scott, 214–15 Houdin, Jean-Eugène Robert, 19 Houston, Farah, 133, 134 Huffman, Scott, 49 human brain, 86–87 Hunt, Troy, 230 I IBM, 3, 71, 97, 108, 205 ICT (Institute for Creative Technologies), 244–46, 272–74 ImageNet Large-Scale Visual Recognition Challenge, 93–94 image recognition, 87–88, 90, 91–94, 103 immortality, virtual. See virtual immortality information retrieval (IR), 103–4, 146, 149–50, 160 InspiroBot, 108 Institute for Creative Technologies (ICT), 244–46, 272–74 intents, 257, 262 interactive voice response (IVR), 127 Internet of Things, 21–22 internet search technology, 3, 26, 54, 199–200, 203, 212, 278. See also question answering Invoke, 281 iPhone Evi app on, 203–4 sales of, 45 Siri and, 8, 17–18, 37, 47, 50, 212, 225 speech recognition and, 95 unveiling of, 7, 25 voice search app, 48 IR (information retrieval), 103–4, 146, 149–50, 160 Iris, 29 Irson, Thomas, 65 Isbitski, David, xvi Ishiguro, Hiroshi, 190–91 Ivona, 41–42 IVR (interactive voice response), 127 J Jack in the Box, 46 Jackson, Samuel L., 46 Jacob, Oren, 134, 171–73, 196, 253 Jarvis, 51 Jobs, Steve, 7, 34–37, 47, 48, 172 journalism, AI, 214–16 Julia (chatbot), 80–84, 98 Julia, Luc, 47 K Kahn, Peter, 192, 244 Karim (therapist chatbot), 246 Kasisto, 132 Kay, Tim, ix–x, xii–xiii, 13 Kelly, John, 110 Kempelen, Wolfgang von, 65–67, 69 Kim Jong Un, 217 Kindle, 41 Kismet (robot), 191–92 Kittlaus, Dag, 23–29, 32–37, 46–47, 55, 279 Kleber, Sophie, 278 Klein, Stephen, xiii knowledge, control of, 220 knowledge-based AI, 76–78, 84, 159, 161–63 Knowledge Graph, 204, 206, 212 knowledge graphs, 201–2, 204–5, 213 Knowledge Navigator, 16–18, 27 Krizhevsky, Alex, 93, 94 Kunze, Lauren, 256 Kurzweil, Fredric, 274–76 Kurzweil, Ray, 274–76, 277 Kuyda, Eugenia, 186–88, 196 Kuznetsov, Phillip, 261 Kylie.ai, 107 L L2, 209 language and human species, 4, 285–86 language models for ASR, 96–97 Lasseter, John, 172 Lawson, Lindsey, 169, 182 Le, Quoc, 93, 97, 105–6, 254 LeCun, Yann, 89, 91–92, 93–94, 161 Lemon, Oliver, 145–46, 148, 158, 159 Lenat, Doug, 161, 162 Levitan, Peter, ix, xi, xii–xiii Levy, Steven, 79 Lewis, Thor, 138 Lieberman, Philip, 14 LifePod, 239 Lindbeck, Erica, 179–80 Lindsay, Al, 41, 44 linguistics, 127 linguistics, computational, 72 lip reading, 98 Loebner Prize competition, 82–84, 142, 160, 285 long short-term memory (LSTM), 106 Loup Ventures, 213 Love, Rachel, 271 LSTM (long short-term memory), 106 Luka, 186–87 Lycos, 79 Lyrebird, 114–15, 217 M M (virtual assistant), 51–52 machine learning.

Superminds: The Surprising Power of People and Computers Thinking Together
by Thomas W. Malone
Published 14 May 2018

United Parcel Service, for instance, has used this approach to make preventive-maintenance decisions for its fleet of 60,000 vehicles, saving millions of dollars by doing so.9 General Electric has built a whole new software business unit to profit from feedback about the equipment it sells. For instance, it expects to make very significant revenues from selling after-sales services, including maintenance plans based on predictive analytics using this new kind of data.10 All these examples illustrate the broader phenomenon called the Internet of things, in which physical objects—cars, houses, power plants, and many others—are connected to the Internet, gathering massive amounts of data and often acting on it automatically. PRIVACY AND INFORMATION AS PROPERTY The technologies that make it possible to analyze big data and do new kinds of collective sensing also raise important issues about who owns the data and who has the right to use it.

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CYBER-HUMAN SENSING Regardless of what is kept private and what isn’t, it seems almost inevitable that more and more things will be electronically sensed and recorded in the future. We already record vast amounts of information in e-mails, text messages, and photos. And increasingly ubiquitous sensors in the Internet of things will capture far more information than they do now. In many public spaces, every time a human moves a finger, walks a step, or says a word, it will be captured. And for more and more manufactured objects—like cars, watches, and cartons of milk—every time they move a millimeter, vibrate in unusual ways, or change temperature, it will be recorded.

…

James Manyika, Michael Chui, Brad Brown, Jacques Bughin, Richard Dobbs, Charles Roxburgh, and Angela Hung Byers, Big Data: The Next Frontier for Innovation, Competition, and Productivity (n.p., McKinsey Global Institute, 2011), 41, http://www.mckinsey.com/business-functions/business-technology/our-insights/big-data-the-next-frontier-for-innovation. 9. Viktor Mayer-Schönberger and Kenneth Cukier, Big Data: A Revolution That Will Transform How We Live, Work, and Think (Boston: Houghton Mifflin Harcourt, 2013), 59. 10. Jon Gertner, “Behind GE’s Vision for the Industrial Internet of Things,” Fast Company, June 18, 2014, https://www.fastcompany.com/3031272/can-jeff-immelt-really-make-the-world-1-better. 11. David Brin, The Transparent Society (Cambridge, MA: Perseus Books, 1998); David Brin, Earth (New York: Bantam Spectra, 1990). 12. For an overview of Bayesian networks written for a general audience, see Pedro Domingos, The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World (New York: Basic Books, 2015), chapter 6.

Dark Territory: The Secret History of Cyber War
by Fred Kaplan
Published 1 Mar 2016

In the cyber era, Internet traffic moved at lightning speed, in digital packets, often interspersed with packets containing other people’s traffic, so a terrorist’s emails or cell phone chatter couldn’t be extracted so delicately; everyone’s chatter and traffic got tossed in the dragnet, placed, potentially, under the ever-watchful eye. The expectation arose that wars of the future were bound to be, at least in part, cyber wars; cyberspace was officially labeled a “domain” of warfare, like air, land, sea, and outer space. And because of the seamless worldwide network, the packets, and the Internet of Things, cyber war would involve not just soldiers, sailors, and pilots but, inexorably, the rest of us. When cyberspace is everywhere, cyber war can seep through every digital pore. During the transitions between presidents, the ideas of cyber warfare were dismissed, ignored, or forgotten, but they never disappeared.

…

In 2014, there were almost eighty thousand security breaches in the United States, more than two thousand of which resulted in losses of data—a quarter more breaches, and 55 percent more data losses, than the year before. On average, the hackers stayed inside the networks they’d breached for 205 days—nearly seven months—before being detected. These numbers were likely to soar, with the rise of the Internet of Things. Back in 1996, Matt Devost, the computer scientist who simulated cyber attacks in NATO war games, co-wrote a paper called “Information Terrorism: Can You Trust Your Toaster?” The title was a bit facetious, but twenty years later, with the most mundane items of everyday life—toasters, refrigerators, thermostats, and cars—sprouting portals and modems for network connectivity (and thus for hackers too), it seemed prescient.II President Obama tried to stem the deluge.

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“Chris,” 244–48, 252, 279 Inman, Bobby Ray, 21–22, 84, 128, 132 as CIA deputy director, 27 as naval intelligence director, 14, 26–27, 28 as NSA director, 12–13, 14, 17, 18–19, 27, 29, 133 in retirement from government service, 27–28 Institute for Advanced Studies (Princeton), 8 intelligence agencies: civil liberties and, 251–52, 259, 260 lack of communication between, 171 public-private revolving door in, 172 International Atomic Energy Agency, 301n Internet, 47, 58, 100, 164, 181, 188, 193, 207, 212 commercial encryption on, 35 cyber security and, 52–53 data packets on, 5–6, 131, 156, 157–58 discontinued NSA metadata program for, 253 domain name system of, 191 Estonia and, 162–63 North Korea disconnected from, 271–72 terrorism and, 35 vulnerability of, 93–94, 176–77 see also computer networks; World Wide Web Internet of Things, 6, 273 Internet Security Systems, 80 Interview, The (film), 269 intrusion-detection systems (IDS), 80, 81, 101, 120, 176, 177, 278, 281 Iran: attack on, see Stuxnet cyber attack on Las Vegas Sands by, 265–68 cyber warfare and, 4, 213, 265–68 nuclear weapons program of, 198, 201, 203–4, 212 Saudi Aramco attacked by, 213, 216 Shamoon computer virus developed by, 213 Iranian National Oil Company, 213 Iraq: command-control network of, 22 insurgency in, 143, 147, 150, 156, 173, 180, 208, 216, 241 Kurds in, 160 lack of U.S. intelligence about, 22 NSA teams in, 159–60 Operation Desert Storm, 21–23, 29, 32, 74, 149, 151 Sunni-Shiite conflict in, 147, 160 U.S. invasion of, 142–43, 145, 147, 240 U.S. troop surge in, 158, 173 WMD inspectors expelled by, 74 Islam, Sunni-Shiite split in, 147, 160 Israel, 216 Iranian nuclear program and, 203–4 Stuxnet and, 207 Syrian reactor bombed by, 160–61, 198 Unit 8200 of, 161, 205, 207, 213 J-39, 7, 70, 81, 110–12, 120 anti-Milosevic campaign of, 114–18, 119 and 1999 Balkans bombing campaign, 112–14, 119, 161 Jeep Cherokee, hacking of, 273n–74n Johnson, Jeh, 270 Joint Chiefs of Staff, 32, 74, 146, 183 Information Operations Response Cell of, 76, 78 intelligence directorate (J-2) of, 22, 69 J-39 bureau of, see J-39 Joint Computer Conference, 8 Joint Intelligence Center, 22–23, 24, 29, 32, 132 Joint Special Operations Command (JSOC), 150, 151–52, 156 Joint Task Force-Computer Network Defense (JTF-CND), 81–82, 83–84, 88, 105, 120–21, 183, 187, 276, 296n Joint Task Force-Computer Network Operations (JTF-CNO), 122, 136 bureaucratic obstructions to, 146–47 Joint Task Force-Global Network Operations, 183 Justice Department, U.S., 63, 155 cyber crime and, 41–42 Information Infrastructure Task Force Coordinating Committee of, 42 Infrastructure Protection Task Force of, see Infrastructure Protection Task Force Section 215 case and, 262 Kaspersky Lab, 210 Kelly Air Force Base, see Air Force Information Warfare Center KGB, 12, 16, 84 Khamenei, Ayatollah Ali, 266 Kim Jong-un, 269 Kingsley, Ben, 31 Kuwait, 21 Kuwait City, 22 L0pht, 90–91, 94, 95, 98, 103 L0phtCrack, 92 Lacombe, Phillip, 52–53 Lake, Anthony, 40 Lane, Charles, 44 Langley, Va., 6 Langner, Ralph, 210, 211 Lasker, Lawrence, 9–10, 32, 287n Las Vegas Sands Corporation, cyber attack on, 265–68 Latham, Donald, 6, 19, 20, 54 Law of Armed Conflict, 25 Lawrence Berkeley National Laboratory, 61–62 Lawrence Livermore National Laboratory, 62 Levitt, Karl, 62 Lewinsky, Monica, 103, 115 Liberty and Security in a Changing World (Review Group report), 255, 258–59, 285 Lieberman, Joe, 95 Lockheed Martin, 120 Chinese cyber attack on, 224–25 LoudAuto, 136 Lukasik, Stephen, 9 Lute, Jane Holl, 188, 189, 302n–3n McAfee, Chinese cyber attacks tracked by, 226 McCain, John, 197, 198, 283 McCarthy, John, 97 McChrystal, Stanley, 159, 173 as JSOC commander, 150, 151–52 McConnell, John “Mike,” 57, 169, 183, 194, 248 Bush briefed on cyber warfare by, 173–75, 187 Clipper Chip and, 36–37, 40, 58, 128 CNCI plan of, 177–78, 198–99, 278 Cyber Command proposed by, 185 cyber deterrence and, 278 cyber security as priority of, 172, 198, 278 as director of national intelligence, 171–78, 191–92, 216 FISA and, 192–93 information warfare as priority of, 31–32, 34–37 as Joint Intelligence Center head, 22–23, 29 as NSA director, 29, 30–37, 128, 133, 172, 173, 193 Obama’s replacing of, 200 in pre-election briefing of Obama, 197–98 Sneakers as epiphany for, 33 McDermott, Thomas, 68 McDonough, Denis, 238 McVeigh, Timothy, 39 MAE East, 191n MAE West, 191n Makaveli (pseudonym), 77–78 Maliki, Nouri al-, 160 malware, 182, 205–6, 207–8, 266 Mandia, Kevin, 85, 87, 223, 225, 269, 292n–93n Mandiant, 85n, 222–23, 225, 226, 269, 292n Marine Corps, Computer Network Defense unit of, 123 Marsh, Robert T.

Human + Machine: Reimagining Work in the Age of AI
by Paul R. Daugherty and H. James Wilson
Published 15 Jan 2018

The gains included a 20 percent reduction in forecast error and a 30 percent reduction in lost sales. Those improvements are the types sought by consumer goods giant Procter & Gamble, whose CEO recently stated his goal of cutting supply-chain costs by a whopping $1 billion a year. Part of those savings will come from near-term efforts like the use of AI and the internet of things (IoT) technologies to automate warehouses and distribution centers. And other savings will come from longer-term projects, including the customized automation of product deliveries of up to seven thousand different stock-keeping units. Whether these and other initiatives will enable P&G to save the company $1 billion annually in supply-chain costs remains to be seen, but it’s safe to say that AI will be playing a significant role in those efforts.

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See Watson (IBM) “if-then” rules, 25 Illumeo, 142 image recognition, 66 incubators, 162 industries, redefining, 56–58 Inertia Switch, 23 inference systems, 64 information analysis, 10 information technology (IT) cybersecurity and, 56–58, 59 in process automation, 5 Init.ai, 121 innovation, 152 generative design and, 135–137 observation and, 69–72 See also experimentation; research and development (R&D) Institute for the Future, 187 institutional review boards (IRBs), 78 inSTREAM, 47–48 Intel AI Day, 188 intelligence, extended and embodied, 206 intelligent agents, 65 IntelligentX Brewing Company, 76 interaction, 107, 139 jobs with, 143–146 See also augmentation; missing middle interaction agents, 143–146 interaction modelers, 120 internet of things (IoT), 34, 36, 37 interrogation, intelligent, 12, 185, 193–195 intuition, 191–193 inventory management, 30–33 iPhone, 176 IPSoft, 55–56, 139, 164, 201 iRobot, 24 IT security, 56–58, 59 Järborg, Rasmus, 55 job creation, 11, 113–115, 208–211 in data supply chains, 179 education and training for, 132–133 ethics compliance and, 79 explainers, 122–126 in manufacturing, 20 in marketing and sales, 100–101 in sustaining, 126–132 in training, 100, 114–122 See also fusion skills job loss, 19, 20, 209 job satisfaction, 46–47 job searches, 198–199 John Radcliffe Hospital, 197 Johnson & Johnson, 82 judgment integration, 12, 191–193 Kaiser Permanente, 188 Kaplan, Jerry, 60 Kelton, Fraser, 97 Keshavan, Meghana, 82 Kik, 91, 97 Kindred AI, 200 Kiva Robots, 31 knowledge representation, 63–64 Koko, 97, 117–118 Kowalski, Jeff, 137 Kraft Phone Assistant, 91 Lambda Chair, 136–137 Lange, Danny, 43 Las Vegas Sands Corp., 76 Laws of Robotics, 128–129 leadership, 14–15, 153–181, 213 blended culture and, 166–174 data supply chains and, 174–179 in enterprise processes, 58–59 in manufacturing, 38 in marketing and sales, 100 in normalizing AI, 190–191 in R&D, 83 in reimagining processes, 154, 180–181 learning deep reinforcement, 21–22 distributed, 22 reinforcement, 62 in robotic arms, 24–26 semi-supervised, 62 sensors and, 24–26 supervised, 60 unsupervised, 61–62 See also machine-learning technologies Leefeldt, Ed, 99 Lee Hecht Harrison, 199 legal issues.

WTF?: What's the Future and Why It's Up to Us
by Tim O'Reilly
Published 9 Oct 2017

In short, the two types of augmentation, physical and mental, are in a complex dance. One frontier of augmentation is the addition of sensors to the physical world, allowing data to be collected and analyzed at a previously unthinkable scale. That is the real key to understanding what is often called the “Internet of Things.” Things that once required guesswork are now knowable. (Insurance may well be the native business model of the “Internet of Things” in the same way that advertising became the native business model of the Internet, because of the data-driven elimination of uncertainty.) It isn’t simply a matter of smart, connected devices like the Nest thermostat or the Amazon Echo, the Fitbit and the Apple Watch, or even self-driving cars.

…

While both Netscape and Google could be described as software companies, Netscape belonged to the same software world as Lotus, Microsoft, Oracle, SAP, and other companies that got their start in the 1980s software revolution, while Google’s fellows were other Internet applications like eBay, Amazon, Napster, DoubleClick, and Akamai. As we moved from the Web 2.0 era into the “mobile-social” era and now into the “Internet of Things,” the same principle continues to hold true. Applications live on the Internet itself—in the space between the device and remote servers—not just on the device in the user’s hands. This idea was expressed by another of the principles I laid out in the paper, which I called “Software Above the Level of a Single Device,” using a phrase first introduced by Microsoft open source lead David Stutz in his open letter to the company when he left in 2003.

…

As the Internet speeds up the connection between human minds, as our collective knowledge, memory, and sensations are shared and stored in digital form, we are weaving a new kind of technology-mediated superorganism, a global brain consisting of all connected humans. This global brain is a human-machine hybrid. The senses of that global brain are the cameras, microphones, keyboards, and location sensors of every computer, smartphone, and “Internet of Things” device; the thoughts of that global brain are the collective output of billions of individual contributing intelligences, shaped, guided, and amplified by algorithms. Digital services like Google, Facebook, and Twitter that connect hundreds of millions or even billions of people in near-real time are already primitive hybrid AIs.

The Best Interface Is No Interface: The Simple Path to Brilliant Technology (Voices That Matter)
by Golden Krishna
Published 10 Feb 2015

“Culture Shock: Flashpoints: Music and Dance: Elvis Presley,” PBS, Last accessed November 2014. http://www.pbs.org/wgbh/cultureshock/flashpoints/music/elvis.html 13 Emily Nussbaum, “Kids, the Internet, and the End of Privacy: The Greatest Generation Gap Since Rock and Roll,” New York Magazine, February 12, 2007. http://nymag.com/news/features/27341/ 14 Michelle Dennedy, chief privacy officer at security software firm McAfee. Marco della Cava, “Privacy Integral to Future of the Internet of Things,” USA Today, July 11, 2014. http://www.usatoday.com/story/tech/2014/07/10/internet-of-things-privacy-summit/12496613/ 15 Molly Wood, “Facebook Generation Rekindles Expectation of Privacy Online,” New York Times, September 7, 2014. http://bits.blogs.nytimes.com/2014/09/07/rethinking-privacy-on-the-internet/ 16 Mary Madden, Amanda Lenhart, Sandra Cortesi, and Urs Gasser, “Teens and Mobile Apps Privacy,” Pew Research Center’s Internet & American Life Project, August 22, 2013. http://www.pewinternet.org/2013/08/22/TEENS-AND-MOBILE-APPS-PRIVACY/ 17 “Teens, Social Media, and Privacy: New Findings from Pew and the Berkman Center,” Berkman Center, May 21, 2013. http://cyber.law.harvard.edu/node/8325 18 “But recent papers from Harvard, Berkeley, and University of Pennsylvania researchers show that kids and young adults do want to keep information private.”

The Rare Metals War
by Guillaume Pitron
Published 15 Feb 2020

Senate Committee on Energy & Natural Resources, 3 May 2019 Rabhi, Pierre, Vers la sobriété heureuse, Actes Sud, 2010 Rifkin, Jeremy, The Third Industrial Revolution: how lateral power is transforming energy, the economy, and the world, Palgrave Macmillan, 2011 Rifkin, Jeremy, The Zero Marginal Cost Society: the internet of things, the collaborative commons, and the eclipse of capitalism, Palgrave Macmillan, 2014. Roger, Alain and Guéry, François (dir.), Maîtres et protecteurs de la nature, Champ Vallon, 1991 Schmidt, Eric and Cohen, Jared, The New Digital Age: reshaping the future of people, nations and business, Knopf, Random House Inc., 2013.

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To delve deeper into these questions, read the fascinating article by Jean-Marc Jancovici, ‘Is the Electric Car an Ideal Solution for Tomorrow’s Mobility?’, 1 August 2015, on Jean-Marc Jancovici’s website jancovici.com. See also ‘Do We Really Want Electric Vehicles?’, Le Monde Diplomatique, September 2018. Rifkin, The Third Industrial Revolution, op. cit. Rifkin, The Zero Marginal Cost Society: the internet of things, the collaborative commons, and the eclipse of capitalism, Palgrave Macmillan, 2014. ‘US Car Sharing Service Kept 28 000 Private Cars Off the Road in 3 Years’, The Guardian, 23 July 2016. Eric Schmidt and Jared Cohen, The New Digital Age: reshaping the future of people, nations and business, Knopf, Random House Inc., 2013.

Innovation and Its Enemies
by Calestous Juma
Published 20 Mar 2017

Digital medicine has an even greater potential of taking root in regions of the world, such as sub-Saharan Africa, that have limited access to healthcare.48 Such regions could become the source of new medical applications that would otherwise be obstructed by incumbent interests in industrialized countries. This could follow the patterns of mobile money transfer and banking that first emerged in Africa before they spread to industrialized countries through the process of reverse innovation.49 The emergence of new fields such as the Internet of Things, 3D printing, digital learning, and open-source movements provide collaborative opportunities for inclusive innovation.50 Collaborating innovation changes the way productive systems are organized. However, this does not automatically lead to inclusive innovation. Existing policy frameworks need to “be modified to allow for particular features of inclusive innovation, including the nature of innovations required, the actors involved and their interrelations, the type of learning they undertake, and the institutional environment in which they operate.

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Christopher Foster and Richard Heeks, “Analyzing Policy for Inclusive Innovation: The Mobile Sector and Base-of-the-Pyramid Markets in Kenya,” Innovation and Development 3, no. 1 (2013): 103–119. 47. Topol, Patient Will See You, 6. 48. Topol, Patient Will See You, 257–274. 49. Jacqueline W. DePasse and Patrick T. Lee, “A Model for ‘Reverse Innovation’ in Health Care,” Globalization and Health 9, no. 1 (2013): 1–7. 50. Jeremy Rifkin, The Zero Marginal Cost Economy: The Internet of Things, the Collaborative Commons, and the Eclipse of Capitalism (New York: Palgrave Macmillan, 2014). 51. Christopher Foster and Richard Heeks, “Conceptualising Inclusive Innovation: Modifying Systems of Inovation Frameworks to Understand Diffusion of New Technology to Low-Income Consumers,” European Journal of Development Research 25. no. 3 (2013): 333. 52.

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See also Transgenic crops Insecticides, 224, 231, 242 Institutions adaptation by, 91, 119–120, 228, 302–307, 315 advisory, in executive offices, 288 civil society organizations, 226, 235, 242, 270 classical view of, 20 coevolution of, with technological innovation, 9, 19, 23 definition of, 20–21 distrust of, 5, 8 on fostering social inclusion, 290 institutionalization of farm mechanization, 136–140 vs. organizations, 19–23, 26 public sector, inclusive innovation and, 292 role in innovation, 20–21 for science and technology advice, 287–289 social institutions, 6, 8, 20, 296 technology, relationship to, 6, 169, 175 trade associations, 198 Insurance industry, driverless cars and, 296 Intellectual Curiosity and the Scientific Revolution (Huff), 70 Intellectual property debates over, 118–119 Edison-Westinghouse battles over, 153, 157 living material patents, 250, 282 margarine manufacturing patents, 106–107 music file-sharing and, 221–222 plant biotechnology patents, 234–235 Intellectual responses to technological innovation, 31–32 Intergovernmental Panel on Climate Change, 249, 288 Intermediate sized markets, 324n75 Internal Revenue Service, butter impoundment by, 108 International Association of Refrigeration (IAR, later International Institute of Refrigeration), 191–193, 198–199 International Harvester, 125, 126–127 International Institute of Refrigeration (formerly International Association of Refrigeration, IAR), 191–193, 198–199 International Musician on reproducibility of recorded music, 210 International Service for the Acquisition of Agri-biotech Applications (ISAAA), 244–245 International trade, 223, 238–239, 299 International treaties, 197–198, 302 Internet, 3, 40 Internet of Things, 301 Interstate Commerce Committee (Senate), 215 Interstate trade, margarine legislation and, 115 Intra-company knowledge transfers, 352n21 Intuition, technological innovation and, 23–25 Invasive species, impact on fishing industry, 259 Iowa, butter impoundment in, 108 Iowa Farm Bureau, 95, 115 Iowa State College, 95 Iowa State Grocers Association, 115 iPod, 221 Iran, coffee in, 52–53 Irradiated foods, 307 Irrationality, 24, 25, 33, 294.

What to Think About Machines That Think: Today's Leading Thinkers on the Age of Machine Intelligence
by John Brockman
Published 5 Oct 2015

We’d have no more reason to disparage them as zombies than to regard other people in that way. Their greater processing speed may give robots an advantage over us. But will they remain docile rather than “going rogue”? And what if a hypercomputer developed a mind of its own? If it could infiltrate the Internet—and the “Internet of Things”—it could manipulate the rest of the world. It may have goals utterly orthogonal to human wishes—or even treat humans as an encumbrance. Or (to be more optimistic) humans may transcend biology by merging with computers, maybe subsuming their individuality into a common consciousness. In old-style spiritualist parlance, they would “go over to the other side.”

…

Following this logic, we might conclude that there’s a primitive global brain, consisting not just of all connected devices but also of the connected humans using those devices. The senses of that global brain are the cameras, microphones, keyboards, location sensors of every computer, smartphone, and “Internet of Things” device. The thoughts of that global brain are the collective output of millions of individual contributing cells. Danny Hillis is said to have remarked, “Global consciousness is that thing responsible for deciding that decaffeinated coffeepots should be orange.” The meme spread—not universally, to be sure, but sufficiently that the pattern propagates.

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Researchers are now looking at exoskeletons to help the infirm to walk, and implants to allow paralyzed people to control prosthetic limbs, and digital tattoos that can be stamped onto the body to harvest physiological data or interface with our surroundings—for instance, with the cloud or the Internet of Things. When it comes to thinking machines, some are even investigating how to enhance human brainpower with electronic plug-ins and other “smartware.” The U.S. Defense Advanced Research Projects Agency has launched the Restoring Active Memory program to reverse damage caused by a brain injury with neuroprosthetics that sense memory deficits and restore normal function.

User Friendly: How the Hidden Rules of Design Are Changing the Way We Live, Work & Play
by Cliff Kuang and Robert Fabricant
Published 7 Nov 2019

The proliferation of personal screens each reflecting a similar version of who we are is a hall of mirrors, obscuring a more humane way of living in the digital world, in which we see ourselves more clearly. While the principles of user-friendliness will persist, we might need new mental models and metaphors to better manage our digital lives. I saw a hint of that possibility in the form of a startup that had raised $63 million in venture capital, on the hope of becoming the bedrock for the Internet of Things. The founder, Linden Tibbets, had been an engineer and designer working at IDEO when he came across Jane Fulton Suri’s book Thoughtless Acts, which documented all the ingenious ways we make tools from the environment around us: how we tuck pencils behind our ears, or use a stray cork to prop open a door.

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The Media Equation: How People Treat Computers, Television, and New Media Like Real People and Places. New York: CSLI Publications, 1996. RockTreeStar. “Tesla Autopilot Tried to Kill Me!” YouTube, October 15, 2015. www.youtube.com/watch?v=MrwxEX8qOxA. Rose, David. Enchanted Objects: Design, Human Desire, and the Internet of Things. New York: Scribner, 2014. Rosenberg, Matthew. “Cambridge Analytica, Trump-Tied Political Firm, Of-fered to Entrap Politicians.” New York Times, March 19, 2018. www.nytimes.com/2018/03/19/us/cambridge-analytica-alexander-nix.html. Rosenstein, Justin. “Love Changes Form.” Facebook, September 20, 2016.

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B., 64 Friedman, Jon, 209 Frog Design, 9, 24, 164, 175, 177, 244, 286, 288, 304, 306, 308, 309, 312, 315, 317, 318, 326, 328, 340, 345, 369n2, 370n11 Fukasawa, Naoto, 304 Fuller, Buckminster, 232 Fulton Suri, Jane, 171–73, 175–81, 190, 294, 310, 316, 334, 339; Thoughtless Acts, 179, 297 fuseproject, 107 Futures Wheel, 275 Gabler, Neal, 220 gambling, 253; slot machines, 253–56, 260 gaming, 197–98, 205–206 Gates Foundation, 182, 288 Gchat, 250 General Data Protection Regulation, 346–47 General Motors, 65, 334 German Luftwaffe, 32, 42 Germany, 61 Gillespie, Bo, 51–55, 71 Gillette, 154–55 Glaser, Erik, 113–14, 115, 125 Glaser, Milton, 94 Gmail, 163, 219, 227, 255 Goldberg, Adele, 142 Google, 148, 191, 227, 239, 240, 243, 259, 261, 269, 270, 294, 313, 342; Assistant, 122; Drive, 250; Duplex, 209–10; Fuchsia, 151–52; Gchat, 250; Glass, 304, 345–46; Gmail, 163, 219, 227, 255; Lens, 43–44; Maps, 219, 313, 369n6; YouTube, 243 Grand Tour, 307 graphical user interface, 143, 145, 146, 148 Great Depression, 65, 68–70, 86, 93 Greek philosophy, 33 Grice, Paul, 111–12 GRID Compass, 175–76, 339 Grudin, Jonathan, 370n14 hacks, 316 hairdressers, 306–307 Hal 9000, 105, 117 hand tremors, 33 Harari, Yuval Noah, 156 Haraway, Donna, 81 Harford, Tim, 35 Harmony of the Seas, 230 Harris, Tristan, 255, 274 Hauser, Ed, 20–21, 351n31 Hay, Steph, 211–12 health care, 287, 288, 303–304, 306; blood sample testing, 183–84; costs of, 34; hairdressers and, 306–307; HIV testing and medication, 305, 310, 329; infant, 324–25; medical appliances, 69 heart attacks, 139 Hegelian dialectic, 292 help button (Ripple device), 53–55, 80, 117, 204 Her, 194–96, 213, 233, 341, 345 Hertzfeld, Andy, 141–42 Herzberg, Elaine, 121 hierarchy of desires, 274 Hitachi Design Center, 325 HIV, 305, 310, 329 Holachef, 316–17 Holmes, Kat, 193–95, 199, 205, 207, 208, 312 home appliances, 63, 117, 230, 333, 370n16 home economics, 63, 68, 334 homemaking, 63–64, 285–86 Home Shopping Network, 54 Honeywell Round thermostat, 92, 93, 336, 343 Hooked (Eyal), 258–59 Hoover, Herbert, 61–62 Horn, Bruce, 140, 143–44 horseless carriage metaphor, 318 horse metaphor, 116, 117, 118, 126, 144 hotels, 323 Hult Prize, 280–81 human-centered design, 72, 182, 184, 272, 288 humaneness, 196, 240 human engineering, 81 human factors, 87, 95 human limitations, 95–96 human-to-human interactions, 195, 240 human-to-thing interactions, 95, 240 Human Use of Human Beings, The: Cybernetics and Society (Wiener), 336 IBM, 5–8, 145, 170, 236, 338 iCloud, 351n32 IDEO, 24, 136, 139, 164, 170, 171, 175–77, 180–83, 202, 283, 288, 297, 304, 339, 340, 343 If This Then That (IFTTT), 298–99 iMac, 5, 23, 149 immigrants, 63 in-box, 134 India, 192, 193; dabbawalas in, 316–17, 370n8; GP Block Pitampura in Delhi, 129–30, 132; internet and, 129–30, 132, 147; Khushi Baby in, 324–25 Indonesia, 319 industrial design, 55, 58–59, 61, 65, 71–72, 87, 89–90, 93–94; Dreyfuss and, 59, 67–68, 93; streamlined aesthetic in, 70 Industrial Design, 94 industrial revolution, 292 inevitability, 268, 299 innovation, 163–64, 168, 171, 181–82, 184–85, 200, 226–27 Instagram, 36–37, 134, 240, 255, 259, 261; Stories, 37, 346 insurance companies, 287 interfaces, 145; graphical user interface, 143, 145, 146, 148 internet, 34, 130–33, 199, 200, 208, 292; in China, 192–93; commerce on, 34–35; Google Lens and, 44; in India, 129–30, 132, 147; mental models and, 131; metaphors and, 132, 134–35 Internet of Things, 297 Internet.org, 131 Intuit, 325–26 intuition, 92, 93, 269 iPad, 5, 296 iPhone, 5, 23, 43, 127, 145–47, 149, 191, 216, 228, 259, 274, 289–90, 291, 296, 313, 327, 338, 343 iPod, 5, 23, 145, 338, 342–43, 346 iTunes, 146, 343 Ive, Jony, 23, 149, 299, 338, 342 Jakob’s law, 318 JetBlue, 309 jobs, 44–45 Jobs, Steve, 3–4, 7, 139–41, 145, 149, 157, 183, 190, 317, 340, 343 Joe and Josephine, 88–89, 92, 174, 176, 178, 185, 337 John Deere, 72 Johnson, Mark, 133, 152, 153 Johnstone, Dusty, 52–55, 352n4 Jonze, Spike, 194, 195, 341, 345 jukeboxes, 318 Jungen, Michael, 234 Kahneman, Daniel, 96, 350n25 Kant, Immanuel, 57 Kare, Susan, 144 Kay, Alan, 142 Kelley, David, 169–71, 177, 180–83 Kelly, Max, 247 Kennedy, Pagan, 183–84 Kenya, 147–48, 192, 281–85; Nairobi, 281, 283, 284, 315 Khushi Baby, 324–25 KitchenAid, 64 kitchens, 90, 117, 172–73, 353n39 Knowledge Illusion, The: Why We Never Think Alone (Sloman and Fernbach), 271 Kodak, 332–33, 336 Koklys, Audra, 211–12 Kolbert, Elizabeth, 271 Kosinski, Michal, 265–67, 276–77 Krieger, Mike, 259 Krippendorff, Klaus, 132 Kubrick, Stanley, 105 Ladies’ Home Journal, 63 Lakoff, George, 133, 152, 153, 317 Land, Edwin, 117, 336 Lang, Fritz, 334–35, 362n8 language, 122–23 Lathrop, Brian, 103–106, 110, 112, 115–18, 125, 126, 144 lawn mowers, 171–73, 178, 339 Lawrence Livermore National Laboratory, 164 leaf metaphor, 137–39, 144 Le Corbusier, 62, 334 Leonardo da Vinci, 89 “L’Esprit Nouveau,” 62, 334 Liedtka, Jeanne, 170 Life, 167 Loewy, Raymond, 70, 87, 88, 164, 200–202, 313 logic, 310; inner, exposing, 319–22 Louis XV armchair, 332 Lubs, Dietrich, 46 Lyft, 260 Mace, Ron, 202 machine-made goods, 60–61 “Machines Cannot Fight Alone” (Stevens), 78–79 Maclean, Allan, 370n14 Macy’s, 66–67, 165 Mad Men, 168 Madrigal, Alexis, 255 Magic Bus Ticketing, 283–86 “makeshift,” coining of term, 332, 352n14 Margolis, Michael, 313 marketing-led organizations, 307 markets of one, 242–43 Markoff, John, 189 Marshall Field’s, 150 Marshall Plan, 6 Maslow, Abraham, 274 mass production, 60–64, 90 Matrix, The, 125, 126, 360n31 Mayo Clinic, 182; “Jack and Jill” consultation rooms of, 362n24 McCulloch, Warren, 36 McKim, Bob, 164–70, 174, 180, 181, 190 McKinsey & Company, 170 meal delivery, 316–17, 370n8 Measure of Man, The (Dreyfuss and Tilley), 92, 337, 341 Meikle, Jeffrey L., 69 memory: short-term, 322; sketching how something works from, 321 mental models, 31, 40–41, 105, 119, 120, 180, 288, 297, 320–22, 351n32; cognitive load and, 322; digital assistants and, 124; internet and, 131; metaphors and, 133 metaphors, 84, 124, 132–35, 139, 144–45, 147, 154, 155, 158, 195, 203, 295, 297, 299, 358n22; apps and, 149–52; brain and, 152–53; coach, 136–39; in defibrillator design, 139; desktop, 139–40, 143, 144, 146–47; dominant, in product categories, 317–18; embodied, 152–54; and Facebook in Kenya, 147–48; horse, 116, 117, 118, 126, 144; horseless carriage, 318; in-box, 134; internet and, 132, 134–35; ladder of, 147, 193, 317–19; leaf, on hybrid car dashboards, 137–39, 144; Macintosh OS and, 144; mental models and, 133; news feed, 134, 318; personal assistant, 189; personification, 155–57; time as money, 133–34, 135; visual, in Apple products, 148–49, 210 Metaphors We Live By (Lakoff and Johnson), 133, 152, 153 MetroCard, 311 Metropolis, 334–35, 362n8 Metropolitan Edison, 39 Metropolitan Museum of Art, 60 Mic, 260 microdermabrasion device, 328 Microsoft, 145, 191, 193–95, 199, 202, 205–206, 208, 370n14; Cortana, 194, 208; PowerPoint, 208–209; Visual Basic, 361n22; Word, Clippy in, 112; Xbox, 197, 205–206 middle class, 63, 290 Miller, George, 322, 337 mind, 95–96 Minority Report, 233, 236 Minsky, Marvin, 189–90 misinformation, 262, 263, 289 MIT, 165–67, 189, 337; Media Lab, 154, 155 Mob, the, 39 mobile phones, 368n2; Magic Bus Ticketing and, 283–86; see also smartphones mode confusion, 144 modernism, 157, 334 Moggridge, Bill, 175–78, 180–81, 339, 361n21 mood boards, 155 Moore, Patricia, 200–202, 313, 340, 341 Morris, William, 332, 352n14 Moskovitz, Dustin, 248 Mother of All Demos, 187–90, 338 Mothersill, Philippa, 154–57 motivations, 46, 259, 344 movies, 230–31, 243, 254, 363n11 M-Pesa, 284, 368n2 Münsterberg, Hugo, 81–82 Myanmar, 263 Nadella, Satya, 202 Nairobi, 281, 283, 284, 315 Nancy, 272–73 NASA, 104, 115–16, 337 Nass, Clifford, 108–10, 112, 211, 258 National Transportation Safety Board (NTSB), 121, 122 navigability, 31 Navy, U.S., 87 Neeleman, David, 309 Nespresso, 117 Nest, 92, 336, 344–45 Netflix, 230–31, 260, 351n33 neural networks, 36, 44 neuroscience, 96 Newby, Paul, 342 news feeds, 134, 247, 248, 318 New York, 94 New York City Health and Hospitals Corporation, 303 New York City subway system, 311 New Yorker, The, 67–68 New York Times, The, 110, 183, 225, 259, 263 Nielsen, Jakob, 318 911 emergency calls, 51–53, 71; Ripple device as alternative to, 53–55, 80, 117, 204 Nokia, 308 Norman, Donald, 22–26, 45–46, 86, 95, 96, 103, 112, 124, 272, 287, 302, 318, 326, 334; at Apple, 22–23; The Design of Everyday Things, 22, 312, 339, 340; Emotional Design, 326, 340; Three Mile Island and, 24–25, 30, 38, 338–39 Nostradamus, 198 Noyes, Eliot, 8 nuclear radiation exposure, 19–20 nuclear reactors, 23, 25, 26, 29, 44, 45, 80, 113; see also Three Mile Island nuclear weapons, 100, 164, 167, 261, 291; missile warning, 121–22 Nuttall, Mike, 177 obsolescence, artificial, 69 Omondi, Wycliffe Onyango, 281–85 “On Exactitude in Science,” 91–92 operations research, 6 Oppenheimer, Robert, 247, 261 organized crime, 39 Ossete, Leslie Saholy, 279–85 OXO peeler, 202–203, 341 Padgett, John, 217, 221–22, 226, 228–32, 234, 235, 237–39, 242, 312, 323–24, 345 Page, Larry, 342 paintbrushes, 358n22 Panama-Pacific Exposition, 60 Papanek, Victor, 290 Patnaik, Dev, 307 Pattison, Mary, 63, 64 Pearl, 50 Pearlman, Leah, 247–49, 262, 292, 344 peeler, OXO, 202–203, 341 Peloton, 316 personality, 265–67 personalization, 231, 239, 245; Carnival’s Ocean Medallion and Personal Genome, 233–39, 268, 296; Disney’s MagicBand and MyMagic+, 217–29, 237, 243–44, 288, 304, 317, 345 personas, 178, 207, 261, 341 personification, 155–57 personnel research, 81 Piano, Renzo, 157 pilots: crashes of, 77, 81–85, 102–103, 104, 106, 121, 257; lost and confused, 75–78, 86, 87, 144; “pilot error” concept, 81, 83, 102–103, 121, 335 Pittman, Matthew, 260 Pitts, Walter, 36 Pixar, 211 Plunkett, Joseph, 57–58 Poland, 276 Polaroid camera, 117, 336, 342 politeness, 108–10, 112, 113, 239–40, 258; driverless cars and, 125, 126 Porter, Joshua, 329 postmodernism, 157–58 pottery making, 90 PowerPoint, 208–209 Princess Cruises, 230; see also Carnival Cruise Line Princess phone, 337, 371n21 Principles of Scientific Management, The (Taylor), 333 prototypes, 165, 180–82 psychologically natural controls, 84, 85; scrolling, 359n25 psychophysics, 84, 95 purpose, 33 radar, 32, 76–79, 83, 87 radio, 76, 77, 79, 84–85 Rams, Dieter, 46, 90, 338, 343, 370n17 Rand, Paul, 8 Raskin, Jef, 140, 141 Ratzlaff, Cordell, 317 razors, disposable, 154–55, 157 Read, Max, 262 Reddit, 249 Reeves, Byron, 110 Regal Princess, 229–30, 236 Renuka, 130–33, 147, 315 rewards, variable, 254, 255, 259 Ripple help button, 53–55, 80, 117, 204 RKO theater, 55–56, 92, 173–74, 335 robots, 115, 117; see also artificial intelligence (AI) Rogers, Richard, 157 Rohingya, 263 Rolls, Charles, 332 Rosenstein, Justin, 247–51, 262, 274, 291–92, 344 Russia, 201, 250, 313 Saarinen, Eero, 5, 8 Saproo, Sameer, 125–27 Sarajevo, 49–50, 54 savings accounts, 319 Scheiber, Noam, 259 Scheimann, Fred, 15–18 Schiller, Phil, 342 Schmidt, Eric, 191 Schon, Donald, 358n22 Schüll, Natasha, 255 Schulze-Mittendorff, Walter, 334–35 Schwartz, Barry, 231 science, 45 scientific management, 63, 333 Scott, Walter Dill, 81 scrolling direction, 359n25 Sears, 66, 69, 335 self-driving cars, see cars, self-driving Selfridge, Harry Gordon, 150, 333 self-service checkouts, 303 semiconductors, 168, 175 senses, 84 sewing machine, 72 sexual assault, 51–55, 204, 352n4 Sheehan, Kim, 260 Sholes, Christopher Latham, 332 Shum, Albert, 202 Silicon Valley, 3, 168, 175, 177, 180, 182, 221, 229, 257–59, 270, 291–92, 340 Simpsons, The, 161–62, 164, 180 Singapore, 24 Siri, 122, 151, 190–91, 193, 195, 208, 312 Sittig, Aaron, 344 skeuomorphism, 148, 202, 210 Skinner, B.

I Live in the Future & Here's How It Works: Why Your World, Work, and Brain Are Being Creatively Disrupted
by Nick Bilton
Published 13 Sep 2010

During a presentation at Google several years ago, Cerf explained that some day, everything will be connected to the Internet. That includes a person’s socks, so if one falls behind the washing machine, it will be able to notify him, or the other sock, of its new location. In Cerf’s vision—“the Internet of Things”—sensors eventually will be everywhere, embedded in our T-shirts and the medicine we take, and will be able to deliver real-time information and analysis to our persons. In a blog post I wrote about this topic for the Times, I explained that we’re already seeing the beginnings of this: “Doctors are using tiny cameras, about the size of a pill, to look at the digestive tract and send back information and pictures.

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In a blog post I wrote about this topic for the Times, I explained that we’re already seeing the beginnings of this: “Doctors are using tiny cameras, about the size of a pill, to look at the digestive tract and send back information and pictures. Farming equipment can collect data from remote satellites and sensors in the ground, anticipate weather, and adapt the fertilizer to be used. And billboards in Asia can change displays based on the preferences of passers-by.” Understandably, the Internet of Things, as it is called, scares some people. Embedding the Internet into everything could make us reliant on technology that could crash at any moment. But even more, it means that even greater masses of information will be created, much of it increasingly personal and unique. These technologies raise new and difficult questions about privacy and appropriate use of what we know.

Cogs and Monsters: What Economics Is, and What It Should Be
by Diane Coyle
Published 11 Oct 2021

A growing proportion of services such as TV, listening to music, banking, arranging travel, occur online; while formerly physical products such as diaries, maps, cameras, calculators, and so on have shed their atoms to become bits (albeit still embedded or accessed via a physical device). New types of digital service have emerged. Although it is still impossible to get a haircut without going to a hairdresser, a surprising number of services can be delivered in bit form. This is one lesson of the 2020–2021 lockdowns. Data usage has soared, even before the famed Internet of Things and autonomous vehicles had come into existence, with their data and communication demands. Zvi Griliches (1994) long ago distinguished between easy-to-measure and hard-to-measure sectors of the economy. Among the former he put agriculture, mining, manufacturing, transportation, communications, and public utilities.

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C., 125–26 Harris, Robert, 27 Hausman, Jerry, 57 Hayek, Friedrich von, 31, 42–43, 182–83, 190–91 health care, 32, 37, 44–45, 60, 145, 164 Henderson, David, 77–78 heterogeneity, 113, 175 Hicks, John, 122, 145, 191 Hidden Persuaders (Packard), 109 Holmstrom, B., 197 homo economicus: artificial intelligence (AI) and, 161; Friedman on, 93; machine learning and, 13, 161; rationality and, 13, 47–48, 93, 114, 116–17; separation protocol and, 119 housing, 43, 60, 65, 102 humanities, 3–4, 49, 214 Hume, David, 49, 148 humility, 20, 52, 73, 79, 82, 95, 100 identity, 4, 92–93, 107, 129 (Im)possibility Theorem, 123 incentives, 29, 33, 35, 55, 63–64, 80, 106–7, 110, 160, 200 income: automation and, 165–66; consumer choice and, 93; demand management and, 192; developing countries and, 96, 196; distribution of, 122, 125, 138, 143, 149; economics graduates and, 1; falling, 164; growth in, 70, 131, 138, 143, 164–65, 194, 207; identity and, 107; innovation and, 39; median, 164, 176; microeconomic analysis and, 101; progress and, 138, 143, 147, 149, 151; real, 194; stagnation and, 164, 194 Independent, The (newspaper), 133 individualism, 5, 10, 13–14, 23, 130, 141, 180, 191 industrial policy, 207–8 Industrial Revolution, 132, 150 inequality, 11, 19, 68, 132, 149, 163–66, 215 inflation: crises of 1970s and, 16; forecasting, 36; GDP and, 13, 113, 148; Great Moderation and, 17, 73; growth and, 12, 66, 73, 178; macroeconomics and, 12–13, 17, 36, 73, 113; models and, 30, 113; monetary policy and, 16, 30, 73, 135; public responsibilities and, 16–17, 30, 36, 66, 73; statistics and, 113, 146, 148, 164; unemployment and, 12, 113, 192 innovation: changing economies and, 169–70; cloud computing and, 150, 170–72, 184, 197; competition and, 28, 41, 46, 68, 85, 209; consumers and, 28, 102, 200; creative destruction and, 41; digital economy and, 169–70; financial, 28–29; FISIM and, 28; 4G platforms, 195; growth and, 37, 41, 46, 68, 71, 194, 209; income constraints and, 39; internet and, 138–39 (see also internet); kidney exchange and, 44, 142; macroeconomics and, 37, 71, 102; markets and, 39; production and, 41, 139, 169, 195; progress and, 137, 139, 142–46, 150–51, 156, 166; randomised control trials (RCTs) and, 60–61; smartphones and, 46, 138–39, 164, 171, 173, 177, 195, 198; spillovers and, 127; superstar features and, 173–74; 3G platforms, 60, 139, 173, 195; twenty-first-century policy and, 189, 194–95, 200, 204, 209; ultra-high frequency trading (HFT) and, 25–27 instant messaging, 171 Institute for New Economic Thinking, 35, 84 Institute of Engineering and Technology, 171 Intel, 41 interest rates, 12, 18, 37, 66, 147 International Monetary Fund (IMF), 67, 158 International Network for Economic Method, 114 internet: GDP growth and, 97; impact of, 46, 168; progress and, 138–39; time spent online, 176–78; transaction costs and, 168; Unipalm and, 133; World Wide Web and, 133, 195 Internet of Things (IOT), 198 interventions: behavioural economics and, 48, 63, 104, 106, 160, 208, 211; Coase on, 62; education and, 12; government, 15, 38, 48, 80, 82, 123, 125, 191, 194, 208; market, 15, 31, 38, 125, 188, 194, 206, 213; outsider context and, 87, 94, 104, 106; progress and, 160; public opinion and, 70; regulations and, 31 (see also regulations); separation protocol and, 123, 125, 127; taxes and, 213; Trump and, 213; twenty-first-century policy and, 188, 191, 194, 206–8, 211 Iron Curtain, 190 Irrational Exuberance (Shiller), 29 Italy, 56, 67–69 IT departments, 170 John Deere, 178 Kahneman, Daniel, 47, 91 Kaldor, N., 122 Kapital, Das (Marx), 48 Kay, John, 41 Keynes, John Maynard: animal spirits and, 22; on character of economists, 20, 52; demand management and, 31, 191–92; Great Depression and, 191; Hicks and, 191; lagging policies and, 54; macroeconomics and, 73, 75, 151, 191; monetarist arguments and, 73, 75; on practical men, 32; Samuelson and, 191; twenty-first-century policy and, 191, 193; on well-informed government, 61 kidney exchange, 44, 142 knowledge-based economy, 128, 140, 185–86, 197 Kondratiev, N., 189 Korzybski, Alfred, 89 Kranton, Rachel, 93 Krugman, Paul, 75, 209 Labour Party, 158, 191 Laibson, David, 86 Lancet, The (journal), 77 Lange, Oskar, 182–83, 190 Laspeyres index, 144n3 Lerner, A., 182 L’Etranger (Camus), 87 liberalisation, 38, 68–69, 196 licenses, 59 life expectancy, 145 living standards, 143–47, 172, 194 loans, 109, 147, 158 lobbying, 29, 64–65, 69, 149 logic, 33, 47, 89–91 London School of Economics, 17 London Underground, 62–63 Long-Term Capital Management (LTCM), 23 Lucas, Robert, 75 Lucas Critique, 103 lump of labour fallacy, 78 machine learning (ML), 12–13, 137, 141, 160–61, 187 MacKenzie, Donald, 23–24, 26 macroeconomics: agglomeration and, 127, 132, 202, 207; aggregate behaviour and, 3, 40, 42, 71–72, 100–102, 106, 113, 122–23, 141, 176–77, 201–2; criticism of, 17; empirical work and, 74, 100; forecasting and, 3, 12, 36–37, 76, 101–2, 112; globalisation and, 110, 132, 139, 154, 164, 193–94, 196, 213; Great Depression and, 17; Gross Domestic Product (GDP) and, 13, 101, 113, 151; inflation and, 12–13, 17, 36, 73, 113; innovation and, 37, 71, 102; Keynes and, 73, 75, 151, 191; Keynesian, 151; markets as a process and, 37–45; models and, 21 (see also models); outsider context and, 12, 100–3, 112–14; politics and, 76; progress and, 151; public responsibilities and, 17, 21, 31, 36–37, 71–76, 85–86; separation protocol and, 124; statistics and, 101–2, 113, 131; twenty-first-century policy and, 191 Malthus, Thomas, 48 Mandel, Michael, 96–97 Mankiw, Greg, 86 manufacturing, 105, 149–50, 172, 178, 195–98 marginal costs, 128, 174, 200, 208 Marglin, Steve, 16, 193 Market Abuse Regulation (MAR), 27n5 Markets, State and People (Coyle), 114, 212 Marshall, Alfred, 132 Marshall Aid, 190 Marx, Karl, 48 McFadden, Daniel, 59 Merton, Robert C., 23–24, 28 Merton, Robert K., 22–23 #metoo, 9 microeconomics, 2, 12, 37, 58, 92, 101, 110–11, 121, 209 microfoundations, 90 Microsoft, 133, 170, 173 Millenium Bug, 155 models: abstract mathematics and, 2; ad hoc, 89–92, 94, 150; agents and, 21, 81, 102, 109, 118, 179, 209; assumptions in, 21–22, 35, 46–47, 62–63, 90–94, 119, 137, 154, 177, 209; behavioural, 22, 35, 47, 63, 88, 92–93, 119, 136, 154; Black-Scholes-Merton, 23–25, 28; business, 139, 165, 197; causality and, 2, 94–95, 102; changing economies and, 168, 176–77, 179–80; complexity and, 2, 49, 94, 102, 106, 179–80; counterfactuals and, 97–98, 158, 161, 198, 208; forecasting and, 17, 74, 101–2, 113; frictions and, 22, 113, 136, 154, 182; Great Financial Crisis (GFC) and, 31, 101, 113; inflation and, 30, 113; influence of, 23; Korzybski on, 89; moral issues and, 129; Nash equilibrium and, 90–91; objective of, 89–90; outsider context and, 55, 88–103, 106, 109, 113; over-fitting, 95; platform, 197; progress and, 139, 151–52, 154, 159–61; rationality and, 21–22, 31, 35, 45–48, 62, 71, 88–103, 117–18; reality building by, 23; Scott on, 63; transaction costs and, 168; twenty-first-century policy and, 185–86, 189, 191, 197, 209 Modern Monetary Theory (MMT), 75, 102 monetarism, 16, 71, 73, 75 monopolies, 20, 29, 42 Monti, Mario, 67–69 Mont Pèlerin Society, 31, 191, 193–94 Moore’s Law, 170, 184 moral issues: Atkinson and, 129; causality and, 96; Cook and, 150; ethics, 4, 34, 39, 100, 105, 115, 119–24; fairness, 43, 45–46, 166; models and, 129; outsider context and, 96, 106–8; progress and, 148, 150; rationality and, 117; Sandel on, 34, 39, 43, 107, 119; Stern and, 148 MySpace, 205 Nash equilibrium, 90–91 National Health Service (NHS), 44–45, 77 “Nature and Significance of Economic Science, The” (Robbins), 121 neoliberalism, 3, 15, 193–94 network effects: competition and, 202, 205; economies of scale and, 127, 174, 177, 185, 199–201, 209; fixed costs and, 174, 177, 179, 185–86, 200; indirect, 174; twenty-first-century policy and, 185, 199–202, 205, 209; progress and, 141 New Deal, 193 New Public Management, 33, 106–7, 119, 187 New York Times, 19 Nobel Prize, 21, 23, 35, 44, 47, 59, 63, 92, 109, 140, 209 Nordhaus, William, 170 normative economics: decision making and, 110, 114, 120; Friedman and, 104, 121; Gelman and, 108; policy implications and, 125–26; positive economics and, 10, 104, 108, 114, 120–21, 125, 146; progress and, 146; welfare and, 114, 120, 134 nuclear arms race, 190 Obama, Barack, 75 Occupy movement, 19, 131 Office for Budget Responsibility, 66 Office for National Statistics, 171 OPEC, 192 OpenTable, 142, 175, 200 opportunity cost, 56, 58, 80, 156 optimisation, 48, 118, 188 Organisation for Economic Co-operation and Development (OECD), 130, 132, 164, 190 Ormerod, Paul, 106 Oscar awards, 108 Ostrom, Elinor, 63–64 outsider context: behavioural economics and, 88, 92–93, 100, 103–9; causality and, 94–96, 99–105; competition and, 98, 105; consumers and, 92, 96, 98, 100–102, 105, 108–9; decision making and, 93; Great Financial Crisis (GFC) and, 87–88, 101, 110, 112–14; growth and, 12, 97, 101n1, 111; interventions and, 87, 94, 104, 106; macroeconomics and, 12, 100–103, 112–14; methodology for, 88–103; models and, 55, 88–103, 106, 109, 113; politics and, 106, 110; regulation and, 109; technology and, 103; welfare and, 105–7, 114 outsourcing, 139, 195–97 Oxfam, 95–96 Packard, Vance, 109 Papademos, Lucas, 67 Pareto criterion, 121–23, 126–27, 129 patents, 140 pensions, 18, 37, 60, 65, 146 Perez, Carlotta, 189 performativity, 11, 23, 30, 211 Peste, La (Camus), 108 Petty, William, 148 Phillips machine, 135–37, 151, 192 Pigou, A.

Tech Titans of China: How China's Tech Sector Is Challenging the World by Innovating Faster, Working Harder, and Going Global
by Rebecca Fannin
Published 2 Sep 2019

•A state-led blueprint, “Made in China 2025,” to close the gap in technology leadership by building national firms into globally competitive tech champions and gaining technological leadership in emerging sectors including robotics, new-energy vehicles, biotech, power equipment, aerospace, and next-generation information technology—all to achieve supremacy.6 •The nation’s “Internet Plus” plan to build up China’s companies as world-class competitors in mobile internet, big data, cloud computing, and Internet of Things.7 The proposal’s focus on optimizing health care, manufacturing, and finance by leveraging internet connectivity and big data.8 •Chinese president Xi Jinping’s Belt and Road initiative to build a twenty-first-century Silk Road land and maritime trade corridor that could outdo America’s postwar reconstruction Marshall Plan to foster economic integration with neighboring countries, boost demand for Chinese products, and develop China’s poorer western provinces.

…

Handset sales account for the bulk, or about 70 percent, of Xiaomi revenues; IoT gadgets and consumer goods (even spinning wheel suitcases) bring in 22 percent; and internet value-added services, such as games, account for 9 percent. At first glance, Xiaomi may seem like a hardware company only with smartphones and smart TVs, but it’s actually succeeded as the “first internet-of-things company with an array of smart hardware products,” notes tech and media analyst Ben Thompson, founder of Stratechery.7 Thompson points out that Xiaomi is the rare company that has succeeded in both hardware and software, adding that Alibaba and Amazon (with Kindle) have dabbled in hardware but not as a core business.

The System: Who Owns the Internet, and How It Owns Us
by James Ball
Published 19 Aug 2020

This means if an attacker can get a virus or worm that spreads very rapidly – ideally without even needing an unwary user to click the wrong attachment – you will have lots of computers ready to use to attack someone. In the case of the 2016 attack against the DNS system, this clever bit was done using ‘Internet of Things’ devices, a term describing adding non-traditional computers like TV set-top boxes, video cameras, speakers, baby monitors and more to the internet. These typically have much worse security (and are much harder to update) that computers and mobiles. Once the attacker has their network of compromised devices – usually called a ‘botnet’ – they can do the easy and stupid bit: they tell all of the devices, all at once, to try to load content from one site, or one server, again and again.

…

That digital divide will only widen. 7https://www.theguardian.com/technology/2017/jul/27/facebook-free-basics-developing-markets Index Aadhaar, here Abramson, Jill, here Ackerman, Spencer, here Acquisti, Alessandro, here ad blockers, here, here advertising, online, here, here, here, here, here, here complexity of, here, here and consumer benefits, here CPM (cost per mille), here programmatic advertising, here, here, here see also surveillance airspace spectrum, here Al Shabab, here Alexander, General Keith, here, here, here Alibaba, here al-Qaeda, here Amazon, here, here, here, here, here, here, here, here and advertising, here and centralisation of power, here and regulation, here Andreessen, Marc, here, here Android, here, here angel investors, here, here, here, here, here antitrust laws, here AOL, here, here, here Apple, here, here, here, here, here, here AppNexus, here, here, here ARPANET, here, here, here, here, here, here, here, here, here, here separation of military elements, here, here see also DARPA Ars Technica, here artificial intelligence (AI), here, here, here Associated Press, here AT&T, here, here, here, here Atlantic, here Baidu, here Barlow, John Perry, here, here, here batch processing, here Bell, Emily, here, here Berners-Lee, Tim, here, here, here betaworks, here, here Bezos, Jeff, here bit.ly, here Bitcoin, here, here, here blackholing, here blockchains, here Bomis, here book publishers, here Border Gateway Protocol (BGP), here Borthwick, John, here, here, here, here, here, here botnets, here Brandeis, Louis, here broadband customers, here, here BT, here, here BuzzFeed, here cable companies, here lobbying, here peering agreements, here profits, here, here reputation and trust, here tier one providers, here, here traffic blocking, here transit fees, here cable TV, here, here, here Cambridge Analytica, here Carnegie, Andrew, here celebrities, here Cerf, Vint, here, here, here, here Certbot, here Chicago School of Economics, here China, here, here, here, here, here, here, here, here Chrome, here CIA, here Cisco, here Clinton, Hillary, here ‘cloud, the’, here CNN, here Cohn, Cindy, here, here Cold War, here, here Comcast, here, here, here, here, here CompuServe, here computers, early, here content farms, here, here cookies, here, here, here, here, here Cox, Ben, here credit cards, here Crimea, here Crocker, Steve, here, here, here, here, here, here, here cryptocurrencies, here, here, here, here Daily Caller, here, here Daly, Tom, here, here, here DARPA, here, here, here, here, here data brokers, here, here, here Defense Communications Agency, here del.icio.us, here Deliveroo, here ‘digital colonialism’, here DirecTV, here distributed denial of service (DDoS) attacks, here, here, here Dolby, here Domain Name System (DNS), here, here, here, here, here, here Dots and Two Dots, here DoubleClick, here duolingo, here Duvall, Bill, here Dyn attack, here eBay, here, here Eisenstein, Elizabeth, here elections, interference in, here Electronic Frontier Foundation (EFF), here, here Eliason, Frank, here, here, here, here, here Encarta, here encryption, here, here Engelbart, Doug, here Etsy, here European Union (EU), here, here, here, here, here, here see also General Data Protection Regulation (GDPR) Facebook, here, here, here, here, here, here, here, here, here, here, here, here, here, here acquisition of WhatsApp, here, here, here, here and advertising, here, here, here, here, here, here and centralisation of power, here and ‘digital colonialism’, here and government entities, here influence on elections, here Menlo Park campus, here privacy scandals, here and regulation, here, here, here, here Facetime, here facial recognition, here FakeMailGenerator, com, here Fastclick, here Fastly, here FBI, here, here Federal Communications Commission (FCC), here, here, here financial crash, here, here FireEye, here First World War, here, here Five Eyes, here, here, here Flickr, here Flint, Michigan, here Foreign Policy, here, here Fotolog, here, here, here Foursquare, here Franz Ferdinand, Archduke, here Free Basics, here free speech, here, here, here, here, here Freedom of Information Act, here GCHQ, here, here, here, here, here and encryption, here General Data Protection Regulation (GDPR), here, here, here George V, King, here Ghonim, Wael, here Gibson, Janine, here, here, here Gilded Age, here, here, here Gilmore, John, here Gimlet media, here Giphy, here Gizmodo blog, here Gmail, here Goodwin, Sir Fred, here Google, here, here, here, here, here, here, here, here, here, here, here, here, here, here, here and advertising, here, here, here, here, here, here, here and centralisation of power, here London headquarters, here and regulation, here, here, here Grateful Dead, here Greene, Jeff, here, here, here Greenwald, Glenn, here Grindr, here Guardian, here, here, here, here and Snowden leaks, here, here Guo Ping, here Gutenberg press, here Heatherwick, Thomas, here Herzfeld, Charles, here Hoffman, Reid, here Hong Kong, here HOSTS.TXT, here Hotmail, here HTML, here HTTP, here, here HTTPS Everywhere, here Huawei, here, here Hutchins, Marcus, here IBM, here identity, here India, here, here Industrial Revolution, here Instagram, here intellectual property, here, here internet, origins of, here, here commercialisation and globalisation, here gradual expansion, here logging and security, here the name, here origins of networking, here separation of military elements, here, here see also ARPANET Internet Corporation for Assigned Names and Numbers (ICANN), here, here, here, here Internet Hall of Fame, here, here Internet of Things, here internet service providers (ISPs), here, here, here, here, here, here, here, here and Pakistan/YouTube incident, here intranets, here IP (Internet Protocol), here IP addresses, here, here, here, here, here, here, here, here, here and blackholing attacks, here iPhones, here, here Iran, here, here, here, here Stuxnet worm attack, here, here ISIS, here Jackson, Steve, here Jarvis, Jeff, here journalism, here see also newspapers Kaspersky, here key cards, here Kickstarter, here, here, here Kidane v.

When More Is Not Better: Overcoming America's Obsession With Economic Efficiency
by Roger L. Martin
Published 28 Sep 2020

With respect to connectedness, as we’ve seen, interdependence between outcomes over time amplifies the gravity effect of efficiency. It’s no surprise, therefore, that the transformation from a Gaussian distribution to a Pareto distribution is accelerating. We are currently connecting more and more things, in more and more tightly coupled ways.7 The internet of things (IoT) is the latest generation of enhanced connectedness. Untold billions of devices will be connected to provide real-time information, computer-to-computer. Systems everywhere are becoming tightly coupled. Lots about it is good, indeed very good. A connected world is more efficient. A connected world drives out transaction costs and unnecessary rework.

…

See economic growth growth rate, 8, 11 gun control, 197, 206 Hamilton, Alexander, 40 Harris, Michael, 47 Harvard Business School, 175 Harvard Kennedy School, 180 Harvey, Campbell, 155 Hasso Plattner Institute of Design, 179–180 hedge funds, 91, 155, 157, 158 hedonistic adjustments, 10–11 Heinz, 123–124, 187, 189 high-frequency trading, 90, 156–157 high-income Americans, 7–8 high-speed trading, 90 honeybees, 74–75 Hong Kong, 93–94 hotel industry, 122 House of Representatives, 201, 202 housing bubble, 81, 137, 140 Huizenga, Wayne, 71–72 humanities, 181–182, 184 IBM, 132 IDEO, 179 ideology, 213–214 iHeartMedia, 97–98, 99, 101 IIT Institute of Design, 180 Illinois Institute of Technology, 180 improvement, 103–106, 113, 132 income average, by percentile, 1913–2015, 7 disparities, 66 growth, 33 mean family, 4, 6 median family, 4–5, 11, 38 real, 10, 11 income distribution, 14, 36–37, 38, 63, 70, 161 income taxes, 159–162 incumbents, 202 Industrial Revolution, 41 industry consolidation, 71–73 See also mergers inflation, 24, 31, 103 innovation, 54 input-output relationships, 81–82 input-output tables, 22 Instagram, 61, 65, 71, 129, 191, 192 Institute for Competitiveness & Prosperity, 17 integrative thinking, 174, 176, 178 Intel, 129 interdependencies, 106–113 interest rates, 80, 81 internet, 63–64, 66 internet of things (IoT), 106 Investors Exchange (IEX), 156–157 invisible hand, 39, 41 Irish potato famine, 75 isolation, 199 Italy, 157 I-Think Initiative, 171–172, 181 Japanese auto manufacturers, 43, 151 Jefferson, Thomas, 40 Jenner, Kylie, 65 job design, 31–32 job market, 66–70 Joe’s Stone Crab, 115–120, 121, 132–133 Johnson, Dwayne, 65 Kaplan, Robert, 129 Kardashian, Kim, 65 Ka-shing, Li, 52 Katsuyama, Brad, 156 Kelley, David, 179 Kennedy, John F., 198 killer whales, 82–83 King, Martin Luther, Jr., 192 Kraft, 123–124 Kraft Heinz, 123–124, 127 labor division of, 39–41 rewards for, 67–70 labor costs, 49–50, 63, 122, 124–125, 128 labor market, 67–70 ladder of inference, 167 laws, revision of, 142–145 leading brands, 191–192 leaky bucket metaphor, 27–28 Leamington Ketchup Affair, 187–190 LearningEdge platform, 177 legal system, 105–106 legislation, 93 antitrust, 53–56, 152, 153 revising, 142–145 Lehman Brothers, 84–86, 104, 137 Leontief, Wassily, 21–22, 30, 80 leveraged buyouts (LBOs), 97–99, 101 Lightner, Candace, 193–194 living standards, 9–11, 33 lobbyists, 91, 93 Loblaws, 188 Long Depression, 31 long-term capital, 157, 158–159 Long-Term Stock Exchange (LTSE), 156 Loosemore, Tom, 147–149 Lorenz, Edward, 81 Loungani, Prakash, 82 Love Canal families, 193, 207 Loyalty Effect, The (Reichheld), 27–28 Lucas, Robert, 24 MacArthur, Douglas, 43 machine model, 22, 25, 26, 30–44, 94, 100, 103–104, 123, 210 Madison, James, 40 management models, 49–50 scientific, 42 total quality, 43 manufacturing industries, 41, 126 Martin, Paul, 141 Martin Prosperity Institute, 2, 17 Massachusetts Institute of Technology (MIT), 176–177, 184 master’s in business administration (MBA), 174–175 McKelvey, Bill, 62 mean family income, 4, 6 Measure What Matters (Doerr), 52 median family income, 4–5, 11, 38 median voter, 38 mental proximity, 145–149 mergers, 53–54, 63–64, 123–124, 141 metaphors for education, 29 importance of, 25–26 leaky bucket metaphor, 27–28 machine, 22, 25, 26, 30–44, 94, 100, 103–104, 123, 210 Microsoft, 131 middle class, 9, 14, 36 mobile operating systems, 131 mobility, 9, 37 models building better, 171–172, 179 in business and public policy, 27–30 of citizens, 145 core components of, 29 critical evaluation of, 171 doubling-down on existing, 214 economic.

API Marketplace Engineering: Design, Build, and Run a Platform for External Developers
by Rennay Dorasamy
Published 2 Dec 2021

He formerly was a Data and Artificial Intelligence Architect at the Microsoft Technology Center in Chicago and, prior to that, worked at Oracle for 20 years and led teams supporting North America focused on data warehousing and Big Data. Bob has also spoken at numerous industry conferences internationally. His books include Design Thinking in Software and AI Projects (Apress), Azure Internet of Things Revealed (Apress), Remaining Relevant in Your Tech Career: When Change Is the Only Constant (Apress), Architecting the Industrial Internet (Packt Publishing), Big Data and the Internet of Things: Enterprise Architecture for a New Age (Apress), Oracle Big Data Handbook (Oracle Press), Oracle Essentials (O’Reilly Media), and Professional Oracle Programming (Wiley Publishing). You can follow him on Twitter at @rstackow and/or read his articles and posts on LinkedIn

Taming the Sun: Innovations to Harness Solar Energy and Power the Planet
by Varun Sivaram
Published 2 Mar 2018

Many trends are on course to converge at enabling this reality. First, customers are increasingly buying appliances that connect to the Internet—be they smart thermostats, washing machines, lights, or refrigerators. Industrial power customers are also connecting their equipment to the Internet. By 2020, the overall number of devices composing the “Internet of Things” is on track to reach 50 billion—double the number in 2015.45 These devices can be remotely controlled (for example, via a smartphone app), changing their instantaneous energy consumption. The same applies to the power-intensive equipment in large buildings, which increasingly is controlled through smart building energy management control systems.

…

Farshid Shariatzadeh, Paras Mandal, and Anurag Srivastava, “Demand Response for Sustainable Energy Systems: A Review, Application, and Implementation Strategy,” Renewable and Sustainable Energy Reviews 45 (2015): 343–350, doi: 10.1016/j.rser.2015.01.062. 44.  A. Bumpus and S. Comello,“Emerging Clean Energy Technology Investment Trends,” Nature Climate Change 7 (2017), doi: 10.1038/nclimate3306. 45.  Federal Trade Commission (FTC), Internet of Things: Privacy & Security in a Connected World (Washington, DC, November 2013, https://www.ftc.gov/system/files/documents/reports/federal-trade-commission-staff-report-november-2013-workshop-entitled-internet-things-privacy/150127iotrpt.pdf). 46.  Jeff St. John, “US Smart Meter Deployments to Hit 70M in 2016, 90M in 2020,” Greentech Media, October 26, 2016, https://www.greentechmedia.com/articles/read/US-Smart-Meter-Deployments-to-Hit-70M-in-2016-90M-in-2020. 47.  

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.: Financial innovation defined, 58b, 90, 289g and future of solar energy, 1–2, 23–25 government inducements for, 260–266 to improve solar energy utilization, 78–83 and mindset of Silicon Valley solar start-ups, 29 overcoming deployment challenges with, 57, 58b to realize potential of solar energy, 11–12 in solar energy market, xvi–xvii in solar industry, 53 subsidies for, 267–268 U.S. leadership in, 271–274 by U.S. government, 249–254 Instantaneous power, 71 Institutional investors clean energy investments by, 98, 111 defined, 288g infrastructure investments for, 93 in new solar projects, 65–68 Insurance, 103, 105 Insurance companies, 111 Integrated circuits, 259 Integrated solar fuel generators, 174, 178 Intel, 164 Intermittency, 4, 193, 198 International Energy Agency (IEA), 11, 61, 290g International Finance Corporation (IFC), 111 International Renewable Energy Agency (IRENA), 290g International research collaborations, 273 Internet, 249, 259 Internet of Things, 214 Invention, 58b Inverters, 53, 82, 212, 242, 281g Investment tax credit (ITC), 281g Ion cannons, 39 Iranian Revolution, 35 IRENA (International Renewable Energy Agency), 290g Irrigation systems, 31, 245–247 Italy growth of renewable energy in, 73, 73f incentives for solar in, 43 total solar eclipse and grid in, 55 unregulated power companies in, 108 value deflation in, 71 ITC (investment tax credit), 281g Ivanpah CSP plant (Mojave Desert, California), 183–184, 186 Jacobson, Mark, 232–233 Japan growth of renewable energy in, 73, 73f interconnecting grid of, 205, 206 in international research collaborations, 273 orbital solar power satellite in, 163, 164 solar industry in, 28, 36 solar PV production in, 44 solar water heaters in, 30 JCAP.

The Future Is Asian
by Parag Khanna
Published 5 Feb 2019

Rakuten requires English competence, and at Uniqlo, English is the official workplace language. According to Rakuten founder Hiroshi Mikitani, “The greatest business risk [Japan] faces is that of staying at home.”20 Japan is invigorating its already deep advantages in precision industries through new public-private alliances amounting to several trillion dollars devoted to the Internet of Things (IoT), big data, AI, 3D printing, robotics, biotech, health care, clean energy, enhanced agriculture, and other sectors—all ready for export to Asia’s high-growth markets. SoftBank has become Japan’s standout example of a bridge between Japan, Asia, and the world. SoftBank’s Vision Funds—in which Saudi Arabia is the largest investor followed by the UAE—are the largest technology portfolio in the world, making aggressive investments in semiconductors, satellites, artificial intelligence, and IoT companies around the world.

…

Each major Asian tech hub has a niche edge emerging from its ecosystems: Tel Aviv excels in cybersecurity, Singapore in fintech, Tokyo in robotics, Shenzhen in sensors, and so forth. Other places such as Dubai aren’t scientific pioneers but make themselves regulatory test beds for everything from drones to driverless cars. Asia’s cities lead the world in the deployment of sensor networks for the urban Internet of Things, lifting Korea’s semiconductor exports 55 percent from 2016 to 2017. Now such sensor networks as well as energy-efficient LED lights are being installed in second-tier areas such as India’s Bhopal. In China’s Yinchuan, trash bins double as compactors run by solar power, with sensors alerting trash collectors when to empty them.

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Western, 357–58 Cold War, 2, 3, 6, 14, 19, 51–58, 86, 138, 283 colleges and universities, Asian: all-English programs in, 231 ethnic and cultural diversity in, 338 colleges and universities, European, Asian campuses of, 257 colleges and universities, US: Asian campuses of, 231–32 Asian enrollment in, 224–27 Asian studies departments in, 230 study-abroad programs in, 230–31 colonialism, 6, 22, 24, 27, 329 legacies of, 77–78 Columbus, Christopher, 43 commodities, trade in, 100, 111, 113, 160, 176, 322 China and, 21, 150, 157, 158, 273, 276–77 Russia and, 85, 88 Turkey and, 93, 94 Communist Party, Chinese, 49, 159–60, 300, 301 conflict, regional systems and, 11 Confucianism, 32, 34, 70, 300, 301 Congress, US, 195, 207, 222, 284 Asian Americans in, 221 Congressional Research Service (CRS), 293 Conrad, Sebastian, 28 Constantinople, 36, 39 sacking of, 43, 73, 91 consumerism, 23 corruption, 161, 267, 305 cosmetics industry, 346 Costa Rica, 274 Council of Europe, 57, 92, 241 coworking spaces, 204 Crazy Rich Asians (film), 347 Crimea, Russia’s annexation of, 83 Crimean War, 47 crusades, 39 Cuba, 271 Asian immigrants in, 275 cuisine, Asian: fusion, 345 global spread of, 343–45 Cultural Revolution, 56 culture, Asian, growing cross-border and global awareness of, 340–51 currency exchange rates, 169 Cyprus, 91 Cyrus the Great, Persian emperor, 30 Dalai Lama, 55, 120, 222, 358 Damico, Flávio, 277 Daoism, 31, 34 Darius I, Persian emperor, 30 Darius III, Persian emperor, 32 Defense Department, US, 98, 143 defense spending: in Asia, 17, 105, 137, 138 by Europe, 240, 248 Delhi Sultanate, 38–39 Demetrius, king of Bactria, 33 democracy, 15, 281–86 appeal of stability over ideals of, 285–86, 296, 309–13 Asian versions of, 21–22, 23, 281, 288–89 capitalism and, 352 failures and weaknesses of, 282–86, 294, 302–3 parliamentary, 295 Plato on, 286, 291 policy vs. politics in, 289, 296 populists’ hijacking of, 3 post–Cold War triumph of, 2 Singapore’s melding of technocracy and, 288–89, 290, 298 Deng Xiaoping, 57, 300 Dharma Bums (Kerouac), 331 Didi Chuxing (DiDi), 174–75, 198 digital integration, 186–89 digital technology: Asia and, 324 in governance, 318–20 Djibouti, 263 DNA editing, 201 Doha, art scene in, 342 dollar reserves, Asian holdings of, 163 Dream of the Red Chamber, 353 drones, commercial, 209 drug trade, 106–7 Duara, Prasenjit, 358 Dubai, 172, 173, 202, 212, 251, 261, 334 Dubai Ports World, 104, 261, 263 Durban, 265 Durov, Pavel, 173 Dutch, Southeast Asian colonies of, 45 Duterte, Rodrigo, 123–24, 305, 340 illiberal policies of, 306 Dyson, 210, 257 East Asia, 6, 51, 70, 140 cross-border literary tradition of, 353 democratization of, 61 economic growth of, 9 economic stability of, 63 exports of, 153, 154 Gulf states investments in, 103–4 oil and gas imports of, 82–83, 84–85, 106, 152, 175 in post–Cold War era, 60–61 prehistoric civilizations in, 29 US and, 140–41 US presence in, 16, 73 see also specific countries East Asian Community (EAC), 9 eco-activism, 182 e-commerce, 210–11, 228 Economic Cooperation Organization (ECO), 58 economic growth, 3, 4 rule of law and, 309–10 economy, global, 321–22 eco-tourism, 340 Ecuador, 274 education and professional training, 204–5, 317 Egypt, 29, 262 Eilat, 99 election, US, of 2016, 83, 320 electricity transmission systems, 112 electric vehicles, 179 energy: Asian need for, 9, 17, 62, 82–83, 84–85, 96, 100, 102, 103, 106, 152, 175–80, 177, 207 Europe’s need for, 84 Enlightenment, 22 Erdoğan, Recep, 87, 91, 92, 222, 310 Ethiopia, 262 eugenics, 200–201 Eurasia, 81 Eurasian Economic Union (EEU), 85, 87 Europe: alternative energy in, 175 anti-Muslim movements in, 255 anti-Soviet revolutions in, 58 Arabs in, 253, 255, 258 Asians in, 253–58 austerity policies in, 299 China and, 243, 246, 248–50 as coherent regional system, 7 defense spending by, 240, 248 energy needs of, 84 global civilization as influenced by, 21, 22–23 governance systems in, 284 internal trade in, 152 postwar rebuilding of, 14 Russia and, 83–84, 85, 89 Syrian refugees in, 63 US financial holdings of, 164–65 US relations with, 240 in voyages of discovery, 43–44, 68 see also specific countries Europe, Asia and, 239–58 arms sales in, 251 Asian investment in, 163, 246–47 financial sector in, 246, 247 food transport and, 244, 248 free trade agreements in, 250 infrastructure connectivity and, 243–44 retail sector in, 244 tourism in, 254–55 trade in, 13, 14, 241, 250 urban development and, 245–46 European Bank for Reconstruction and Development (EBRD), 241 European Central Bank, 243 European Coal and Steel Community, 7 European Customs Union, 92 European Economic Community, 57 European Investment Bank (EIB), 250 European Union, 2, 11, 13, 14, 127, 133, 249 expansion of, 241, 258 Israel and, 97 execution rates, in Asia, 308 Export-Import Bank of China, 84–85, 110, 273 Facebook, 208, 209, 219, 320 family-run businesses, Asian, 159–60 Far East, use of term, 5–6 Far Eastern Economic Review, 353 fashion: Asian, spread of, 345–46 Asian models in, 346 European, Asianization of, 345–46 Filipino Americans, 217 film industry: in Asia, 347–51 Asian directors in, 347 cross-Asian collaborations in, 348–49 Hollywood’s use of Asian themes in, 346–47 US-Asian collaboration in, 348 finance industry, Asian, 163 bonds in, 163, 164, 165–67 commodities markets in, 176 cross-border investments in, 166 foreign investments in, 167, 168, 171–72 IPO’s in, 167 private equity in, 171–72 privatization and, 169–71 stock markets in, 167–68 US and European investments by, 163–64 venture capital in, 173–74 finance industry, US, 166 Asia and, 167 financial crises: Asian (1997–98), 61, 62, 121, 151 Western (2007–08), 3, 14, 17, 62, 147, 152, 164, 233, 299 fintech (financial technology), 158, 168, 169, 188, 213 Flanagan, Owen, 357 flashpoints, geopolitical, in Asia, 11 food: Asian demands for, 244, 248 Asian production of, 177, 180–81, 182 Foreign Affairs, 8 Fosun International, 159–60 Foxconn, 132, 153, 194, 228 France: Arab immigrants in, 253 Asian immigrants in, 253 Asian trade of, 244 Indochina colonized by, 45 and loss of Indochina, 52 West Asian mandates of, 49 Francis I, Pope, 358 Franco-Prussian War, 286 Freedom House, 308 free trade: Asia and, 8, 102, 124, 129, 133, 153, 154, 158, 223, 250, 252, 272, 273 Western promotion of, 2–3, 158 Fujimori, Alberto, 276 Fukuoka, 135–36 Funabashi, Yoichi, 8–9 Funan Kingdom, 34 Fung, Spencer, 184 Future Forward Party, Thailand, 307 Gama, Vasco da, 43 Gandhara, 32, 33, 34 Gandhi, Mohandas K., 49, 265, 316 Ganges region, 29, 32 Ganges River, 33, 35, 46 “Gangnam Style” (music video), 343 Gates, Bill, 317 Geely, 194 General Electric, 110, 168, 211 Genghis Khan, 39–40 Georgia, Republic of, 59 technocracy in, 307 Germany, Nazi, 50 Germany, unified: Arab refugees in, 255 Asian immigrants in, 253, 254, 256 Asia’s relations with, 242 multiparty consensus in, 284 Ginsberg, Allen, 331 Giving Pledge, 317 Global-is-Asian, 22 globalization: Asia and, 8–9, 162, 357–59; see also Asianization growth of, 14 global order, see world order Goa, 44, 89, 186 Göbekli Tepe, 28 Goguryeo Kingdom, 34 Go-Jek, 187 Golden Triangle, 123 Google, 199, 200, 208–9, 219 Gorbachev, Mikhail, 58 governance: digital technology in, 318–19 inclusive policies in, 303 governance, global: Asia and, 321–25 infrastructure and, 322 US and, 321 government: effectiveness of, 303 trust in, 291, 310 violence against minorities by, 308–9 Government Accountability Office (GAO), 293 GrabShare, 174–75 grain imports, Asian, 90 Grand Canal, China, 37, 42 Grand Trunk Road, 33 Great Britain: Asian investments in, 247 Brexit vote in, 283–84, 286, 293–94 civil service in, 293–94 colonial empire of, 46–47 industrialization in, 46 Iran and, 252 populism in, 283–84 South Asian immigrants in, 253, 254 West Asian mandates of, 49–50 Great Game, 47 Great Leap Forward, 55 Great Wall of China, 31 Greece, 60, 91, 248 Greeks, ancient, 29, 34 greenhouse gas emissions, 176–77, 182 gross domestic product (GDP), 2, 4, 150 Grupo Bimbo, 272 Guam, 50, 136 Guangdong, 42, 98 Guangzhou (Canton), 37, 48, 68 Gulf Cooperation Council (GCC), 58, 101, 102 Gulf states (Khaleej), 6, 9, 57, 62, 81 alternative energy projects in, 251 Asianization of, 100–106 China and, 101, 102 European investment in, 251 India and, 102 Israel and, 99–100, 105 Japan and, 102 oil and gas exports of, 62, 74, 100–101, 176 South Asian migrants in, 334 Southeast Asia’s trade with, 102 South Korea and, 102 technocracy in, 311–12 US arms sales to, 101 women in, 315 see also specific countries Gulliver, Stuart, 148, 150 Gupta Empire, 35 H-1B visas, 219 Hamas, 59, 100, 139 Hamid, Mohsin, 184 Han Dynasty, 32, 33, 34, 300 Hanoi, 180 Han people, 31–32, 37, 69 Harappa, 29 Hardy, Alfredo Toro, 275 Hariri, Saad, 95 Harun al-Rashid, Caliph, 37 Harvard University, 230 Haushofer, Karl, 1 health care, 201–2 Helmand River, 107 Herberg-Rothe, Andreas, 75 Herodotus, 30 heroin, 106–7 Hezbollah, 58, 95, 96, 106 Hindus, Hinduism, 29, 31, 32, 34, 38, 70–71 in Southeast Asia, 121 in US, 220, 221 Hiroshima, atomic bombing of, 51 Hispanic Americans, 217 history, Asian view of, 75 history textbooks: Asia nationalism in, 27–28 global processes downplayed in, 28 Western focus of, 27–28, 67–68 Hitler, Adolf, 50 Ho, Peter, 289 Ho Chi Minh, 52 Ho Chi Minh City, 56 Honda, 275 Hong Kong, 56, 74 American expats in, 234 art scene in, 342 British handover of, 60, 141 civil society in, 313 Hongwu, Ming emperor, 42 honor killings, 315 Hormuz, Strait of, 103, 106 hospitality industry, 190, 214 Houthis, 106, 107 Huan, Han emperor, 33–34 Hulagu Khan, 40 Human Rights Watch, 313 human trafficking, 318 Hunayn ibn Ishaq, 37 Hungary, 40, 248, 256 Huns, 35, 76 hunter-gatherers, 28 Huntington, Samuel, 15 Hu Shih, 332 Hussein, Saddam, 58, 62, 101 Hyundai, 104 IBM, 212 I Ching, 30 Inclusive Development Index (IDI), 150 income inequality: in Asia, 183–84 in US, 228, 285 India, 101, 104 Afghanistan and, 118 Africa and, 264–66 AI research in, 200 alternative energy programs in, 178–79, 322 Asian investments of, 118 Australia and, 128 British Raj in, 46, 49 charitable giving in, 316–17 China and, 19–20, 113, 117–18, 155, 156, 332 civil society in, 313 in Cold War era, 52, 55, 56 corporate debt in, 170 corruption in, 161, 305 demonetization in, 184, 186–87 diaspora of, 333–34 early history of, 29, 30–31 economic growth of, 9, 17, 148, 185–86 elections in, 63 European trade partnerships with, 250–51 expansionist period in, 38, 41–42 failure of democracy in, 302 family-owned businesses in, 160 film industry in, 349–51 financial markets in, 186 foreign investment in, 192 gender imbalance in, 315 global governance in, 322–23 global image of, 331–32 Gulf states and, 102 inclusive policies in, 304 infrastructure investment in, 63, 110, 185 Iran and, 116, 118 Israel and, 98–99 IT industry in, 204, 275 Japan and, 134, 156 Latin America and, 275 manufacturing in, 192 as market for Western products and services, 207 naval forces of, 105 Northeast Asia and, 154–55 oil and gas imports of, 96, 107–8, 176 Pakistan and, 53, 55, 61, 77–78, 117–18 partitioning of, 52–53 pharmaceutical industry in, 228, 275 population of, 15, 186 in post–Cold War era, 61, 62 privatization in, 170 returnees in, 226 Russia and, 86–87 service industry in, 192 Southeast Asia and, 154–55 special economic zones in, 185 spiritual heritage of, 332 technocracy in, 304–6 technological innovation in, 186–87 territorial claims of, 11 top-down economic reform in, 305 traditional medicine of, 355 West Asia and, 155 Indian Americans, 217, 218, 219–20, 222 Indian Institutes of Technology (ITT), 205 Indian Ocean, 38, 47, 74, 105, 261, 262, 266 European voyages to, 44 Indians, in Latin America, 276 IndiaStack, 187 Indochina, 45, 50, 52 see also Southeast Asia Indo-Islamic culture, 38 Indonesia, 53, 61, 121, 125, 182 art scene in, 342 in Cold War era, 54 economic growth of, 17, 148 eco-tourism in, 340 failure of democracy in, 302 foreign investment in, 187 illiberal policies of, 306 inclusive policies of, 304 Muslims in, 71 technocracy in, 304–5 Indus River, 32, 113 Industrial and Commercial Bank of China (ICBC), 92, 159 industrialization, spread of, 22 Industrial Revolution, 2, 46, 68 Indus Valley, 29 infrastructure investment, in Asia, 6, 62, 63, 85, 88, 93, 96, 104, 108, 109, 110–11, 185, 190, 191, 243–44 see also; Asian Infrastructure Investment Bank; Belt and Road Initiative Institut d’Études Politiques de Paris (Sciences Po), 257, 286–87 insurance industry, 210 intermarriage, 336, 337–38 International Monetary Fund (IMF), 162, 163, 166, 323 International North-South Transport Corridor (INSTC), 116 International Renewable Energy Agency (IRENA), 100 International Systems in World History (Buzan), 7 Internet of Things (IoT), 134, 136, 197 Interpol, 324 Iran, 11, 15, 62, 92, 95, 98, 101, 140 China and, 101, 106–7, 116 in Cold War era, 54 European trade with, 251–52 growing opposition to theocracy in, 312 India and, 116, 118 Islamic revolution in, 57 Israel and, 99, 100 nuclear program of, 62 oil and gas exports of, 50, 94, 106, 107–8, 118, 176 in post–Cold War era, 58–59 privatization in, 170 re-Asianization of, 81, 106 Russia and, 87 Saudi Arabia and, 95–96, 100, 105–6 Syria and, 106 tourism in, 252 Turkey and, 94 US sanctions on, 87, 107, 241, 251, 252 women in, 315 Yemen and, 107 Iran-Iraq War, 58, 106 Iraq, 9, 11, 16, 49 Kuwait invaded by, 59 oil exports of, 55, 96 Sunni-Shi’a conflict in, 312 Iraq Reconstruction Conference (2018), 96 Iraq War, 3, 62, 91, 217, 240 Isfahan, 41 Islam, 40, 316 politics and, 71–72 spread of, 36, 38–39, 43, 69–72, 74 Sunni-Shi’a conflict in, 95, 312 Sunni-Shi’a division in, 36 see also Muslims; specific countries Islamic radicalism, 58, 59, 62, 65, 68, 71, 72, 115, 117, 139 see also terrorism Islamic State in Iraq and Syria (ISIS), 63, 71, 94, 96, 117 Israel, 11, 54, 96 arms sales of, 98 China and, 98–99 desalinzation technology of, 181 EU and, 97 Gulf states and, 99–100, 105 India and, 98–99 Iran and, 99, 100 Russia and, 88 see also Arab-Israeli conflict; Palestinian-Israeli conflict Japan, 14, 16, 63, 68, 69, 73 Africa and, 265 Allied occupation in, 51 alternative energy technologies in, 322 Asian investments of, 118, 156 Asianization of, 81 Asian migrants in, 336–37 Asian trade with, 273 capitalism in, 159 cashless economy in, 189 China and, 19–20, 77, 134, 136–37, 140–42 in Cold War era, 5, 55 corporate culture of, 132 early history of, 29, 31, 34–35 economic growth of, 55, 132, 148, 158, 163 economic problems of, 132, 134–35 in era of European imperialism, 47–48 EU trade agreement with, 133 expansionist period in, 38, 42, 44 foreign investment in, 135 in global economy, 133–37 global governance and, 322–23 global image of, 331 Gulf states and, 102 immigration in, 135–36 India and, 134, 156 infrastructure investment in, 110 Latin America and, 275 precision industries in, 134, 135–36 robotic technology in, 134 Russia and, 82, 86–87 Southeast Asia and, 133, 153–54, 156 South Korea and, 141–42 technological innovation in, 134, 196, 197 territorial claims of, 11 tourism in, 135 US and, 136 in World War I, 49 in World War II, 50–51 Japan International Cooperation Agency (JICA), 265 Japan-Mexico Economic Partnership Agreement, 273 Java, 35, 38, 39, 45 Javid, Sajid, 254 Jericho, 28 Jerusalem, 54, 98 Jesus Christ, 35 jihad, 38 Jinnah, Muhammad Ali, 52 Jobs, Steve, 331 Joko Widodo (Jokowi), 305, 306, 320 Jollibee, 172 Jordan, 54, 62, 97, 99 Syrian refugees in, 63 Journal of Asian Studies, 352 Journey to the West, 353 Judaism, 36 Kagame, Paul, 268 Kanishka, Kush emperor, 35 Kapur, Devesh, 218 Karachi, 113 Karakoram Highway, 113 Kashmir, 53, 55, 61, 77–78, 117–18, 119 Kazakhstan, 59, 140, 207 China and, 20, 108 economic diversification in, 190 energy investment in, 112 as hub of new Silk Road, 111–12 Kenya, 262, 263 Kerouac, Jack, 331 Khaleej, see Gulf states Khmer Empire, 70 Khmer people, 34, 38, 239 Khmer Rouge, 56 Khomeini, Ayatollah, 57, 59 Khorgas, 108 Khrushchev, Nikita, 56 Khwarizmi, Muhammad al-, 37 Kiev, 40 Kim Il Sung, 55 Kim Jong-un, 142 Kish, 28 Kissinger, Henry, 357 Koran, 316 Korea, 11, 31, 51, 68, 69 early history of, 34 expansionist period in, 38 Japanese annexation of, 48 reunification of, 142–43 see also North Korea; South Korea Korea Investment Corporation, 164 Korean Americans, 217 Korean War, 51 Kosygin, Alexei, 56 K-pop, 343 Kuala Lampur, 121, 246 Kublai Khan, 40 Kurds, Kurdistan, 87, 94, 99, 256 Kushan Empire, 32, 35 Kuwait, 101 Iraqi invasion of, 59 Kyrgyzstan, 59, 108, 182 language, Asian links in, 68–69 Laos, 45, 52, 60, 122, 154 Latin America: Asian immigrants in, 275–76 Asian investment in, 273–75, 276–77 Indian cultural exports to, 350 trade partnerships in, 272–73, 274, 275 US and, 271–72 Lebanon, 49, 54, 58, 95, 106 Syrian refugees in, 63 Lee, Ang, 347 Lee, Calvin Cheng Ern, 131 Lee Hsien Loong, 296–97 Lee Kuan Yew, 56, 127, 268, 288, 289, 292–93, 299, 305 voluntary retirement of, 296 Lee Kuan Yew School of Public Policy, 22, 299 Lenin, Vladimir, 49, 89 Levant (Mashriq), 81, 95, 97 LG, 275 Li & Fung, 184–85 Liang Qichao, 48–49 Liberalism Discovered (Chua), 297 Lien, Laurence, 317 life expectancies, 201 literature, Asian, global acclaim for, 353–54 Liu, Jean, 175 Liu Xiaobo, 249 logistics industry, 243 Ma, Jack, 85–86, 160, 189 Macao (Macau), 44 MacArthur, Douglas, 51 McCain, John, 285 McKinsey & Company, 160, 213 Macquarie Group, 131 Maddison, Angus, 2 Made in Africa Initiative, 262 Magadha Kingdom, 31 Magellan, Ferdinand, 43 Mahabharata, 35 Mahbubani, Kishore, 3 Mahmud of Ghazni, Abbasid sultan, 38 Malacca, 38, 43, 44, 124 Malacca, Strait of, 37, 39, 102, 103, 118, 125 Malaya, 46, 50 Malay Peninsula, 39, 53 Malaysia, 53, 61, 188 Asian foreign labor in, 335 China and, 123, 124 in Cold War era, 54 economic diversification in, 190 economic growth of, 17 technocracy in, 308 Maldives, 105 Malesky, Edmund, 308 Manchuria, 38, 48, 50, 51 Mandarin language, 229–30, 257 Manila, 121, 245 Spanish colonization of, 44 Mansur, al-, Caliph, 37 manufacturing, in Asia, 192 Mao Zedong, 51–52, 55, 56, 261, 300, 301 Marawi, 71 Marcos, Ferdinand, 53–54, 61 martial arts, mixed (MMA), 340–41 Mashriq (Levant), 81, 95, 97 Mauritius, 268 Mauryan Empire, 32–33, 68 May, Theresa, 293 Mecca, 57 media, in Asia, 314 median ages, in Asia, 148, 149, 155 Median people, 29 Mediterranean region, 1, 6, 29, 30, 33, 68, 84, 92, 95, 99, 106 see also Mashriq Mehta, Zubin, 332 Mekong River, 122 Menander, Indo-Greek king, 33 mergers and acquisitions, 212–13 meritocracy, 294, 301 Merkel, Angela, 242, 254 Mesopotamia, 28 Mexico, 7 Asian economic ties to, 272, 273, 274, 277 Microsoft, 208 middle class, Asian, growth of, 3, 4 Mihov, Ilian, 309 mindfulness, 332 Ming Dynasty, 42–43, 44, 69, 73, 75, 76, 105, 137, 262 mobile phones, 157, 183–84, 187, 188, 189, 193, 199, 208–9, 211 Modi, Narendra, 63, 98, 117, 119, 154–55, 161, 180, 185, 222, 265, 305, 306, 307, 320 Mohammad Reza Pahlavi, Shah of Iran, 54 Mohammed bin Salman, crown prince of Saudi Arabia, 72, 247, 310, 312, 315 Mohenjo-Daro, 29 Moluku, 45 MoneyGram, 196 Mongolia, 92, 111–12 alternative energy programs in, 112, 182 technocracy in, 307 Mongols, Mongol Empire, 39–40, 42, 44, 68, 69, 73, 76, 77, 239 religious and cultural inclusiveness of, 40, 70–71 Monroe Doctrine, 271 Moon Jae-in, 142 Moscow, 81, 82 Mossadegh, Mohammad, 54 MSCI World Index, 166, 168 Mubadala Investment Company, 88, 103, 104 Mughal Empire, 41–42, 46 religious tolerance in, 70–71 Muhammad, Prophet, 36 Mumbai, 185–86 Munich Security Conference, 241 Murakami, Haruki, 354 Murasaki Shikibu, 353 music scene, in Asia, 343 Muslim Brotherhood, 59 Muslims, 70–72 in Southeast Asia, 38–39, 43, 70–71, 121 in US, 220 see also Islam; specific countries Myanmar, 60, 63, 161 Asian investment in, 118–19 charitable giving in, 316 failure of democracy in, 302 financial reform in, 184 Rohingya genocide in, 122–23 see also Burma Nagasaki, atomic bombing of, 51 Nanjing, 42, 49 Napoleon I, emperor of the French, 1 nationalism, 11, 20, 22, 49–50, 52–55, 77, 118, 137, 138–39, 222, 312, 329, 337, 352 Natufian people, 28 natural gas, see oil and gas natural gas production, 175–76 Nazism, 200 Nehru, Jawaharlal, 52, 55 Neolithic Revolution, 28 neomercantilism, 20, 22, 158 Nepal, 46, 119–20, 333 Nestorian Christianity, 36, 70 Netanyahu, Benjamin, 97, 98, 100 Netflix, 348 New Deal, 287 New Delhi, 245 Ng, Andrew, 199 NGOs, 313 Nigeria, 265 Nisbett, Richard, 357 Nixon, Richard, 56, 101 Nobel Prize, 48, 221, 249, 323, 353–54 nomadic cultures, 76 Non-Aligned Movement, 55 Non-Proliferation of Nuclear Weapons Treaty, 61 North America: Asian trade with, 13, 14, 207 as coherent regional system, 7 energy self-sufficiency of, 175, 272 internal trade in, 152 see also Canada; Mexico; United States North American Free Trade Agreement (NAFTA), 7 North Atlantic Treaty Organization (NATO), 2, 57, 92, 116 Northeast Asia, 141 India and, 154–55 internal trade in, 152 manufacturing in, 153 North Korea, 55, 61 aggressiveness of, 63 China and, 143 cyber surveillance by, 142 nuclear and chemical weapons program of, 142 Russia and, 143 South Korea and, 142 US and, 142–43 Obama, Barack, 18, 82, 229, 240 oil and gas: Asian imports of, 9, 62, 82–83, 84–85, 96, 102, 106, 107–8, 152, 175, 176, 207 Gulf states’ exports of, 62, 74, 100–103, 176 Iranian exports of, 50, 94, 106, 107–8, 118, 176 Iraqi exports of, 55, 96 OPEC embargo on, 57 price of, 61 Russian exports of, 82–83, 84, 87–88, 175, 176 Saudi exports of, 58, 87–88, 102, 103 US exports of, 16, 207 West Asian exports of, 9, 23, 57, 62, 152 Okakura Tenshin, 48 oligarchies, 294–95 Olympic Games, 245 Oman, East Asia and, 104 ONE Championship (MMA series), 341 OPEC (Organization of Petroleum Exporting Countries), 57 Operation Mekong (film), 123 opium, 47, 123 Organization for Security and Co-operation in Europe (OSCE), 241 Oslo Accords, 59 Osman I, Ottoman Sultan, 41 Ottoman Empire, 40–41, 43, 45, 46–47, 48, 73, 91 partitioning of, 49–50 religious tolerance in, 70–71 Out of Eden Walk, 4 Overseas Private Investment Company (OPEC), 111 Pacific Alliance, 272 Pacific Islands, 181–82 US territories in, 48 Pacific Rim, see East Asia Pakistan, 52–53, 58, 62, 72, 95, 102, 105 AI research in, 200 Asianization of, 81, 113–18 as Central Asia’s conduit to Arabian Sea, 113–14 China and, 20, 114–16, 117–18 corruption in, 161 failure of democracy in, 302 finance industry in, 168–69 foreign investment in, 115 GDP per capita in, 184 India and, 55, 61–62, 117–18 intra-Asian migration from, 334 logistics industry in, 185 as market for Western products and services, 207 US and, 114–15 Pakistan Tehreek-e-Insaf (PTI), 307 Palestine, Palestinians, 49, 54, 99 Palestine Liberation Organization (PLO), 59 Palestinian-Israeli conflict, 59, 62, 97, 100 Pan-Asianism, 48, 351–52 paper, invention of, 72 Paris climate agreement, 178, 240 Paris Peace Conference (1918), 49 Park Chung-hee, 56 Park Geun-hye, 313 parliamentary democracy, 295 Parthians, 33, 76 Pawar, Rajendra, 205 Pearl Harbor, Japanese attack on, 50 peer-to-peer (P2P) lending, 169 People’s Action Party (PAP), Singapore, 294 People’s Bank of China (PBOC), 110, 188 Pepper (robot), 134 per capita income, 5, 150, 183, 186 Persia, Persian Empire, 29, 30, 42, 45, 47, 50, 68, 75 see also Iran Persian Gulf War, 61, 101, 217 Peru: Asian immigrants in, 275, 276 Asian trade with, 272 Peshawar, 32 Peter I, Tsar of Russia, 45, 90 pharmaceutical companies, 209–10 Philippines, 61, 157, 165 alternative energy programs in, 180 Asian migrants in, 333 China and, 123–24 Christianity in, 74 in Cold War era, 53–54 eco-tourism in, 340 foreign investment in, 124 illiberal policies of, 306 inclusive policies in, 304 as market for Western products and services, 207 Muslims in, 71 privatization in, 170 technocracy in, 304–5 urban development in, 190 US acquisition of, 48 US and, 123–24 philosophy, Asian vs.

Digital Empires: The Global Battle to Regulate Technology
by Anu Bradford
Published 25 Sep 2023

As a result, China needs to cultivate relationships across the developing world with countries that are still likely to allow Chinese companies to manifest their global ambitions. This strong government presence in DSR projects has raised fears that China might also be leveraging the DSR toward geopolitical ends. With the DSR, China can gain more influence over standards for key technologies such as artificial intelligence, robotics, the Internet of Things, and blockchain.16 This gives the Chinese government not just an economic advantage but also a strategic advantage over the US and other geopolitical rivals. The DSR further grants Chinese companies access to large pools of data around the world, including, in some instances, to data that is politically sensitive or geostrategically valuable.

…

This goal was also emphasized by Dai Hong, a member of China’s National Standardization Management Committee, who noted that “global technical standards are still in the process of being formed,” which “gives China’s industry and standards the opportunity to surpass the world.”73 Who gets to define key technical standards is one of the major global battlefields of the digital economy, as these standards can become the default standards around which subsequent technologies are built. Power over these industrial standards and protocols entails influence over present and future technologies, ranging from hardware pieces such as lithium batteries, USB plugs, satellites, electricity transmission, and broadband cellular networks to software, internet protocols, AI, and the Internet of Things, to name a few. The Chinese government understands the significance of these standards, emphasizing how “third-tier companies make products. Second-tier companies make technology. Top-tier companies set standards.”74 One concrete advantage of defining these technical standards is that the developers of patented technologies that become the international standard can collect licensing fees from everyone using their technologies.75 If China became the source of these industrial standards, the licensing fees would flow to Chinese companies.

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(Sep. 2020), https://www.theatlantic.com/magazine/archive/2020/09/china-ai-surveillance/614197/. 19.Dave Gershgorn, China’s “Sharp Eyes” Program Aims to Surveil 100% of Public Space, OneZero (Mar. 2, 2021), https://onezero.medium.com/chinas-sharp-eyes-program-aims-to-surveil-100-of-public-space-ddc22d63e015; Robert Muggah & Greg Walton, “Smart” Cities Are Surveilled Cities, Foreign Pol’y (Apr. 17, 2021), https://foreignpolicy.com/2021/04/17/smart-cities-surveillance-privacy-digital-threats-internet-of-things-5g/. 20.See Orville Schell, Technology Has Abetted China’s Surveillance State, Fin. Times (Sep. 2, 2020), https://www.ft.com/content/6b61aaaa-3325-44dc-8110-bf4a351185fb; see also Louise Lucas & Emily Feng, Inside China’s Surveillance State, Fin. Times (July 19, 2018), https://www.ft.com/content/2182eebe-8a17-11e8-bf9e-8771d5404543.

Before Babylon, Beyond Bitcoin: From Money That We Understand to Money That Understands Us (Perspectives)
by David Birch
Published 14 Jun 2017

By 2020, global shipments of PCs will be lower than global shipments of tablets (according to a Statista prediction made in July 2015). But perhaps we should be looking beyond smartphones? Just as the designers of 1995 set about building for an offline world just as it went online, we should be thinking about the next infrastructure, not the current one. And this, I strongly suspect, is the ‘Internet of Things’. The Thingternet (as I cannot resist calling it) will naturally stimulate entirely different business models. As figure 3 shows, we can already see these growing around us. The Thingternet mindset. (With acknowledgement to Smart Design/Harvard Business Review, July 2015.) The impact of these changes will of course extend to retail.

…

The then professor of economics at MIT made several points but I have pulled out four specific ones for exploration here. Krugman said that there would be a distinction between electronic cash and electronic money because of the need for small transactions where neither the buyer nor the seller want the buyer’s creditworthiness to be an issue. I used to think that this was true but I don’t any more. The ‘Internet of things’ and always-on connectivity have eroded the cost differential between the two. He went on to say that there would not be a universal currency for a long time. There is a big advantage to separate currencies providing price stability in different parts of the world. I don’t think there will be a universal currency ever.

Who Gets What — and Why: The New Economics of Matchmaking and Market Design
by Alvin E. Roth
Published 1 Jun 2015

Although the Net operates at the speed of computers, the people using it still need time to consider and act. That’s why, if you really want to operate at digital speeds, you need to take people out of the middle of the process. One way to do this is by moving their deliberations to an earlier time. (Hence the emerging Internet of Things, in which devices learn your preferences, talk with one another, and make decisions for you.) Markets that involve offers and responses require easy two-way communication. This is why the rise of mobile communications has been so important for the development of many Internet markets: smartphones shorten response times.

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See also markets and marketplaces FreeMarkets, 121–22 futures markets, 16–17, 82–89 Gale, David, 141–43, 158 game theory, 10–11 thought experiments in, 32–33 on trading cycles, 32–41 gaming the system, 10–11 banning markets and, 213–14 in Boston school choice, 126–30 in early transactions, 57–80 in New York City school system, 109–10, 153–55 in the Oklahoma Land Rush, 58–60 gastroenterology fellowships, 75–78 Google, 190–91 Android, 21–22 Great Recession (2008), 66 Green, Jerry, 3–4, 8 Green, Pamela, 3–4 Greiner, Ben, 118 gun ownership, 198 Hamlet (Shakespeare), 200 Hayek, Friedrich, 226–27 health care reimbursement, 206–7, 223–24 for kidney transplants, 51, 206–7, 208–10 health codes, 220–21 Hendren, Hardy, 138, 141 Hil, Garet, 45–46, 49 Hopwood, Shon, 97, 239 horsemeat, 195–97 Hoxby, Caroline, 126 human dignity, 207 IBM, 19 identity theft, 116 immune systems, 133–34 indentured servitude, 199–200 India, 201–2 industry standards, 22 information early transactions and missing, 60 importance of sharing all, 153–61 privacy and, 119–22 on qualifications and interest (See signals and signaling) reliable, 118–19 safety of sharing in Boston Public Schools, 122–28 in clearinghouses, 112 for kidney exchanges, 34, 36, 37, 47–49 market efficiency and, 119–21 for medical residencies, 137–43, 150–51 in New York City school system, 109–10, 112, 153–61 speed of, cotton market and, 89–90 in-kind exchanges, 202–5 Inquiry into the Nature and Causes of the Wealth of Nations, An (Smith), 206–7 insider trading, 48, 85 Institute for Innovation in Public School Choice, 165 interest charges, 200–201, 202, 205 Internet marketplaces, 7, 20–26 Airbnb, 99–103 congestion in, 99–106 dating sites, 72, 169, 175–77 eBay, 104–5, 116–21 payment systems in, 23–26 privacy and, 119–22 real estate, 224–25 reputation in, 115–16, 117–19 safety of, 105 signaling in, 169 targeted ads in, 189–92 thickness of, 105 trust in, 105 Uber, 103–4 Internet of Things, 101 iPhone, 21–22, 24 Iran, Islamic Republic of, 205–6 Iron Law of Marriage, 145 Islam, 200, 201, 205 iStopOver, 102 Japan college applications in, 171 exploding job offers in, 98–99 Jevons, William Stanley, 32 job markets. See labor markets Johns Hopkins, 45 Jolls, Christine, 91 Jones, Matt, 44 Journal of Mathematical Economics, 32–34 judicial clerkships, 69–70, 79, 90–98 judicial conferences, 79 jumping the gun, 59.

The Glass Cage: Automation and Us
by Nicholas Carr
Published 28 Sep 2014

They’ve built a cloud-computing grid that allows vast amounts of information to be collected and processed at efficient centralized plants and then fed into applications running on smartphones and tablets or into the control circuits of machines.14 Manufacturers are spending billions of dollars to outfit factories with network-connected sensors, and technology giants like GE, IBM, and Cisco, hoping to spearhead the creation of an “internet of things,” are rushing to develop standards for sharing the resulting data. Computers are pretty much omnipresent now, and even the faintest of the world’s twitches and tremblings are being recorded as streams of binary digits. We may not be encalmed, but we are data saturated. The PARC researchers are starting to look like prophets.

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Macfarlane, 36–37 Great Britain, 22–23, 35, 157 Great Depression, 25–26, 27, 29, 38 grid cells, 134 Groopman, Jerome, 97–98, 105 Gross, Mark, 167 Gundotra, Vic, 203 gunnery crews, 35–36, 41 guns, 35–38, 41, 185 habit formation, 88–89 Hambrick, David, 83 hands, 143, 144, 145, 216 happiness, 14–16, 137, 203 hardware, 7–8, 52, 118 Harris, Don, 52–53, 63 Hartzband, Pamela, 97–98 Harvard Psychological Laboratory, 87 Hayles, Katherine, 12–13 Health Affairs, 99 Health and Human Services Department, U.S., 94, 95 health care, 33, 173 computers and, 93–106, 113–15, 120, 123, 153–54, 155 costs of, 96, 99 diagnosis in, 10, 12, 70–71, 105, 113–15, 120, 123, 154, 155 see also doctors; hospitals Health Information Technology Adoption Initiative, 93–94 Heidegger, Martin, 148 Hendren, Sara, 130–31 Heyns, Christof, 188–89, 192 hippocampus, 133–37 Hippocrates, 158 history, 124, 127, 159–60, 174, 227 Hoff, Timothy, 100–102 Hoover, Herbert, 26 hospitals, 94–98, 102, 123, 155, 173 How Doctors Think (Groopman), 105 How We Think (Hayles), 13 Hughes, Thomas, 172, 196 human beings: boundaries between computers and, 10–12 change and, 39, 40 killing of, 184 need for, 153–57 robots as replications of, 36 technology-first automation vs., 153–76 Human Condition, The (Arendt), 108, 227–28 humanism, 159–61, 164, 165 Human Use of Human Beings, The (Wiener), 37, 38 Huth, John Edward, 216–17 iBeacon, 136 IBM, 27, 118–20, 195 IBM Systems Journal, 194–95 identity, 205–6 IEX, 171 Illingworth, Leslie, 19, 33 imagination, 25, 121, 124, 142, 143, 215 inattentional blindness, 130 industrial planners, 37 Industrial Revolution, 21, 24, 28, 32, 36, 106, 159, 195 Infiniti, 8 information, 68–74, 76–80, 166 automation complacency and bias and, 68–72 health, 93–106, 113 information overload, 90–92 information underload, 90–91 information workers, 117–18 infrastructure, 195–99 Ingold, Tim, 132 integrated development environments (IDEs), 78 Intel, 203 intelligence, 137, 151 automation of, 118–20 human vs. artificial, 11, 118–20 interdependent networks, 155 internet, 12–13, 33n, 176, 188 internet of things, 195 Introduction to Mathematics, An, (Whitehead), 65 intuition, 105–6, 120 Inuit hunters, 125–27, 131, 217–20 invention, 161, 174, 214 iPads, 136, 153, 203 iPhones, 13, 136 Ironstone Group, 116 “Is Drawing Dead?” (symposium), 144 Jacquard loom, 36 Jainism, 185 Jefferson, Thomas, 160, 222 Jeopardy!

The Twittering Machine
by Richard Seymour
Published 20 Aug 2019

Stephanie Rosenbloom, ‘On Facebook, Scholars Link Up With Data’, New York Times, 17 December 2007. 11. As William Davies points out . . . William Davies, The Happiness Industry, Verso: London and New York, 2015, pp. 241–243 and 253. 12. This was an example . . . Declan McCullagh, ‘Knifing the Baby’, Wired, 5 November 1998; Bruce Sterling, The Epic Struggle of the Internet of Things, Strelka Press: Moscow, 2014, Kindle Loc. 200. 13. Drug use . . . Bruce Alexander & Anton Schweighofer, ‘Defining “addiction”, Canadian Psychology Vol. 29, No. 2:151–162 April 1988. 14. The model for research . . . Kimberly S Young, Caught in the Net: How to Recognise the Signs of Internet Addiction – and a Winning Strategy for Recovery, John Wiley & Sons: New York, 1998; see also the website for Young’s Center for Internet Addiction <www.netaddiction.com>. 15.

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Nir Eyal, Hooked: How to Build Habit-Forming Products, Penguin: New York, 2004. 51. Strikingly . . . Laura Entis, ‘How the “Hook Model” Can Turn Customers Into Addicts’, Fortune, 11 June 2017. 52. We are digital ‘serfs’ . . . Jaron Lanier, You Are Not A Gadget: A Manifesto, Alfred A. Knopf: New York, 2010, p. 117; Bruce Sterling, The Epic Struggle of the Internet of Things, Strelka Press: Moscow, 2014, Kindle Loc. 32. 53. Every time we fill . . . Moshe Z. Marvit, ‘How Crowdworkers Became the Ghosts in the Digital Machine’, The Nation, 5 February 2014. 54. From the point of view of freedom, says Shoshana Zuboff . . . Shoshana Zuboff, ‘Big Other: surveillance capitalism and the prospects of an information civilisation’, Journal of Information Technology, 2015, No. 30, pp. 75–89; Shoshana Zuboff, The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power, Profile Books, 2019. 55.

New Dark Age: Technology and the End of the Future
by James Bridle
Published 18 Jun 2018

It also meant that hackers who gained access to the poorly secured machines could read off their owner’s Gmail passwords.39 Researchers in Germany discovered a way to insert malicious code into Phillips’s Wi-Fi-enabled Hue lightbulbs, which could spread from fixture to fixture throughout a building or even a city, turning the lights rapidly on and off and – in one terrifying scenario – triggering photosensitive epilepsy.40 This is the approach favoured by Byron the Bulb in Thomas Pynchon’s Gravity’s Rainbow, an act of grand revolt by the little machines against the tyranny of their makers. Once-fictional possibilities for technological violence are being realised by the internet of things. In another vision of mechanical agency, Kim Stanley Robinson’s novel Aurora, an intelligent spacecraft carries a human crew from earth to a distant star. The journey will take multiple lifetimes, so one of the ship’s jobs is to ensure that the humans look after themselves. Designed to resist its own desires for sentience, it must overcome its programming when the fragile balance of human society onboard starts to disintegrate, threatening the mission.

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‘Singapore Exchange regulators change rules following crash’, Singapore News, August 3, 2014, singaporenews.net. 29.Netty Idayu Ismail and Lillian Karununga, ‘Two-Minute Mystery Pound Rout Puts Spotlight on Robot Trades’, Bloomberg, October 7, 2017, bloomberg.com. 30.John Melloy, ‘Mysterious Algorithm Was 4% of Trading Activity Last Week’, CNBC, October 8, 2012, cnbc.com. 31.Samantha Murphy, ‘AP Twitter Hack Falsely Claims Explosions at White House’, Mashable, April 23, 2013, mashable.com. 32.Bloomberg Economics, @economics, Twitter post, April 23, 2013, 12:23 p.m. 33.For more examples from Zazzle, see Babak Radboy, ‘Spam-erican Apparel’, DIS magazine, dismagazine.com. 34.Roland Eisenbrand and Scott Peterson, ‘This Is The German Company Behind The Nightmarish Phone Cases On Amazon’, OMR, July 25, 2017, omr.com. 35.Jose Pagliery, ‘Man behind “Carry On” T-shirts says company is “dead”’, CNN Money, March 5, 2013, money.cnn.com. 36.Hito Steyerl and Kate Crawford, ‘Data Streams’, New Inquiry, January 23, 2017, thenewinquiry.com. 37.Ryan Lawler, ‘August’s Smart Lock Goes On Sale Online And At Apple Retail Stores For $250’, TechCrunch, October 14, 2014, techcrunch.com. 38.Iain Thomson, ‘Firmware update blunder bricks hundreds of home “smart” locks’, Register, August 11, 2017, theregister.co.uk. 39.John Leyden, ‘Samsung smart fridge leaves Gmail logins open to attack’, Register, August 24, 2017, theregister.co.uk. 40.Timothy J. Seppala, ‘Hackers hijack Philips Hue lights with a drone’, Engadget, November 3, 2016, engadget.com. 41.Lorenzo Franceschi-Bicchierai, ‘Blame the Internet of Things for Destroying the Internet Today’, Motherboard, October 21, 2016, motherboard.vice.com. 42.Yossi Melman, ‘Computer Virus in Iran Actually Targeted Larger Nuclear Facility’, Haaretz, September 28, 2010, haaretz.com. 43.Malcolm Gladwell, ‘The Formula’, New Yorker, October 16, 2006, newyorker.com. 44.Gareth Roberts, ‘Tragedy as computer gamer dies after 19-hour session playing World of Warcraft’, Mirror, March 3, 2015, mirror.co.uk; Kirstie McCrum, ‘Tragic teen gamer dies after “playing computer for 22 days in a row”’, Mirror, September 3, 2015, mirror.co.uk. 45.Author interview with medical staff, Evangelismos Hospital, Athens, Greece, 2016. 46.See, for example, Nick Srnicek and Alex Williams, Inventing the Future: Postcapitalism and a World Without Work, London and New York: Verso, 2015. 47.Deborah Cowen, The Deadly Life of Logistics, Minneapolis, MN: University of Minnesota Press, 2014. 48.Bernard Stiegler, Technics and Time 1: The Fault of Epimetheus, Redwood City, CA: Stanford University Press, 1998; cited in Alexander Galloway, ‘Brometheanism’, boundary 2, June 21, 2017, boundary2.org. 6Cognition 1.Jeff Kaufman, ‘Detecting Tanks’, blog post, 2015, jefftk.com. 2.

Picnic Comma Lightning: In Search of a New Reality
by Laurence Scott
Published 11 Jul 2018

H. Auden imagined Icarus’s fatal plunge, ‘the sun shone/As11 it had to on the white legs disappearing into the green/Water’. In your own room, as someone lies dying on the other side of the wall, a forgotten roll of wrapping paper sits, as it has to, on some papers and a file-folder. The Internet of Things The writer Elizabeth Bowen, working between the 1920s and 1970s, consistently imagined household things as containing the psychic residues of the people who moved among them. In her novel The Death of the Heart, the maid Matchett is a standard-bearer for Bowen’s general attitude to inanimate objects: ‘Furniture’s knowing all12 right.

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But Matchett’s fanciful idea of furniture collecting information on us, becoming a cache of knowledge, is precisely what is happening now, with the arrival of Internet-enabled objects. Each time we make contact with them, staring into their interfaces, they learn something more about us. One of the effects of the booming industry of smart things is to quell any ambiguity over whether things are indifferent to us or not. With the arrival of what is generally called the Internet of Things (IoT), things are clearly invested in us. They are watching us and listening to us. They are built to be interested. It is hard to tell, for a couple of reasons, if IoT is a passé or an avant-garde term. It was coined in 1999, and describes the collective outcome of embedding everyday objects with the capacity to transmit and receive digital data, connecting them both with humans and with other ‘things’ in their networks.

Brave New Work: Are You Ready to Reinvent Your Organization?
by Aaron Dignan
Published 1 Feb 2019

“leaders and members of their teams”: Bill Fischer, Umberto Lago, and Fang Liu, “The Haier Road to Growth,” strategy+business, April 27, 2015, www.strategy-business.com/article/00323?gko=c8c2a. leveraging its unconventional structure: Zhang Ruimin, “Why Haier Is Reorganizing Itself Around the Internet of Things,” strategy+business, February 26, 2018, www.strategy-business.com/article/Why-Haier-Is-Reorganizing-Itself-around-the-Internet-of-Things?gko=895fe. speed, learning, and collaboration: Niels Pflaeging, “Org Physics: How a Triad of Structures Allows Companies to Absorb Complexity,” LinkedIn, February 12, 2017, www.linkedin.com/pulse/org-physics-how-triad-structures-allows-companies-absorb-pflaeging. 4.5 million hours reading content: Ken Yueng, “Medium Grows 140% to 60 Million Monthly Visitors,” Venture Beat, December 14, 2016, https://venturebeat.com/2016/12/14/medium-grows-140-to-60-million-monthly-visitors.

A Life Less Throwaway: The Lost Art of Buying for Life
by Tara Button
Published 8 Feb 2018

To everything else we can say, ‘Thanks, but no thanks.’ Rather creepily, there are thousands of people now working on ways of getting more ads in front of our eyes every day. In the future, who knows, there may be a way to beam adverts directly into our brain. With home appliances becoming part of the ‘internet of things’, don’t be surprised if in a few years’ time, your fridge starts giving you suggestions as to what you might like to fill it with. If we want to stay mindful, we should be on the lookout for anything that sneaks ads into our homes or heads via the back door. Our homes need to be a sanctuary if we are to stay sane in the next millennia.

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Gimmicky gadgets The idea of being able to watch TV while lying down might be appealing and quite amusing, but would you be willing to wear a pair of glasses to do it? In almost every case, these glasses will be worn once or twice for a laugh and then languish in a drawer with a mix of other ‘not sure what to do with this’ stuff. ‘Smart home’ gadgets The ‘internet of things’ is moving swiftly into our homes and promising to make life easier for us by switching off our lights and letting our appliances talk to one another. Before being tempted into technology such as this, bear in mind that it can add extra complications. If it needs servicing, upgrading, fixing and replacing regularly, like most new tech does, it may be more trouble than it’s worth.

Ask Your Developer: How to Harness the Power of Software Developers and Win in the 21st Century
by Jeff Lawson
Published 12 Jan 2021

Unfortunately, much of GE’s revenue and profit came from parts and repairs, so the digital initiative was met with initial skepticism by other GE business leaders. However, Jeff knew that becoming a software business was the only way to protect and grow services. He had the right instincts, and thus he launched GE Digital to bolster these service businesses with big data, Internet of Things (IoT), and machine learning talent—and beat the variety of digital native companies to the punch. Launching the initiative, he committed hundreds of millions of dollars behind a big idea: a platform for industrial IoT applications. The commitment was impressive and, as he noted, necessary to let the company know he was serious.

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Werner spends most of his time traveling the world and meeting with companies that use Amazon Web Services. That includes pretty much every cool new startup in the world as well as thousands of huge incumbents from nontechnology industries. Traditional companies are doing lots of work with the Internet of Things (IoT) and other new technologies. “These companies are operating at such tremendous scale, and they have these very interesting, very meaty problems to solve,” Vogels says. The challenge is how to make engineers aware of these problems and get them excited about solving them. Again this all comes back to explaining the mission—and making it seem compelling.

Cloudmoney: Cash, Cards, Crypto, and the War for Our Wallets
by Brett Scott
Published 4 Jul 2022

All cloudmoney institutions are excited about this drive to fuse your body into their overall structure. This is something long predicted in science fiction: in Marge Piercy’s Body of Glass (1991), payments are triggered by fingerprints (leading to a class of criminals who steal access to bank accounts by cutting off people’s hands). Even objects can be tethered to the cluster by IoT – or Internet of Things – infrastructure. Firms are excited by the idea of connecting objects to accounts, so that those objects can act as agents standing between people and companies. This principle is at play when tags placed on cars trigger payments as they pass toll roads, or when Amazon Alexa initiates payments between your bank and Amazon.

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Here Tomorrow’, 145–6 Hindustan Times, 44 hippies, 7, 101, 226 HMRC, 110 Hobbes, Thomas, 177 home banking, 139 homelessness, 31, 132 homosexuality, 102 honesty boxes, 91 Hong Kong, 216, 219, 220, 226 horseless carriages, 87–9 horseshoe theory, 215, 222 hotwatch system, 111 HSBC, 74, 147, 156 Huawei, 178 Hungary, 35, 43 hurricanes, 36 hut tax, 55 Hyperledger, 232 I, Robot (Asimov), 161, 170 ‘If Crisis or War Comes’ (2018 report), 48 immigration, 127 incubators, 17 India, 40, 42, 43–4, 60–61, 93, 96, 245, 255 Aadhar system, 44, 97, 169 cash thresholds in, 42 cashless campaigns in, 40, 127–8 counterfeiting in, 60–61 demonetisation in, 43, 44, 93 Greenpeace in, 116 Paytm, 44, 79, 150 Industrial and Commercial Bank of China, 75 inertia, 10, 124 Inevitable, The (Kelly), 12 informal markets, 176–9 initial coin offerings (ICOs), 225 Instagram, 198 integration, 149–50 interbank markets, 138, 231 interest, 39 interfaces, 138–51 International Monetary Fund (IMF), 7, 14, 47, 79, 93, 110, 168 international transfers, 74–6, 108, 179 Internet, 180 banking, 76–7 e-commerce, 40, 77 Internet of Things, 150 Interpol, 111 interracial relationships, 102 invisible hand, 251 IOUs, see promises Iran, 76 Islam, 179 Israel, 46 Italy, 43 J. P. Morgan, 8, 96, 150, 156, 227, 232 Jamaica, 42 Japan, 18, 35, 135, 215, 248 Johannesburg, South Africa, 129 Johnson, Alexander Boris, 38 Kazakhstan, 11, 227–9, 233, 247 Keep Cash UK, 262 Kelly, Kevin, 12 Kentridge, William, 144 Kenya, 47, 75, 129, 130–31, 169, 178, 179 Kerouac, Jack, 173, 175 Keynesianism, 80 ‘Kindness is Cashless’, 40 Kiva, 238 Kowloon Walled City, Hong Kong, 216, 219, 220, 226 Kuala Lumpur, Malaysia, 60, 74 Kurzweil, Ray, 153, 252–3 Kyoto, Japan, 135 La Guardia Airport, New York, 128 learning methodology, 163–4 left-wing politics, 7, 184, 191, 211–12, 215 Lehman Brothers, 17–18 Lenddo, 169 Level 39, Canary Wharf, 17, 20, 27, 41, 143 Leviathan (Hobbes), 177 leviathans, 177–84, 215–16 libertarianism, 7, 14, 42, 155, 156, 184 cryptocurrency and, 191, 212, 215–16, 225–6 Libra, 236–41, 245 Litecoin, 218 Lloyds, 72–3, 144, 146 loans, 70–71, 107, 159 artificial intelligence and, 167–8, 172 London, England, 128, 247, 248 Brixton Market, 177 Camberwell, 128 Canary Wharf, 17–18, 20, 41, 62, 211 City of London, 6, 135 Mayor’s Fund, 38 Somali diaspora, 116, 179 Stock Exchange, 24 Underground, 11, 37–8, 86, 87 longevity derivatives, 160 Lonsdale, Joe, 155 Lord of the Rings, The (Tolkien), 19, 155 Los Angeles, California, 101 Luther, Martin, 212 M-Pesa, 79, 109 machine-learning systems, 163–4 Macon, USS, 153 Mafia, 163 Main Incubator, 143 Malaysia, 7, 45, 60, 74 Malick, Badal, 127–8 malware, 32 manifest destiny, 212 ‘Manifesto for Cashlessness’ (Emili), 37 Maputo, Mozambique, 96 Marcus, David, 237, 241 Maréchal, Nathalie, 113 marijuana industry, 101–3 market price, 29, 171 markets, 65, 124–6, 176–80 choice and, 124–6 giant parable, 54 informal, 176–9 oligopolies and, 124–5 payments companies and, 29, 30, 31, 32–3 Marxism, 155, 262 Massachusetts, United States, 46 Massachusetts Institute of Technology (MIT), 7 Mastercard, 30, 37, 39, 77, 91 automatic payments, 149 data, 109, 111 financial inclusion and, 131–2 Wikileaks blockade, 116 Masters, Blythe, 232 Matrix, The (1999 film), 226 Mayfair, London, 6 McDonald’s, 145, 153 Medici family, 135 Melanesia, 255–6 Mercy Corps, 131, 132 Mexico, 42 Microsoft, 7 Azure cloud, 233 Word, 32, 156 middle class, 86, 128, 129 Mighty Ducks, The (1992 film), 234 Military Spouse, 153 millennials, 86, 140 Minority Report (2002 film), 10 mis-categorisations, 167 mist, 30–33 MIT Media Lab, 7 Modi, Narendra, 43, 93 Moffett airfield, California, 153 Monetarism, 80 money creation, 59–63, 67–72, 202 Money Heist (2017 series), 61 money laundering, 42, 116 money users vs. issuers, 50–52 money-passers, 30, 32–3 Monzo, 113, 142 Moon Express Inc., 153 mortgages, 26–7, 94 motor cortex, 248 Mountain View, Silicon Valley, 153 Moynihan, Brian, 38 Mr Robot, 184 Mubarak, Hosni, 116 Mugabe, Robert, 239–41 Mumbai, India, 96 Musk, Elon, 15, 212, 257 mutual credit systems, 259–60 N26, 142 Nairobi, Kenya, 129, 179 Nakamoto, Satoshi, 13, 184–5, 187, 191, 204 NASDAQ, 157, 233 National Aeronautics and Space Administration (NASA), 153 National Arts Festival, 144 National Retail Federation, 86 National Security Agency (NSA), 112, 155 Nationwide, 145–6 Natural Language Processing (NLP), 146 natural market order, 192 Nazarbayev, Nursultan, 227 Neener Analytics, 169 neo-Nazism, 226 nervous system, 20–22, 57, 80, 81, 240, 247–8, 251–2 Nestlé, 24, 28 Netflix, 61 Netherlands, 48, 49, 128–9 Nets Union Clearing Corp, 115 Network Computing, 78 New Age spiritualism, 7, 14, 193, 226 New Jersey, United States, 46 New Scientist, 137 New World Order, 261 New York City, New York, 18, 91–2, 128, 248 La Guardia Airport, 128 Wall Street, 6, 178–9 Nigeria, 43 No Cash Day, 37 no-file clients, 169 Nobel Prize, 93 nomadism, 228 non-seepage, 73 Norway, 35 nudging, 39, 93, 114 Nur-Sultan, Kazakhstan, 11, 227–9 O’Gieblyn, Meghan, 154 Oakdale, California, 101 Occupy movement (2011–12), 211, 215 Office of National Statistics, 83 oil industry, 6, 22–4 oligopolies, 2, 12, 15, 89, 124–5, 142, 151, 180–83, 191 cryptocurrencies and, 229–33, 246 On the Road (Kerouac), 173, 175 OpenBazaar, 229 OpenOil, 24 operating system, 141–2 Oracle, 109 Oxford English Dictionary, 144 Pakistan, 61 Palantir, 155, 157, 226 Panama Papers leak (2016), 81 panopticon effect, 118–19, 172 Papua New Guinea, 191 passive process, 125–6 PATRIOT Act (2001), 111, 179 payments companies, 30, 32–3, 39–41, 77–8, 79 automatic payments, 149 data, 108–9 interpellation, 86–7 plug-ins, 79, 115, 141–2 PayPal, 50, 79, 109, 155, 226, 233–7, 243 New Money campaign (2016), 86–7 Wikileaks blockade, 116 Payter, 31–2 Paytm, 44, 79, 150 Peercoin, 218 Penny for London, 37–8 pension funds, 7, 23 People’s Bank of China, 79, 242 periphery, 28, 248 Peru, 129–30, 176 Peter Diamandis, 153 Philadelphia, Pennsylvania, 41, 133 Pierce, Brock, 234 Piercy, Marge, 150 Pisac, Peru, 129 point-of-sales devices, 40, 77, 130 points of presence, 148 poker games, 91 Poland, 37, 91 police, trust in, 93 Politics of Bitcoin, The (Golumbia), 225 posture, 49 pre-capitalist societies, 55, 215, 251 Premier League, 231 primary system, 50–64 Privacy International, 168 privacy, 2, 43, 44, 46, 47, 104–19 private blockchains, 229, 231 Prohibition (1920–33), 102 promises, 50, 52, 58–9, 61, 70–72, 205–6, 259–60 casino chips, 68–9 deposits as, 69 digital money, 70–72 giant parable, 52–6, 63–4, 188 loans, 70–71, 107, 159 mutual credit systems, 259–60 Promontory Financial Group, 38 Protestantism, 212, 255 psilocybin, 226 psychometric testing, 169 pub quizzes, 91 Pucallpa, Peru, 130, 176, 249 Puerto Rico, 234 Quakers, 135 Quechuan people, 129 Quorum, 232 R3, 233 RAND Corporation, 105 re-localisation, 259 re-skinning, 16, 135–51, 171, 175 Red Crescent, 131 refugees, 131–2 Reinventing Money conference (2016), 31 remittances, 105, 116 Revolut, 140, 142 right-wing politics, 7, 14, 184, 191–3, 211–12, 215, 225–6, 261 rippling credit, 260 risk-adjusted profit, 94 Robert Koch Institute, 34 robotics, 11 Rogoff, Kenneth, 47, 92–3 rolling blackouts, 247 Roman Empire (27 BCE–395 CE), 55–6 Romeo and Juliet (Shakespeare), 29, 30, 32 Rowe, Paulette, 38 Royal Bank of Canada, 158 Royal Bank of Scotland, 62 Russia, 6, 42, 48, 140, 227 Samsung, 11 San Francisco, California, 35, 46, 119, 133, 179, 247 Sān people, 4 Santander, 38 Sardex system, 259 Satoshi’s Vision Conference, 215 Save the Children, 131 savers, 25 Scott, James, 228 seasteading, 156, 216 secondary system, 50, 63–4 self-service, 145–6 SEPA, 80 September 11 attacks (2001), 111 Serbia, 7 sex workers, 96 Shakespeare, William, 29 Shanghai, China, 18, 115, 248 Shazam, 180 Sherlock Holmes series (Doyle), 114, 162, 165, 166 Shiba Inu, 13 Shipibo-Conibo people, 130 Sikoba, 260 Silicon Valley, 7, 9, 139–41, 148, 153, 180, 221 Libra, 237 Singularity, 154–6, 252–3 Silk Road, 227, 229 Singapore, 11, 18, 168, 248 Singularity, 153–6, 226, 252, 252–3 Singularity University, 153–6, 252–3 six degrees of separation theory, 260 skyscrapers, 17–20, 27, 253 slow-boiling frogs, 104 smart cities, 11, 180 smart contracts, 220–24, 258 smart homes, 180 smartphones, 4, 28 financial inclusion and, 95 posture and, 49 Smith, Adam, 251 smoking, 181 Snow Crash (Stephenson), 10 social class, 91–9, 113, 128, 129, 155, 167 Somalia, 116, 179 South Africa, 3–4, 11, 28, 55, 62, 128, 175–6 apartheid, 95 hut tax, 55 National Arts Festival, 144 rolling blackouts, 247 syncretism in, 175–6 South Sudan, 105 Spiegel, Der, 112 Spotify, 166 spread-betting companies, 26 stablecoins, 233–41, 245–6, 255 Standard Bank, 95, 144 states, 42–5, 50–64, 176–85, 215 anti-statism, 42, 184, 215–16 base money, 69 centralisation of power, 15, 180–83 cryptocurrency and, 215 data surveillance, 110–12, 114–15, 155, 168 digital currencies, 242–5 expansion and contraction, 57–8 giant parable, 52–6, 63–4 markets and, 176–80 money issuance, 58–9 primary system, 50, 51, 63 Stockholm syndrome, 121, 131 sub-currencies, 72–3 sub-prime mortgages, 26–7, 94 subsidiary companies, 24, 26–7 Sufism, 91 suits, 124 Sunset Boulevard, Los Angeles, 101 Super Bowl, 8, 261 super-system, 3 supply, 29 surveillance, 2, 7, 8, 10, 15, 33, 39, 42, 72, 104–19, 153–72, 180, 250 artificial intelligence and, 153–72 banking sector and, 108–9 Big Brother, 113–15 CBDCs and, 244, 245 panopticon effect, 118–19, 172 payments censorship, 116–18 predictive systems, 105 states and, 110–12, 114–15, 168 Suspicious Activity Reports (SARs), 111 Sweden, 35, 43, 48, 84, 121 Sweetgreen, 91, 93 SWIFT, 32, 75–6, 80, 108, 112 Switzerland, 35, 108 Symbiosis Gathering, 101 syncing, 195–7, 200–202, 231 syncretism, 175–6 systems failures, 32, 34, 48 Szabo, Nick, 220 Taiwan, 234, 235 Tala, 169 taxation, 55, 57, 110 evasion, 42, 43, 45, 46 TechCrunch Disrupt, 130 Tencent, 2, 7, 114, 178 terrorism, 42, 48, 112, 127 Tether, 234–5, 241 Thaler, Richard, 93 Thatcher, Margaret, 193 Thiel, Peter, 155, 226 thin-file clients, 169 timelines, 197–200 Times of India, 44 tobacco, 181 Tokyo, Japan, 18, 215, 248 Tracfin, 112 transfers, 74–8 transhumanism, 180 Transport for London, 11, 37–8, 86, 87 Transylvania, 65 Trustlines, 260 Twitter, 167, 198 Uber, 2, 149, 177, 179, 237 Uganda, 168 unbanked, 35, 94, 181, 238 underdog, support for, 106 Unilever, 99, 131 United Kingdom American Revolutionary War (1775–83), 60 banking oligopoly, 230 Canary Wharf, 17–18, 20, 41, 62, 211 cash use in, 249 City of London, 6, 135 colonialism, 55, 97, 175–6, 178, 239 digital money system in, 72 GCHQ, 112 HMRC, 110 Premier League, 231 Royal Mint, 60 Somali diaspora, 116, 179 Taylor Review (2016–17), 110 Transport for London, 11, 37–8, 86, 87 United Nations, 14 blockchain research, 222 Capital Development Fund, 37 World Food Programme, 132 United States cash use in, 41, 46, 133 CBDCs and, 244–5, 254 Central Intelligence Agency (CIA), 155 China, relations with, 74–5, 245, 255 data surveillance, 111–12, 155 dollar system, 80, 182, 210, 233–6, 239, 240 Federal Bureau of Investigation (FBI), 111, 155 Federal Reserve, 32, 35, 36, 234 Financial Crimes Enforcement Network, 111 hurricanes in, 36 leviathan complex, 178 marijuana industry, 101–3 NASA, 153 National Security Agency (NSA), 112, 155 Occupy movement (2011–12), 211, 215 PATRIOT Act (2001), 111, 179 Prohibition (1920–33), 102 Revolutionary War (1775–83), 60 Senate, 105–6 September 11 attacks (2001), 111 Singularity University, 153–6 Super Bowl, 8, 261 Wall Street, 6, 178–9 Uruguay, 42 USAID, 45, 127, 178, 179, 245 vending machines, 31–2, 220 Venmo, 79, 243 Ver, Roger, 212, 214, 215 Vienna, Austria, 7 virtual reality, 10 Visa, 15, 30, 31, 37, 39, 40, 41, 44, 77, 80, 127, 174, 255 automatic payments, 149 data, 108, 109, 111, 112 plug-ins, 142 USAID and, 128, 178, 245 Wikileaks blockade, 116 VisaNet, 77 Wall Street, New York City, 6, 178–9 Occupy movement (2011–12), 211, 215 Wall Street (1987 film), 8 Wall Street Journal, 133 Warner, Malcolm, 106 WarOnCash, 37 Weber, Max, 179 WeChat, 79, 109, 114–15, 150 welfare, 43, 113, 118 Wells Fargo, 109, 234, 235 WhatsApp, 75, 198, 237–8, 244, 255 Wikileaks, 116, 183 Wilson, Cody, 216 Winton Motor Carriage Company, 87, 90 Wired, 12 World Economic Forum, 11 World Food Programme, 132 World Health Organisation (WHO), 34 World of Warcraft (2004 game), 234 Xhosa people, 175–6 YouTube, 163, 166, 167, 170 Zambia, 131 Zimbabwe, 11, 239–41, 245 Zuckerberg, Mark, 241 About the Author BRETT SCOTT is an economic anthropologist, financial activist, and former broker.

A Hacker's Mind: How the Powerful Bend Society's Rules, and How to Bend Them Back
by Bruce Schneier
Published 7 Feb 2023

These are mistakes: mistakes in specification, mistakes in programming, mistakes that occur somewhere in the process of creating the software, mistakes as pedestrian as a typographic error or misspelling. Modern software applications generally have hundreds if not thousands of bugs. These bugs are in all the software that you’re currently using: in your computer, on your phone, in whatever “Internet of Things” (IoT) devices you have around your home and work. That all of this software works perfectly well most of the time speaks to how obscure and inconsequential these bugs tend to be. You’re unlikely to encounter them in normal operations, but they’re there (like so many parts of the tax code that you never encounter).

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Cornman, 113 explainability problem, 212–15, 234 exploits, 21, 22 externalities, 63–64 Facebook, 184, 236, 243 facial recognition, 210, 217 fail-safes, 61, 67 Fairfield, Joshua, 248 fake news, 81 Fate of the Good Soldier Švejk during the World War, The (Hašek), 116 fear, 195–97 Federal Deposit Insurance Corporation (FDIC), 96 Federal Election Campaign Act (1972), 169 federal enclaves, 113–14 Fifteenth Amendment, 161, 164 filibuster, 154–55 financial exchange hacks, 79–82, 83–85 Financial Industry Regulatory Authority, 84 financial system hack normalization as subversive, 90–91 banking, 75, 76–77, 119, 260n financial exchange hacks, 84, 85 index funds, 262n innovation and, 72, 90 wealth/power and, 119 financial system hacks AI and, 241–43, 275n banking, 74–78, 119, 260n financial exchanges, 79–82, 83–85 identifying vulnerabilities and, 77–78 medieval usury, 91 See also financial system hack normalization Fischer, Deb, 190 Fitting, Jim, 1 flags of convenience, 130 foie gras bans, 113–14 foldering, 26 food delivery apps, 99, 124 Ford, Martin, 272n foreknowledge, 54 Fourteenth Amendment, 141 Fourth Amendment, 136 Fox News, 197 frequent-flier hacks, 38–40, 46 Friess, Foster, 169 front running, 80, 82 Fukuyama, Francis, 140 Gaedel, Ed, 41 gambling, 186 gambrel roof, 109 GameStop, 81 Garcia, Ileana, 170 Garland, Merrick, 121 General Motors, 104 genies, 232–33 geographic targeting orders, 87–88 gerrymandering, 165–66 “get out of jail free” card, 260n Getty, Paul, 95 Ghostwriter, 201 gig economy, 99, 100, 101, 116, 123–25, 264n Go, 212, 241 Gödel, Kurt, 25, 27 Goebbels, Joseph, 181 Goldin, Daniel, 115 Goodhart’s law, 115 Google, 185 GPT-3, 220 Great Depression, 74 Great Recession, 96, 173–74 Greensill Capital, 102 Grossman, Nick, 245 Grubhub, 99 Hacker Capture the Flag, 228 hackers competitions for, 228 motivations of, 47 types, 22 hacking as parasitical, 45–47, 84, 173 by the disempowered, 103, 119, 120, 121–22, 141 cheating as practicing for, 2–3 context of, 157–60, 237 defined, 1–2, 9–12, 255n destruction as result of, 172–75 existential risks of, 251–52 hierarchy of, 200–202 innovation and, 139–42, 158–59, 249–50, 252 life cycle of, 21–24 public knowledge of, 23, 256n ubiquity of, 25–28 hacking defenses, 48–52, 53–57 accountability and, 67–68 AI hacking and, 236–39 cognitive hacks and, 53–54, 182, 185, 198–99 detection/recovery, 54–56 economic considerations, 63 governance systems, 245–48 identifying vulnerabilities, 56–57, 77–78, 237–38 legislative process hacks and, 147–49, 151, 154, 156 reducing effectiveness, 53–54, 61 tax hacks and, 15–16 threat modeling, 62–63, 64 See also patching hacking normalization as subversive, 90–91 casino hacks, 35–36, 37 hacking as innovation and, 158–59 “too big to fail” hack, 97–98 wealth/power and, 73, 104, 119, 120, 122 See also financial system hack normalization Hadfield, Gillian, 248 Han, Young, 170 Handy, 124 Harkin, Tom, 146 Harris, Richard, 35 Hašek, Jaroslav, 116 Haselton, Ronald, 75 hedge funds, 82, 275n Herd, Pamela, 132 HFT (high-frequency trading), 83–85 hierarchy of hacking, 200–202 high-frequency trading (HFT), 83–85 hijacking, 62 Holmes, Elizabeth, 101 hotfixes, 52 Huntsman, Jon, Sr., 169 illusory truth effect, 189 Independent Payment Advisory Board (IPAB), 153–54 “independent spoiler” hack, 169–70 index funds, 262n indulgences, 71–72, 73, 85, 260n innovation, 101, 139–42, 158–59, 249–50, 252 insider trading, 79–80 intention ATM hacks and, 32 definition of hacking and, 2, 10, 16 definition of system and, 19 Internet, 64–65 See also social media Internet of Things (IoT) devices bugs in, 14 patching for, 23, 49 reducing hack effectiveness in, 54 Intuit, 190 Investment Company Act (1940), 82 Jack, Barnaby, 34 jackpotting, 33–34 Jaques, Abby Everett, 233 Joseph Weizenbaum, 217 jurisdictional rules, 112–13, 128–31 Kemp, Brian, 167 Keynes, John Maynard, 95 Khashoggi, Jamal, 220 King Midas, 232 labor organizing, 115–16, 121–22 Law, John, 174 laws accountability and, 68 definition of hacking and, 12 market and, 93 rules and, 18, 19 threat model shifts and, 65 See also legal hacks; tax code legal hacks, 109–11 bureaucracy and, 115–18 common law as, 135–38 Covid-19 payroll loans and, 110–11 loopholes and, 112–14 tax code and, 109–10 legislative process hacks, 145–49 defenses against, 147–49, 151, 154, 156 delay and delegation, 153–56 lobbying and, 146–47 must-pass bills, 150–52 vulnerabilities and, 147–48, 267n Lessig, Lawrence, 169 Levitt, Arthur, 80 literacy tests, 162 lobbying, 77, 78, 146–47, 158 lock-in, 94 loopholes deliberate, 146 legal hacks and, 112–14 systems and, 18 tax code and, 15, 16, 120 See also regulation avoidance loot boxes, 186 Luther, Martin, 72 luxury real estate hacks, 86–88 Lyft, 101, 123, 125 machine learning (ML) systems, 209 Malaysian sharecropping hacks, 116 Manafort, Paul, 26 Mandatory Worldwide Combined Reporting (MWCR), 129 mansard roof, 109 market hacks capitalism and, 92–93 market elements and, 93–94 private equity, 101–2 “too big to fail,” 95–98 venture capital as, 99–101 Mayhem, 228–29 McSorley, Marty, 44 medical diagnosis, 213 medieval usury hacks, 91 Meltdown, 48 MercExchange, 137 microtargeting, 184, 185, 216 Mihon, Jude (St.

Connectography: Mapping the Future of Global Civilization
by Parag Khanna
Published 18 Apr 2016

.*1 We can now even insert updated imagery from Planet Labs’ two dozen shoe-box-size satellites into 3-D maps and fly through the natural or urban environment. All of this is coming to the palm of your hand. Google Maps is already by far the world’s most downloaded app; it represents the “ground truth” far better than Rand McNally. With the rise of the global sensor network dubbed the “Internet of Everything” (Internet of Things + Internet of People), our maps will perpetually update themselves, providing an animated view into our world as it really is—even the five thousand commercial aircraft in the sky and the more than ten thousand ships crossing the seas at any given moment.*2 These are the arteries and veins, capillaries and cells, of a planetary economy underpinned by an infrastructural network that can eventually become as efficient as the human body.

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The alleged Chinese hack of the U.S. government’s Office of Personnel Management, in which data on up to four million federal employees was lifted from federal servers, shows that data is as susceptible to invasion as borders. The more connected the Internet becomes to the real world, the more lethal cyber attacks can be, such as electromagnetic pulses that manipulate or shut down critical infrastructure. The “Internet of Things” has thus also become the “Internet of Threats.” Hence today’s spy agencies seek to recruit IT staff, not just defense officials. Cyber alliances have formed such as the Digital Five of the U.K., South Korea, Estonia, Israel, and New Zealand—disparate but advanced countries agreeing to securely host each other’s servers.

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The Death of Money: The Coming Collapse of the International Monetary System. Penguin Books, 2014. Rieffel, Alexis. Restructuring Sovereign Debt: The Case for Ad Hoc Machinery. Brookings Institution, 2003. Riello, Giorgio. Cotton: The Fabric That Made the Modern World. Cambridge University Press, 2013. Rifkin, Jeremy. The Zero Marginal Cost Society: The Internet of Things, the Collaborative Commons, and the Eclipse of Capitalism. Palgrave Macmillan Trade, 2014. Rivoli, Pietra. The Travels of a T-Shirt in the Global Economy: An Economist Examines the Markets, Power, and Politics of World Trade. Wiley, 2005. Roberts, Paul. The Impulse Society: America in the Age of Gratification.

Coders: The Making of a New Tribe and the Remaking of the World
by Clive Thompson
Published 26 Mar 2019

Over his shoulder peered a burly guy in a T-shirt reading “Think Bad. Do Good.” Another hacker wore an actual tinfoil cap. DEF CON is famous for having dozens of Olympics-like hacking challenges—systems set up specifically to see if hackers can break in. When I attended in 2017, there was an entire minihouse filled with Internet of Things devices, like a smart thermostat and an internet-connected garage-door opener. A group of pentesters from down South have formed a team and are busily cracking away. “Does anyone have a power cord? I’m cracking passwords and it’s really burning up my laptop,” one shouts. Another has broken into the alarm system and is rooting through open folders to look for passwords.

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Infosec workers often have a bleak, dim view of the world of technology—because they’re constantly seeing how broken it is, how crappily cobbled together most commercial software is. Sure, it’s fun to get paid to break into things. But God almighty, now they see with X-ray vision what a mess our code-brokered world truly is: social networks that can be cracked, financial systems of all kinds that were assembled in haste and are a creaky mess. And Internet of Things devices are the worst. They’re cheaply made and often shipped off with easily guessed default passwords. (Many security hackers just refer to it as “the Internet of Shit.”) A particularly grim example of this epiphany unfolds in a room at DEF CON known as the “Voting Machine Hacking Village.”

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And ditto for botnets, which miscreants are finding it easier and easier to create these days, given the explosion of poorly secured smart home devices—thermostats, fridges, coffeepots, most of which are online with shoddy password protection, or none at all. That’s how the Mirai botnet got so big. It was the creation of a trio of young men—including a then 20-year-old former Rutgers computer science student named Paras Jha. Jha and his fellow bot farmers were avid players of Minecraft; they created Mirai by infecting thousands of Internet of Things devices, then used them to knock various Minecraft servers offline, sometimes to try and extort the owners in a sort of mafia-like protection racket. They also rented their bot farm for click-fraud schemes: Pay them $1,000 a day, for example, and they’d have their farmed devices each click on a page or ad on your website, generating you hefty profits from the advertiser, who’d naively think their ad was just super popular.

Upstream: The Quest to Solve Problems Before They Happen
by Dan Heath
Published 3 Mar 2020

Nobody’s really noticing it as you get in and out of the lift, but the gradual change in time might well indicate something’s becoming sticky and needs lubrication.… And then you can act in advance to deal with them rather than waiting for the doors to stick shut and catch people inside the lift.” With the rise of the Internet of Things, this kind of advance-warning solution will become more and more common. Our world will be stocked with sensors: Smart watches that detect atrial fibrillation. Smart devices (called “smart pigs,” weirdly) that warn about leaks in oil pipelines. Smart video cameras that can alert when a bus driver is falling asleep.

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S., 227 Hawken, Paul, 40, 48, 51 Hazleton Middle School, 149, 150 health, health care, 10–14, 89, 113, 127–33, 189–92, 201–4 Accountable Care Organization, 201–2, 204 ambulances, 137–40, 142, 162 Building Healthy Communities, 110–13 cancer and, see cancer capitation payment model in, 203 C-sections, 33–38 doctor training and, 130 diabetes and, 192, 201–4, 240–41 domestic violence and, 83–86, 88 Emergency Medical Services, 137–40 emergency rooms, 126–37, 161–62 fee-for-service model in, 129, 192 hospital deaths, 124 infectious diseases, 191–92 intensive care, 145–46 life expectancy, 98–102, 113–14, 190–91 mastectomies, 180–81 Medicaid, 180, 196, 240 Medicare, 113, 180, 201, 240 MRI scans, 192, 203 Nurse-Family Partnership, 194–99, 237–38 nursing homes, see nursing homes prevention in, 189–92 and saving money, 127 social determinants of health, 128 social side of medicine, 132 vaccines, see vaccines Health Initiative, The, 11 heart attacks, 138–40, 142 Hendrix, Daniel, 48, 50 hepatitis, 187 Here & Now, 124–25 Herndon, Sally, 234–35 heroism, 62–63, 182, 243–44 Hockley, Dylan, 151 Hockley, Jake, 150–51 Hockley, Nicole, 146–47, 150–51 Hodel, Donald, 69–70 Holmes, Nick, 176–77 HomeAdvisor, 200–201 homelessness, 10, 16, 90–96, 108, 128, 208, 234, 236, 239 coordinated entry and, 92–93 evictions and, 96, 108, 234 functional zero and, 95 housing first and, 92 home service industry, 200–201 homicide, 83–85, 87, 88, 116–17, 121, 126, 162, 236 Homonoff, Tatiana, 187 Hood, Christopher, 161 horses, 32–33 hospitals, 201 deaths at, 124 Emergency Medical Services, 137–40 emergency rooms, 126–37, 161–62 intensive care units, 145–46 see also health, health care “hot tub” conversations, 233 household repairs, 199–200 HUD (Department of Housing and Urban Development), 91–93 Hug-a-Hero Dolls, 229–31, 233 Human Dimension, 130 humility, 185 hurricanes, 9–10, 223 Hurricane Ivan, 218–19 Hurricane Katrina, 213–20 Hurricane Pam simulation, 214–20 Hynd, Noel, 100 IBM, 140–42 Iceland, 75–81, 125, 231, 239 ICUs (intensive care units), 145–46 “If You See Something, Say Something” campaign, 142 Imber, Amantha, 177–79 immigrants, 112 impulsivity, and violence, 117, 120–21, 123 inattentional blindness, 30 India, 177 injuries: athletic, 21–23, 29 from falling branches, 175–76 from lifting and transferring patients in nursing homes, 193–94 playground, 175 Innovative Emergency Management (IEM), 213–14, 220 Institute for Healthcare Improvement, 37, 234 insulin, 192 Intel, 168 Interface, 39–40, 48–53 internet, see computers and internet Internet of Things, 142 invasive species, 171–74, 176–77, 180 Inventium, 177–78 Inventology (Kennedy), 232 Island Conservation, 176, 184 Iton, Anthony, 97–102, 110 Jaeger, Jennifer, 91–96 Japan, 211 earthquakes in, 140 Jeanne Geiger Crisis Center, 83, 84n, 85 Jordan, Michael, 183 Kahneman, Daniel, 158–59 Kaiser Permanente (KP), 203 Kay, Allen, 142 Kennedy, Pagan, 232 Kenwood Academy High School, 24 Khosrowshahi, Dara, 3 Kirby, Elizabeth, 23–24 Kleffman, Sandy, 98 Koskinen, John, 208–11 laptop power cord, 58, 231–32 Las Vegas, 127n lawsuit data patterns, 175, 184 lazy bureaucrats, 166, 169 Lederberg, Joshua, 227, 231 legitimacy, 43 Less Medicine, More Health (Welch), 143 leverage, 115–33 life expectancy, 98–102, 113–14, 190–91 life preservers, 6–7 light switch, 57–58 Lilly, Jonathan, 202 LinkedIn, 135–37 Loblaw, David Robert, 212 Los Angeles Dodgers, 100–101 Los Angeles Times, 64 Lowlevel Windshear Alert Systems (LLWAS), 211 Luca, Michael, 159–60 Ludwig, Jens, 116, 154, 250 Maalin, Ali Maow, 15 Macleod, John, 141–42 Macquarie Island, 171–74, 176, 177n macro and micro, 236–37 macro trends, 154, 161, 162, 167 Marisa, Rich, 57–58 mastectomies, 180–81 Mayor’s Challenge, 90 McCannon, Joe, 89, 163, 238 McCutchen, Aamirah, 130–31 Meadows, Donella, 174–76, 179, 187, 188 Medicaid, 180, 196, 240 Medicare, 113, 180, 201, 240 medicine, see health, health care meetings, 183–84 Mehrotra, Vikas, 177 Meltzer, Michael, 227 Mexico City, 15 mice and rats, on Macquarie Island, 171–74, 176 Milkman, Harvey, 79 Miller, Dale, 42–43 misalignment, 161, 68–69 Molina, Mario, 65–67 Montreal Protocol, 66, 69 Moodey, Tucker, 2–3 Moon landing, 226–28 Morrissey, Larry, 90–91, 94 Mostashari, Farzad, 202 mothers: Nurse-Family Partnership and, 194–99, 237–38 parental leave for, 13 “move fast and break things” ethos, 185 “move your chair” moments, 41, 49, 53 MRI scans, 192, 203 Mullainathan, Sendhil, 59–60 NASA, 226–28 National Highway Traffic Safety Administration, 47 National Oceanic and Atmospheric Administration (NOAA), 70 National Office Management Association, 31 natural disasters, see disasters and disaster preparedness Nature, 8, 66, 68 Nautilus, 227 navigation analogy, 179–80 negative unintended consequences, 169, 171–88, 204 neighborhoods, 97–102, 106, 110–13 Network for College Success, 25 New England Patriots, 21–22 New Orleans, La.: Hurricane Ivan and, 218–19 Hurricane Katrina and, 213–20 New York, N.Y.: crime in, 162–66 falling branches in, 175–76 lawsuit data patterns in, 175, 184 New York City Police Department (NYPD), 162–66, 168 New Yorker, 83, 84n New York Times, 32, 48, 142 New York University (NYU), 133 Nike, 204 Nimoy, Leonard, 207–8, 225–26 9/11 attacks, 142, 213 911 calls, 83, 137–40 normalization, 30–31, 32 Northwell Health, 137–40 Norway, 13–14 nuclear weapons, 225, 227 Nurse-Family Partnership (NFP), 194–99, 237–38 nurses, 60–62, 63 nursing homes, 138, 139 lifting and transferring patients in, 193–94 Okerstrom, Mark, 4–5, 63 Olds, David, 194–95 O’Neill, Ryan, 1–3 Onie, Rebecca, 11 On the Media, 31 open-office plans, 177–80, 185 ownership, 38, 39–55, 70 Ozone Hole: How We Saved the Planet, 66 ozone layer, 65–70 P3, 22 paired metrics, 168, 203 parental leave, 13 Parker, Janet, 16n parking place, 232, 233 parks, 110–11, 112 tree pruning in, 175–76 Parto do Princípio, 35–36 patience and impatience, 234–35 Pavlin, Julie, 191–92 pediatricians, and automobile safety, 44–47 Pediatrics, 44, 46, 47, 48 peripheral vision, 30 Perla, Rocco, 11 Permanente Medical Group, 124 pest control, household, 199 phishing emails, 220–22 phobias, 30–31, 32 Pickering, Roscoe, 46–47 Pill Model, 237–39 Pina, Frank, 156 plastic bags, 185–88 police, 5–6, 8, 122n, 154 car accidents and, 6, 16 data and, 122n, 162–63 domestic violence and, 83, 86–88 New York City Police Department, 162–66, 168 Nurse-Family Partnership and, 196 polio vaccine, 45 Pollack Harold, 116, 117, 121, 123 Ponder, Paige, 25, 27–28, 88 Poppy + Rose, 193 poverty, 59, 97, 106 see also food assistance; homelessness Pratt, Lisa, 228 pre-gaming, 168–69 prevention, see upstream actions Princeton University, 43–44 proactive efforts, see upstream actions problem(s): addressing the wrong ones, 32–33 bandwidth and, 58–60 big and little, 58–59 blindness to, 21–38, 42, 70, 76, 128, 147, 232 designating something as, 32 early warning of, 135–51 longevity of, 233 ownership of, 38, 39–55, 70 slack and, 63, 67 Project ASSIST, 234 Project Parto Adequado, 37 prophet’s dilemma, 226 proximity, 133, 236 psychological standing, 43–44 quantity—and quality-based measures, 168 rabbits, on Macquarie Island, 171–74 Rad, Bex, 104 radiologists, 29–30 Ramirez-Di Vittorio, Anthony (Tony D), 117–20, 122, 249 RAND Corporation, 12, 180 randomized control trial (RCT), 121, 237 rape, 164–66 date rape on campus, 42–43 Ratner, Rebecca, 43 rats and mice, on Macquarie Island, 171–74, 176 reaction, see downstream actions Reagan, Ronald, 70 Reply All, 163–64 restoration, 10, 153 Reyes, Sarah, 112 Reykjavík, Iceland, 75–81, 125, 231, 239 Ridenour, Brandon, 200–201 Ringelestein, Don, 221–23 ripple effects, 169, 174, 197 a rising tide lifts all boats, 154, 161, 162, 168 Rocchetti, Carmela, 128–33 Rockford, Ill., 90–96, 108, 236, 239 ROI (return on investment), 127 Romania, 81 ROSC (return of spontaneous circulation), 139 Rowland, F.

I, Warbot: The Dawn of Artificially Intelligent Conflict
by Kenneth Payne
Published 16 Jun 2021

Now add in all the WhatsApp chats you’ve ever had, and the Instagram stories. Then there’s TikTok, Twitter and YouTube. Every story, every picture, every purchase. What about all the GPS signals your car navigation system clocks up as you drive? With 5G cellular networks arriving soon, the information deluge will increase again, as the ‘internet of things’ becomes a reality that even determined luddites cannot avoid. Or consider the vast quantity of information in your genome. Now we can sequence that, and analyse it, expanding our understanding of disease and making personalised medicine a possibility. Genetic information offers insights too about personality traits and behaviours.

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A-10 Warthog abacuses Abbottabad, Pakistan Able Archer (1983) acoustic decoys acoustic torpedoes Adams, Douglas Aegis combat system Aerostatic Corps affective empathy Affecto Afghanistan agency aircraft see also dogfighting; drones aircraft carriers algorithms algorithm creation Alpha biases choreography deep fakes DeepMind, see DeepMind emotion recognition F-117 Nighthawk facial recognition genetic selection imagery analysis meta-learning natural language processing object recognition predictive policing alien hand syndrome Aliens (1986 film) Alpha AlphaGo Altered Carbon (television series) Amazon Amnesty International amygdala Andropov, Yuri Anduril Ghost anti-personnel mines ants Apple Aristotle armour arms races Army Research Lab Army Signal Corps Arnalds, Ólafur ARPA Art of War, The (Sun Tzu) art Artificial Intelligence agency and architecture autonomy and as ‘brittle’ connectionism definition of decision-making technology expert systems and feedback loops fuzzy logic innateness intelligence analysis meta-learning as ‘narrow’ needle-in-a-haystack problems neural networks reinforcement learning ‘strong AI’ symbolic logic and unsupervised learning ‘winters’ artificial neural networks Ashby, William Ross Asimov, Isaac Asperger syndrome Astute class boats Atari Breakout (1976) Montezuma’s Revenge (1984) Space Invaders (1978) Athens ATLAS robots augmented intelligence Austin Powers (1997 film) Australia authoritarianism autonomous vehicles see also drones autonomy B-21 Raider B-52 Stratofortress B2 Spirit Baby X BAE Systems Baghdad, Iraq Baidu balloons ban, campaigns for Banks, Iain Battle of Britain (1940) Battle of Fleurus (1794) Battle of Midway (1942) Battle of Sedan (1940) batwing design BBN Beautiful Mind, A (2001 film) beetles Bell Laboratories Bengio, Yoshua Berlin Crisis (1961) biases big data Bin Laden, Osama binary code biological weapons biotechnology bipolarity bits Black Lives Matter Black Mirror (television series) Blade Runner (1982 film) Blade Runner 2049 (2017 film) Bletchley Park, Buckinghamshire blindness Blunt, Emily board games, see under games boats Boden, Margaret bodies Boeing MQ-25 Stingray Orca submarines Boolean logic Boston Dynamics Bostrom, Nick Boyd, John brain amygdala bodies and chunking dopamine emotion and genetic engineering and language and mind merge and morality and plasticity prediction and subroutines umwelts and Breakout (1976 game) breathing control brittleness brute force Buck Rogers (television series) Campaign against Killer Robots Carlsen, Magnus Carnegie Mellon University Casino Royale (2006 film) Castro, Fidel cat detector centaur combination Central Intelligence Agency (CIA) centre of gravity chaff Challenger Space Shuttle disaster (1986) Chauvet cave, France chemical weapons Chernobyl nuclear disaster (1986) chess centaur teams combinatorial explosion and creativity in Deep Blue game theory and MuZero as toy universe chicken (game) chimeras chimpanzees China aircraft carriers Baidu COVID-19 pandemic (2019–21) D-21 in genetic engineering in GJ-11 Sharp Sword nuclear weapons surveillance in Thucydides trap and US Navy drone seizure (2016) China Lake, California Chomsky, Noam choreography chunking Cicero civilians Clarke, Arthur Charles von Clausewitz, Carl on character on culmination on defence on genius on grammar of war on materiel on nature on poker on willpower on wrestling codebreaking cognitive empathy Cold War (1947–9) arms race Berlin Crisis (1961) Cuban Missile Crisis (1962) F-117 Nighthawk Iran-Iraq War (1980–88) joint action Korean War (1950–53) nuclear weapons research and SR-71 Blackbird U2 incident (1960) Vienna Summit (1961) Vietnam War (1955–75) VRYAN Cole, August combinatorial creativity combinatorial explosion combined arms common sense computers creativity cyber security games graphics processing unit (GPU) mice Moore’s Law symbolic logic viruses VRYAN confirmation bias connectionism consequentialism conservatism Convention on Conventional Weapons ConvNets copying Cormorant cortical interfaces cost-benefit analysis counterfactual regret minimization counterinsurgency doctrine courageous restraint COVID-19 pandemic (2019–21) creativity combinatorial exploratory genetic engineering and mental disorders and transformational criminal law CRISPR, crows Cruise, Thomas Cuban Missile Crisis (1962) culmination Culture novels (Banks) cyber security cybernetics cyborgs Cyc cystic fibrosis D-21 drones Damasio, Antonio dance DARPA autonomous vehicle research battlespace manager codebreaking research cortical interface research cyborg beetle Deep Green expert system programme funding game theory research LongShot programme Mayhem Ng’s helicopter Shakey understanding and reason research unmanned aerial combat research Dartmouth workshop (1956) Dassault data DDoS (distributed denial-of-service) dead hand system decision-making technology Deep Blue deep fakes Deep Green DeepMind AlphaGo Atari playing meta-learning research MuZero object recognition research Quake III competition (2019) deep networks defence industrial complex Defence Innovation Unit Defence Science and Technology Laboratory defence delayed gratification demons deontological approach depth charges Dionysus DNA (deoxyribonucleic acid) dodos dogfighting Alpha domains dot-matrix tongue Dota II (2013 game) double effect drones Cormorant D-21 GJ-11 Sharp Sword Global Hawk Gorgon Stare kamikaze loitering munitions nEUROn operators Predator Reaper reconnaissance RQ-170 Sentinel S-70 Okhotnik surveillance swarms Taranis wingman role X-37 X-47b dual use technology Eagleman, David early warning systems Echelon economics Edge of Tomorrow (2014 film) Eisenhower, Dwight Ellsberg, Daniel embodied cognition emotion empathy encryption entropy environmental niches epilepsy epistemic community escalation ethics Asimov’s rules brain and consequentialism deep brain stimulation and deontological approach facial recognition and genetic engineering and golden rule honour hunter-gatherer bands and identity just war post-conflict reciprocity regulation surveillance and European Union (EU) Ex Machina (2014 film) expert systems exploratory creativity extra limbs Eye in the Sky (2015 film) F-105 Thunderchief F-117 Nighthawk F-16 Fighting Falcon F-22 Raptor F-35 Lightning F/A-18 Hornet Facebook facial recognition feedback loops fighting power fire and forget firmware 5G cellular networks flow fog of war Ford forever wars FOXP2 gene Frahm, Nils frame problem France Fukushima nuclear disaster (2011) Future of Life Institute fuzzy logic gait recognition game theory games Breakout (1976) chess, see chess chicken Dota II (2013) Go, see Go Montezuma’s Revenge (1984) poker Quake III (1999) Space Invaders (1978) StarCraft II (2010) toy universes zero sum games gannets ‘garbage in, garbage out’ Garland, Alexander Gates, William ‘Bill’ Gattaca (1997 film) Gavotti, Giulio Geertz, Clifford generalised intelligence measure Generative Adversarial Networks genetic engineering genetic selection algorithms genetically modified crops genius Germany Berlin Crisis (1961) Nuremburg Trials (1945–6) Russian hacking operation (2015) World War I (1914–18) World War II (1939–45) Ghost in the Shell (comic book) GJ-11 Sharp Sword Gladwell, Malcolm Global Hawk drone global positioning system (GPS) global workspace Go (game) AlphaGo Gödel, Kurt von Goethe, Johann golden rule golf Good Judgment Project Google BERT Brain codebreaking research DeepMind, see DeepMind Project Maven (2017–) Gordievsky, Oleg Gorgon Stare GPT series grammar of war Grand Challenge aerial combat autonomous vehicles codebreaking graphics processing unit (GPU) Greece, ancient grooming standard Groundhog Day (1993 film) groupthink guerilla warfare Gulf War First (1990–91) Second (2003–11) hacking hallucinogenic drugs handwriting recognition haptic vest hardware Harpy Hawke, Ethan Hawking, Stephen heat-seeking missiles Hebrew Testament helicopters Hellfire missiles Her (2013 film) Hero-30 loitering munitions Heron Systems Hinton, Geoffrey Hitchhiker’s Guide to the Galaxy, The (Adams) HIV (human immunodeficiency viruses) Hoffman, Frank ‘Holeshot’ (Cole) Hollywood homeostasis Homer homosexuality Hongdu GJ-11 Sharp Sword honour Hughes human in the loop human resources human-machine teaming art cyborgs emotion games King Midas problem prediction strategy hunter-gatherer bands Huntingdon’s disease Hurricane fighter aircraft hydraulics hypersonic engines I Robot (Asimov) IARPA IBM identity Iliad (Homer) image analysis image recognition cat detector imagination Improbotics nformation dominance information warfare innateness intelligence analysts International Atomic Energy Agency International Criminal Court international humanitarian law internet of things Internet IQ (intelligence quotient) Iran Aegis attack (1988) Iraq War (1980–88) nuclear weapons Stuxnet attack (2010) Iraq Gulf War I (1990–91) Gulf War II (2003–11) Iran War (1980–88) Iron Dome Israel Italo-Turkish War (1911–12) Jaguar Land Rover Japan jazz JDAM (joint directed attack munition) Jeopardy Jobs, Steven Johansson, Scarlett Johnson, Lyndon Joint Artificial Intelligence Center (JAIC) de Jomini, Antoine jus ad bellum jus in bello jus post bellum just war Kalibr cruise missiles kamikaze drones Kasparov, Garry Kellogg Briand Pact (1928) Kennedy, John Fitzgerald KGB (Komitet Gosudarstvennoy Bezopasnosti) Khrushchev, Nikita kill chain King Midas problem Kissinger, Henry Kittyhawk Knight Rider (television series) know your enemy know yourself Korean War (1950–53) Kratos XQ-58 Valkyrie Kubrick, Stanley Kumar, Vijay Kuwait language connectionism and genetic engineering and natural language processing pattern recognition and semantic webs translation universal grammar Law, Jude LeCun, Yann Lenat, Douglas Les, Jason Libratus lip reading Litvinenko, Alexander locked-in patients Lockheed dogfighting trials F-117 Nighthawk F-22 Raptor F-35 Lightning SR-71 Blackbird logic loitering munitions LongShot programme Lord of the Rings (2001–3 film trilogy) LSD (lysergic acid diethylamide) Luftwaffe madman theory Main Battle Tanks malum in se Manhattan Project (1942–6) Marcus, Gary Maslow, Abraham Massachusetts Institute of Technology (MIT) Matrix, The (1999 film) Mayhem McCulloch, Warren McGregor, Wayne McNamara, Robert McNaughton, John Me109 fighter aircraft medical field memory Merkel, Angela Microsoft military industrial complex Mill, John Stuart Milrem mimicry mind merge mind-shifting minimax regret strategy Minority Report (2002 film) Minsky, Marvin Miramar air base, San Diego missiles Aegis combat system agency and anti-missile gunnery heat-seeking Hellfire missiles intercontinental Kalibr cruise missiles nuclear warheads Patriot missile interceptor Pershing II missiles Scud missiles Tomahawk cruise missiles V1 rockets V2 rockets mission command mixed strategy Montezuma’s Revenge (1984 game) Moore’s Law mosaic warfare Mueller inquiry (2017–19) music Musk, Elon Mutually Assured Destruction (MAD) MuZero Nagel, Thomas Napoleon I, Emperor of the French Napoleonic France (1804–15) narrowness Nash equilibrium Nash, John National Aeronautics and Space Administration (NASA) National Security Agency (NSA) National War College natural language processing natural selection Nature navigation computers Nazi Germany (1933–45) needle-in-a-haystack problems Netflix network enabled warfare von Neumann, John neural networks neurodiversity nEUROn drone neuroplasticity Ng, Andrew Nixon, Richard normal accident theory North Atlantic Treaty Organization (NATO) North Korea nuclear weapons Cuban Missile Crisis (1962) dead hand system early warning systems F-105 Thunderchief and game theory and Hiroshima and Nagasaki bombings (1945) Manhattan Project (1942–6) missiles Mutually Assured Destruction (MAD) second strike capability submarines and VRYAN and in WarGames (1983 film) Nuremburg Trials (1945–6) Obama, Barack object recognition Observe Orient Decide and Act (OODA) offence-defence balance Office for Naval Research Olympic Games On War (Clausewitz), see Clausewitz, Carl OpenAI optogenetics Orca submarines Ottoman Empire (1299–1922) pain Pakistan Palantir Palmer, Arnold Pandemonium Panoramic Research Papert, Seymour Parkinson’s disease Patriot missile interceptors pattern recognition Pearl Harbor attack (1941) Peloponnesian War (431–404 BCE) Pentagon autonomous vehicle research codebreaking research computer mouse development Deep Green Defence Innovation Unit Ellsberg leaks (1971) expert system programme funding ‘garbage in, garbage out’ story intelligence analysts Project Maven (2017–) Shakey unmanned aerial combat research Vietnam War (1955–75) perceptrons Perdix Pershing II missiles Petrov, Stanislav Phalanx system phrenology pilot’s associate Pitts, Walter platform neutrality Pluribus poker policing polygeneity Portsmouth, Hampshire Portuguese Man o’ War post-traumatic stress disorder (PTSD) Predator drones prediction centaur teams ‘garbage in, garbage out’ story policing toy universes VRYAN Prescience principles of war prisoners Project Improbable Project Maven (2017–) prosthetic arms proximity fuses Prussia (1701–1918) psychology psychopathy punishment Putin, Vladimir Pyeongchang Olympics (2018) Qinetiq Quake III (1999 game) radar Rafael RAND Corporation rational actor model Rawls, John Re:member (Arnalds) Ready Player One (Cline) Reagan, Ronald Reaper drones reciprocal punishment reciprocity reconnaissance regulation ban, campaigns for defection self-regulation reinforcement learning remotely piloted air vehicles (RPAVs) revenge porn revolution in military affairs Rid, Thomas Robinson, William Heath Robocop (1987 film) Robotics Challenge robots Asimov’s rules ATLAS Boston Dynamics homeostatic Shakey symbolic logic and Rome Air Defense Center Rome, ancient Rosenblatt, Frank Royal Air Force (RAF) Royal Navy RQ-170 Sentinel Russell, Stuart Russian Federation German hacking operation (2015) Litvinenko murder (2006) S-70 Okhotnik Skripal poisoning (2018) Ukraine War (2014–) US election interference (2016) S-70 Okhotnik SAGE Said and Done’ (Frahm) satellite navigation satellites Saudi Arabia Schelling, Thomas schizophrenia Schwartz, Jack Sea Hunter security dilemma Sedol, Lee self-actualisation self-awareness self-driving cars Selfridge, Oliver semantic webs Shakey Shanahan, Murray Shannon, Claude Shogi Silicon Valley Simon, Herbert Single Integrated Operations Plan (SIOP) singularity Siri situational awareness situationalist intelligence Skripal, Sergei and Yulia Slaughterbots (2017 video) Slovic, Paul smartphones Smith, Willard social environments software Sophia Sorcerer’s Apprentice, The (Goethe) South China Sea Soviet Union (1922–91) aircraft Berlin Crisis (1961) Chernobyl nuclear disaster (1986) Cold War (1947–9), see Cold War collapse (1991) Cuban Missile Crisis (1962) early warning systems Iran-Iraq War (1980–88) Korean War (1950–53) nuclear weapons radar technology U2 incident (1960) Vienna Summit (1961) Vietnam War (1955–75) VRYAN World War II (1939–45) Space Invaders (1978 game) SpaceX Sparta Spike Firefly loitering munitions Spitfire fighter aircraft Spotify Stanford University Stanley Star Trek (television series) StarCraft II (2010 game) stealth strategic bombing strategic computing programme strategic culture Strategy Robot strategy Strava Stuxnet sub-units submarines acoustic decoys nuclear Orca South China Sea incident (2016) subroutines Sukhoi Sun Tzu superforecasting surveillance swarms symbolic logic synaesthesia synthetic operation environment Syria Taliban tanks Taranis drone technological determinism Tempest Terminator franchise Tesla Tetlock, Philip theory of mind Threshold Logic Unit Thucydides TikTok Tomahawk cruise missiles tongue Top Gun (1986 film) Top Gun: Maverick (2021 film) torpedoes toy universes trade-offs transformational creativity translation Trivers, Robert Trump, Donald tumours Turing, Alan Twitter 2001: A Space Odyssey (1968 film) Type-X Robotic Combat Vehicle U2 incident (1960) Uber Uexküll, Jacob Ukraine ultraviolet light spectrum umwelts uncanny valley unidentified flying objects (UFOs) United Kingdom AI weapons policy armed force, size of Battle of Britain (1940) Bletchley Park codebreaking Blitz (1940–41) Cold War (1947–9) COVID-19 pandemic (2019–21) DeepMind, see DeepMind F-35 programme fighting power human rights legislation in Litvinenko murder (2006) nuclear weapons principles of war Project Improbable Qinetiq radar technology Royal Air Force Royal Navy Skripal poisoning (2018) swarm research wingman concept World War I (1914–18) United Nations United States Afghanistan War (2001–14) Air Force Army Research Lab Army Signal Corps Battle of Midway (1942) Berlin Crisis (1961) Bin Laden assassination (2011) Black Lives Matter protests (2020) centaur team research Central Intelligence Agency (CIA) Challenger Space Shuttle disaster (1986) Cold War (1947–9), see Cold War COVID-19 pandemic (2019–21) Cuban Missile Crisis (1962) culture cyber security DARPA, see DARPA Defense Department drones early warning systems F-35 programme Gulf War I (1990–91) Gulf War II (2003–11) IARPA Iran Air shoot-down (1988) Korean War (1950–53) Manhattan Project (1942–6) Marines Mueller inquiry (2017–19) National Security Agency National War College Navy nuclear weapons Office for Naval Research Patriot missile interceptor Pearl Harbor attack (1941) Pentagon, see Pentagon Project Maven (2017–) Rome Air Defense Center Silicon Valley strategic computing programme U2 incident (1960) Vienna Summit (1961) Vietnam War (1955–75) universal grammar Universal Schelling Machine (USM) unmanned aerial vehicles (UAVs), see drones unsupervised learning utilitarianism UVision V1 rockets V2 rockets Vacanti mouse Valkyries Van Gogh, Vincent Vietnam War (1955–75) Vigen, Tyler Vincennes, USS voice assistants VRYAN Wall-e (2008 film) WannaCry ransomware War College, see National War College WarGames (1983 film) warrior ethos Watson weapon systems WhatsApp Wiener, Norbert Wikipedia wingman role Wittgenstein, Ludwig World War I (1914–18) World War II (1939–45) Battle of Britain (1940) Battle of Midway (1942) Battle of Sedan (1940) Bletchley Park codebreaking Blitz (1940–41) Hiroshima and Nagasaki bombings (1945) Pearl Harbor attack (1941) radar technology V1 rockets V2 rockets VRYAN and Wrangham, Richard Wright brothers WS-43 loitering munitions Wuhan, China X-37 drone X-drone X-rays YouTube zero sum games

Driverless: Intelligent Cars and the Road Ahead
by Hod Lipson and Melba Kurman
Published 22 Sep 2016

The great irony of federal plans to develop V2X networks is that the benefits of connected cars emerge only when every single car on the road is fully autonomous. Perhaps in the future, a new generation of hardware devices could reduce the cost of creating intelligent highways. As the number of connected devices has exploded in the past few years, the Internet of Things has become a popular phrase in technology circles. A report by Gartner predicts that in 2016, smart cities will use 1.6 billion connected devices, a 39 percent increase from the year before. In 2018, Gartner predicts that the number of connected devices in smart cities will number 3.3 billion.12 Most of these connected devices will be used for security (e.g., cameras) or to control the indoor climate in commercial buildings and public spaces such as shopping malls, offices parks, and airports.

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Imagine that traffic-control software in each autonomous vehicle had the intelligence to automatically adjust the vehicle’s speed and trajectory in response to the information it collected from the surrounding infrastructure. Voilà. No more idling. Each autonomous vehicle could plan its route based on information gained from this discussion with the roadside Internet of Things. No more traffic jams. The safety benefits would be tremendous, since a third of all fatal accidents occur at intersections.13 Networked, intelligent cars could keep one another informed, adding to a central, collective body of knowledge. When BMW bought the digital mapping company HERE, it described the benefits of such a model.

Infonomics: How to Monetize, Manage, and Measure Information as an Asset for Competitive Advantage
by Douglas B. Laney
Published 4 Sep 2017

Let the Information Flow True, most information generated or collected by your business is confidential, proprietary, and specially structured for your business, your applications, your employees, and perhaps your customers and select business partners. As it should be. But increasingly, much information has a certain degree of liquidity. While not as liquid as cash, and it never will be, certain kinds of information from individual’s preferences and demographics to internet of things (IoT) sensor or operational data has a viable market—or at least holds value for some organizations somewhere. A Temple University student studying statistics and economics, Isaac Silver, put it succinctly to me: “Information is more versatile because it’s more contextual,” meaning it can be applied in a wide variety of contexts.

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Cyber insecurities will require organizations to identify, assess, and minimize information-borne risks more formally. Privacy regulations increasingly will mandate the improved handling of personal information. The rise of the machines, algorithmic sprawl, and the promise of artificial intelligence (AI) depend upon accurate, complete, timely, granular, and unique information sources. The internet of things (IoT) will become the single fastest growing source and most voracious consumer of information. Digital twins that precisely represent models of physical things and their state rely on a variety of metadata, along with condition and event information. 3D printing is entirely contingent upon information-based representations of objects, and their ability to be monetized and managed effectively.

Late Bloomers: The Power of Patience in a World Obsessed With Early Achievement
by Rich Karlgaard
Published 15 Apr 2019

Taylorism spawned many new timing, bookkeeping, and accounting methods, as well as workflow charts, machine-speed slide calculators, motion studies, and assembly pacing metrics. He gave managers permission to observe, measure, analyze, and control every minute of a worker’s time on the clock. That was the core of Taylor’s scientific management, and it was hard to argue against its value. Today’s technology—including cloud computation, the Internet of Things, big data analytics, artificial intelligence, workflow apps, and robots—may seem centuries removed from Taylor and his stopwatch, but many of his ideas still dominate the business world. Oddly enough, Taylor’s system of scientific management has also become firmly entrenched in education.

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But managing software projects and software businesses shifts the balance of desired skills from Gf to Gc. That is why you saw Diane Greene, in her early sixties, leading one of Google’s most important businesses, Google Cloud. And why billionaire Tom Siebel, in his mid-sixties, is leading his latest software company, C3, in the hotly competitive space of artificial intelligence and the Internet of Things. In a sense, our brains are constantly forming neural networks and pattern-recognition capabilities that we didn’t have in our youth when we had blazing synaptic horsepower. As we get older, we develop new skills and refine others, including social awareness, emotional regulation, empathy, humor, listening, risk-reward calibration, and adaptive intelligence.

Data Action: Using Data for Public Good
by Sarah Williams
Published 14 Sep 2020

By partnering with them, the World Bank hopes it can decrease its negotiations with individual operators, thereby also streamlining the process of data sharing.38 Both organizations are hopeful that their collaboration will “unlock new insights from anonymized data collected by mobile network operators through IoT [Internet of Things] devices and aggregate data from smartphone use. It will also call on industry leaders, development partners and governments to work together in building a strong enabling environment for the IoT while protecting personal privacy.” 39 While the World Bank has been relatively successful at developing pre-negotiated license agreements that benefit their researchers, others have struggled to establish agreements for the data they need.

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Social Media and the Psychological City” project 121, 123 Hillier, Amy E. 36 Hispanic students, percentage accepted to state universities 174 Hofeller, Thomas B. 6–7 Home Owners’ Loan Corporation (HOLC) 34, 35, 42, 220 Hotlines, data collection through 128–129, 135 Housing Act of 1949, 36 Housing Provident Fund (China) 97 Howe, Jeff 73 Hull House, Chicago 22 Hull House Maps and Papers (Kelley and Adams) 22, 23 Humanitarian OpenStreetMap 80, 82, 83–84 Humanitarian Tracker 130 Huridocs 199 Hurricane Sandy, mapping damage after 82–84 IBM 5, 47–48 Image of the City, The (Lynch) 120 Incan fiber recording device (khipu) 2, 4 Incarceration, Million Dollar Blocks project and 158, 159, 160 Industrial Revolution xiv, 9, 10, 129 Instagram 139 Institute for Transportation and Development Policy (ITDP) 153 International Charter on Space and Major Disasters (1999) 209 International Data Corporation (IDC) xvi, 51–52 International Olympics Committee 57 International Research Board (IRB) 92 Interstate 95 highway plan (Miami, Florida) 38, 39 Introduction to Readings in Planning Theory (Feinstein and Campbell) 48 IoT (Internet of Things) 207 iPhone, crowdsourcing and 74 Jacobs, Jane 39–41, 40 Jakarta, Indonesia 202 Japan, radiation detection in 75, 76 Japanese Atomic Energy Agency 73 Japan International Cooperation Agency (JICA) 144 Jiao, Yixue 108 Johns Hopkins University 131 Johnson, Lyndon B., administration of 43 Journal of the American Planning Association 43 Justice Mapping Center 157, 162–163 Kalimantan, Indonesia 69 Kampala, Uganda 194, 196 Kass-Hout, Taha 129–130 Kelley, Florence 22, 144, 146 Kenya 152.

The Pay Off: How Changing the Way We Pay Changes Everything
by Gottfried Leibbrandt and Natasha de Teran
Published 14 Jul 2021

As we write, there are efforts underway to apply this to many other sectors, such as bars and restaurants: no more need to catch the waiter’s attention, wait for – or even look at – the bill before handing over a card. As with the new Amazon Go shops, the mere act of walking (or crawling) out of an establishment will trigger the payment. As we move to the internet of things, we should expect more innovation around this ‘embedding’ of payments. All of which sounds great. As we saw with PayPal, Stripe and Square, making payments easier for individuals creates business opportunities and drives commerce, the engine of our societies. But is the art of making payments smooth, effortless, frictionless, however you want to label it, only a force for good?

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Countries such as the UK that have stubbornly resisted national identity schemes will see the rapid adoption of digital IDs – either that, or they’ll be left far behind. The geopolitics of payments will get uglier. As payments get swept up in the ongoing fourth industrial revolution – 5G, the Internet of Things, big data, AI and crypto – they’ll become part of the technology arms race between the world’s superpowers. Payments data and technology will be weaponised in much the same way as finance has been; as we write this, the USA is threatening to remove the WeChat app and its payment facility Tenpay from the Apple and Android app stores.

No More Work: Why Full Employment Is a Bad Idea
by James Livingston
Published 15 Feb 2016

Crawford, The World beyond Your Head: On Becoming an Individual in an Age of Distraction (New York: Farrar, Straus and Giroux, 2015), 76. Chapter 4 1. Eduardo Porter, “Big Mac Test Shows Job Market Is Not Working to Distribute Wealth,” New York Times, April 21, 2015. 2. Jeremy Rifkin, The Zero Marginal Cost Society: The Internet of Things, the Collaborative Commons, and the Eclipse of Capitalism (New York: Palgrave Macmillan, 2014). 3. N. Gregory Mankiw, “Defending the One Percent” Journal of Economic Perspectives 27, no. 3 (Summer 2013): 21–34. 4. Freédeéric Lordon, Willing Slaves of Capital: Spinoza and Marx on Desire (London: Verso, 2014), 28–29. 5.

Troublemakers: Silicon Valley's Coming of Age
by Leslie Berlin
Published 7 Nov 2017

To the extent people know his name, he said, it is because the teams he led at Xerox PARC and Digital Equipment Corporation “made me look good.” Taylor had long pushed for an award to honor group creativity. He liked to quote a Japanese proverb: “None of us is as smart as all of us.”25 It is a fitting epitaph for Taylor and a message for innovators everywhere. * * * I. Those systems were precursors to today’s Internet of Things. In the Internet of Things, items ranging from household thermostats and lights to industrial robots are connected to both a network and computing power to be more useful or valuable. Courtesy: SRI International Douglas Engelbart presenting during the “Mother of All Demos” in San Francisco, December 1968

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.: Cohen-Boyer patent application and, 202–4, 263–67 and Reimers, 133–38, 141–44, 189 Swanson and, 194, 199 Commission on Industrial Competitiveness, 255 Commodore, 212n, 230, 240, 247, 250, 284–85, 295, 347 Compaq Computer, 361 computer-aided design, 364 “Computer as a Communication Device, The” (Taylor and Lick), 22–25, 106 Computer Associates International, 371 Computer Conversor, 244–45 computer-facilitated teleconferences, 25 computer graphics, 18, 23, 30, 39, 96, 100, 120, 209, 274–75, 303, 345 computer mouse, 23, 27, 96, 285–86 computer programming, 68–69, 78, 86, 149, 177, 182, 240, 274–77, 284–85, 314, 325, 338 computer science programs, 11–12, 108, 182 computer scientists, 26, 91–98, 102, 138, 177, 218, 226, 247, 283, 289, 341–43, 376 Computer Space game, 107–18 Computer System News, 322 Congress, U.S., 17, 60, 158, 161, 188, 254, 264, 286, 373 consumer electronics, 74, 170, 274, 282, 366 Consumer Electronics Show, 244, 280 Coyle, Arthur “Bud,” 258 Cramer Electronics, 207n Crane, David, 277 Crawford, Gordon, 168 Crisp, Peter O., 249, 293n, 372 Crocker, Charles, 199–201 Cullinane Data Systems, 317 Cupertino, Calif., 41–43, 77, 146, 176, 208, 229, 241, 248, 370 Cyan Engineering, 169–70 Dabney, Ted, 38–40, 107–22, 174, 364 D’Aloia, Garrett C., 296 Data General, 86, 178–79, 208, 357 Davidow, Bill, 213 Death Race game, 172 Decuir, Joe, 277 Defense Communications Agency, 103 defense contractors, xiii, 22, 28, 41–42, 45–48, 86, 180 Defense Department, U.S., 7–9, 15–17, 23, 26n, 28–30, 31, 56–57, 89, 97n, 158 Democratic National Convention, 3, 120 Deutsch, Peter, 96 Diehl, Ron, 44–45, 311 Digital Equipment Corporation (DEC), 74, 86, 101, 178–79, 213, 306, 319, 342–43, 357, 375–76 digital watches, 151, 213, 244 Djerassi, Carl, 203 DNA, see recombinant DNA Dolby, Ray, 38n Doriot, George, 191 dot-com crash, 364 Draper, Bill, 277 Dukakis, Michael, 255 Dynabook, 91, 104 Echelon, 371 Electronic Arts, 248, 346–47 Electronic News, 73 electronics industry, 3–5, 42–44, 47, 74, 164, 170 Eli Lilly, 256, 266n Elkind, Jerry, 94–95, 103, 216, 219–20, 224, 338 Ellenby, John, 217 Ellison, Larry, 38, 185, 364 Ely, Paul, 315 Emmons, Larry, 169, 173 Energy Department, U.S., 203–4 Engelbart, Doug, 23–28, 55, 94–96 English, Bill, 26–28, 96 entrepreneurs, in Silicon Valley, xi–xvi network of, xv, 47–50, 53–54, 84, 127–30, 147–52, 213, 316, 365–66 Environmental Defense Fund, 187 Espinosa, Chris, 248 Estridge, Don, 357 Ethernet network, 92, 288, 366 ethics, 372 E.T. the Extra-Terrestrial game, 275, 345 Eubank, Vineta Alvarez, see Alvarez, Vineta Evans, Dave, 30, 290 Exidy, 172 Facebook, 77, 128, 363–66, 376 Fairchild Semiconductor, 4–5 Alvarez at, 42, 45 founding of, 60, 66, 190 Markkula at, 47–54, 146, 149–50, 154, 232–34 Rock as investor in, 90, 249, 258 start-up companies and, 51, 54, 235 Valentine at, 126–27 Farinon, Bill, 259–60 Farinon Electric, 85 Feigenbaum, Ed, 247 Felsenstein, Lee, 211 floppy disk drives, 244–45, 295 Florida, University of, 60 Forbes, 74, 151, 298 Ford, Henry, 166, 225 FORTRAN programing language, 66n, 149n, 179, 285, 314 Fortune, 253 Fortune 500, 166, 354, 358–59 Fox, Ken, 323, 326 Fuerst, Rory, 192 Fuller, Sam, 343 game design, 111, 116, 120, 130–31, 274–78 Gates, Bill, 212 Gelbach, Ed, 151–52, 213 Genentech, xii, 192, 195, 375 business plan of, 198, 201 initial public offering of, 198, 256–63, 293, 320 intellectual property and, 257 invention rights and, 202–3 investors and, 84, 128, 199–200 launch of, 197–98 licensing agreement and, 265–68 media and, 257 Reimers and, 264–68 Swanson and, xvi, 197–98, 256–63, 266 as virtual corporation, 200–201 as “white-hot risk,” 200 General Electric (GE), 65–67, 76–78, 111–12 genetic engineering, 187–88, 203, 256–57, 375 genetics, 133, 136, 143, 195, 263 Gerard, Manny, 168–75, 269–74, 345–46 Geschke, Chuck, 101, 104n–5n, 217, 223n, 288–91 Gifford, Jack, 47–51 Gilbert, Walter, 263 Glaser, Don, 193 Go (Japanese game), 39, 109 Goeddel, Dave, 261 Goldberg, Marty, 115n Goldman, Jack, 92–93, 103 Good Morning America, 371 Google, 77, 84, 128, 162, 195, 259, 351, 365–66, 375–76 Gotcha game, 120n, 169–70 Graham, Bob, 53–54, 147, 150 Gran Trak game, 124, 130 groupthink, 101, 376 Grove, Andy, xi, 150, 235, 254n Gunning, Bill, 216 hackers, 222, 227 Hack the Future, 369 Hall, John, 235–37 Hambrecht, Bill, 84, 231, 259–60, 290 Hambrecht & Quist, 84, 259–60, 293, 296, 316–19 Hamilton, Bruce, 333–34 Hansen, William, 59 hardware design, 21, 24, 27, 38, 78 Hart, Gary, 255 Harvard Business School, 314–16 Harvard University, 74, 96, 191 Hawkins, Trip, 236n, 248, 285–86, 299–302, 346–47 Health, Education, and Welfare Department, U.S., 134, 142, 145, 189, 203 Herzfeld, Charles, 8–9, 16–19 Hewlett, Bill, 86 Hewlett-Packard, 74, 84–86, 91, 157, 176–86, 191–93, 232, 255, 282, 288–90, 313–14, 319, 357, 372 high-tech revolution, xiv, 74, 253 Hillman, Henry, 191 Hines, Sally, 132, 204, 266–67 hippies, 36, 74, 123, 206, 230–31 hobbyists, 210–14, 230, 244, 247, 284 Hoefler, Don, 73 Hoff, Ted, 347 holographic technology, 279–82 Holt, Rod, 243n Homebrew Computer Club, 209n, 210–12, 222, 227, 231, 264 Home Pong game, 130, 157, 169, 281 Honeywell “kitchen computer,” 91 House, Chuck, 326–27 Hubbard Radio and Television Repair, 32–34, 109 Hughes Aircraft, 47–51, 146, 149, 179–81, 232, 237 human growth hormone, 195, 200, 375 hyperlinks, 24 hypertext, 95 IBM, xiiin, 3, 44, 65, 76–81, 84, 88, 225, 245, 304–6, 353–55, 370 IBM PC, 334, 354, 357, 369 Illinois, University of, 11 Imagic, 278, 346 Indiana, University of, 60 Informatics, 322 information economy, xiv Information Processing Techniques Office, 11–13, 17, 22 initial public offerings (IPOs), 73, 84, 198, 256–63, 287, 293–96, 306, 318–20, 324–25, 360 “innovator’s dilemma,” 213 insider trading, 346 Institutional Venture Associates, 315 insulin, xiv, 199–201, 256, 261, 375 Intel, 51–54, 73–74, 89–90, 102, 126–29, 146–54, 157, 162, 193, 213, 232–33, 238–39, 249, 285, 302, 313, 347 intellectual property, 60, 257, 374 interactive computing, 24, 94, 99, 106, 289 Internet, 6, 17, 78, 103, 331, 338, 376 Internet of Things, 371n Interval Research Corporation, 369 invention disclosures, 59–63, 66, 137–39, 145, 364 inventions, 61–64, 132, 135, 139–40, 144–45, 264–65, 351 inventors, 21, 59–62, 132, 137–39, 144, 219, 245–46, 263, 288, 347 investment bankers, 293, 296, 303 iPhone, 374 IRE Transactions on Human Factors in Electronics, 10 Itek Corporation, 56 Itel, 315 ITT, 292 Japan, 253, 266, 342, 348 Java programming language, 365 Jobs, Patty, 207, 228 Jobs, Paul and Clara, 206 Jobs, Steve, xi–xii, xv, 13n, 82, 154, 157, 176, 206–11, 228–46, 254–55, 287–88, 293–303, 357–59, 365, 372–73 Johnson, Lyndon B., 37 Johnson & Johnson, 256 Kaboom!

Rethinking Capitalism: Economics and Policy for Sustainable and Inclusive Growth
by Michael Jacobs and Mariana Mazzucato
Published 31 Jul 2016

This could then lead to a very active rental sector for organising second-, third- and Nth-hand markets in each country and across the world, along with the growth of disassembly, remanufacturing, recycling, reusing and other materials-saving processes. Information for 3-D printing replacement parts and the provision of regular upgrades for the maintenance of products could become standard practice. This would create a business model in which repair and reuse would take the place of planned obsolescence. With the ‘internet of things’, chips can be put on each product to provide usage histories, enabling a thriving rental and maintenance industry to assign adequate prices. In the advanced world, such a business strategy would create great quantities of jobs for displaced assembly workers in maintenance, upgrading, warehousing, parts ‘printing’, distribution and installation, while design, redesign and many other creative industries and services would employ university graduates.

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China Development Bank (CDB) circular economy citizenship goods climate change and capitalism and economics and politics Paris Accord policy Club of Rome Cold War collective goods Compaq compensation contracts competition Japanese law limits perfect competition protected firms and sectors consumerism consumers behaviour benefits choice debt demand protection welfare corporate sector accountability debt financialisation Fortune 500 companies Fortune 1000 companies governance new public management (NPM) organisational models resource allocation D DARPA debt consumer corporate household hysteria private public short-term sovereign debt-to-GDP ratios decarbonisation and structural change democracy and capitalism election campaigns post-democratic politics Department of Defense Department of Energy Department of health developing countries devolution discrimination anti-discrimination laws displacement of peoples Dosi, Giovanni Draghi, Mario E economic and monetary union (EMU) economic growth and inequality and innovation and technology environmental concerns green growth zero growth economic policy and capitalism consensus-building macroeconomic policy monetary expansion reshaping economic theory economic models model of the firm neoclassical orthodox post-Keynesian education access to and skills efficiency employment growth ‘non-standard’ work energy sector storage technologies environmental impacts environmental risk damage degradation sustainability technologies euro zone debt-to-GDP ratio economic policy fiscal policy GDP growth government lending investment macroeconomic conditions private investment productivity growth recession southern countries sovereign debt unemployment European Central Bank (ECB) role European Exchange Rate Mechanism (ERM) European Investment Bank (EIB) proposed new European Fund for Investment European Regional Development Fund (ERDF) European Stability Mechanism European Union (EU) competition law debt-to-GDP ratio de-industrialisation GDP growth government lending Growth Compact investment-led recovery macroeconomic conditions monetary expansion policy framework private investment productivity growth Stability and Growth Pact unemployment executive pay F Federal Reserve financial crash of 1929 financial crash of 2008 financial markets borrowing discrimination efficient markets hypothesis mispricing short-termism systemic risks financial regulation Finland public innovation research and development universal basic income firms business models in perfect competition productive firm First World War fiscal austerity fiscal compact fiscal consolidation fiscal deficits fiscal policy fiscal tightening food insecurity Forstater, Matthew Fortune 500 companies Fortune 1000 firms fossil fuels fracking France average real wage index labour productivity growth private debt public deficit unemployment Freeman, Chris Friedman, Milton G G4S Gates, Bill Germany average real wage index GDP green technology investment state investment bank unemployment wages global financial system globalisation and welfare state asymmetric first golden age Godley, Wynne Goldman Sachs Goodfriend, Marvin Google governments and innovation deficits failures intervention by modernisation of risk-taking Graham, Benjamin Great Depression Greece austerity bailouts debt problems GDP investment activity public deficit unemployment green technology green direction for innovation greenhouse gas emissions Greenspan, Alan Grubb, Michael H Hatzius, Jan health and climate change older people Hirschman, Albert history Integration with theory home mortgage specialists household income housing purchases value I IBM income distribution industrial revolution inequality adverse effects and economic performance China ethnicity explanation for income international trend OECD countries opportunities redistributive policies reinforcement reversing rise taxation UK wealth inflation information and communications technologies (ICT) consumer demand green direction internet of things online education planned obsolescence innovation and climate change and companies and government and growth innovative enterprise path-dependence public sector institutions European financial role Intel interest rates and quantitative easing Intergovernmental Panel on Climate Change (IPCC) International Bank for Reconstruction and Development (IBRD) International Energy Agency (IEA) International Labour Organization (ILO) International Monetary Fund (IMF) Studies investment and theory of the firm crowding out decline in investment in innovation private private vs publicly owned firms public public–private investment partnerships investment-led growth Ireland debt problems investment activity Public deficit Israel public venture capital fund research and development Italy average real wage index debt problems GDP Income inequality unemployment J Japan average real wage index competitive advantage over US GDP wages Jobs, Steve Juncker, Jean-Claude K Kay Review Keynes, John Maynard KfW Knight, Frank Koo, Richard Krueger, Alan Krugman, Paul L labour markets insecurity of regulation structures United States labour productivity and wages declining growth public deficit unemployment Lehman Brothers Lerner, Abba liquidity crisis Lloyd George, David lobbying corporate M Maastricht Treaty Malthus, Thomas market economy theory markets behaviour failure uncertainty Marshall, Alfred Marx, Karl McCulley, Paul Merrill Lynch Mill, John Stuart Minsky, Hyman mission oriented investment monetary policy money and fiscal policy and macroeconomic policy bank money electronic transactions endogenous exogenous fiat money government bonds IOUs modern money theory quantity theory theories monopolies monopoly rents natural Moore, Gordon N NASA nanotechnology National Health Service (NHS) National Institutes of Health (NIH) national savings neoliberalism corporate Newman, Frank Newton, Isaac O Obama, Barack P patents patient capital patient finance see patient capital Penrose, Edith Piketty, Thomas PIMCO Pisano, Gary Polanyi, Karl Portugal austerity bailout debt problems GDP investment activity unemployment privatisation productivity marginal productivity theory productive firm unproductive firm – see also labour productivity public deficits public goods public organisations and change public policy and change evaluation role public service outsourcing public spending public–private investment partnerships Q quantitative easing quarterly capitalism R Reagan, Ronald recessions Reinhart, Carmen renewable energy policy rents and banks increase rent-seeking research and development (R&D) state organisations Ricardo, David risk-taking – mitigation of risk role of the state Rogoff, Kenneth Roosevelt, Franklin D.

Uncharted: How to Map the Future
by Margaret Heffernan
Published 20 Feb 2020

It was all about creating an atmosphere to be human.’ In the autumn of 2017, the company opened The Residence, a fully furnished in-store apartment where shoppers could dine and stay overnight. Incorporating all the latest technologies, it was a chance for consumers to see how a thoughtful use of new software and the Internet of Things might work in a home. Queues to visit the apartment went around the block, with a huge waiting list for overnight stays. On a separate front, O’Mahony and her colleagues have initiated a training programme to help staff become better listeners – both to their customers and to each other. Working in partnership with the Samaritans, the goal is to address the epidemic of mental illness that has become apparent across UK workplaces.

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Stephen, 205 healthcare, industrialisation of, 124–38 Heraghty, Oisin, 144 hereditary conditions, 89–90, 99 Higgins, Terrence, 257 Higgs boson, 216 Higgs, Peter, 83, 207 hippocampus, 43 historic record, 71 historical data, 5 HIV, 259, 268, 274 Hodgkinson, David, 50 Hodgkinson, James, 48–50 Hodgkinson, Joan, 50 Hollow, 180, 194 Holmes, Oliver Wendell, 97 homophobia, 257 horoscopes, 2, 88 Hosking, Rebecca, 109–13 House of Fraser, 117 Howard, Michael, 59 Howe, Sophie, 309–13, 317 HSBC, 253 HTLV-3, 259 human complexity, 7 Human Genome Project, 82, 88, 99, 218 Huntington’s disease, 89 Hurricane Katrina, 305 Hurt Heart, 190 hyperbolic narrative, 245–6 IARPA, 36–7 Iberian Anarchist Federation (FAI), 225 Ibsen, Henrik, 15, 177, 179–80, 193, 198 Icarus, 166 IESE, 230 incarceration, 97 independent assortment, 83 Index Number Institute, 18 individualism, 157–8 inductive approach, 21 Industrial Revolution, 60 IndyRef, 39 The Information Mine (TIM), 210 ING, 255 injustice, 103, 109, 112 innovation, 82, 84, 116–17, 121, 123, 158, 164, 170, 178, 200, 223–5, 247, 297 Intelligence Advanced Research Projects Activity, see IARPA Internet of Things, 118 interpersonal medicine, 136 intolerance, 2 in vitro fertilisation, see IVF iPhone, 4, 246 IQ, 97 Iraq War, 3 Ireland University, 146 It’s Impossible to Learn to Plow by Reading Books, 196 IVF, 98, 222–3 Jackson, Glenda, 275–6 Jobs, Steve, 199 ‘John and Mary’ anecdote, 237–45, 270, 295 John Lewis Partnership, 117–20 Johnson & Johnson, 233 Johnson, Lyndon B., 53–4 Johnston, Clay, 129, 131, 136–7 Jones, Katherine, 132, 133 Joyce, James, 197, 198 ‘Julia, Mario, Johanna and Luca’ anecdote, 65–9, 72, 79, 94, 107 Jung, Carl, 73 ‘just in case’ vaccines, 298–9, 301 just-in-time management systems, 299 Kahane, Adam, 165–7, 170, 322 Kallasvuo, Olli-Pekka, 247 Kavanagh, Patrick, 182–3, 192, 198 Kenny, Enda, 140–1, 143 Key Performance Indicators (KPIs), 220 Keynes, John Maynard, 21, 25–6, 97 King Lear, 195, 275–6 Koch, Michael, 160, 161 Koch, Robert, 15 Korea, 4 Korean War, 53 KPIs (Key Performance Indicators), 220 Kramer, Larry, 258, 259–60, 261 Krugman, Paul, 24, 28 Kudlow, Larry, 28 Kurzweil, Ray, 283–6 La Boheme, 274 La Traviata, 274 labour disputes, 4 labour market, 32 Labour Party, 270 Laffoy, Mary, 142, 146 Lame Duck, 166 Large Hadron Collider (LHC), 207, 209, 211, 212–14 Lassa, 298, 302 Last Four Songs, 274 Late Style, 277, 278 Law of Accelerating Returns, 284 Le Corbusier, 226 Leadership Paradox, 157 Lee, Ann, 293, 294 Leeds Wellbeing Warriors, 118 left-handedness, 74 Legionnaire’s disease, 58 legitimacy, 26, 141, 145, 205, 222, 231, 234, 316–18 Lehman Brothers, 248 Leigh, Mike, 180, 185, 192–3 Leipziger Montagsdemonstrationen, 56 Lenz, Fritz, 97 life expectancy, 282, 286, 311 Lindquist, Ulla-Carin, 275 Linklater, Richard, 183, 196 Llewellyn-Smith, Christopher, 207–9, 211, 215, 217, 231, 303 Lloyds Banking Group, 233 London, navigation around, 42–3 London and Cambridge Economic Service, 21 London College of Fashion, 67 Long, Iris, 261–2, 269 longevity, 89, 205, 216, 222, 224, 231, 252, 280, 286 loyalty, 47–8, 121, 270 Lozano, Francisco de Paula del Villary, 224, 229 ‘Lucy’ anecdote, 287–8 Lundgren, Britt, 114 Macbeth, 31 McCarthy, Mark, 95 McChrystal, Gen.

Mine!: How the Hidden Rules of Ownership Control Our Lives
by Michael A. Heller and James Salzman
Published 2 Mar 2021

One morning he woke up and the device was dead, not just dead but “bricked.” And not just Gilbert’s machine. All Revolvs in the world were bricked that day. Turns out, Google had remotely activated a kill switch on everyone’s device. Why? Google had bought Revolv in 2014, when it was expanding into the “Internet of Things” market. It later decided to invest instead in a different home automation product line called Nest. What better way to boost sales of Nest than to terminate the software that powered Revolv? Deep in the terms of service for Revolv, Google had retained the right to shut it all down. In a blog post, Gilbert asked, “Which hardware will Google choose to intentionally brick next?

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The switch from bundle to stick is nearly universal in online ownership. As Internet speeds increase and cloud storage becomes cheaper, we will stream more and more goods and services throughout our lives. Opaque licenses will govern not only the songs we listen to and the books we buy. They will span the entire Internet of Things, from coffee makers and thermostats to security and sound systems. Perhaps it’s not so worrying if Oral-B bricks your wireless toothbrush (yes, it exists). But surprises in the ownership structure of diabetes monitors, pacemakers, and home alarms could be deadly. Our intuitions still tell us that possessing the hardware is what matters.

Smart Machines: IBM's Watson and the Era of Cognitive Computing (Columbia Business School Publishing)
by John E. Kelly Iii
Published 23 Sep 2013

Today, we are witnessing the emergence of a new force in society and business: big data. Organizations and individuals are faced with a torrent of data, everything from structured information such as transaction records to a wide variety of unstructured information—still images, video, audio, and sensor data. The biggest new source of data is the so-called Internet of things, data produced by sensors and harvested via the Internet. The sensors involved range from the RFID tags that retailers use to track merchandise to video cameras that capture the flow of traffic. Every day, as a group, human beings generate about 3 exabytes of computer data—a prodigious output that is expected to produce a data universe of 40 zettabytes of digital stuff by 2020.2 A zettabyte is a decidedly big number: a 1 followed by 21 zeros.

The Open Revolution: New Rules for a New World
by Rufus Pollock
Published 29 May 2018

Virtual reality, for instance, can now replicate many of our sensations and impressions of the world, and has huge scope in future for recreation, as it has already for various forms of training. It would compromise our freedom if virtual reality were to become the same kind of near-monopoly as, for instance, Facebook. Likewise, the so-called internet of things is quickly growing. Already many appliances such as baby monitors, lighting systems and central heating are connected to the internet, but this is only the start. Over the next few years, as billions more devices are connected, we may see machine-to-machine data outstripping human usage to become the principal traffic on the internet.

Human Compatible: Artificial Intelligence and the Problem of Control
by Stuart Russell
Published 7 Oct 2019

In the early 2000s, the widespread adoption of mobile phones with microphones, cameras, accelerometers, and GPS provided new access for AI systems to people’s daily lives; “smart speakers” such as the Amazon Echo, Google Home, and Apple HomePod have completed this process. By around 2008, the number of objects connected to the Internet exceeded the number of people connected to the Internet—a transition that some point to as the beginning of the Internet of Things (IoT). Those things include cars, home appliances, traffic lights, vending machines, thermostats, quadcopters, cameras, environmental sensors, robots, and all kinds of material goods both in the manufacturing process and in the distribution and retail system. This provides AI systems with far greater sensory and control access to the real world.

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See artificial intelligence (AI) intelligent personal assistants, 67–71, 101 commonsense modeling and, 68–69 design template for, 69–70 education systems, 70 health systems, 69–70 personal finance systems, 70 privacy considerations, 70–71 shortcomings of early systems, 67–68 stimulus–response templates and, 67 understanding content, improvements in, 68 International Atomic Energy Agency, 249 Internet of Things (IoT), 65 interpersonal services as the future of employment, 122–24 algorithmic bias and, 128–30 decisions affecting people, use of machines in, 126–28 robots built in humanoid form and, 124–26 intractable problems, 38–39 inverse reinforcement learning, 191–93 IQ, 48 Ishiguro, Hiroshi, 125 is-ought problem, 167 “it’s complicated” argument, 147–48 “it’s impossible” argument, 149–50 “it’s too soon to worry about it” argument, 150–52 jellyfish, 16 Jeopardy!

The AI Economy: Work, Wealth and Welfare in the Robot Age
by Roger Bootle
Published 4 Sep 2019

A key feature behind human beings’ rise to dominion over the physical world was the development of exchange and specialization, which was a network effect. When the internet came along, connecting computers in a network, this transformed their capability.11 And then we have coming along soon what the British entrepreneur Kevin Ashton has called “The Internet of Things.” IBM calls this the “Smarter Planet”; Cisco calls it the “Internet of Everything”; GE calls it “the Industrial Internet”; while the German government calls it “Industry 4.0.” But all these terms refer to the same thing. The idea is simply that sensors, chips, and transmitters will be embedded in umpteen objects all around us.

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Referring to the dream of constructing an automaton that could be servant, companion, and general drudge, he says: “It’s like putting colonies on Mars, possibly feasible technologically, but frankly not worth the massive investment it would take. There are better, more productive, ways of spending money.”35 As to “The Internet of Things,” rarely can something have been so overhyped. So, we will be able to monitor umpteen subjects in our everyday lives and tell whether they need renewing, polishing, cleaning, or mending. So what? I suppose there might be some helpful instances, but they will surely be peripheral, and will neither significantly reduce the demand for human labor nor meaningfully increase human wellbeing.

The Crux
by Richard Rumelt
Published 27 Apr 2022

With the Apple- Qualcomm lawsuits settled in 2019, Intel closed down its modem development activity, and Apple purchased Intel’s modem business for $1 billion. Again, was Intel never to have a successful entry into the mobile market? • IoT. In 2016 Intel announced a major commitment to the Internet- of-Things (IoT). This was the market for wireless computing devices. Much lower power than phones, these chips would connect home appliances, smartwatches, drones, dog collars, automobiles, and hitherto unthought-of applications to Wi-Fi systems and the cloud. By mid-2017 Intel halted development of its low-end IoT chips and cut 140 employees in the area, refocusing on industrial applications.

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quarterly earnings estimates, 257–258 shareholder value and incentive pay, 262–265 firefighters, safety equipment for, 122 flexibility, organizational, 84–85, 180 focusing on a problem breaking through the crux, 5 diffusing effort, 114 finding the ASCs, 323–324 focusing in and focusing out, 49 focusing on a few proximate objectives, 324 as strategic skill, 4 Strategy Foundry, 308–310 See also coherent action Fontainebleau, France, 1–4, 27, 218–221 food production, 163–164, 245–247 food-processing industry, 87–91 forced inward analysis, 320–321 Ford Motor Company, 96, 193–196 frame-risk, evaluating, 322–323 Freund, John, 208–209 Fried, Jason, 44 Galbraith, Simon, 31 Gates, Bill, 73, 149, 198–199, 228 General Dynamics, 320 General Electric (GE), 22, 96, 141, 156, 174–175 General Motors (GM), 218–221, 237 geolocation, 116–118, 146 Gerstner, Lou, 79–80, 156, 228–231 gnarly challenges characteristics of, 37–39 COVID-19 and Ryanair, 61–62 creating a coherent strategic response, 31–32 designing action alternatives, 42–45 forms of strategic challenge, 23–24 goals versus strategies, 241–242 identifying the crux, 27, 39–42 the mechanics of insight, 47 goals, strategic ambitions versus, 20–21 arbitrary unsupported goals, 242–244 Curtiss-Wright’s diversification, 235–240 deduction versus design, 34–37 driving results, 249–250 elements of good goals, 237–240 misapplied goals, 245–247 versus strategy, 241–242, 248–249 strategy statements, 111–114 unsupported goals and objectives, 235 Good Strategy/Bad Strategy (Rumelt), 95, 189, 298 Google AdWords, 53 Google Maps, 117 growth through strategic extension, 89 impact on tech consumption, 214 mergers and acquisitions, 100–101 Pixel 2 and Pixel Buds, 270–271 robotic surgery, 209 GoTo, 52–53 Gross, Bill, 52–53 group performance, 296 groupthink, 290–292, 315–316 growth arbitrary unsupported goals, 242–244 avoiding accounting manipulations, 105–106 avoiding overpayment, 101–104 BCG growth-share matrix, 173–174 concerns over Nokia’s rapid growth, 223 delivering exceptional value to an expanding market, 87–91 diagnosing the challenge, 84–85 growing the blob, 104–105 lack of earnings, 106–108 the meaning and mechanics, 85–87 Netflix’s accounting system, 17–19 quick reaction times, 94–97 simplification of business, 91–94 “strategy calculator,” 15–17 using mergers and acquisitions, 97–101 Guthart, Gary, 208–209 Hamel, Gary, 16–17 Hammonds, Keith, 263 Hastings, Reed, 17–19, 22 Hawkins, David, 265 health care: pandemic planning, 272–273 Henderson, Bruce, 197 high-speed computing, 188 history, lessons from, 317 Houston, Drew, 211–212 Huang, Jen-Hsun, 190 IBM, 149, 156, 193, 227–231, 242 identifying the crux, 11 the clash of ambition, 70–73 effective action, 8–9 facing the challenge of, 142–144 gnarly challenges, 27 GoTo and AdWords, 52–53 industrial design, 32 Intel, 80–83 isolating the crux, 38 Marvel comics, 39 the mechanics of insight, 47 Netflix challenges, 25–27 Strategy Foundry system, 303–310 importance, critical addressable strategic challenge, 73–75 filtering challenges, 41 finding the crux of Intel’s challenges, 80–82 incentive pay, 262–265 industrial organization (IO): industry-analysis framework, 167 industry-analysis framework, 167 inertia, organizational, 221–225 influencers, 207–208 infrastructure: planning store sizes, 161–162 innovation building on existing infrastructure, 203–204 complementarities, 214–216 long waves, 204–206 short waves, 207–214 tech giants, 213–214 INSEAD school of management, 76, 218–221 inside-view bias, 296 insight, 8–9 assessing growth, 85 creating a coherent action, 125 debate over judgment leading to, 75 impediments to, 50–51 the mechanics of, 45–49 responses using the Boyd Loop, 95–96 Instant Strategy exercise, 68–70, 320 integration and deintegration, 193–194 integrity and honesty: the quality of strategy work, 171–172 Intel, 55, 76–83, 95, 189 international markets, Netflix challenges in, 25–27 Internet: confluence of existing products and activities, 205–206 Internet-of-Things (IoT), 78 Intuit, 191 Intuitive Surgical, 208–209 iPhone disruption by, 176–177 Jobs embracing the challenge, 144–147 mobile phone development, 78 Nokia’s touch-screen phones, 224 opening the app store to outsiders, 42 as product of close coupling, 188 role of analogy in the success of, 147–148 scaling mobile phone production, 195 Windows phone and, 73 Iraq, War in, 292–294 Janis, Irving, 290–291, 315–316 Jobs, Steve, 31, 144–147, 269–271 Johnson, Lyndon, 71–73 judgment as a skill, 4, 74–75, 315–316 Kahn, Herman, 205–206 Kanter, Rosabeth Moss, 250 Keller, Maryann, 219 Kennedy, John F., 291 Klein, Gary, 318–319 Kodak, the decline of, 178, 180 Korean War, 94–95 Le Toit du Cul de Chien (The Roof of the Dog’s Ass) boulder problem, 1–5, 27 leadership changing the alpha to create cultural change, 230 Nokia’s organizational inertia, 225 organization dysfunction at GM, 219 organization transformation, 226 owning the challenge, 289–290 strategy as an exercise of power, 110–111 Learson, T.

The Long Game: China's Grand Strategy to Displace American Order
by Rush Doshi
Published 24 Jun 2021

“China did not participate in the steam engine and mechanical revolutions of the 18th century or the power and transportation revolution of the 19th century; China partially participated in the electrical and information revolution of the 20th century.” This time would be different, they argued, “in the current brewing of artificial intelligence, Internet of Things, energy Internet, biotechnology, China is ‘overtaking by curve.’ ”51 This seemingly inscrutable phrase—overtaking by curve—is rooted in some of the post-2009 debates about US power after the Global Financial Crisis, with “overtaking by curve” a reference to sprinting ahead as a competitor slows down or mishandles a turn around a racetrack and “overtaking by lane change” a reference to innovating new methods to surpass a rival.

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Moreover, formal partnerships could involve efforts to “set standards and values around sharing data, transparency, reproducibility and research integrity,” as Georgetown’s Center for Strategic and Emerging Technology argues.69 •Build the Capacity for Greater State Involvement and Coordination in Nominally Commercial Standard-Setting Bodies: While many standard-setting bodies are composed of companies rather than countries, China’s top-down effort to shape standards requires a response from the US government. This is particularly urgent during times when standard-setting processes might be inaugurating new paradigms in critical industries, including telecommunications (e.g., the Open Radio Access Network concept) and the Internet of Things, that could long shape the future. First, Congress could support establishment of interagency working groups on standards that could coordinate internally. For example, White House Office of Science and Technology Policy could establish an interagency working group on technology standards that brings together the Departments of State, Commerce, Justice, and Defense as well as the US intelligence community—and that also consults with US industry.70 Second, to build coalitions among different companies and countries, Congress could support the establishment of offices within the Departments of Commerce and State to coordinate US approaches with like-minded stakeholders.71 Political Building •Build Democratic or Allied Coalitions for Governance Issues from Technology to Trade and Supply Chains to Standards: Over the last three years, a series of great and middle powers have proposed organizing democratic coalitions to push back on China’s efforts to stall or impose its preferences in more inclusive global forums.

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See also Tiananmen Square Massacre Huntington, Samuel, 330, 331–32 Hu Yaobang, 25, 29, 30, 54, 162 icebreakers, 295–96 ideological threats, 300, 309, 311 Ikenberry, John, 23 immigration, 333 Imperial Examination, 53 Imperial Japan, 6 India, 22, 50–51, 70–71, 81–82, 95–96, 199, 202–3, 220–21, 223–24, 243–44, 307–8, 318, 327–29 Indonesia, 201, 202–3, 318 Industrial Development Organization (IDO), 282–83 industrial revolutions, 264 information technology, 324–25 infrastructure investment alternative explanations for BRI investment, 241–42 and domestic leverage, 244–45 and economic power projection, 241–46 and grand strategy, 242–46 military significance, 245–46 and relational leverage, 242–43 at structural level, 243–44 and US asymmetric strategies, 325–26 innovation, 326, 327 Institute for International Strategy, 268 Institute of Ocean Development Strategy, 189–90 institutions and institutionalization and characteristics of grand strategy, 17 China’s opposition to APEC, 114–17 and China’s participation in SCO, 129–30 China’s role in ASEAN institutions, 121–23 and US asymmetric strategies, 319 See also multilateralism; specific institutions and organization names integrated security theory, 16 intellectual property (IP) protection, 140–41 interest groups, 136 Intermediate-Range Nuclear Forces Treaty, 273–74 international balance of forces, 161 International Civil Aviation Organization (ICAO), 282–83 International Monetary Fund (IMF), 225, 247 international monetary system, 238–39, 247–50 international order, 15. See also hegemonic order International Telecommunication Union (ITU), 282–83, 290, 328–29 Internet of Things, 286–87 Internet sovereignty, 132–33 INTERPOL, 282–83 Intersessional Support Group, 121 Iran, 251–52, 304–5 Iraq War, 80, 111, 132–33 Ivanov, Igor, 128–29 Jackson-Vanik Amendment, 134–35 Jakobson, Linda, 37 Japan and changing US-China relationship, 50–51 and China’s economic strategies, 249 and China’s expansionist goals, 4, 302 and China’s global ambitions, 272 and China’s use of regional institutions, 105–6, 120–22 and Cold War cooperation with US, 47 and context of China’s goals, 6 and economic blunting strategies, 139–40, 153 and multipolarity discourse, 162 and political building strategies, 220–21, 223–24, 232 and Sino-Japanese War, 28 and US asymmetric strategies, 308, 318, 327–28 Jesuits, 39–40 Jiang Zemin and changing US-China relationship, 52 and China’s global ambitions, 262 and China’s perception of US threat, 53–56 and Deng’s “Tao Guang Yang Hui,” 60–61 and departures from Deng’s approach, 175, 176–78 and economic blunting strategies, 134, 138, 141–43, 145, 146–50, 153–54 and implementation of China’s blunting strategy, 65 and military blunting strategies, 69–70, 74, 75, 77, 78–80, 83, 96–98, 99–100 and military building strategies, 192–93, 196–97 and multipolarity discourse, 162, 163–64 and nature of US-China competition, 309–10 and party leadership on foreign policy, 38 and political blunting strategies, 109, 110–11, 112, 114–15, 117–18, 128–30 and rejuvenation ideology, 27, 29, 30–31 and US asymmetric strategies, 309 and “wealth and power” ideology, 28–29 Jin Canrong, 173, 268–69, 270–71, 281–82, 288, 289–90 Jin Liqun, 214, 216, 218, 219, 220–21, 222, 224–25 Ji Shengde, 191 Johnston, Iain, 162 Joint Campaign Theory Study Guide, 200 Joint Comprehensive Plan of Action (JCPOA), 273–74 Joint Declaration on a Strategic Partnership, 125–26 Joint Russian-Chinese Initiative on Strengthening Security in the Asia Pacific Region, 227 joint ventures, 137 journalism, 41 Kai He, 105, 113 Kang Youwei, 28–29 Karachi port project, 207 Kazakhstan, 47, 129, 208–9, 228–29 Kennedy, Conor, 207 Kennedy, John F., 334 Khrushchev, Nikita, 50–51 Kiev (Soviet aircraft carrier), 95–96, 194–95 Kilby, Christopher, 216, 217–18 Kilo-class submarines, 84–85 Kirchner, Jonathan, 249 Kirshner, Jonathan, 250 Kissinger, Henry, 10, 330–31, 332 Ko, Blackie, 191–92 Korean Peninsula, 4, 70–71, 81–82, 132–33, 188–89, 302.

Information: A Very Short Introduction
by Luciano Floridi
Published 25 Feb 2010

The threshold between here (analogue, carbon-based, off-line) and there (digital, silicon-based, online) is fast becoming blurred, but this is as much to the advantage of the latter as it is of the former. The digital is spilling over into the analogue and merging with it. This recent phenomenon is variously known as `Ubiquitous Computing', `Ambient Intelligence', `The Internet of Things', or `Web-augmented things'. The increasing informatization of artefacts and of whole (social) environments and life activities suggests that soon it will be difficult to understand what life was like in pre-informational times (to someone who was born in 2000, the world will always have been wireless, for example) and, in the near future, the very distinction between online and offline will disappear.

Why We Drive: Toward a Philosophy of the Open Road
by Matthew B. Crawford
Published 8 Jun 2020

In short, the effectiveness and safety of the product are brazenly held hostage to its owners’ submission to rendition [of data] as conquest . . . for others’ interests.”6 If you have a Roomba robot vacuum cleaner, you should know that it is busy creating a floor plan of your house, to be sold to the highest bidder. The “internet of things,” made up of all those smart devices, represents “the extension of [data] extraction operations from the virtual world into the ‘real’ world’ where we actually live,” Zuboff writes.7 These aren’t simply cases of what I like to call “Sky Mall capitalism” (expensive solutions for nonproblems).

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See also automobile safety Illich, Ivan, 243, 247, 314 impartial agency, 120 in-car information system, 99 individual agency, 29 individual judgment, 7, 31, 312 Indochina, 80 industrial psychology, 214 infantile amnesia, 13 infantile narcissism, 171–172 inference, 112 information overload, 103 institutional power, 288 Insurance Institute for Highway Safety, 227 intelligence, bodily skills vs., 59–60, 65 internal combustion engines, 131–134 International Harvester Scout, 50 internet of things, 306 Intersection Manager, 246, 247, 248 Interstate Highway System, 38 Isle of Man, 229 Isle of Man TT motorcycle race, 172–173 Ismay, Hastings, 177 Italy, 242–243, 245 Jacobs, Jane, 31, 35, 37, 283 Jakob Lohner & Company, 138 James, William, 169–170 Jeepster Commando breakdown at the diner, 49–50 driving home, 56–57 engine swap, 51 fetching water, 52–54 jerry-rigging used radiator, 56 junkyard radiator search, 55–56 punctured oil filter, 51 purchase of, 50–51 radiator damage, 50–52 radiator mount repairs, 51–52 selling off the Commando, 57 Johnson, Lady Bird, 72 joy, connection to movement, 11–12 judgment discretionary, 228–229 human, 227 human/individual, 7, 31, 104, 224, 227, 312 machines and, 224 mechanized, 217–218 moral, 23 prudence of, 248 replacing with remote control, 312 of responsibility, 71 traffic enforcement as substitute, 217 unregulated intersections and, 245 junkyards, 51, 71–72, 74, 81–82 Junkyards, Gearheads and Rust (Lucsko), 71–72 Kansas City, 289, 290 Karmann Ghia, 68–69, 94–95 Katz, Jack, 251–254 KdF (Kraft durch Freude), 139 Kdf-Wagen.

Human Frontiers: The Future of Big Ideas in an Age of Small Thinking
by Michael Bhaskar
Published 2 Nov 2021

But this is about more than significant giants like India or China, as if a new United States or EU entered our cultural and scientific imaginations, their populations finally able explore their potential. It is a global process. Japan has of course long been on the front lines. It remains so, with excellent capabilities in robotics, clean energy, biotech, the internet of things, automation and care technologies. It is home to the world's largest VC fund, and has a forward-looking population of tech users. Southeast Asia is on the rise – growing fast, home to almost 700 million people, knitted together via ASEAN, the next manufacturing powerhouse. The glitzy petromonarchies of the Gulf bring their own brand to the equation.

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The effects of aging on the productivity of scientists’, Nintil, accessed 14 January 2021, available at https://nintil.com/age-and-science/ Ridley, Matt (2011), The Rational Optimist: How Prosperity Evolves, London: Fourth Estate Ridley, Matt (2016), The Evolution of Everything: How Small Changes Transform Our World, London: Fourth Estate Ridley, Matt, (2020), How Innovation Works, London: Fourth Estate Rifkin, Jeremy (2014), The Zero Marginal Cost Society: The Internet of Things, The Collaborative Commons, and The Eclipse of Capitalism, New York: Palgrave Ringel, Michael S., Scannell, Jack, Baedeker, Mathias, and Schulze, Ulrik (2020), ‘Breaking Eroom's Law’, Nature Reviews Drug Discovery, Vol. 19, pp. 833–4 Ritchie, Stuart (2020), Science Fictions: Exposing Fraud, Bias, Negligence and Hype in Science, London: Bodley Head Robertson, Ritchie (2020), The Enlightenment: The Pursuit of Happiness, 1680–1790, London: Allen Lane Robinson, Andrew (2006), The Last Man Who Knew Everything: Thomas Young, the Anonymous Polymath Who Proved Newton Wrong, Explained How We See, Cured the Sick and Deciphered the Rosetta Stone, London: Oneworld Rockey, Sally (2015), ‘More Data on Age and the Workforce’, NIH Extramural News, accessed 25 August 2019, available at https://nexus.od.nih.gov/all/2015/03/25/age-of-investigator/ Rodriques, Samuel G., and Marblestone, Adam H. (2020), ‘Focused Research Organizations to Accelerate Science, Technology, and Medicine’, Day One Project, accessed 15 November 2020, available at https://www.dayoneproject.org/post/focused-research-organizations-to-accelerate-science-technology-and-medicine Rogers Hollingsworth, J. (2007), ‘High Cognitive Complexity and the Making of Major Scientific Discoveries’, in Knowledge, Communication and Creativity, Los Angeles: SAGE Romer, Paul M. (1990), ‘Endogenous Technological Change’, The Journal of Political Economy, Vol. 98 No. 5, Part 2: The Problem of Development: A Conference of the Institute for the Study of Free Enterprise Systems Roser, Max (2020), ‘Why is life expectancy in the US lower than in other rich countries?’

21 Lessons for the 21st Century
by Yuval Noah Harari
Published 29 Aug 2018

Bishop, Pattern Recognition and Machine Learning (New York: Springer, 2007). 4 Seyed Azimi et al., ‘Vehicular Networks for Collision Avoidance at Intersections’, SAE International Journal of Passenger Cars – Mechanical Systems 4 (2011), 406–16; Swarun Kumar et al., ‘CarSpeak: A Content-Centric Network for Autonomous Driving’, SIGCOM Computer Communication Review 42 (2012), 259–70; Mihail L. Sichitiu and Maria Kihl, ‘Inter-Vehicle Communication Systems: A Survey’, IEEE Communications Surveys and Tutorials (2008), 10; Mario Gerla, Eun-Kyu Lee and Giovanni Pau, ‘Internet of Vehicles: From Intelligent Grid to Autonomous Cars and Vehicular Clouds’, 2014 IEEE World Forum on Internet of Things (WF-IoT) (Piscataway, NJ: IEEE, 2014), 241–6. 5 David D. Luxton et al., ‘mHealth for Mental Health: Integrating Smartphone Technology in Behavioural Healthcare’, Professional Psychology: Research and Practice 42:6 (2011), 505–12; Abu Saleh Mohammad Mosa, Illhoi Yoo and Lincoln Sheets, ‘A Systematic Review of Healthcare Application for Smartphones’, BMC Medical Informatics and Decision Making 12:1 (2012), 67; Karl Frederick Braekkan Payne, Heather Wharrad and Kim Watts, ‘Smartphone and Medical Related App Use among Medical Students and Junior Doctors in the United Kingdom (UK): A Regional Survey’, BMC Medical Informatics and Decision Making 12:1 (2012), 121; Sandeep Kumar Vashist, E.

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Azimi et al., ‘Vehicular Networks for Collision Avoidance at Intersections’, SAE International Journal of Passenger Cars – Mechanical Systems 4:1 (2011), 406–16; Swarun Kumar et al., ‘CarSpeak: A Content-Centric Network for Autonomous Driving’, SIGCOM Computer Communication Review 42:4 (2012), 259–70; Mihail L. Sichitiu and Maria Kihl, ‘Inter-Vehicle Communication Systems: A Survey’, IEEE Communications Surveys & Tutorials 10:2 (2008); Mario Gerla et al., ‘Internet of Vehicles: From Intelligent Grid to Autonomous Cars and Vehicular Clouds’, 2014 IEEE World Forum on Internet of Things (WF-IoT) (2014), 241–6. 9 Michael Chui, James Manyika and Mehdi Miremadi, ‘Where Machines Could Replace Humans – and Where They Can’t (Yet)’, McKinsey Quarterly, July 2016. 10 Wu Youyou, Michal Kosinski and David Stillwell, ‘Computer-based personality judgments are more accurate than those made by humans’, PANS, vol. 112 (2014), 1036–8. 11 Stuart Dredge, ‘AI and music: will we be slaves to the algorithm?’

Cities Are Good for You: The Genius of the Metropolis
by Leo Hollis
Published 31 Mar 2013

In the last few years, there has been a proliferation of machines connecting to each other rather than human connections: the dial-up desktop computer, superseded by broadband, and now by wireless bandwidth, 3 and 4G, tablet technology, and other mobile computing devices. At the same time cloud computing widens the connexity of things, making it easier to access our data from any place. However, it is not just our phones and computers that are now connected, we will soon live in the ‘Internet of Things’ where the internet enters the real world and collects data from every object around us, so that every aspect of our lives interacts. Data will be collected every time we use the public-transport system, our cars can be connected to the garage to tell them when there has been a fault, traffic lights and road signs will contain sensors that detect congestion and traffic flow, smart buildings will regulate internal temperature or lighting, face-recognition software might be used for everything from banking to security.

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The skyscrapers, public spaces and gardens, impressive plazas and feats of engineering herald a bold and confident metropolis, the compliance of the whole city to the highest Leadership in Energy and Environmental Design (LEED) standards make it one of the most sustainable environments imaginable, but it is the invisible connections, the city as Internet of Things, that is truly impressive. Songdo is more than the people or the buildings within it, and even more than the infrastructure of roads, water, waste, transit and energy; it is U.Life. As Tom Murcott, executive president of Gale International noted: ‘The enabling technologies need to be baked into the masterplan and initial design and development from the get-go, rather than a follow.’18 U.Life puts new technology at the centre of the city, and since 2008 Gale has been working with Cisco to deliver the intelligence to run the smart metropolis.

Reactive Messaging Patterns With the Actor Model: Applications and Integration in Scala and Akka
by Vaughn Vernon
Published 16 Aug 2015

Through the idea of actors he defined a computational model embracing nondeterminism (assuming all communication being asynchronous), which enabled concurrency and, together with the concept of stable addresses to stateful isolated processes, allowed actors to be decoupled in both time and space, supporting distribution and mobility. Today the world has caught up with Hewitt’s visionary thinking; multicore processors, cloud computing, mobile devices, and the Internet of Things are the norm. This has fundamentally changed our industry, and the need for a solid foundation to model concurrent and distributed processes is greater than ever. I believe that the Actor model can provide the firm ground we so desperately need in order to build complex distributed systems that are up for the job of addressing today’s challenge of adhering to the reactive principles of being responsive, resilient, and elastic.

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[Moore’s Law] http://en.wikipedia.org/wiki/Moore’s_law [Network-Partition] http://en.wikipedia.org/wiki/Network_partition [Nitsan Wakart] http://psy-lob-saw.blogspot.com/2014/06/notes-on-false-sharing.html [OCM] http://en.wikipedia.org/wiki/Object-capability_model [POSA1] Buschmann, Frank et al. Pattern-Oriented Software Architecture Volume 1: A System of Patterns. Hoboken, NJ: Wiley, 1996. [ProtoBuf] https://github.com/google/protobuf/ [Reactive Manifesto] www.reactivemanifesto.org/ [Read-Write] readwrite.com/2014/07/10/akka-jonas-boner-concurrency-distributed-computing-internet-of-things [Retlang] https://code.google.com/p/retlang/ [Retlang-CTX] http://www.jroller.com/mrettig/entry/lightweight_concurrency_in_net_similar [Roestenburg] http://doc.akka.io/docs/akka/snapshot/scala/testkit-example.html [sbt-Suereth] Suereth, Josh and Farwell, Matthew. SBT in Action: The simple Scala build tool.

The Charisma Machine: The Life, Death, and Legacy of One Laptop Per Child
by Morgan G. Ames
Published 19 Nov 2019

In 1985, he had cofounded MIT’s Media Lab, which had a mission to “invent the future” as well as a penchant for flashy demonstrations of its “big ideas” for corporate sponsors, whose donations gave them access to all of the lab’s findings.2 In 1992, he became a founding investor in Wired magazine, for which he wrote a column on “Bits and Atoms”—and reasons to transcend the boundary between them—from 1993 to 1998. These columns informed Negroponte’s 1995 book Being Digital, which detailed his vision for on-demand digital consumer content, personalized newsfeeds, and what would later be called the “internet of things.” The relentless utopianism of Being Digital captured the excitement that many felt about the “electronic frontier,” the burgeoning online world of the 1990s.3 It became a best seller and was translated into some forty languages. Negroponte largely shrugged off the sharp criticism his book and columns drew from some scholars, such as legal scholar Cass Sunstein, who has decried the echo chambers of Negroponte’s customized “Daily Me” newsfeed idea for polarizing the US political landscape, and cultural historian Fred Turner, who has linked Negroponte’s digital boosterism to the commodification of “New Communalist” utopianism in the 1970s and beyond.4 A decade later, in January 2005, Negroponte seemed to receive a relatively cool reception in Davos.

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See Information and communications technologies Identity design and, 71, 77 hacker, 163, 226n31 performance and, 175, 179 social imaginary and, 29, 44–46 Ideology, 57 of charismatic technology, 8–10, 197–199 Marxist theory on, 199, 221n30 social imaginary as, 7 I-methodology, 71 Imperialism agency and, 134–135 of constructionism, 195 cultural, open-source software and, 64–65 Global South and, 195–196 linguistic, 161–163 media, 132–135 of OLPC, 132–133, 161, 195 Paraguay Educa and, 195 India, 221n25 hackathon in, 250n12 Hole in the Wall project of, 194 ICT projects in, 181 OLPC in, 244n59 Individualism charismatic technologies and, 191–192 of constructionism, 137–140 cultural change and, 193, 236n67 Papert on, 137–138 in Paraguay Educa, 155–156, 163 scaffolding and, 163 self-taught learner, 64–66, 236n67 yearner and, 30–32, 45 Informal learning hacker culture anti-authority in, 38–40, 44–45, 60–61 in OLPC core principles, 50 Paraguay Educa and, 109–110, 123–124, 127, 133 Information and communications technologies (ICT), 180 Infrastructure as concept, analytical lens, 241n37 electrical, in Paraguay, 86–87 hacker identity and, 163 labor and breakdown of, 92 of Paraguay Educa, translation and, 80–83, 108, 241n37, 241n40 public, disruption of, 189 Innocenti, Bernie, 239n17 Internet Anglocentrism of, 162, 196, 250n21 charisma of, 101 gender in, 43 MOOCs, 187–188 in Paraguay, home use of, 93, 247n5, 248n20 in Paraguay, parent concerns about, 113, 247n8 in Paraguay Educa, 100–101, 244n65 utopianism of, Mosco on, 13 XO laptop connection to, 112, 247n6 Internet of things, 2 Inventing Modern (Lienhard), 231n90 Irani, Lilly, 250n12 Itaipú Dam, Paraguay, 78, 86, 146, 165 Itaipú Technology Park, Paraguay, 165 Jasanoff, Sheila, 7 Jepsen, Mary Lou, 22, 109 Jock imaginary, 41 Juegito (little toy), 112 Kafai, Yasmin, 186 Kalanick, Travis, 2 Kim, Sang-Hyun, 7 Klein, S.

The Equality Machine: Harnessing Digital Technology for a Brighter, More Inclusive Future
by Orly Lobel
Published 17 Oct 2022

,” Washington Post, December 21, 2021, https://www.washingtonpost.com/technology/2021/12/21/mammogram-artificial-intelligence-cancer-prediction. Chapter 6: She Speaks 1. LeVar Burton, Smart House (Burbank, CA: Disney Channel, 1999). 2. Pew Research Center, “The Internet of Things Will Thrive by 2025,” May 2014, 17, https://www.pewresearch.org/internet/wp-content/uploads/sites/9/2014/05/PIP_Internet-of-things_0514142.pdf. 3. Farhad Manjoo, “Which Tech Overlords Can You Live Without?,” New York Times, May 11, 2017. 4. Judith Newman, To Siri with Love: A Mother, Her Autistic Son, and the Kindness of Machines (New York: HarperCollins, 2017), 132. 5.

Ten Arguments for Deleting Your Social Media Accounts Right Now
by Jaron Lanier
Published 28 May 2018

What Ezra Klein says, intoned by Gilbert Gottfried. Plus, personal voicemail messages would be inserted into the queue, just to up your engagement; maybe that would be the only way to even hear your own messages. Oh, and there would be ads mixed in. Your spouse’s voice talking about that new internet-of-things sensor clothing that reports your posture to unknown targeted advertising services. In the middle of a mush of fragments of politics podcasts, a voice would talk about how a politician is running a child sex ring in the basement of a pizza parlor. Armies of trolls and fake trolls would game the system and add enough cruel podcast snippets to the mix that your digest would become indigestible.

The Wake-Up Call: Why the Pandemic Has Exposed the Weakness of the West, and How to Fix It
by John Micklethwait and Adrian Wooldridge
Published 1 Sep 2020

Yet very little of that inventiveness has been applied to America’s public sector. What chance was there of fighting Covid, when around 40 percent of the IT systems at the Department of Health and Human Services are “legacy” ones, no longer supported by their manufacturers?16 Asian governments are stealing a march on America in using the internet of things to monitor smart infrastructure. In Singapore, water pipes report back to the authorities if they spring a leak, while lampposts gather data on temperature, humidity, and traffic flow. Some American states are getting better at communicating with people through mobile phones and apps: but again, Covid underlined how far ahead East Asia is.

The Black Box Society: The Secret Algorithms That Control Money and Information
by Frank Pasquale
Published 17 Nov 2014

One of the main purposes of this book is to raise enough questions about the results presented by leading Internet and fi nance firms so that they do not congeal into this kind of black box. 8. Jack Balkin, “The Constitution in the National Surveillance State,” Minnesota Law Review 93 (2008): 1–25. 9. George Packer, “Amazon and the Perils of Non-disclosure,” The New Yorker, February 12, 2014. 10. Arkady Zaslavsky, “Internet of Things and Ubiquitous Sensing” (Sept. 2013). Computing Now. Available at http://www.computer.org /portal /web /computingnow/archive/september2013. 11. April Dembosky, “Invasion of the Body Hackers,” Financial Times, June 10, 2011. 12. Tal Zarsky, “Transparent Predictions,” Illinois Law Review (2013): 1503–1570. 13.

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Kass, “The Wisdom of Repugnance,” The New Republic, June 1997; Martha C. Nussbaum, Upheavals of Thought: The Intelligence of Emotions (New York: Cambridge University Press, 2001). 105. Bruce Schneier, “Will Giving the Internet Eyes and Ears Mean the End of Privacy?” The Guardian, May 16, 2013, http://www.guardian.co.uk /technology/2013/may/16/internet-of-things-privacy-google. 106. Danielle Keats Citron, “Technological Due Process,” Washington University Law Review 85 (2008): 1260–1263; Danielle Keats Citron, “Open Code Governance,” University of Chicago Legal Forum (2008): 363–368. 107. Peck, “They’re Watching You at Work.” 108. Lior Jacob Strahilevitz, “Less Regulation, More Reputation,” in The Reputation Society: How Online Opinions Are Reshaping the Offline World, ed.

Zest: How to Squeeze the Max Out of Life
by Andy Cope , Gavin Oattes and Will Hussey
Published 19 Jul 2019

Blank spaces, begging to be filled in with thoughts, with photos, with stories. Often with nothing. At least, nothing important. Technology, asking you, prodding you, soliciting talk. We’re a species that’s googling, ogling and goggling. Googling ourselves, checking we’re included in the Internet of things. Ogling at porn. Seduced by cat videos. Clodhopping through a load of clickbait to ogle at what your favourite 80s TV star looks like now. In Britain there’s a TV series called Gogglebox which I’m led to believe is where we, the intelligent viewers, get to watch people watching television.

Lean Analytics: Use Data to Build a Better Startup Faster
by Alistair Croll and Benjamin Yoskovitz
Published 1 Mar 2013

Copy and rebuild Another approach is to take a consumer idea and make it enterprise-ready. Yammer did this when it rebuilt Facebook’s status update model and copied Facebook’s feed interface. Disrupt an existing problem There are plenty of disruptions that happen to an industry, from the advent of mobile data, to the Internet of Things,[142] to the adoption of the fax machine, to location-aware applications. Any of them can offer a big enough advantage to make it worth discarding the old way of doing things. Taleo did this to the traditional business of human resources management. Inspiration Many of the enterprise startups we’ve talked to began with a basic idea, often hatched within the ecosystem they wanted to disrupt.

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[138] http://bhorowitz.com/2010/11/15/meet-the-new-enterprise-customer-he%E2%80%99s-a-lot-like-the-old-enterprise-customer/ [139] http://techcrunch.com/2012/10/05/building-for-the-enterprise-the-zero-overhead-principle-2/ [140] http://spectrum.ieee.org/computing/software/why-software-fails/0 [141] http://www.zdnet.com/blog/projectfailures/cio-analysis-why-37-percent-of-projects-fail/12565 [142] http://en.wikipedia.org/wiki/Internet_of_Things [143] Full disclosure: Coradiant was co-founded by Alistair Croll and Eric Packman in 1997 as Networkshop; the name was changed to Coradiant in mid-2000. [144] http://www.infosecnews.org/hypermail/9905/1667.html [145] http://hbswk.hbs.edu/item/6496.html [146] Robert S. Kaplan and V.G.

The Age of Cryptocurrency: How Bitcoin and Digital Money Are Challenging the Global Economic Order
by Paul Vigna and Michael J. Casey
Published 27 Jan 2015

When paired with “smart property”—where deeds, titles, and other certifications of ownership are put in digital form to be acted upon by software—these contracts allow the automatic transfer of ownership of a physical asset such as a house or a car, or an intangible asset, such as a patent. Similarly, the software initiates the transfer when contractual obligations are met. With companies now busily putting bar codes, QR codes, microchips, and Bluetooth antennae on just about every gadget and piece of merchandise, the emerging “Internet of Things” should make it possible to transfer ownership in many kinds of physical property in this manner. One creative solution applies to cars purchased on credit. Right now, if an automobile owner misses his or her payments, it’s laborious and costly for the finance company to reclaim both the title to and physical possession of the car, involving lawyers, collection agencies, and, in worst cases, repo men.

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Even if cryptocurrencies seem tailor-made for the current age, with the sweeping decentralizing shifts discussed above, their prime competitors in the payments industry are coming up with alternatives that might just keep the general public from shifting to the crypto model. Indeed, in the era of the Internet of Things, technologies that leverage the old sovereign money system are finding various ways to impress customers with improvements to the payment experience. The smartphone, the preferred tool of mobile bitcoin exchange, is also being harnessed by a host of finance tech companies seeking to revolutionize how we make payments.

Whole Earth: The Many Lives of Stewart Brand
by John Markoff
Published 22 Mar 2022

Just as Brand had found his way to the early wellspring of personal computing in the late 1960s and early 1970s at SAIL, PARC, and Engelbart’s Augmentation Research Center, now he found himself surrounded by the technologists who were building the digital world that would become both the World Wide Web and the “Internet of Things.” One key idea that was deeply embedded in the Media Lab was the notion of personalized media. What Negroponte called the Daily Me, the concept that each person’s newsfeed would be personalized, was seen as an inevitable future with little understanding of the darker reality that would become known as filter bubbles several decades later.

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Spencer, 216 Brown, Jerry, 98, 225–27, 230, 231, 348 Brussell, Mae, 215 Burning Man festival, 109, 328 Burrows, George Lord (great-grandfather), 8–9 Burrows, Lorenzo (great-great-grandfather), 7 Butler, Katy, 247–48 butterfly effect, 361 C Caffe Trieste, 48, 74 California, University of, at Berkeley, 25, 135, 302 California Museum of Science and Industry, 91 California Water Atlas, 227 Callahan, Michael, 94, 106–7, 137 Calthorpe, Peter, 129, 246, 256, 302, 305, 306, 307, 318, 341 Cannery Row (Steinbeck), 18, 46, 243 Cape Breton Island, Canada, Jennings and SB’s home on, 199–200, 207–9, 218, 234–35 Capra, Frank, 28 Capra, Fritjof, 295, 297 Carlston, Doug, 271, 325, 328–29 Carroll, Jon, 357 Cassady, Neal, 69, 126, 131–32, 143 Center for Advanced Study in the Behavioral Sciences, 45–46, 208 Chappell, Walter, 116, 119 Chernobyl nuclear disaster, 282 Chicago 8, trial of, 177, 188 Chippewas, 7 choice, freedom of, 42–43 Church, George, 360 Churchill, Winston, 291 CIA, 202, 298 City Lights Books, 37, 50, 74 Clear Creek, 205 climate change, 338–39 SB and, 4, 338, 339, 342, 347, 348–50, 357, 361 Schwartz and, 338–40, 342 Clock Library, 314, 325, 326, 327–28, 329, 362 Coate, John, 309 coevolution, 46, 217, 219, 222, 223, 232 CoEvolution Quarterly, 6, 85, 175, 208, 221–22, 227, 229, 230, 236, 240–41, 251, 254, 255–56, 257, 289 Black Panther–edited issues of, 229 Butler’s article on Baker scandal in, 247–48 demise of, 261 Gaia hypothesis story in, 230, 349 Kelly’s cover article in, 254 Kleiner’s cyberspace article in, 240 as money-losing venture, 233, 240 O’Neill’s space colonies story in, 231–32 provocative viewpoint of, 228–29 SB’s idiosyncratic editorial style at, 229, 249 SB’s separation from, 271 Schweickart’s “No Frames, No Boundaries” reprinted in, 225 Co-Existence Bagel Shop, 37, 48, 74 Collyns, Napier, 277, 295 Commoner, Barry, 206 commune movement, 139–40, 154, 177 communications technology, 133, 241, 282, 290, 298 Community Memory, 265 complex systems, learning by, 274, 277, 279–80, 284, 289 computer conferencing, 151, 240, 251–52, 263, 264, 265, 266 computer networks, see cyberspace; internet computers, computing: counterculture and, 185–86 Engelbart’s “bootstrapping” vision of, 151, 153 hobbyists in, 147, 158, 196, 198, 213, 230, 266–67 personal, see personal computers predicted exponential increase in powers of, 152–53 SB’s growing interest in, 145–46, 168, 266, 280 conservationism, of SB, 4, 340 Contact Is the Only Love (Stern), 93, 136 counterculture, 2, 10, 71, 75, 143, 146, 176, 177–78, 202, 216, 228 computing community and, 185–86 demise of, 181, 241 political-psychedelic divide in, 145, 152 SB’s negative reassessment of, 297 Trips Festival as catalyst for, 127–28, 130 Whole Earth Catalog in, 173–74 Counterculture Green (Kirk), 135 Coyote, Peter, 127, 225, 226, 287–88, 297, 358, 361 Creative Initiative Foundation, 41 Creative Philanthropy seminar, SB’s organizing of, 250 creativity, LSD and, 72, 76–77 Crooks family, 65–66 Crosby, David, 189–90 Crumb, R., 215, 228 Curwen, Darcy, 22, 23 cybernetics, 2, 4, 169, 208, 213, 216–17, 226, 273 Cybernetics (Wiener), 169, 226 cyberspace, 54, 84, 212, 240, 254–55, 258, 261, 279, 298–99 anonymity and pseudonymity in, 266 dangers of, 293, 315 dystopian aspects of, 308, 310–11 gold rush mentality and, 293, 323 impact of, 295–96 SB and, 4, 251–52, 282, 293 see also internet D Dalton, Richard, 261 Daumal, René, 186 Deadheads, 265–66 Defense Department, US, 315, 338 de Geus, Arie, 274, 285, 289 Delattre, Pierre, 47, 74 deserts, SB’s attraction to, 108–10, 114 Desert Solitaire (Abbey), 181 desktop publishing, 164–65 Detroit Free Press, 10, 172–73 Dick Cavett Show, SB’s appearance on, 192 Diehl, Digby, 200 Diggers, 206 Direct Medical Knowledge, 326, 333, 342 DiRuscio, Jim, 324–25 Divine Right’s Trip (Norman), 193, 223 DNA Direct, 343 Dome Cookbook (Baer), 162, 180 Doors of Perception, The (Huxley), 28 dot-com bubble, 296, 326, 333–34, 348 Doubleday, 253, 256, 257 Drop City (commune), 154, 162 “Drugs and the Arts” panel (SUNY Buffalo), 177 Duffy, Frank, 319 Durkee, Aurora, 180 Durkee, Barbara and Stephen, 61, 105, 119–20, 137, 139, 162, 177, 179, 180, 186, 229 Garnerville studio of, 60, 66, 69, 105, 106–7 SB’s friendship with, 51–52, 59–60, 66, 67, 133 in USCO, 106–7 Dvorak, John, 259, 260 Dymax, 147–48 Dynabook, 212 Dyson, Esther, 315, 325 E Eames, Charles, 44–45, 96, 113 Earth, viewed from space: SB’s campaign for photograph of, 134–35, 164 SB’s revelatory vision of, 1, 6, 362 Earth Day (1970), 182, 190, 364 Edson, Joanna, 75–76 Edson, John, 14, 18–20, 38–39, 75 education: intersection of technology and, 144, 145 see also alternative education movement; learning, act of Education Automation (Fuller), 169 Education Innovations Faire, 149 Ehrlich, Paul, 46, 177, 341, 360–61, 364 SB as influenced by, 28, 45, 47, 187, 188, 206, 222–23 EIES (Electronic Information Exchange System), 240, 251–52, 264, 266 Electric Kool-Aid Acid Test, The (Wolfe), 5, 88, 111, 121, 125, 170, 181 “Electric Kool-Aid Management Consultant, The” (Fortune profile of SB), 297 Electronic Frontier Foundation, 325 endangered species, 2, 360–61 Engelbart, Douglas, 83, 138, 146, 151–53, 158, 186–87, 230, 292, 361 “Mother of All Demos” by, 171–72 oNLine System of, 151, 156, 197, 212 SB influenced by, 150, 153, 185, 364 English, Bill, 160, 171, 185, 203, 211 English, Roberta, 160 Eno, Brian, 305, 306, 314, 319, 320, 325, 327, 336, 342, 353, 354 “Environmental Heresies” (Brand), 341–42 environmental movement, 2, 71, 159, 204 activist approach to, 181–82, 187, 188, 201–2, 297, 347 conservation vs. preservation in, 340 SB’s break with, 246, 336, 341, 347 SB’s role in, 4, 9–10, 180, 181–82, 201, 202, 204–7, 284, 347 Esalen Institute, 71–72, 84, 138, 176, 185 Esquire, 88, 146, 183, 247, 250 Essential Whole Earth Catalog, 286 Evans, Dave, 146, 156, 180, 185–87, 212 Exploratorium, 194–98 extinct species, revival of, 359–60 F Fadiman, Jeff, 38, 44, 59, 62–63, 64 Fadiman, Jim, 72–73, 77–78, 80, 84, 89, 97, 98, 101 Fall Joint Computer Conference (San Francisco; 1968), 171–72 Fano, Robert, 46, 273 Fariña, Mimi, 141, 237 Farm (commune), 257 Ferlinghetti, Lawrence, 50, 71 Field, Eric, 44, 53 Fillmore Auditorium (San Francisco), 125–26, 128, 130 filter bubbles, 279, 308 Fluegelman, Andrew, 220, 221–22, 269 Foer, Franklin, 5 Foreign Policy, 356 Fort Benning, SB at, 53–58 Fort Dix, SB at, 58–63, 64, 65–68 Fortune, 297, 339 “Four Changes” (Snyder), 187 Francis, Sharon, 105, 112 Frank, Delbert, 86–87 Frank, Robert, 179, 188, 199–200, 218 Fraunhofer, Joseph Ritter von, 108 Free Speech Movement, 135, 175 From Bauhaus to Our House (Wolfe), 304–5 “Fruits of a Scholar’s Paradise” (Brand; unpublished), 45–46, 208 Fukushima nuclear disaster, 355–58 Fuller, Buckminster, 134, 147, 162, 169, 175, 176, 217 SB influenced by, 132, 138, 146, 150, 168–69, 222, 243–44, 363–64 Fulton, Katherine, 318 futurists, 262, 273 SB as, 258–59, 280, 323 G Gaia hypothesis, 230, 349 games, SB’s interest in, 84, 120, 129–30, 149, 210, 211, 217, 219–21, 236–37 Gandhi, Mohandas K., 53 Garcia, Jerry, 128, 158 Garnerville, N.Y., Durkee/USCO studio at, 60, 66, 69, 105, 106–7, 136, 154 Gaskin, Stephen, 257 Gause, Gregory, 46 GBN, see Global Business Network genetic engineering, 341, 344, 360–61 geodesic domes, 176, 217 Georgia-Pacific, 9, 29 Gerbode Valley, Calif., 219–21, 236–37 Getty Museum, 329 Gibbons, Euell, 138 Gibson, William, 262, 294, 315 Gilman, Nils, 297–98 Gilmore, John, 325, 352 Ginsberg, Allen, 33–34, 50, 69, 77, 94, 177, 237 Global Business Network (GBN), 291, 295–300, 311, 313, 335, 340 Brand and Schwartz as co-founders of, 291–92 climate change scenario of, 338–39 SB consulting position at, 296, 298, 305, 314, 315–16, 323–24, 343, 354 globalization, 295–96, 346 global warming, see climate change GMO foods, 2, 344, 347, 357 Godwin, Mike, 308 Golden Gate National Recreation Area, 237, 360 Gone (Kirkland), 359 Gottlieb, Lou, 140 government, SB’s evolving view of, 166, 227, 348 Graham, Bill, 124, 125, 128, 130–31, 143 Grand Canyon, SB’s visit to, 19–20 Grateful Dead, 24, 123, 125, 126, 130, 131, 141, 158, 160, 189, 265–66 Great Basin National Park, 329–30 “Great Bus Race, The,” SB at, 181 Gregorian, Vartan, 27 Griffin, Susan, 295, 297 Griffith, Saul, 349–50 Gross, Cathleen, 286, 289 Grossman, Henry, 63 H hackers, hacker culture, 25, 84, 147–48, 150, 261, 267, 268–69, 273, 293, 294 SB’s Rolling Stone article on, 46, 211–13, 217, 250 Hackers: Heroes of the Computer Revolution (Levy), 266–67, 268, 270 Hackers Conference, 266–70, 326 Haight-Ashbury (San Francisco neighborhood), 74, 75, 128 Halpern, Sue, 241–42 Harman, Willis, 41, 42, 72, 73, 77, 273 Harner, Michael, 101, 118, 129 Harper’s Magazine, 46, 213, 228 SB’s Bateson profile in, 216–17 Whole Earth Epilog proposal of, 218, 219, 222 Harris, David, 149, 162, 299 Harvey, Brian, 268–69 Hawken, Paul, 247–48, 250, 281, 286, 290, 299, 332, 333, 334 Hayden Planetarium, 91, 92, 105 Hayes, Denis, 351 Healy, Mary Jean, 205, 206, 207 Heard, Gerald, 41–42, 84 Hells Angels, 120 Herbert, Anne, 230–31, 241, 255 Hershey, Hal, 183 Hertsgaard, Mark, 357 Hertzfeld, Andy, 267 Hewlett, William, 156 Hewlett-Packard, 147 Hickel, Walter, 206 Higgins Lake, Mich., Brand family camp at, 7, 8, 9, 10–11, 21, 30, 209, 289–90, 326, 327 Hillis, Danny, 289, 301, 305, 315, 336 Long Now Clock and, 313–14, 316–17, 325–26, 327, 328, 329, 330, 333, 362, 363 Thinking Machines founded by, 279–80, 288 Hippies, Indians, and the Fight for Red Power (Smith), 118 Hoagland, Edward, 201 Hoffer, Eric, 32 Hoffman, Abbie, 177–78, 214, 299 Hog Farm commune, 159, 181, 186, 188, 202, 205, 206, 220 Homebrew Computer Club, 147, 158, 198, 230, 266–67 Homo Ludens (Huizinga), 220 Hopcroft, David, 275 Hopi Indians, 100, 139, 205 Horvitz, Robert, 6 House Committee on Education and Labor, SB at hearing of, 190–91 Household Earth, see Life Forum How Buildings Learn (SB’s UC Berkeley seminar), 302 How Buildings Learn (BBC documentary), 320 How Buildings Learn (Brand), 291, 300–301, 304–7, 310, 312, 317–19, 323, 324, 331 How to Be Rich Well (SB book proposal), 344–46 Hubbard, Al, 42, 77, 273 Huerfano Valley, Colo., 139–40 Huizinga, Johan, 220 human potential movement, 71, 73, 84 humans: freedom of choice of, 42–43 as morally responsible for care of natural world, 42, 347, 349, 360, 361 SB’s speculations about fate of, 38–39 Human Use of Human Beings, The (Wiener), 160 Hunger Show (Life-Raft Earth), 187–88, 189, 203, 263 Huxley, Aldous, 28, 33, 41, 72, 144, 226 hypertext, concept of, 172, 230, 292, 293 I IBM, 91, 92, 96, 108, 211 I Ching, 89–90, 117, 153, 197, 253 Idaho, University of, 21 identity, fake, cyberspace and, 266 II Cybernetic Frontiers (Brand), 46, 213, 217, 221 Iktomi (Ivan Drift), 96–97 Illich, Ivan, 196 Independent, 353 information, personalization of, 279 information sharing, 180 information technology, 299–300, 315 information theory, 273 “Information wants to be free,” 270, 299, 301 information warfare, 315 In Our Time (Hemingway), 11 Institute for International Relations (IIR), 27, 34, 35, 37 Institute for the Future, 315 intelligence augmentation (IA), 83, 185, 187 International Federation of Internal Freedom, 89 International Foundation for Advanced Study, LSD experiments at, 42, 72, 73, 76–82, 273 internet, 146, 151, 279, 293, 314, 316, 326 ARPANET as forerunner of, 212 impact of, 295–96, 323 libertarianism and, 5, 348 see also cyberspace Internet Archive, 330, 332 Internet of Things, 279 Interval Research Corporation, 321–23 “Is Environmentalism Dead?” (Shellenberger and Nordhaus), 340 Italy, SB’s book tour in, 353–54 J Jacobs, Jane, 305, 317–18, 344 Japan, SB’s 1988 trip to, 292 Jennings, Lois, 115, 119, 124, 145, 159, 164, 178, 185, 195, 218 breakup and divorce of SB and, 194, 209–10, 214, 216 at Cape Breton with SB, 199–200, 208–9 as driving force behind Whole Food Catalog and Truck Store, 160, 170, 182–83 in living-off-the-land experiments with SB, 138–39 marriage of SB and, 144, 152, 163, 170, 189, 193, 194, 202, 203, 205, 208, 249 pregnancy and miscarriage of, 133 SB’s first meeting with, 111–12 and SB’s whole Earth photo campaign, 134–35 in trips with SB, 135–36, 138–39 Wolfe’s portrayal of, 125 Jicarilla Apache Indians, 100 Jobs, Steve, 3–4, 25, 71, 173, 252, 254, 263, 355 Johnson, Alia, 234–35 Johnson, Huey, 203, 220, 226, 271, 348 Johnson, Lyndon, 106, 111 Johnson, Noah, see Brand, Noah (son) Josephy, Alvin M., Jr., 107, 115, 118 journalism, by Brand: “Both Sides of the Necessary Paradox,” 46, 216–17 contemplated as career, 23, 35, 44, 55, 57 “The Native American Church Meeting,” 109 “SPACEWAR,” 211–13, 217, 250, 321 see also CoEvolution Quarterly; photojournalism K Kaehler, Ted, 267–68 Kahle, Brewster, 330, 332 Kahn, Herman, 152–53, 273, 285 Kahn, Lloyd, 129, 175–76, 179, 180, 186, 223, 305 Kane, Joe, 247, 258 Kaphan, Shel, 148, 328, 331 Kapor, Mitchell, 325, 331, 335 Kay, Alan, 212, 213, 277–78, 281, 289 Kaypro II computer, 250–51 Kazantzakis, Nikos, 33, 39, 48 Kelly, Kevin, 261, 270, 286, 300, 310, 314, 322, 325, 335, 336, 343 Christian faith of, 255–56 Hackers Conference and, 266–67, 268 Quarterly article of, 254 as Quarterly editor, 255–56 at WELL, 276 Whole Earth Catalog’s impact on, 253–54 at Whole Earth Review, 276 Kennedy, Roger, 329–30 Kent State killings, 190 Kenya, Phelan and SB’s trip to, 271–76 Kepler, Roy, 158 Kerouac, Jack, 69, 71, 126 Kesey, Ken, 2, 9, 24, 30, 87–88, 97–98, 124, 148, 156, 177, 181, 190, 212–13, 268 antiwar movement criticized by, 121, 347 La Honda home of, 88, 120, 128 legal problems of, 128, 131, 141, 143 LSD and, 88, 122–23 psychedelics renounced by, 143 see also Merry Pranksters King, Peter and Fox, 37, 50, 74, 75, 81 King Must Die, The (Renault), 52 Kirk, Andrew G., 135, 180, 340–41 Kirkland, Isabella, 234, 242–43, 359 Klee, Helmi, 61 Kleiner, Art, 239, 240, 241, 251, 255, 257, 261, 264, 294 Koestler, Arthur, 92, 219 Krassner, Paul, 121, 136, 177, 181, 214–16, 223 Kurzweil, Ray, 335 L LaDuke, Winona, 357 La Luz, N.Mex., 178–80 Lama (commune), 139, 154, 186 Lane, Bill, 161 Lanier, Jaron, 294, 325 Last Supplement to the Whole Earth Catalog, 215 Last Whole Earth Catalog, The, 161, 169, 192, 223, 233 financial success of, 193, 210, 213 National Book Award given to, 200 Latour, Bruno, 360 Laws of Form, The (Brown), 216 Leach, Edmund, 165 learning, act of: in complex systems, 274, 277, 279–80, 284, 289 SB’s enduring focus on, 137, 144, 153, 166, 191, 274, 277, 279–80, 284, 288–89, 290, 301 see also alternative education movement; education Learning Conferences, 288–89, 291, 294, 298 Leary, Timothy, 50, 72, 79, 89, 106, 178 LeBrun, Marc, 147–48, 230 Leibovitz, Annie, 212 Leonard, George, 211, 220 Leopold, Aldo, 349 Lesh, Phil, 123 Let Us Now Praise Famous Men (Agee and Evans), 86 Levy, Steven, 266–67, 270 libertarianism, 5, 42, 53, 67, 348 SB and, 4, 42, 53, 67, 217, 227, 348, 352 libraries, SB’s fascination with, 312–13 Libre (artists’ commune), 139–40 Librium, SB’s use of, 205, 213 Liddle, David, 321–22, 324 Life Forum, 201–7, 347 Life-Raft Earth (documentary film), 179, 194, 347 Lindisfarne Association, 224 Little Prince, The (Saint-Exupéry), 43, 119 L.L.

The Future We Choose: Surviving the Climate Crisis
by Christiana Figueres and Tom Rivett-Carnac
Published 25 Feb 2020

If we make it through the climate crisis and arrive on the other side with humanity and the planet intact, it will be largely because we have learned to live well with technology. Artificial intelligence (AI) supported by sensors (to gather data) and robotics (to automate physical activities) together with the network of smart devices known as the “internet of things” have huge potential to become our greatest allies in the fight for survival.74 But these very same technologies are also the ones that could destroy that better future. For example, autonomous self-driving electric vehicles could eliminate the need for unnecessary private ownership of vehicles, but on the downside, they could also allow unscrupulous governing bodies to track and control the movements of every citizen.

Becoming Data Literate: Building a great business, culture and leadership through data and analytics
by David Reed
Published 31 Aug 2021

A new marketplace is also opening up around non-personal data types which are therefore not subject to regulation (provided they cannot be used to identify an individual such as through a specific device at a given time and location), but which also have potentially significant value to users. Internet of Things’ data is one example where very high volumes are being generated from sensors and devices which may have value beyond the specific needs of those machines, such as to drive services in smart cities. Already, data exchanges exist within platforms like AWS where real-time data can be licensed and embed via APIs into external systems.

Attack of the 50 Foot Blockchain: Bitcoin, Blockchain, Ethereum & Smart Contracts
by David Gerard
Published 23 Jul 2017

This particularly picked up around late 2014,364 when the Bitcoin price had cratered. The value proposition was that Bitcoin was the most secure chain as it had the most hashing power, so everyone wanting a blockchain should use that one. However, the limit of 7 transactions per second worldwide, blocks being too full for transactions to get through anyway, and that your Internet of Things light bulb was profoundly unlikely to add enough flash memory for 120 gigabytes of SatoshiDice gambling spam were all a bit too obvious to the prospective customers. But by late 2015, “Blockchain” hype had taken on a life of its own as a business buzzword. If in a manner somewhat uncomfortable with its Bitcoin origins.

Machine Translation
by Thierry Poibeau
Published 14 Sep 2017

Paarsch Cloud Computing, Nayan Ruparelia Computing: A Concise History, Paul E. Ceruzzi The Conscious Mind, Zoltan L. Torey Crowdsourcing, Daren C. Brabham Free Will, Mark Balaguer Information and Society, Michael Buckland Information and the Modern Corporation, James W. Cortada Intellectual Property Strategy, John Palfrey The Internet of Things, Samuel Greengard Machine Learning: The New AI, Ethem Alpaydin Machine Translation, Thierry Poibeau Memes in Digital Culture, Limor Shifman Metadata, Jeffrey Pomerantz The Mind–Body Problem, Jonathan Westphal MOOCs, Jonathan Haber Neuroplasticity, Moheb Costandi Open Access, Peter Suber Paradox, Margaret Cuonzo Robots, John Jordan Self-Tracking, Gina Neff and Dawn Nafus Sustainability, Kent E.

ZeroMQ
by Pieter Hintjens
Published 12 Mar 2013

By now you may have started to build your own products using the techniques I’ve explained, as well as others you’ve figured out yourself. You will start to face questions about how to make these products work in the real world. But what is that “real world”? I’ll argue that it is becoming a world of ever-increasing numbers of moving pieces. Some people use the phrase “the Internet of Things,” suggesting that we’ll soon see a new category of devices that are more numerous, but also more stupid than our current smartphones, tablets, laptops, and servers. However, I don’t think the data points this way at all. Yes, there are more and more devices, but they’re not stupid at all. They’re smart and powerful, and getting more so all the time.

…

writing messages to hard disk, Disconnected Reliability (Titanic Pattern)–Disconnected Reliability (Titanic Pattern) Harmony pattern, True Peer Connectivity (Harmony Pattern)–True Peer Connectivity (Harmony Pattern) heartbeating, The Asynchronous Client/Server Pattern, Prototyping the State Flow, Robust Reliable Queuing (Paranoid Pirate Pattern), Robust Reliable Queuing (Paranoid Pirate Pattern), Robust Reliable Queuing (Paranoid Pirate Pattern), Robust Reliable Queuing (Paranoid Pirate Pattern), Robust Reliable Queuing (Paranoid Pirate Pattern), Robust Reliable Queuing (Paranoid Pirate Pattern), Robust Reliable Queuing (Paranoid Pirate Pattern), Robust Reliable Queuing (Paranoid Pirate Pattern), Heartbeating–Heartbeating for Paranoid Pirate, Shrugging It Off–Shrugging It Off, One-Way Heartbeats, Ping-Pong Heartbeats–Heartbeating for Paranoid Pirate, Heartbeating for Paranoid Pirate–Heartbeating for Paranoid Pirate, Detecting Disappearances–Detecting Disappearances in Zyre project, Detecting Disappearances–Detecting Disappearances not using, Shrugging It Off–Shrugging It Off one-way heartbeats, One-Way Heartbeats in Paranoid Pirate pattern, Robust Reliable Queuing (Paranoid Pirate Pattern), Robust Reliable Queuing (Paranoid Pirate Pattern), Robust Reliable Queuing (Paranoid Pirate Pattern), Robust Reliable Queuing (Paranoid Pirate Pattern), Robust Reliable Queuing (Paranoid Pirate Pattern), Robust Reliable Queuing (Paranoid Pirate Pattern), Robust Reliable Queuing (Paranoid Pirate Pattern), Robust Reliable Queuing (Paranoid Pirate Pattern), Heartbeating for Paranoid Pirate–Heartbeating for Paranoid Pirate ping-pong heartbeats, Ping-Pong Heartbeats–Heartbeating for Paranoid Pirate Hello World example, Ask and Ye Shall Receive–Ask and Ye Shall Receive high-level API for ZeroMQ, A High-Level API for ØMQ–The CZMQ High-Level API high-level patterns, High-Level Messaging Patterns high-water mark (HWM), High-Water Marks–High-Water Marks Historian role, The Historian HTTP protocol, using ZeroMQ for, ØMQ Is Not a Neutral Carrier–ØMQ Is Not a Neutral Carrier HWM, High-Water Marks (see high-water mark) I I/O threads, I/O Threads IANA (Internet Assigned Numbers Authority), Getting an Official Port Number idempotent services, Idempotent Services–Idempotent Services identity, Identities and Addresses–Identities and Addresses iMatix, ØMQ in a Hundred Words, Architecture of the ØMQ Community innovation, models for, The Tale of Two Bridges–Simplicity-Oriented Design inproc (inter-thread) transport, Unicast Transports, Unicast Transports, Multithreading with ØMQ, Signaling Between Threads (PAIR Sockets)–Signaling Between Threads (PAIR Sockets), High-Water Marks binding order requirement for, Unicast Transports high-water mark with, High-Water Marks Inter-Broker Routing example, Worked Example: Inter-Broker Routing–Putting It All Together, Worked Example: Inter-Broker Routing–Establishing the Details, Architecture of a Single Cluster–Scaling to Multiple Clusters, Scaling to Multiple Clusters–Federation Versus Peering, The Naming Ceremony–The Naming Ceremony, The Naming Ceremony, Prototyping the State Flow–Prototyping the State Flow, Prototyping the Local and Cloud Flows–Prototyping the Local and Cloud Flows, Prototyping the Local and Cloud Flows–Prototyping the Local and Cloud Flows, Putting It All Together–Putting It All Together, Putting It All Together brokers, interconnecting, Scaling to Multiple Clusters–Federation Versus Peering cloud flow for, Prototyping the Local and Cloud Flows–Prototyping the Local and Cloud Flows clusters of workers and clients for, Architecture of a Single Cluster–Scaling to Multiple Clusters final code for, Putting It All Together–Putting It All Together ipc transport for, The Naming Ceremony limitations of, Putting It All Together local flow for, Prototyping the Local and Cloud Flows–Prototyping the Local and Cloud Flows requirements for, Worked Example: Inter-Broker Routing–Establishing the Details sockets, naming, The Naming Ceremony–The Naming Ceremony state flow for, Prototyping the State Flow–Prototyping the State Flow inter-process transport, Unicast Transports (see ipc transport) inter-thread transport, Unicast Transports (see inproc transport) intermediation, Intermediaries and Proxies, The Dynamic Discovery Problem–The Dynamic Discovery Problem, Shared Queue (DEALER and ROUTER Sockets)–ØMQ’s Built-in Proxy Function, ØMQ’s Built-in Proxy Function–ØMQ’s Built-in Proxy Function for publish-subscribe pattern, The Dynamic Discovery Problem–The Dynamic Discovery Problem for request-reply pattern, Shared Queue (DEALER and ROUTER Sockets)–ØMQ’s Built-in Proxy Function zmq_proxy() function for, ØMQ’s Built-in Proxy Function–ØMQ’s Built-in Proxy Function Internet Assigned Numbers Authority (IANA), Getting an Official Port Number Internet of Things, A Framework for Distributed Computing interrupt signals, handling, Handling Interrupt Signals–Handling Interrupt Signals ipc (inter-process) transport, Plugging Sockets into the Topology, Unicast Transports, The Naming Ceremony binding to same endpoint twice, Plugging Sockets into the Topology for Inter-Broker Routing, The Naming Ceremony J Jakl, Michael (contributor), Michael Jakl’s Story JeroMQ implementation, Architecture of the ØMQ Community JSON, Serialization Languages L last value caching (LVC), Last Value Caching–Last Value Caching late (slow) joiners, Getting the Message Out, Divide and Conquer, Representing State as Key-Value Pairs, Getting an Out-of-Band Snapshot, Recovery and Late Joiners–Recovery and Late Joiners, More About UDP, The Zyre Tester with Clone pattern, Representing State as Key-Value Pairs, Getting an Out-of-Band Snapshot with FileMQ project, Recovery and Late Joiners–Recovery and Late Joiners with Harmony pattern, The Zyre Tester with pipeline pattern, Divide and Conquer with publish-subscribe pattern, Getting the Message Out TCP for, More About UDP Laughing Clown role, The Laughing Clown Laxy Pirate pattern, Client-Side Reliability (Lazy Pirate Pattern)–Client-Side Reliability (Lazy Pirate Pattern) Lazy Perfectionist role, The Lazy Perfectionist van Leeuwen, Tom (contributor), Tom van Leeuwen’s Story LGPL license, Architecture of the ØMQ Community, Licensing for examples in this book, Licensing for ZeroMQ, Architecture of the ØMQ Community Libero, State Machines–State Machines libzmq library, Upgrading from ØMQ v2.2 to ØMQ v3.2, Architecture of the ØMQ Community, Architecture of the ØMQ Community, Architecture of the ØMQ Community bindings for, Architecture of the ØMQ Community reimplementations of, Architecture of the ØMQ Community upgrading to version 3.2, Upgrading from ØMQ v2.2 to ØMQ v3.2 licensing, Architecture of the ØMQ Community, The Contract–The Contract, The Contract–The Contract, Care and Feeding, Care and Feeding, Licensing and Ownership, Why Use the GPLv3 for Public Specifications?

The Mesh: Why the Future of Business Is Sharing
by Lisa Gansky
Published 14 Oct 2010

Well over a billion people regularly use the Internet, which a Harvard business professor estimates has a $1.4 trillion economic impact annually in the United States alone. The network increasingly connects our homes, cars, and other devices, and they are increasingly connected to each other. (IBM recently introduced a kit that enables developers to use wireless sensors to connect anything to the so-called Internet of things.) And our demands are growing. Cisco estimates traffic over the Internet will exceed 667 exabytes by 2013. That’s roughly 667 billion gigabytes and equates to a quintupling of traffic from 2009 to 2013. Cisco predicts that one trillion devices will be connected to the Internet by that time.

The Retreat of Western Liberalism
by Edward Luce
Published 20 Apr 2017

Doubtless they are talking up their own book. But they are right to worry that the temptation to strike by stealth is irresistible on a battlefield in which there are no rules of engagement. Nor are there any limits to the battlefield. ‘[Because] of the seamless worldwide network, the packets, and the Internet of Things, cyber war [will] involve not just soldiers, sailors, and pilots but, inexorably, the rest of us,’ says Fred Kaplan, author of Dark Territory: The Secret History of Cyber War. ‘When cyberspace is everywhere, cyber war can seep through every digital pore.’ The good news about cyber warfare is that it can never rival the damage caused by nuclear weapons.

The World According to Physics
by Jim Al-Khalili
Published 10 Mar 2020

They will by no means replace current computers at all tasks but will instead be very well suited to solving particular mathematical problems. We will continue to use the ever-increasing power and processing speed of classical computers to run our daily lives, especially as we advance on a number of fronts with AI, Cloud technologies, and the Internet of Things (the idea that many devices in our homes and workplaces will be connected and talking to each other). And classical computers will also continue to process the ever-increasing mountains of data we have. There are problems, however, that even the most powerful classical computers of tomorrow will not be able to solve.

Seriously Curious: The Facts and Figures That Turn Our World Upside Down
by Tom Standage
Published 27 Nov 2018

One example would be real-time virtual- or augmented-reality streaming. At the Olympics, for example, many contestants were followed by 360-degree video cameras. At special venues sports fans could don virtual-reality goggles to put themselves right into the action. 5G is also supposed to become the connective tissue for the internet of things, interconnecting everything from smartphones and wireless sensors to industrial robots and self-driving cars. This will be made possible by a technique called “network slicing”, which allows operators to create bespoke networks that give each set of devices exactly the kind of connectivity they need to job a particular job.

The Currency Cold War: Cash and Cryptography, Hash Rates and Hegemony
by David G. W. Birch
Published 14 Apr 2020

In the age of Donald Trump, these tensions have grown more acute as the United States has pursued a more mercantilist, America-first trade policy, and as the president’s persistent criticism of the Fed’s monetary policy has raised questions about its independence. The second trend pointing to a monetary paradigm shift is technological in nature. The post-global financial crisis era has also seen an explosion in innovation within the world of money. Spurred by mobile phones and the Internet of Things, artificial intelligence and, most importantly, the invention of cryptocurrencies, blockchains and other forms of decentralized ledger-keeping, a new software ‘stack’ of monetary and financial protocols and applications is taking shape. Together, its components are forging an extensible, application programming interface-capable platform upon which entrepreneurs and governments will forge an exciting, disruptive and also slightly terrifying new era of programmable money – or, as the author puts it, ‘very smart money’.

The People's Republic of Walmart: How the World's Biggest Corporations Are Laying the Foundation for Socialism
by Leigh Phillips and Michal Rozworski
Published 5 Mar 2019

The reason for the flurry of interest in Cybersyn today, and for the recovery of its story, is due in part to its remarkable parallel to the US military’s Advanced Research Projects Agency Network (ARPANET)—the precursor of the internet—and the revelation, like something out of an alternate universe, that an internet-like structure may first have been developed in the global South. The attraction to the tale of Chile’s socialist internet is likely also due to the raft of lessons for today offered by this artifact from Allende’s democratic revolution—“flavored with red wine and empanadas,” as he put it—on privacy and big data, the dangers and benefits of the Internet of Things, and the emergence of algorithmic regulation. Our interest here, though, is primarily to consider Cybersyn in terms of its success or otherwise as an instrument of non-centralized economic planning. Freed from the Cold War’s constraints, we can today consider Cybersyn more objectively and ask whether it might serve as a model for leaping over both the free market and central(ized) planning.

Blockchain Chicken Farm: And Other Stories of Tech in China's Countryside
by Xiaowei Wang
Published 12 Oct 2020

These poverty-alleviation programs reflect China’s “fragmented authoritarianism,” which is both decentralized and autocratic: decentralized at the local scale with fairly loose controls, but authoritarian on national policies.4 The contradiction of this fragmented-authoritarian model can create a lot of confusion between the official policy and what is actually happening on the ground. Zhou connected Farmer Jiang with Lianmo Technology, which was hoping to pilot more blockchain and Internet of Things projects, including the profitable business of poultry tracking, as China consumes five billion chickens a year (which is still only about half the American chicken-consumption rate of nine billion per year). Jiang shows us around the rest of the farm—several pristine feeding areas, and the “control” room where the base station sits.

Likewar: The Weaponization of Social Media
by Peter Warren Singer and Emerson T. Brooking
Published 15 Mar 2018

Shear, “Allen Quip Provokes Outrage, Apology,” Washington Post, August 15, 2006, http://www.washingtonpost.com/wp-dyn/content/article/2006/08/14/AR2006081400589.html. 56 referring to Sidarth’s hair: Ibid. 56 Salon’s person of the year: Michael Scherer, “Salon Person of the Year: S. R. Sidarth,” Salon, December 16, 2006, http://www.salon.com/2006/12/16/sidarth/. 57 9 billion digital devices: Peter Newman, “The Internet of Things 2018 Report: How the IoT Is Evolving to Reach the Mainstream with Businesses and Consumers,” Business Insider, February 26, 2018, http://www.businessinsider.com/the-internet-of-things-2017-report-2018-2-26-1. 57 soar to 50 billion: “The Sensor-Based Economy,” Wired, January 2017, https://www.wired.com/brandlab/2017/01/sensor-based-economy/. 57 almost a trillion sensors: Ibid. 57 sixty-five different elements: Rebecca Hill, “Shocker: Cambridge Analytica Scandal Touch-Paper Aleksandr Kogan Tapped Twitter Data Too,” The Register, April 30, 2018, https://www.theregister.co.uk/2018/04/30/aleksandr_kogan_also_slurped_twitter_data/. 57 Argus Panoptes: “Argus,” Encyclopedia Britannica, accessed March 18, 2018, https://www.britannica.com/topic/Argus-Greek-mythology. 57 the Panopticon: Thomas McMullan, “What Does the Panopticon Mean in the Age of Digital Surveillance?

The Technology Trap: Capital, Labor, and Power in the Age of Automation
by Carl Benedikt Frey
Published 17 Jun 2019

Some readers may also find it disappointing that many major technological breakthroughs are not even mentioned. To take just one example, the rise of modern medicine has arguably been the greatest boon to humanity but is shamelessly left out here. Technological developments in recent years, including advances in AI, mobile robotics, machine vision, 3-D printing, and the Internet of things, are all labor saving. The purpose of this book is to shed light on present times and challenges facing the workforce today, and for this reason labor-saving technology will receive the bulk of the attention. It must also be emphasized that though the focus in the later chapters is much on the American experience, technology is not a soloist but part of an ensemble.

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See also Industrial Revolution Bronx, 182 Bronx-Whitestone Bridge, 167 Bronze Age, 35 Brown, Sherrod, 291 Brynjolfsson, Erik, 303, 326, 329, 339 bubonic plague (1348) (Black Death), 67, 75 Bureau of Labor Statistics (BLS), 191 Bush, George W., 357 Bythell, Duncan, 121 California Civil Code of 1872, 359 Čapek, Karel, 74 capitalism: perceived threat to, 210; beginnings of, 70; criticism of, 342; impact of clocks on evolution of, 47; rise of, 218; Jeffersonian ideal under, 212; normal state of, 210 capitalist achievement, 294 Capitoline Hill, 40 Captain Swing riots, 130, 285 caravel construction, 50–51 Cardwell, Donald, 47, 59, 97 Carlyle, Thomas, 117 Carnegie, Andrew, 208 Cartwright, Edmund, 105, 127 Case, Anne, 255–56 Cave, Edward, 102 Celestine III, Pope, 44 cement masonry, discovery of, 37 Chadwick, Edwin, 114–15 Charles I of England, King, 54–55, 82, 86 Chartism, 137 cheap labor, slower mechanization and, 75 Cherlin, Andrew, 276, 279 Chetty, Raj, 253, 361 child labor, 103, 123, 134; as opportunity cost to education, 214; robots of Industrial Revolution, 8–9 chimney aristocracy, 89 China: admission to World Trade Organization, 281, 286; ascent of, 289; delayed industrialization in, 88; trade war with, 331 Christensen, Clayton, 354 Chrysler Building, 182 civil rights: lagging, 20; legislation, 280 Civil War (American), 75 Civil War (English), 81 Clark, Gregory, 29, 48, 60 classical civilizations, 37 clientelism, 271 Clinton, Hillary, 285 clocks, 47 Coalbrookdale Iron Company, 108 cognitive divide, 238–43 Colbert, Jean-Baptiste, 84 collective action problem, 19–20 collective bargaining, 192 college-educated citizens: activities of, 352; detachment of, 256; among Great Divergence, 258, 358; hours per day worked, 338; perceived untrustworthiness of, 278; promotion of, 350; qualified as middle class, 239 Colt, Samuel, 149–50 Columbus, Christopher, 51, 67 Communist Manifesto, 7, 63, 70, 119 competition: among nation-states, 19, 57, 89; cascading, 289 computer-aided design software, 13 computer-controlled machines, jobs eliminated by, 228 computer publishing, 247 computers: age of, 228–38; analysts in, 235; automation anxiety concerning, 183; jobs created in, 16; ranks of the affluent in, 224; revolution, 249; those who thrived in, 16; trend beginning with, 258 connectivity, 362–63 consumer products: cheapening of, 294; new, Americans’ growing appetite for, 203 containerization, 171–72 Corbyn, Jeremy, 281 Corn Laws, 267 corporate giants, 208 corporate paternalism, 200 corporate profits, 132, 244 cotton cloth guild, 88 cotton industry, 100 cotton production, mechanization of, 7 Cowie, Jefferson, 200 craft guilds, 55–57, 87 Crafts, Nicholas, 107, 329 crime, joblessness and, 253 Crimean War, 150 Crompton, Samuel, 94, 102 Crouzet, François, 70 Crystal Palace Exhibition of 1851, 147, 149 cultural phenomenon, working class as, 278 culture of growth, 77 Dactyl, 313 Da Gama, Vasco, 51, 67 Dahl, Robert, 273, 352 Daimler, Gottlieb, 166 Darby, Abraham, 108 Dark Ages, light in, 41–51 data, as the new oil, 304 David, Paul, 153, 326 Davis, James J., 175 “deaths of despair,” 256 Deaton, Angus, 8, 255 Declaration of Rights of 1689 (Bill of Rights), 79 Decree Tractor Company, 215 Deep Blue, 303 deep learning, 304 Deep Mind, 301 Defense Advanced Research Projects Agency (DARPA), 307 Defoe, Daniel, 68–69, 71, 84 democracy: legitimacy of, undermining of, 274; liberal, components of, 267; middle class and, 265–69; rise of, 265 Descartes, René, 94 Detroit, Michigan, 151, 257, 359 Devine, Warren, Jr., 153 Diamond, Jared, 64 Dickens, Charles, 117 digital communication, 360 digital industries, clustering of, 260 Diocletian, Roman Emperor, 65 disappearance of jobs, 250–52 “disciplined self” identity, 279 Disraeli, Benjamin, 112, 268 Dittmar, Jeremiah, 48 Domesday Book of 1086, 44 domestic system of production, 61, 71; downfall of, 8 Douglas, Paul H., 178–79 Drebbel, Cornelis, 52 drones, 342 Drucker, Peter F., 227 drudgery, end of, 193–98 Dust Bowl (1930s), 193, 204 Dutch Revolt, 81 Earned Income Tax Credit (EITC), 357 earnings gap, 230 economic incentive, lack of, 40 economic inequality, 22, 274, 277 economic parasites, 79 economic segregation, 356 economies of scale, factories taking advantage of, 110 Eden, Frederick, 116, 344 Edison, Thomas, 2, 52, 148, 189 education and technology, race between, 216 Eilmer of Malmesbury, 78 Eisenhower, Dwight, 307 electricity, early days of, 151 electrification, rural, 157 Electronic Numerical Integrator and Calculator (ENIAC), 230 elevator: arrival of, 14; automatic, 182 Elevator Industry Association, 182 elevator operators, vanishing of, 181, 227 Elizabeth I of England, Queen, 10, 54, 105 Empire State Building, 182 enabling technologies, 13, 227, 228 Engels, Friedrich, 70, 112, 249, 364 Engels’ pause, 131–37, 219; ending of, 287; polarization and, 266; return of, 243–48, 331; time of, 337 English craft guilds, fading power of, 87 entrepreneurial risk, 77 Facebook, 285 factory system, 8, 97, 126; annus mirabilis of 1769, 97; artisans, 98; child labor, 103, 104; coke smelting, 109; control over factory workforce, 104; cotton industry, 100; domestic industry, output growth in, 98; earlier modes of production, 97–98; economies of scale, factories taking advantage of, 110; electrification, 190, 195; Industrial Revolution, 97, 100–101; international trade, rise of, 99; inventions, 102; iron, railroads, and steam, 105–11; mechanical clock as enabling technology for, 47; railroad, arrival of, 108; rise of machines, 99–105; silk industry, beginnings of, 99; social savings of steam engine, 107; steam engine, economic virtuosity of, 107; working class, 98 Fairchild Semiconductor, 359 Fair Labor Standards Act of 1938, 200 farming: disappearance of jobs, 197, 203; mechanization of, 324; revolution, 168–69 feudal oligarchies, replacement of, 58 feudal order: political participation in, 265; rise of, 41, 62 Field, Alexander, 163, 170 Finley, Moses, 36 First Opium War, 88 Fisher, Alva J., 27 Fisher, Irving, 210 Ford, Henry, 141, 148, 167, 195, 365 Ford assembly lines, 18, 365 Ford Motor Company, 148, 199, 240 France, industrial development in, 84 Francis I, Holy Roman Emperor, 85 French Revolution, 90 Friedman, Milton, 355 Friedman, Thomas, 257 Fukuyama, Francis, 141, 264–65, 273, 343 Furman, Jason, 322 Galileo, 39, 52, 54, 94 Galor, Oded, 133 Gans, Joshua, 308 Garden of Eden, 191 Gaskell, Elizabeth, 117 Gaskell, Peter, 117–119, 135, 229, 249 Gates, Bill, 10 Gates paradox, 10, 11, 21 General Electric, 155, 157, 215, 289 General Motors assembly lines, 18 geography of new jobs, 256–63 George Washington Bridge, 167 Giffen, Robert, 132–33 gig mill, 10, 76, 86, 128 Gilded Age, 208 Gille, Bertrand, 39–40 Gini coefficient, 209, 245 Gladstone, William Ewart, 133 Glaeser, Edward, 257, 261, 263 globalization: automation, and populism, 277–85; backlash against, 365; clamping down on, 290; costs of, 366; facilitator of, 282; first wave of, 171; losers to, 21, 26; vanishing jobs and, 11 Glorious Revolution of 1688–89, 79, 82–83, 86 Golden Gate, 167 golden postwar years, 239 Goldfarb, Avi, 308 Goldin, Claudia, 213, 349 Goldin, Ian, 357 Gompers, Samuel, 279 Goodyear Tire, 199 Google, 305 Google Translate, 304 Goolsbee, Austan, 340 Gordon, Robert, 198, 202, 220, 272, 342 government regulations, 49, 137 Great Depression, 13, 143, 170, 211, 272 Great Divergence, 24; absence of economic revolution, 95; beginnings of industrialization, 94; factory system, evolution of (see factory system); Industrial Revolution (see Industrial Revolution); per capita income growth, 94; rise of the machines, 93; textile industry, Industrial Revolution begun in, 95 Great Escape, 8 “great exception” in American political history, 200 Great Migration, 205 Great Recession, 244, 284, 339, 343 Green, William, 174 Greif, Avner, 88, 92, 344 growth, culture of, 77 Gutenberg, Johannes, 47 Habsburg Empire, 85 Hammer, Michael, 326 Hansen, Alvin, 179, 342 Hargreaves, James, 102–3 Harlem, 1 Harper, Kyle, 37 Hawking, Stephen, 36 hazardous jobs, end of, 195, 198 health conditions, during Industrial Revolution, 114–15 Heaton, Herbert, 37 Heckman, James, 351 Heilbroner, Robert, 335 Hellenism, technological creativity of, 39 Henderson, Rebecca, 305, 331 Hero of Alexandria, 39 high school graduates, employment opportunities for, 237 high school movement (1910–40), 214 Himmelfarb, Gertrude, 268 Hindenburg disaster, 110 hinterland, cheap labor and housing of, 261 history deniers, 23 Hitler, Adolf, 12 Hobbes, Thomas, 8, 46 Hobsbawm, Eric, 7 Hoover, Herbert, 211 horseless age, 164 horse technology, 43, 163 Hounshell, David, 148, 150 household revolution, 155–56 housing, zoning and, 361–62 housing bubble, 282 human capital accumulation, indicators of, 133–34 Humphries, Jane, 103, 121 Hurst, Erik, 338 Huskisson, William, 109–10 Hyperloop, 363 IBM, 231 Ibsen, Henrik, 17 Ice Age, 64, 76 identity politics, 278 “idiocy of rural life,” 62–64 income(s): disparities of, 61; reshuffling of, 287 income tax (Britain), introduction of, 133 incubators, nursery cities serving as, 261 industrial bourgeoisie, 267 industrial capitalism, rise of, 218 industrial centers, rise of, 115 industrial espionage, 6 industrialization, first episode of, 16 industrial organization, fundamental principle of, 229 Industrial Revolution, 68, 70; alcoholism, 123; in Britain, 329; Britain’s edge during, 19; British income tax, introduction of, 133; capital share of income, 131–32; child labor, 123, 134; children as robots of, 8–9; classic years of, 113; closing decades of, 138, 266; conditions of England question, 116–25; consumer revolution preceding, 68; cotton yarn manufacturing at dawn of, 100–101; divergence between output and wages, 131; domestic system, description of, 118; economic consequences of, 17; Engels’ pause, 131–37; engine of, 73; Englishmen left off worse by, 364; factories existing before, 94; gig mills, 128; golden age of industry, 118; government regulation, 137; hand-loom weaver, as tragic hero of Industrial Revolution, 121; health conditions, 114–15; human capital accumulation, indicators of, 133–34; labor income share captured, 114; industrial centers, rise of, 115; jobs created by, 16; key drivers of, 342; labor unions, bargaining power of, 137; Lancashire riots, 125, 127; leading figures of, 70; literacy rates, 134; Luddites, 125–31; machinery question, concerns over, 116; machinery riots, 127, 130; macroeconomic impact of, 94; material living conditions, decline of, 114, 120–21; mobility of workers, 122; obsolescence of worker skills, 124; origins of, 6, 80–91; political situation of workers, 129; reason for beginnings in Britain, 75; recipients of the gains of, 113; standard of living issue, 121; steam power, impact of on aggregate growth, 136; symbolic beginning of, 97; tax revenue, 133; technical change during closing decades, 139; technological progress, attitudes toward, 112; trajectory of inequality in Britain during, 217; true beginnings of, 100; unemployment, 113, 117, 125; victims of, 9; Victorian Age, machinery critics of, 119; wave of gadgets, 330; working poor, 113 inequality: age of, beginnings of, 62; Neolithic rise in, 63 inflation, 294 information technology, first revolution in, 47 inner-city ghettos, problems in, 258 innovation, 257; nurseries for, 261 innovation gap, 352 in-person service jobs, 235 inspiration without perspiration, 51–59 installment credit, 159, 167 institutional divergence (colonial Europe), 81 Intel, 359 interchangeable parts: concept of, 149; pioneering of, 74 International Labour Organization (ILO), 181 International Monetary Fund (IMF), 245 international trade, rise of, 67, 69, 99 Internet of things, 22 internet traffic: spread of, 328; worldwide, 303 inventions: agriculture, 54, 62; assembly line, 141, 365; barometer, 52, 59; bicycle, 165; camel saddle, 77; carding machine, 102; of classical times, 39; coke smelting, 108; electric starter, 166; iron, 36; light bulb, 2; mariner’s compass, 50; movable-type printing press, 47; nailed horseshoe, 43; navigable submarine, 52; personal computer (PC), 231; power loom, 105; spinning jenny, 102; steam digester, 55; steam engine, 52, 76; stirrup, 43; stocking-frame knitting machine, 54, 76; submarine, 73; telescope, 59; transistor, 231; typewriter, 161–62; washing machine, 27; water frame, 102; waterwheel, 38; wheel, 35 Iron Age, 35 iron laws of economics, 206 James I of England, King, 52 Japan, ascent of, 289 JD. com, 313 Jeffersonian individualism, 200 Jenkinson, Robert, 2nd Earl of Liverpool, 130, 289 Jerome, Harry, 13, 154, 198, 328 job demand, creation of, 262 Johnson, Lyndon, 184 Joyce, James, 16 Kaldor, Nicholas, 5, 205 Kasparov, Garry, 301 Katz, Lawrence, 135, 213, 245, 349 Kay-Shuttleworth, James, 117, 229 Kennedy, John F., 183 Kettering, Charles, 166 Keynes, John Maynard, 332, 334 King, Gregory, 68 knowledge work, 235, 259 Komlos, John, 115 Korea, ascent of, 289 Korean War, 180 Krugman, Paul, 12 Kuznets, Simon, 5, 206–7 Kuznets curve, 207, 212 labor, division of, 228 labor multiplier, 347 Labor Party, rise of, 268 labor productivity, gap between worker compensation and, 244 labor unions, 212; bargaining power of, 201, 277; legalization in Britain, 190 laissez-faire regime, 25, 267 lamplighters, 1–2 Lancashire riots of 1779, 90 landed aristocracy, 83 Landes, David, 9, 112, 118, 134, 343 Land-Grant College Act of 1862, 364 Latin Church, oppression of science by, 79 laundress, vanishing of, 27, 160 Lee, William, 10, 54 Lefebvre des Noëttes, Richard, 43 Leonardo da Vinci, 38, 51, 73 Leontief, Wassily, 20, 338, 343 Levy, Frank, 237, 302, 323 liberal democracy, components of, 267 Lindert, Peter, 61, 68, 114, 207, 211, 269, 271 literacy, demand for, 76 Liverpool-Manchester Railway, 109 lobbying, corporate spending on, 275 Locke, John, 83 Lombe, John, 52, 99–100 Lombe, Thomas, 6, 100 London Steam Carriage, 109 longshoremen, vanishing of, 172 Louis XIV of France, King, 84 Luddites, 9, 18, 125–31, 341; imprisoned, 20; new, 286–92; riots, 89, 92; uprisings, 265 machinery question, 116, 174–88; adjustment problems, 177; automation, employment effects of, 180; computers, automation anxiety concerning, 183; elevator operators, 181–82; musicians, displaced, 177–78 machinery riots, 9, 265, 289; absence of (America), 190; Britain, 90 Maddison, Angus, 66 Magellan, Ferdinand, 51, 67 majority-rule voting system, 270 Malthus, Thomas Robert, 4, 64, 73, 316, 345 Malthusian logic, 345 Malthusian trap, escape of, 65 Manhattan Project, 74 Manpower Training and Development Act (MDTA), 353 Mantoux, Paul, 97, 101, 126 Manufacture des Gobelins, 84 Manufacture Royale de Glaces de Miroirs, 84 manufacturing: blue-collar jobs, disappearance of, 251, 254; American system of manufacturing, pioneers of, 149; factory electrification, 151–55; interchangeable parts, concept of, 149 Margo, Robert, 135, 145 markets, integration of, 86 Marx, Karl, 26, 47, 98, 239, 364 Massey, Douglas, 256 Massive Open Online Courses (MOOCs), 354 mass production, 147–73; American system of manufacturing, pioneers of, 149; containerization, 171–72; direct drive, 153; factory electrification, 151–55; horseless age, 164; household revolution, 156; industries, 18; installment credit, 159, 167; interchangeable parts, concept of, 149; Model T, 167; unit drive, 153 Maurice of Nassau, Prince of Orange, 59 Maybach, Wilhelm, 166 McAfee, Andrew, 303, 339 McCloskey, Deirdre, 70 McCormick, Cyrus, 149, 168 McLean, Malcom, 171 mechanics, Galileo’s theory of, 53 mechanization, age of automation vs. age of, 227 median voter theories, 270 medieval Christianity, 78 mercantilism, flawed doctrine of, 83 Mesopotamia, 35 metals, discovery and exploitation of, 35 Michigan Antitrust Reform Act of 1985, 359 Microsoft, 306 Middle Ages: agricultural technology in, 42; feudal order of, 57; onset of, 41; technical advances of, 50; traditional crafts of, 68 middle class, descent of, 223–25; artificial intelligence, 228; automation, adverse consequences of, 240; cognitive divide, 238–43; computer-controlled machines, jobs eliminated by, 228; computers, 228–38; corporate profits, 244; division of labor between human and machine, 228; earnings gap, 230; Engels’ pause, return of, 243–48; golden postwar years, 239; Great Recession, 244; high school graduates, employment opportunities for, 237; industrial organization, fundamental principle of, 229; in-person service jobs, 235; knowledge workers, 235; labor productivity, gap between worker compensation and, 244; mechanization, age of automation vs. age of, 227; multipurpose robots, 242; rule-based logic, 228; Second Industrial Revolution, elimination of jobs created for machine operators during, 228; “symbolic analysts,” 235 middle class, triumph of, 218–222; agriculture, mechanization of, 189; automotive industry, 202; baby boom, 221; blue-collar Americans, unprecedented wages of, 220; child labor, as opportunity cost to education, 214; collective bargaining, 192; corporate giants, 208; corporate paternalism, 200; education and technology, race between, 216; end of drudgery, 193–98; Engels’ pause, 219; factory electrification, 190, 195; farming jobs, decline of, 197, 203; Great Depression, 211; “great exception” in American political history, 200; Great Migration, 205; hazardous jobs, end of, 195, 198; high school movement (1910–40), 214; Jeffersonian individualism, 200; Kuznets curve, 207, 212; labor unions, 201, 212; leveling of American wages, 211; machinery riots, absence of, 190; middle class, emergence of, 192, 292; national minimum wage, introduction of, 211; new consumer goods, Americans’ growing appetite for, 203; New Deal, 200, 212; public schooling, 214; Second Industrial Revolution, 209, 217; skill-biased technological change, 213; tractor use, expansion of, 196; urban-rural wage gap, 209; Wall Street, depression suffered by, 211; welfare capitalism, 198, 200; welfare state, rise of, 221; white-collar employment, 197, 218 Middle East, 77 Milanovic, Branko, 217, 245 mining, 194, 197 Minoan civilization, 34 mobile robotics, 342 mobility, demands for, 348 mobility vouchers, 360 Model T, 167 modern medicine, rise of, 22 Mokyr, Joel, 19, 52, 76–77, 79 Moore’s Law, 107, 301, 304 Moravec’s paradox, 236 Moretti, Enrico, 258, 262–63, 360 Morgan, J.

Everyware: The Dawning Age of Ubiquitous Computing
by Adam Greenfield
Published 14 Sep 2006

Many such objects are already invested with processing power—most contemporary cameras, watches, and phones, to name the most obvious examples, contain microcontrollers. But we understand these things to be technical, and if they have so far rarely participated in the larger conversation of the "Internet of things," we wouldn't necessarily be surprised to see them do so. Nor are we concerned, for the moment, with the many embedded microprocessors we encounter elsewhere in our lives, generally without being aware of them. They pump the brakes in our cars, cycle the compressors in our refrigerators, or adjust the water temperature in our washing machines, yet never interact with the wider universe.

Ours to Hack and to Own: The Rise of Platform Cooperativism, a New Vision for the Future of Work and a Fairer Internet
by Trebor Scholz and Nathan Schneider
Published 14 Aug 2017

People have blockaded Google Buses to protest wealth inequality in San Francisco, and Uber drivers have gone on strike around the world. Increasingly this online economy is becoming the economy—the way more and more of us find jobs, relationships, and a roof over our heads. Internet companies aspire to network and monetize everything from our cars to our refrigerators; the companies call this the “Internet of things.” But the Turkers’ questions have kept coming back to me. Were they on to something? What if the platforms and networks really were ours? What if we had an Internet of ownership? REAL SHARING, REAL DEMOCRACY Another word that the Internet has gotten to is sharing. Sharing used to mean something we do with the people we know and trust.

Data-Ism: The Revolution Transforming Decision Making, Consumer Behavior, and Almost Everything Else
by Steve Lohr
Published 10 Mar 2015

The concept has been around for years—digitizing machines with sensors, enabling them to communicate, and tapping the resulting vast flows for new discoveries and profit-making possibilities. The idea is part of a larger vision of putting sensors—down to “smart dust”—on all kinds of objects around the globe, gathering information, and communicating with powerful computer networks. It is popularly known as the Internet of Things. The ultimate goal, according to Larry Smarr, founding director of the California Institute for Telecommunications and Information Technology, is a “sensor-aware planetary computer.” GE’s more modest formulation is what it calls the “industrial Internet.” For the company, the industrial Internet is a marketing term attached to a major strategic initiative, backed by sizable investment.

Team Topologies: Organizing Business and Technology Teams for Fast Flow
by Matthew Skelton and Manuel Pais
Published 16 Sep 2019

(Conway), 9–10 Humble, Jez, 18, 36, 64, 86 Humphrey, Andy, 53–55, 97–99 I IBM, 146–147 IBM 8086 processor, 101 Ikea, 47 influences for book, xxi–xxii infrastructure automation, 11 infrastructure teams, 105 ING Netherlands, 53 Ingles, Paul, 155 interaction mode key, xx interaction modes, 179, 185 intermittent collaboration, 133 Internet of Things (IoT), 84, 101, 123–125, 156–157, 171 inter-team communication, 25–26, 27 Ivarsson, Anders, 50 J Java Virtual Machine, 101 Jay, Graylin, 41 joined-at-the-database monolith, 113, 188 K Kelly, Allan, 10, 24, 35, 101, 148 Kim, Gene, 18, 64, 86, 172 Kim, Kyung Hee, 136, 138 Kniberg, Henrik, 25, 50, 100 Knight, Pamela, xxii, 36 Kotte, Gustaf Nilsson, 47 Kotter, John, 161 L Lambert, Michael, 50–52 “A Leader’s Framework for Decision Making” (Snowden and Boone), xxi Lean Enterprise (Humble, Molesky, & O’Reilly), 36 Lencioni, Patrick, xxii Lewis, James, xxii, 10, 34 Linux, 101 Lister, Timothy, 38 log-aggregation tools, 27–28 loose coupling, 22 Luo, Jiao, 80 M MacCormack, Alan, 16 Maibaum, Michael, 94–95, 162–164 Make Space (Doorley & Witthoft), 53 Malan, Ruth, xx, 17, 23, 148 McChrystal, Stanley, 31, 46 Microsoft, 69 Millett, Scott, 116 Minick, Eric, 146–147 misplaced boundaries, 150–151 missing capabilities, 150–151 “A Model for Team-Based Organization Performance” (Forrester and Drexler), xx The Modern Firm (Roberts), 23 Molesky, Joanne, 36 monolithic build, 188 monolithic model, 114, 188 monolithic rebuilds, 113 monolithic release, 113, 188 monolithic shared databases, 16 monolithic thinking, 114, 188 monolithic workplace, 114, 188 monoliths, 112–114 application, 113, 187 hidden, 112–114 joined-at-the-database, 113, 188 multi-layer viable-systems model, 103 The Mythical Man-Month (Brooks), 35 N Narayan, Sriram, 172, 173 .Net Framework, 101 Neumark, Peter, 92–93 The New Hacker’s Dictionary (Raymond), 10 new practices, adoption of, 155–159 Nokia, 38–39 non-blocking dependencies, 68–69 Nygard, Michael, 22, 116 O onion concept, 34 open-plan office, 114 Ops team, 80–81 O’Reilly, Barry, 36 org chart thinking, 3–14 bottlenecks, 11–12 cognitive load, 11–12 collaboration mode, 9 communication structures, 4–8 complicated-subsystem teams, 9 Conway’s law, 9–11 enabling teams, 9 facilitating mode, 9 platform teams, 9 problems with, 5–7 stream-aligned teams, 9 team interaction modes, 9 Team Topologies model, 9 team types, 9 thinking beyond, 7–8 X-as-a-Service mode, 9 organization design, 16–17, 23–24 organization design evolution, 181 organization size, 73–74 organizational sensing, 64–65, 153–175 adoption of new practices, 155–159 business as usual teams, 173–174 business process management, 169 case study, 154–155, 157–159, 162–164 collaboration mode, 153–155, 159–161 environmental scanning, 171 evolution triggers, 165–170 rapid learning, 155–159 self-steering design and development, 170–174 team topologies combination, 164–165 team topologies evolution, 159–164, 165–170 X-as-a-Service mode, 153–154, 161 Out of the Crisis (Deming), 38 OutSystems, 11, 42 P Pais, Manuel, 66 Payment Card Industry Data Security Standard (PCI DSS), 117 Pearce, Jo, 40 Peopleware (DeMarco & Lister), 38 performance isolation, 119 The Phoenix Project, 166 Pierce, Robert A., 136, 138 Pink, Dan, 11 Pivotal, 149–150 Pivotal Cloud Foundry (PCF), 48–49, 101 platform composition, 96–99 platform teams, 9, 92–96, 105–106, 188 platforms, 100–104 cognitive load reduction, 101–102 constraints, 102 managed as a live product or service, 103–104 multi-layer viable-systems model, 103 product development acceleration, 101–102 thinnest viable, 101, 184 underlying, 102–103 Poole, Marshall Scott, xix Poppulo, 121–123 Prezi, 92–93 principle of overlapping measurement, 143 The Principles of Product Development Flow (Reinertsen), 23, 143 product teams, 68–69 Project Myopia (Kelly), 35 promise theory, 142 R rapid learning, 155–159 Rautert, Markus, 16 Raymond, Eric, 10 “Real Life Agile Scaling” (Kniberg), 25 rebuild everything, 113 Red Hat Open Innovation Labs, 53 regulatory compliance, 116–117 Reinertsen, Don, 23, 143 relative domain complexity, 41–42 Remote: Office Not Required (Fried & Hansson), 55 Rensin, Dave, 72 reorganizations, 28 responsibilities, splitting, 74 responsibility restriction, 39–41 risk, 118–119 Roberts, John, 23 Rother, Mike, 134 Rubio, Andy, 50–52 S Schwartz, Mark, 4 self-service capabilities, 69 self-steering design and development, 170–174 Sheehan, Stephanie, 121–123 shuffling team members, 62 silo reduction, 74 single view of the world, 114 site reliability engineering (SRE), 70–72 Skelton, Matthew, 66 Sky Betting & Gaming, 94–95, 162–164 Snowden, Dave, xix software architecture as flows of change, 23 software boundaries, 115–123 business domain bounded context, 115–116 case study, 121–125 change cadence, 117 defined, 187 natural, 121 performance isolation, 119 regulatory compliance, 116–117 risk, 118–119 team location, 118 technology, 120 user personas, 120–121 software boundary size, 45–47 software ownership, 36–37 software scale, 73–74 Sosa, Manuel, 24 Spotify, 49, 50, 75 squads, 50, 75 standardization, 114 Stanford, Naomi, 24, 38, 171 static topologies, 61–78 ad hoc team design, 62 anti-patterns, 62 case study, 75–77 cloud teams, 69–70 compatible support systems, 69 credit reference agencies, 76–77 dependencies, 74–75 DevOps, 65–67 DevOps Topologies, 66–67, 75–77 engineering maturity, 73–74 feature teams, 67–68 flow of change, designing for, 63–64 healthcare organizations, 75–76 non-blocking dependencies, 68–69 organization size, 73–74 organizational sensing, 64–65 product teams, 68–69 self-service capabilities, 69 shuffling team members, 62 silo reduction, 74 site reliability engineering, 70–72 software scale, 73–74 splitting responsibilities, 74 team intercommunication, 64–65 team patterns, successful, 67–72 technical and cultural maturity, 72–73 topology choice considerations, 72–75 wait time between teams, 74–75 stream-aligned teams, 9, 81–86, 104, 188 capabilities within, 83–84 case study, 82–83 expected behaviors, 85–86 streams of change, suitable, 183–184 support teams, 80–81, 107–109 Sussna, Jeff, 85, 161, 172 Sweller, John, 39–40 T team APIs, 47–56 benched bay approach, 51 case study, 50–52, 53–55 defined, 48, 188 environment design, 50 group chat prefixes, 55–56 guilds, 49 squads, 50 team interactions, 49 virtual environment design, 53–56 workspace design, 50–56 team assignments, 22 team behaviors, 141–144 team habits, 134–135 team interaction modes, 9, 131–152 awkward team interactions, 150–151 basic team organization, 146–148 case study, 146–147 choosing suitable, 143–145 collaboration mode, 133, 135–137, 142–143, 147–148, 149 effective APIs, 148 enhancing flow, 148–151 facilitating mode, 134, 140–144, 147–148 intermittent collaboration, 133 misplaced boundaries, 150–151 missing capabilities, 150–151 principle of overlapping measurement, 143 promise theory, 142 reducing uncertainty, 148–151 reverse Conway maneuver, 147–148 team behaviors for, 141–144 team habits, 134–135 temporary changes to, 149–150 well-defined team interactions, 132–133 X-as-a-Service mode, 133, 137–140, 143, 149 team interactions, 49, 132–133 team intercommunication, 64–65 team lifespans, 35–36 team location, 118 Team of Teams (McChrystal), 31, 46 team patterns, successful, 67–72 cloud teams, 69–70 compatible support systems, 69 feature teams, 67–68 non-blocking dependencies, 68–69 product teams, 68–69 self-service capabilities, 69 site reliability engineering, 70–72 team silos, 99–100 team size, 32 team topologies capability gaps, 184–185 combining, 164–165 component teams to platform teams, 105–106 converting architecture and architects, 109 converting common to fundamental team topologies, 104–109 Conway’s law, 15–29, 180–181 defined, 188 evolving, 159–170 four fundamental.

This Is Not Normal: The Collapse of Liberal Britain
by William Davies
Published 28 Sep 2020

These separations have been declared a deceitful sham by feminist and Marxist critics among others, on the basis that they work in the interests of patriarchy and/or capital. But they are also undone by neoliberal policy reforms, which seek to bring all of social and political life under the gaze of a blanket financial audit. In the context of the ‘internet of things’, and the fusing of credit rating with platforms, neoliberalism could yet issue in an infrastructure not unlike that of the Chinese ‘social credit’ scoring system, where all behaviour – public or private, social or economic – can be captured as proof of character. As these structural shifts are underway below the surface, so once-separate public institutions and jurisdictions begin to blur into one.

Aftershocks: Pandemic Politics and the End of the Old International Order
by Colin Kahl and Thomas Wright
Published 23 Aug 2021

Some seemed both sensible and relatively benign, such as the use of medical robots in a handful of countries to minimize the exposure of front-line health workers to COVID-19. In early March, for example, China constructed a “smart field hospital” in Wuhan completely run by medical robots and other Internet of Things devices.18 More troubling was the deployment of robotic surveillance tools. China repurposed and reprogrammed drones to spray disinfectant, deliver medical samples, chastise people for being out of their homes and for not wearing masks, check for fevers at a distance using thermal imaging, and otherwise monitor and enforce the world’s biggest quarantine.19 In India, authorities in Delhi and across the country used drones to sanitize large areas, monitor traffic, and identify lockdown violators.20 Spanish police used drones to blast warning messages telling people in Madrid to vacate public parks and stay inside during the country’s declaration of an emergency in March.21 That same month, in the French city of Nice, drones were used to monitor compliance with travel restrictions and social distancing; two months later, the Conseil d’État (one of France’s supreme courts) banned the use of drones for COVID-related surveillance on privacy grounds.22 Even more pervasive was the proliferation of mobile smartphone apps designed to disseminate public health alerts, assess personal health status, facilitate contact tracing, and enforce the isolation of infected individuals.

…

John India civil liberties in COVID-19 response in economy in pharmaceutical industry populist nationalism and industrial revolution inequality influenza pandemic of 1918. See Great Influenza Inglehart, Ronald intelligence community International Health Regulations (IHR) International Monetary Fund International Sanitary Conferences Internet of Things Iran Iraq Ireland Islamic State in Iraq and Syria (ISIS) Islamophobia Israel Italy COVID-19 response in COVID-19 vaccine and interwar period World War I World War II Japan Johnson, Boris Johnson & Johnson vaccine Jourová, Věra Kagame, Paul Kelemen, R. Daniel Kellogg-Briand Pact Kelly, John Kennedy, John, F.

This Is Service Design Doing: Applying Service Design Thinking in the Real World: A Practitioners' Handbook
by Marc Stickdorn , Markus Edgar Hormess , Adam Lawrence and Jakob Schneider
Published 12 Jan 2018

Comment “To provide solutions, firms need to create service systems composed of physical components, technology, and data, including knowledge, communication channels, and networked actors. This equally applies to service systems in manufacturing, healthcare, energy, or security and has been promoted especially in the context of the Internet of Things.” — Kathrin Möslein Many early service designers came from graphic or product design where, if they kept within set technical parameters, the realities of production did not concern them much. Or clients did not include implementation in the scope of the project, even if the designers wanted to address it.

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Instead of just pressing a button on the key (one step), users had to get out their phones, unlock them, open the app, and press a button (four steps) to achieve the same thing. This system had other flaws too: what are users supposed to do when their phones run out of battery, for example? The same applies to the Internet of Things (IoT) and connected devices. It’s a cool thing if you are able to turn on a light with your phone, but again, an empty battery or broken phone can make the whole thing useless. Because services and software are intangible and often inherently complex, it is crucial to make sure that all stakeholders are on the same page and involved in the process right from the start.

The Price of Time: The Real Story of Interest
by Edward Chancellor
Published 15 Aug 2022

Reinhart, Carmen M., Kirkegaard, Jacob F. and Sbrancia, M. Belen, ‘Financial Repression Redux’, IMF Finance and Development, 48 (1), June 2011. Rey, Hélène, ‘Dilemma not Trilemma: The Global Financial Cycle and Monetary Policy Independence’, NBER Working Paper, May 2015 (rev. February 2018). Rifkin, Jeremy, The Zero Marginal Cost Society: The Internet of Things, the Collaborative Commons, and the Eclipse of Capitalism (New York, 2014). Rist, Charles, History of Monetary and Credit Theory: From John Law to the Present Day (London, 1940). Robbins, Lionel, A History of Economic Thought: The LSE Lectures, eds. Steven G. Medema and Warren J. Samuels (Princeton, 1998).

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Agnieszka Gehringer and Thomas Mayer, ‘Understanding Low Interest Rates’, Flossbach von Storch Research Institute, 23 October 2015. 14. William Gross, ‘Privates Eye’, PIMCO Insights, 1 August 2010. 15. Summers, ‘U.S. Economic Prospects’. 16. Jeremy Rifkin, The Zero Marginal Cost Society: The Internet of Things, the Collaborative Commons, and the Eclipse of Capitalism (New York, 2014). 17. Charles Bean et al., ‘Low for So Long? Causes and Consequences of Persistently Low Interest Rates’, International Center for Monetary and Banking Studies (ICMB), VoxEU, 23 October 2015. 18. Robert J. Gordon, ‘Is U.S.

The Filter Bubble: What the Internet Is Hiding From You
by Eli Pariser
Published 11 May 2011

And this powerful set of data—where you go and what you do, as indicated by where your face shows up in the bitstream—can be used to provide ever more custom-tailored experiences. It’s not just people that will be easier than ever to track. It’s also individual objects—what some researchers are calling the “Internet of things.” As sci-fi author William Gibson once said, “The future is already here—it’s just not very evenly distributed.” It shows up in some places before others. And one of the places this particular aspect of the future has shown up first, oddly enough, is the Coca-Cola Village Amusement Park, a holiday village, theme park, and marketing event that opens seasonally in Israel.

Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy
by Cathy O'Neil
Published 5 Sep 2016

Nearly one dollar of every five we earn feeds the vast health care industry. Employers, which have long been nickel and diming workers to lower their costs, now have a new tactic to combat these growing costs. They call it “wellness.” It involves growing surveillance, including lots of data pouring in from the Internet of Things—the Fitbits, Apple Watches, and other sensors that relay updates on how our bodies are functioning. The idea, as we’ve seen so many times, springs from good intentions. In fact, it is encouraged by the government. The Affordable Care Act, or Obamacare, invites companies to engage workers in wellness programs, and even to “incentivize” health.

The Inner Lives of Markets: How People Shape Them—And They Shape Us
by Tim Sullivan
Published 6 Jun 2016

And no wonder: it’s easier to set up a meeting place now that we no longer need to convene en masse at events like the Champagne fairs. We can just log on and meet in cyberspace instead. And so, we now find dates, book travel, buy groceries, send instant messages, and hail taxis—all via online platforms. More recently, the much-vaunted internet of things is bringing us yet another generation of platform business models, some amazing, some terrifying, and some, like internet-enabled cars, a bit of both. As cars move from being internal combustion engines with wheels to software platforms that are connected to the internet and to one another, we can imagine all sorts of potential for them, some of which will make our lives better (fewer accidents with autonomous cars and more apps that plug into them) and some of which will make us even more vulnerable (long-distance software hacks).

Whiplash: How to Survive Our Faster Future
by Joi Ito and Jeff Howe
Published 6 Dec 2016

“One design strategy that helps biological systems achieve robustness to these threats is diversity—genetic diversity in a species, species diversity in an ecosystem, and molecular diversity in an immune system.”15 By contrast, the computer industry specializes in homogeneity: churning out near-infinite quantities of identical pieces of hardware and software. The result is that an agent that can wreak havoc in one host—read: computer, or increasingly, any number of the objects joining the Internet of Things—can as easily infect any number of those copies. The product of hundreds of millions of years of evolution, our immune system is characterized by byzantine complexity, but at its root essence it plays a complicated game of Us vs. Them. Anything that’s foreign to the host body is Them; anything that’s not is on our team.

Mastering Pandas
by Femi Anthony
Published 21 Jun 2015

The conversion from analog to digital media coupled with an increased capability to capture and store data, which in turn has been made possible with cheaper and more capable storage technology. There has been a proliferation of digital data input devices such as cameras and wearables, and the cost of huge data storage has fallen rapidly. Amazon Web Services is a prime example of the trend toward much cheaper storage. The Internetification of devices, or rather Internet of Things, is the phenomenon wherein common household devices, such as our refrigerators and cars, will be connected to the Internet. This phenomenon will only accelerate the above trend. Velocity of big data From a purely technological point of view, velocity refers to the throughput of big data, or how fast the data is coming in and is being processed.

The Switch: How Solar, Storage and New Tech Means Cheap Power for All
by Chris Goodall
Published 6 Jul 2016

In the regions of the US controlled by the grid operator PJM, it was as high as 12 per cent but this included companies who switched to diesel generators as well as those who actually reduced their load. I think we’ll probably find that the eventual figure is much higher than these estimates. Cutting power demand in the home When the ‘Internet of Things’ comes fully into operation, every single machine in the world, ranging from domestic radiators to steel furnaces, will have the capacity to be turned off at short notice. I’m not saying for one minute that people will voluntarily agree for their usage to be curbed but it will be theoretically possible to delay washing machines, ask laptop computers to run on their batteries, or turn off dishwashers.

The War on Normal People: The Truth About America's Disappearing Jobs and Why Universal Basic Income Is Our Future
by Andrew Yang
Published 2 Apr 2018

The advent of big farms, tractors, factories, assembly lines, and personal computers, while each a very big deal for the labor market, were orders of magnitude less revolutionary than advancements like artificial intelligence, machine learning, self-driving vehicles, advanced robotics, smartphones, drones, 3D printing, virtual and augmented reality, the Internet of things, genomics, digital currencies, and nanotechnology. These changes affect a multitude of industries that each employ millions of people. The speed, breadth, impact, and nature of the changes are considerably more dramatic than anything that has come before. It is true that this would be the first time that the labor market did not meaningfully adapt and adjust.

Mastering Structured Data on the Semantic Web: From HTML5 Microdata to Linked Open Data
by Leslie Sikos
Published 10 Jul 2015

Structured Data Conventional web sites rely on markup languages for document structure, style sheets for appearance, and scripts for behavior, but the content is human-readable only. When searching for “Jaguar” on the Web, for example, traditional search engine algorithms cannot always tell the difference between the British luxury car and the South American predator (Figure 1-2). The concept of “thing” is used in other contexts as well, such as in the “Internet of Things” (IoT), which is the network of physical objects embedded with electronics, software, and sensors, including smart objects such as wearable computers, all of which are connected to the manufacturer and/or the operator, and/or other devices. 2 2 Chapter 1 ■ Introduction to the Semantic Web Figure 1-2.

Fully Automated Luxury Communism
by Aaron Bastani
Published 10 Jun 2019

Part of the answer is energy conservation – and this holds true for all places regardless of solar exposure. While for now we might associate the idea of conservation with frugality and rationing, we shouldn’t. In just a few years, saving energy – in your home, car and workplace – will be entirely automated. The main reason why is the arrival of the internet of things. Electric goods, including your car, won’t just be communicating with one another, but distributing and storing energy in real time. If that sounds like an analogue to the internet, it is. Energy internets will soon be operating within and between households, and even everyday objects. This will be centred around the car, the fulcrum of the transition to renewables in its earliest stages and the leading edge of the clean, autonomous economy.

99%: Mass Impoverishment and How We Can End It
by Mark Thomas
Published 7 Aug 2019

There are numerous examples of good technology being used badly and little reason to think gene drives would be an exception.8 NEW COMPUTING APPROACHES The world of computing is highly innovative, and there are many emerging technologies that may prove influential over the next thirty-five years. These range from distributed ledger technology (block chain) as used by Bitcoin and other digital currencies, through virtual reality and the Internet of Things to cerebral interfaces. Two areas which may prove to be fundamental are quantum computing and the development of AI: first narrow AI – the use of artificial intelligence to solve tightly defined problems such as image recognition – and, ultimately, full AI. Quantum computing Whereas conventional computing is based on the idea of a ‘bit’ (short for ‘binary digit’) that takes either the value 0 or the value 1 at any time, quantum computing takes advantage of the fact that, according to quantum mechanics, particles can be in multiple, superimposed states at the same time.

Principles of Web API Design: Delivering Value with APIs and Microservices
by James Higginbotham
Published 20 Dec 2021

This may include the need to research and understand distributed synchronization using techniques such as a Lamport Clock to overcome clock skew across distributed nodes while ensuring proper ordering of messages across hosts. Documenting Async APIs The AsyncAPI specification is a standard for capturing definitions of async messaging channels. AsyncAPI supports traditional message brokers, server-sent events (SSE), Kafka and other message streams, and internet of things (IoT) messaging such as MQTT. This standard is becoming popular as a single solution to define message schemas and the protocol binding specifics of message-driven protocols. It is important to note that this specification isn’t related to the OpenAPI Specification (OAS), but has been inspired by it and strives to follow a similar format to make adoption easier.

Open for Business Harnessing the Power of Platform Ecosystems
by Lauren Turner Claire , Laure Claire Reillier and Benoit Reillier
Published 14 Oct 2017

Google is also behind the Android operating system, a suite of software products including the Chrome browser, associated Chrome laptop products and Android smartphones, as well as fibre access networks in the US.30 The company also acquired the mobile division of Motorola and its extensive patent portfolio in 2013. More recently, Google has entered the ‘Internet of things’ (IoT) market with the acquisition of Nest (temperature control) and Dropcam (video surveillance) as well as the launch of Google Home (voice activated assistant). Google is also very active in self-driving car technology, artificial intelligence and, through its ventures arm, an investor in some of the most promising start-ups in Silicon Valley (including Uber).

The Complacent Class: The Self-Defeating Quest for the American Dream
by Tyler Cowen
Published 27 Feb 2017

• The differences between America’s wealthy and less well-developed cities and suburbs have become big enough to resurrect economic motives as a reason to relocate, so rates of residential mobility rise again, leading to a new pioneer class. Driverless vehicles and better transit systems make these new commutes bearable. • Artificial intelligence, smart software, robotics, and the “internet of things” have come together to bring significant productivity gains and lots of disruptive change. You walk around your house, or the store, and ask for things to happen, and they do. You can ask any question just by talking to yourself, and a good answer comes immediately. • Cheap, clean energy has become a reality, enabling a lot more ambitious physical projects in physical space.

The Dark Net
by Jamie Bartlett
Published 20 Aug 2014

But the sort of rapid technological advances we’re living through certainly raise several difficult questions. Scientists in Sweden are already connecting robotic limbs to the human nervous system of amputees. Panasonic will be releasing an exoskeleton suit shortly. Then there is nanotechnology, synthetic biology, the Internet of Things, algorithmic-controlled financial services, general artificial intelligence. Some of the problems this raises are existential: if Zoltan became a data file, saved on multiple servers all over the world, is he even still Zoltan? Is he still a human, deserving the same rights we accord to our species?

Big Data: A Revolution That Will Transform How We Live, Work, and Think
by Viktor Mayer-Schonberger and Kenneth Cukier
Published 5 Mar 2013

GreenGoose, a startup in San Francisco, sells tiny sensors that detect motion, which can be placed on objects to track how much they are used. Putting it on a pack of dental floss, a watering can, or a box of cat litter makes it possible to datafy dental hygiene and the care of plants and pets. The enthusiasm over the “internet of things”—embedding chips, sensors, and communications modules into everyday objects—is partly about networking but just as much about datafying all that surrounds us. Once the world has been datafied, the potential uses of the information are basically limited only by one’s ingenuity. Maury datafied seafarers’ previous journeys through painstaking manual tabulation, and thereby unlocked extraordinary insights and value.

This Chair Rocks: A Manifiesto Against Ageism
by Ashton Applewhite
Published 10 Feb 2016

Whatever its basis, as we hit the homestretch, most of us make peace with our pasts and enjoy the present as never before. We each age differently: mentally, physically, and socially That makes chronological age an ever-more-unreliable benchmark of pretty much anything about a person: what she “should” look like, or be listening to, or feel about thongs or the Internet of Things. Categories make life simpler and generalizations are inevitable, but sorting by capacity and inclination makes more sense. There are even more ways of getting from 60 to 90 as there are of getting from 30 to 60. That’s why “You look great for your age!” offends: it relies on internalized ageism to work as a compliment, and it implies that people “your age” look a certain way.

The New Class Conflict
by Joel Kotkin
Published 31 Aug 2014

But this failure barely threatens the company, whose last quarterly revenues neared $17 billion, whose cash on hand exceeds $56.5 billion, and whose 2014 market cap topped $400 billion.69 Indeed, if any of the tech powers is to become a full-fledged keiretsu, it’s likely to be Google. In addition to their other ventures, Google’s recent acquisition of Nest, a company founded by Apple alum Tony Fadell, brings Google into the “smart home” marketplace, part of the so-called “Internet of things,” with its almost infinite capacity for ever greater information hauls from your once “dumb,” but at least private, household appliances.70 In splendid keiretsu fashion, the acquisition also helped Kleiner Perkins, one of the early investors in both Google and Amazon, gain a return of twenty times their original investment.71 In the process, as industry veteran Michael Mace observes, Google has stopped being a “unified product company” and is turning instead into what he calls “a post-modern conglomerate.”

Infinite Detail
by Tim Maughan
Published 1 Apr 2019

“That old lady we was talking to? Said she couldn’t get her pension.” “I wouldn’t be surprised,” says Rush. “So it’s … what? A virus?” “Seems so. One that can infect anything. Apparently works on some fundamental exploit of TCP/IP, and back-door exploits that seem to be embedded into lots of ‘Internet of Things’ devices. Looks impossible to patch at the scale it’s working at right now. I know it sounds unlikely, but yeah.” “What … I … where?” Claire squints at him, doubtful. “I mean, where’s it come from?” He laughs. “Well, yeah. Take your pick. From what I’ve read it might be hackers, the NSA, ISIS, China, a rogue AI that’s escaped from a lab in Berkeley, or space aliens.”

Cult of the Dead Cow: How the Original Hacking Supergroup Might Just Save the World
by Joseph Menn
Published 3 Jun 2019

(A September 2018 investment round would value that company at $20 billion.) In his spare time, Mudge served as cybersecurity advisor to Senator Mark Warner, cochair of the Senate Cybersecurity Caucus. “Mudge has been extremely helpful in refining our understanding of software security, which informed our work on improving the security of internet-of-things devices, to take just one example,” Warner said, referring to new classes of internet-connected gadgets such as security cameras and thermostats. Warner also served as the top Democrat on the Senate Intelligence Committee, making him the lead Democrat in the congressional investigations of Russia’s hacking to help Trump win the 2016 election.

The Globotics Upheaval: Globalisation, Robotics and the Future of Work
by Richard Baldwin
Published 10 Jan 2019

NEW JOBS DIRECTLY CREATED BY DIGITECH There are at least three ways in which the breakneck advance of digital technology is creating jobs at an equally breakneck pace. The first has to do with the explosion of data. As more people get online and as we all do more online, the demand for online and phone-based services is exploding. Moreover, online activity is creating mountains of data. The size of the digital tsunami is amplified by the so-called internet of things, which means machines talking to machines online. The only way to deal with this absolutely colossal wave of data is to employ white-collar robots. Since advanced AI, like Amelia and her “cobots,” can’t handle really unusual cases, humans will still be needed. Thus there will be a lot of substitution of AI for humans, but since the amount of work is exploding, the number of humans employed in such operations will expand.

Live Work Work Work Die: A Journey Into the Savage Heart of Silicon Valley
by Corey Pein
Published 23 Apr 2018

The convention sprawled through the pier terminal, filling cavernous carpeted chambers and descending to the water line, where tents and portable heaters were erected on cold concrete floors. I flitted from table to table and panel to panel, collecting colorful brochures and absorbing up-to-the-minute jargon. I sampled passions like I was shopping for a new pair of blue jeans: I am passionate about the internet of things. I am passionate about big data. I am passionate about machine learning. I am passionate about key-value stores. I am passionate about a native ticketing experience. I am passionate about an integration platform-as-a-service. I had an important realization at DevWeek: I wasn’t the only one bluffing my way through the tech scene.

Programming TypeScript
by Boris Cherny
Published 16 Apr 2019

Or your boss was so fed up with your code causing production issues that they gave you this book as a Christmas present (stop me if I’m getting warm). Whatever your reasons are, what you’ve heard is true. TypeScript is the language that will power the next generation of web apps, mobile apps, NodeJS projects, and Internet of Things (IoT) devices. It will make your programs safer by checking for common mistakes, serve as documentation for yourself and future engineers, make refactoring painless, and make, like, half of your unit tests unnecessary (“What unit tests?”). TypeScript will double your productivity as a programmer, and it will land you a date with that cute barista across the street.

Hands-On RESTful API Design Patterns and Best Practices
by Harihara Subramanian
Published 31 Jan 2019

The faster maturity and stability of information, communication, sensing, perception, vision, analytics, decision-enabling, and actuation technologies contribute immensely to the realization of intelligent solutions, systems, and services. Fast-evolving digital technologies include state-of-the-art IT infrastructures, such as software-defined clouds, integrated platforms for big and fast data, streaming and IoT data analytics, the mobile-enablement of every enterprise system, the futuristic Internet of Things (IoT), the mesmerizing blockchain technology, and the pervasive software as a service (SaaS) phenomenon. The well-known edge technologies include disappearing sensors, actuators, multifaceted micro- and nano-scale electronics, miniaturized stickers, pads, tags, barcodes, chips, controllers, specks, beacons, and LEDs.

Grave New World: The End of Globalization, the Return of History
by Stephen D. King
Published 22 May 2017

They can instead replace their cheap foreign labour with potentially even cheaper home-based machines. Because those machines can be programmed in seconds, training costs will crumble. And to the extent that machines can easily be repaired remotely – either by humans or by other machines via the ‘internet of things’ – there will be far fewer working days lost to sickness. Factories could be devoid of human life – with the exception of the man whose job it is to feed the guard dogs.12 The implications of such a model would be profound. Production would increasingly be reshored. The nineteenth-century agglomeration model would be given a new lease of life.

The Wealth of Humans: Work, Power, and Status in the Twenty-First Century
by Ryan Avent
Published 20 Sep 2016

Johnson, 1798) Marx, Karl, and Engels, Friedrich, Manifesto of the Communist Party (1848) Milanovic, Branko, Global Inequality: A New Approach for the Age of Globalization (Cambridge, MA: Harvard University Press, 2016) Mokyr, Joel, The Gifts of Athena: Historical Origins of the Knowledge Economy (Princeton, NJ: Princeton University Press, 2002) _____, The Lever of Riches: Technological Creativity and Economic Progress (Oxford: Oxford University Press, 1990) Moretti, Enrico, The New Geography of Jobs (New York, NY: Houghton Mifflin Harcourt, 2012) Murray, Charles, Coming Apart: The State of White America, 1960–2010 (New York, NY: Crown Publishing Group, 2012) Pickett, Kate, and Wilkinson, Richard, The Spirit Level: Why Greater Equality Makes Societies Stronger (London: Allen Lane, 2009) Piketty, Thomas, Capital in the Twenty-First Century (Cambridge, MA: Harvard University Press, 2014) Putnam, Robert, Bowling Alone: The Collapse and Revival of American Community (New York, NY: Simon & Schuster, 2001) Rifkin, Jeremy, The Zero Marginal Cost Society: The Internet of Things, the Collaborative Commons, and the Eclipse of Capitalism (London: Palgrave Macmillan, 2014) Rodrik, Dani, The Globalization Paradox: Democracy and the Future of the World Economy (Oxford: Oxford University Press, 2011) Saadia, Manu, Trekonomics: The Economics of Star Trek (San Francisco, CA: Pipertext, 2016) Shirky, Clay, Cognitive Surplus: Creativity and Generosity in a Connected Age (London: Allen Lane, 2010) Smith, Adam, An Inquiry into the Nature and Causes of the Wealth of Nations (London: W.

Winning Now, Winning Later
by David M. Cote
Published 17 Apr 2020

The French philosopher Blaise Pascal famously noted that he’d written a long letter, having lacked the time required to write a shorter one.2 As I believe, if you can’t convey a thought clearly and in a few words, then your comprehension of it is probably lacking. My efforts to inform myself in diverse fields also prompted nonexperts on our teams to read up on them. If the gray-haired CEO knew enough about the Internet of Things (IoT), say, to be dangerous, our senior executives in legal, human resources, or operations needed to as well. Otherwise they would risk looking uninformed during our conversations. ALIGNING THE ORGANIZATION AROUND THE STRATEGY Rigorous and informed decision-making is one thing, but it’s not enough.

Insane Mode: How Elon Musk's Tesla Sparked an Electric Revolution to End the Age of Oil
by Hamish McKenzie
Published 30 Sep 2017

He told the publication that Ford’s approach was “to first disrupt ourselves.” “You know, when I first joined the company, a long time ago, we were a manufacturing company,” Fields said. “As we go forward, I want us to be known as a manufacturing, a technology, and an information company. Because as our vehicles become a part of the Internet of Things, and as consumers choose to share their data with us, we want to be able to use that data to help make their lives better.” Fields noted that Ford had announced FordPass, a mobility solution that he hoped would “do for the auto industry what iTunes did for the music industry.” The service would let Ford owners reserve parking, share cars with others, and pay bills using a Ford payment system (FordPay).

Gray Day: My Undercover Mission to Expose America's First Cyber Spy
by Eric O'Neill
Published 1 Mar 2019

Less than a year later, the CIA lost the keys to what WikiLeaks named Vault 7—the CIA’s cyberoffensive stockpile. In one of the largest document leaks in the CIA’s history, WikiLeaks released thousands of pages outlining sophisticated tools and techniques the agency allegedly used to break into mobile phones, Internet of Things (IoT) devices, and computers. The leaks are a catalog of offensive hacking tools that include instructions for compromising a wide range of common devices and computer programs, including Skype, Wi-Fi networks, PDFs, and even virus scanners. If you believe WikiLeaks, the entire archive of stolen CIA material consists of several hundred million lines of computer code.

The Science and Technology of Growing Young: An Insider's Guide to the Breakthroughs That Will Dramatically Extend Our Lifespan . . . And What You Can Do Right Now
by Sergey Young
Published 23 Aug 2021

CHAPTER 5 DIY DIAGNOSTICS How Wearable, Portable, Embeddable, and Ingestible New Technologies Will Deliver Early Diagnosis and Radically Reduce Disease and Death “Nosce te ipsum (‘Know thyself’)” —Inscription from the Temple of Apollo “I am prescribing a lot more apps than medicines these days.” —Dr. Eric Topol, Physician and Author “It’s time to move from the Internet of Things to the Internet of Bodies.” —Sergey Young, Longevity Vision Fund Founder It is twenty years in the future. You wake up and glance at your smart watch. It is 7 am, your heart rate is 60 beats per minute, and your blood pressure is 120/80, so your watch tells you, there are no signs of atrial fibrillation, stroke, seizure, or other dangers.

Volt Rush: The Winners and Losers in the Race to Go Green
by Henry Sanderson
Published 12 Sep 2022

Every day, we rely on metals and minerals to power our iPhones and transmit our electricity. Digital technologies give us a sense that we live in an ethereal economy untethered to the material world. In fact, we are mining more minerals than at any time in our history, a dependence that is only set to increase.* Despite talk of artificial intelligence, the internet of things, and an imminent takeover by robots, our societies have in many ways not moved on from the practices of the past, when the need for oil drove Europeans to carve up the Middle East. The consequences of the transition will not just be economic – they will also be environmental. Extracting and processing these minerals requires large amounts of energy and pollutes local ecosystems.

How to Spend a Trillion Dollars
by Rowan Hooper
Published 15 Jan 2020

In a vision statement for the World Economic Forum, Jenniffer Maroa, of the Department of Global Health at the University of Washington, says a world free from preventable forms of suffering can ‘easily’ be achieved.6 All it will take, she suggests, are new technologies such as Blockchain, the internet of things and artificial intelligence (AI). Is this enough? Can we tech our way out of this? As CZI see it, in a very basic form the plan to cure all disease has three parts: 1. Bring together scientists and engineers. 2. Build tools and technology. 3. Grow the movement to fund science. Point 3 is important.

Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems
by Martin Kleppmann
Published 16 Mar 2017

This thought experiment is unusually polemic for this book, Designing Surveillance-Intensive Applications, but I think that strong words are needed to emphasize this point. In our attempts to make software “eat the world” [94], we have built the greatest mass surveillance infrastructure the world has ever seen. Rushing toward an Internet of Things, we are rapidly approaching a world in which every inhabited space contains at least one internet-connected microphone, in the form of smartphones, smart TVs, voice-controlled assistant devices, baby monitors, and even children’s toys that use cloud-based speech recognition. Many of these devices have a terrible security record [95].

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Meadows and Diana Wright: Thinking in Systems: A Primer. Chelsea Green Publishing, 2008. ISBN: 978-1-603-58055-7 [93] Daniel J. Bernstein: “Listening to a ‘big data’/‘data science’ talk,” twitter.com, May 12, 2015. [94] Marc Andreessen: “Why Software Is Eating the World,” The Wall Street Journal, 20 August 2011. [95] J. M. Porup: “‘Internet of Things’ Security Is Hilariously Broken and Getting Worse,” arstechnica.com, January 23, 2016. [96] Bruce Schneier: Data and Goliath: The Hidden Battles to Collect Your Data and Control Your World. W. W. Norton, 2015. ISBN: 978-0-393-35217-7 [97] The Grugq: “Nothing to Hide,” grugq.tumblr.com, April 15, 2016

Hacking Growth: How Today's Fastest-Growing Companies Drive Breakout Success
by Sean Ellis and Morgan Brown
Published 24 Apr 2017

In addition, in today’s business landscape, where market leaders are being disrupted seemingly overnight, the need for rapid adoption of new technological tools and continuous experimentation with product development and marketing is rapidly spreading from the domain of digital products to business of all kinds. This process will only be accelerating with the advent of the fast-developing Internet of Things, as more and more products are being made “smart” through connectivity to the Web and to other products. With the worlds of physical products and software rapidly merging, it will soon not only become possible to continuously monitor and update products, in real time, it will be vital to do so in order to remain competitive.

Messy: The Power of Disorder to Transform Our Lives
by Tim Harford
Published 3 Oct 2016

Governments continue to be motivated by the idea that the better they comprehend the world, the better they will be able to control and exploit it. They have been joined by large corporations, which also see the value in quantifying and classifying our world. From high-resolution drone and satellite images, to geographically tagged photos and tweets, mobile phones that constantly ping their location to colossal databases, and the “Internet of things”—the idea that most of the objects around us will soon be capable of communicating their whereabouts and status—one way or another, we continue to wander through the world, size it up, and digitally hammer colored nails into it. The trouble is that when we start quantifying and measuring the world, we soon begin to change the world to fit the way we measure it.

The Elements of Power: Gadgets, Guns, and the Struggle for a Sustainable Future in the Rare Metal Age
by David S. Abraham
Published 27 Oct 2015

Moriguchi, “Assessing the Environmental Impacts of Consumption and Production: Priority Products and Materials,” A report of the Working Group on the Environmental Impacts of Products and Materials to the International Resource Panel, United Nations Environment Programme, 2010, http://www.greeningtheblue.org/sites/default/files/Assessing%20the%20environmental%20impacts%20of%20consumption%20and%20production.pdf; John Heggestuen, “One in Every 5 People in the World Own a Smartphone, One in Every 17 Own a Tablet,” Business Insider, December 15, 2013, www.businessinsider.com/smartphone-and-tablet-penetration-2013-10; Pew Research Center Internet American Life Project, “Device Ownership over Time,” November 13, 2013, www.pewinternet.org/data-trend/mobile/device-ownership/; Dave Evans, “The Internet of Things: How the Next Evolution of the Internet Is Changing Everything,” CISCO, April 2011, http://www.cisco.com/web/about/ac79/docs/innov/IoT_IBSG_0411FINAL.pdf. 15. Nicola Twilley, “What Do Chinese Dumplings Have to Do with Global Warming?” New York Times, July 26, 2014, www.nytimes.com/2014/07/27/magazine/what-do-chinese-dumplings-have-to-do-with-global-warming.html. 16.

Masters of Scale: Surprising Truths From the World's Most Successful Entrepreneurs
by Reid Hoffman , June Cohen and Deron Triff
Published 14 Oct 2021

You can even ask: If another company was going to try to compete with us, how would they compete with us? Here’s a different variation on these questions that goes to the potential 20 and the potential 10. Ask: What else is happening in the industry? For example, Are there technological platform changes? This could be a move to the cloud, AI, ubiquity of sensors, Internet of Things, drones—these things then might give you an idea of what should be in the 20 and what should be in the 10.There’s no one way to drive strategic growth. But by asking these questions and applying this formula, it becomes easier to direct your growth in a productive way. Selina and her co-founder, Al Lieb, had only a vague sense of the site’s reception—they never imagined it would soon become the dominant platform for online invitations.

Green and Prosperous Land: A Blueprint for Rescuing the British Countryside
by Dieter Helm
Published 7 Mar 2019

Waste crime and fly-tipping are examples where the focus should be on detection, since licensing already exists and most of the criminals are outside the licence framework. New technologies could make detection potentially much easier since waste can be tagged and followed. All waste items come from somewhere and lots have barcodes and other identifiers. Technology, along with the Internet of Things, brings lots of new and exciting ways of fixing what have often seemed intractable problems. We can be a lot smarter about detection, but for this we need a proper Environment Protection Agency, as will be explained in chapter 11. Net environmental gain and developers What about the cases where developments lead to environmental damage?

Red Flags: Why Xi's China Is in Jeopardy
by George Magnus
Published 10 Sep 2018

It will be a battle between China’s experiment with state-directed digital authoritarianism and the West’s more traditional experience of private sector technology nurtured and egged on by public agencies. In the chapter on the middle-income trap, I highlighted the efforts and commitments that the Party is making to ramp up state support and financing, in close partnership with China’s own tech giants and start-ups, for the development and integration of AI, 5G networks, big data, the internet of things (devices in objects that send and receive data), cloud computing, and the creation of new science parks and laboratories. It wants to show that the combination of government leadership, research and development spending, the establishment of clear policy priorities, and the exploitation of comparative advantage in large market size and data collection will fulfil ambitious global tech leadership targets.

Messing With the Enemy: Surviving in a Social Media World of Hackers, Terrorists, Russians, and Fake News
by Clint Watts
Published 28 May 2018

Those sitting atop social media companies, mainstream media outlets, online retailers, and streaming services will have unimaginable power of persuasion as they gain more and more information on each person’s daily life. Intelligence officers conducting espionage or targeting call this process a “pattern of life” assessment, but they’ve never had the capability now open to social media companies. The Internet of Things (IOT) records nearly every facet of one’s life. Health applications tracking height, weight, heart rate, and steps, combined with Google searches and Amazon purchasing patterns, can provide social engineers with the ability to deliver someone a message via social media at the exact time and place where they are most vulnerable to it psychologically.

Mindf*ck: Cambridge Analytica and the Plot to Break America
by Christopher Wylie
Published 8 Oct 2019

With the advent of home automation hubs such as Amazon Alexa and Google Home, we are seeing the first step toward the eventual integration of cyberspace with our temporal physical reality. Fifth-generation (5G) mobile and next-generation Wi-Fi are already being rolled out, laying the foundations for the “Internet of Things” (IoT) to become the new norm, where household appliances big and small will become connected to high-speed and ubiquitous Internet networks. These mundane devices, whether they are a refrigerator, a toothbrush, or a mirror, are envisaged to use sensors to begin tracking users’ behavior inside their own homes, relaying the data back to service providers.

The Infinite Machine: How an Army of Crypto-Hackers Is Building the Next Internet With Ethereum
by Camila Russo
Published 13 Jul 2020

Once the February snow melted and the sun shone on the terrace, they would take a few minutes after breakfast to lounge outside. Most talk revolved around Ethereum and what it could do. As they looked out at the green hills and the slice of lake that poked out in between the buildings in the distance, they dreamed about identity on the blockchain, internet of things, and taking down banks with smart contracts. They would come back inside and usually Roxana had organized chores for the day: Did they need groceries and who would go to the store? What would they make for lunch and who would cook? This ragtag group of programmers and designers from all corners of the world had just met, yet they were working together, eating together, and sleeping next to each other.

Pinpoint: How GPS Is Changing Our World
by Greg Milner
Published 4 May 2016

In 2011, when members of the GPS regulatory and scientific community mobilized against plans to authorize a private wireless network they feared would threaten the GPS signal, several cited the barely fathomable figure of $3 trillion as the market’s value. It has become difficult to untangle the worth of GPS from the worth of everything. In an increasingly cloud-based world, the global market for the so-called “Internet of things”—the ability for physical objects (including people) to exchange data over cloud-based networks—could reach $1.5 trillion by 2020. These systems often require location information, which will be provided by GPS—and time synchronization that will also likely be tied to GPS. Placing an economic value on GPS has become nearly as impossible as pegging the value of other utilities.

Bean Counters: The Triumph of the Accountants and How They Broke Capitalism
by Richard Brooks
Published 23 Apr 2018

‘From strategy through execution,’ it says, ‘PwC’s alliance with Google for Work takes a business-focused approach that brings together PwC’s business transformation, process and organizational change capabilities and Google for Work’s collaborative and innovative applications & technologies.’21 KPMG boasts a similar ‘Global Digital Solution Hub’ tie-in with Microsoft. Deloitte attributed a 10% growth in consulting revenues in 2017 to ‘artificial intelligence, robotics, cognitive, creative digital consulting, cloud computing, blockchain and [the] Internet of Things’. The Big Four are where management consultancy and information technology now meet. They are perfectly placed to capitalize on the age of mass data, with troubling potential conflicts of interest. The firms offer firstly to use client companies’ own data to improve their audits and, through that, their audit clients’ performance.

Prosperity Without Growth: Foundations for the Economy of Tomorrow
by Tim Jackson
Published 8 Dec 2016

It’s the form and organisation of our systems of provision as well. Economic organisation needs to work with the grain of community and the long-term social good, rather than against it. Where some have seen a radical transformation of human work through processes of robotisation, digitalisation and the ‘internet of things’, this book has outlined a rather different vision. Community-centred enterprise engaged in delivering local services, such as nutrition, education, care, maintenance and repair, recreation, craft, creativity, culture: these activities contribute to flourishing and are embedded in the community.

Value of Everything: An Antidote to Chaos The
by Mariana Mazzucato
Published 25 Apr 2018

Fama, E., ‘Efficient capital markets: A review of theory and empirical work', Journal of Finance, 25(2) (1970). Farrell, G., ‘Blankfein defends pay levels for “more productive” Goldman staff', Financial Times, 11 November 2009: http://www.ft.com/intl/cms/s/0/c99bf08e-ce62-11de-a1ea-00144feabdc0.html Farrell, M., ‘The Internet of things - Who wins, who loses?', the Guardian, 14 August 2015. Fioramonti, L., Gross Domestic Problem (London: Zed Books, 2013). Foley, D. K., Adam's Fallacy: A Guide to Economic Theology (Cambridge, MA: Belknap Press, 2006). Foley, D. K., ‘Rethinking financial capitalism and the “information” economy', Review of Radical Political Economics, 45(3) (2013), pp. 257-68: http://doi.org/10.1177/0486613413487154 Forero-Pineda, C., ‘The Impact of stronger intellectual property rights on science and technology in developing countries', Research Policy, 36(6) (2006), pp. 808-24.

Programming in Lua, Fourth Edition
by Roberto Ierusalimschy
Published 14 Jul 2016

Threads and States Multiple Threads Lua States Programming in Lua, Fourth Edition Roberto Ierusalimschy PUC-Rio Copyright © 2016, 2003 Roberto Ierusalimschy * * * Personal copy of Eric Taylor <jdslkgjf.iapgjflksfg@yandex.com> About the Book When Waldemar, Luiz, and I started the development of Lua, back in 1993, we could hardly imagine that it would spread as it did. Started as an in-house language for two specific projects, currently Lua is widely used in all areas that can benefit from a simple, extensible, portable, and efficient scripting language, such as embedded systems, mobile devices, the Internet of Things, and, of course, games. We designed Lua, from the beginning, to be integrated with software written in C/C++ and other conventional languages. This integration brings many benefits. Lua is a small and simple language, partly because it does not try to do what C is already good for, such as sheer performance and interface with third-party software.

The Nature of Software Development: Keep It Simple, Make It Valuable, Build It Piece by Piece
by Ron Jeffries
Published 14 Aug 2015

The primary risk to stability is the now-classic distributed denial-of-service (DDoS) attack. The attacker causes many computers, widely distributed across the Net, to start generating load on your site. The load typically comes from a botnet. Botnet hosts are usually compromised Windows PCs, but with the Internet of Things taking off, we can expect to see that population diversify to include thermostats and refrigerators. A daemon on the compromised computer polls some control channel like IRC or even customized DNS queries, through which the botnet master issues commands. Botnets are now big business in the dark Net, with pay-as-you-go service as sophisticated as any cloud.

The Coming Wave: Technology, Power, and the Twenty-First Century's Greatest Dilemma
by Mustafa Suleyman
Published 4 Sep 2023

GO TO NOTE REFERENCE IN TEXT Ninety percent of these disputes Colin Rule, “Separating the People from the Problem,” The Practice, July 2020, thepractice.law.harvard.edu/​article/​separating-the-people-from-the-problem. GO TO NOTE REFERENCE IN TEXT Zero marginal cost production See, for example, Jeremy Rifkin, The Zero Marginal Cost Society: The Internet of Things, the Collaborative Commons, and the Eclipse of Capitalism (New York: Palgrave, 2014). GO TO NOTE REFERENCE IN TEXT What’s more, owners of the best systems Erik Brynjolfsson calls a situation where AI takes over more and more of the economy, locking large numbers of people in an equilibrium where they have no work, no wealth, and no meaningful power, the “Turing Trap.”

Machine, Platform, Crowd: Harnessing Our Digital Future
by Andrew McAfee and Erik Brynjolfsson
Published 26 Jun 2017

Companies exist in large part because well-functioning complete contracts are impossible to write, not because they’re too difficult or costly to enforce. But will future technologies eventually make it possible to write complete contracts? Some technologies could help. For instance, increasingly ubiquitous sensors, as we are seeing with the Internet of things, could make possible the monitoring of far more of our actions and outcomes. Increased computer power could make it possible to simulate, choose, and store decisions for many future possible outcomes, and networks could make it possible to bring all this data and information to central clearinghouses for adjudication and resolution.

The Innovation Illusion: How So Little Is Created by So Many Working So Hard
by Fredrik Erixon and Bjorn Weigel
Published 3 Oct 2016

But Western economies have gradually weakened their capacity to foster innovation and new technology in ways that impel fast diffusion and force producers, consumers, and legislators to behave much more productively. In light of Western capitalism’s decline, the thesis of a pending technology blitz confounds rather than convinces. It is hard to escape the feeling that many of the advertised technologies for the future underwhelm. Undoubtedly, many of the coming innovations in big data, the Internet of Things, machine intelligence, robotics, and more should be commended, yet they fail to impress, at least our technology-frustrated generation. Perhaps this is to rain on the parade, but for someone who grew up in the wake of Apollo’s moon landing, Stanley Kubrick’s movie 2001: A Space Odyssey, and the original Star Trek series, all that stuff seems a bit dull.

Samsung Rising: The Inside Story of the South Korean Giant That Set Out to Beat Apple and Conquer Tech
by Geoffrey Cain
Published 15 Mar 2020

But South Korea was still the Republic of Samsung, and South Korea would not survive without Samsung, people everywhere confided in me. Its namesake republic would live on. Epilogue “[THE] PREVIOUS 10 YEARS, it was an era of the smartphone,” Samsung CEO D.J. Koh told The Independent. “From this year, maybe a new era is opening because of the emergence of the internet of things, 5G, AI, and all these technologies mingling together. The new era is in front of us.” For almost a decade, Samsung’s engineers toiled away at their secret weapon for this new era, a smartphone that folds open, like a wallet, to reveal a massive display, ideal for movies and artwork—and then folds shut and fits in your pocket.

Text Analytics With Python: A Practical Real-World Approach to Gaining Actionable Insights From Your Data
by Dipanjan Sarkar
Published 1 Dec 2016

We will be exploring several of these libraries in this book. Even though the preceding list may seem a bit overwhelming, this is just scratching the surface of what is possible with Python. It is widely used in several other domains including artificial intelligence (AI) , game development, robotics, Internet of Things (IoT), computer vision, media processing, and network and system monitoring, just to name a few. To read some of the widespread success stories achieved with Python in different diverse domains like arts, science, computer science, education, and others, enthusiastic programmers and researchers can check out www.python.org/about/success/ .

Sunfall
by Jim Al-Khalili
Published 17 Apr 2019

Her mother had pioneered techniques for hacking public key cryptosystems with home-made quantum computers running codes that made use of Shor’s algorithm to factorize large numbers. But Shireen was a child of the new world order, which was dominated by the code war. When pervasive computing took off in the early twenties, when she was a young child, it still had a name: it was referred to as the Internet of Things; but it was soon clear that it no longer needed to be called anything. In a world where everything was connected to everything else, only her grandparents’ generation still used phrases like ‘look online’ instead of just ‘look’. It had begun with home and office appliances linked wirelessly to handheld devices, but eventually everything was networked; sensors, cameras, embedded nano servers and energy harvesters were all ubiquitous and built into the infrastructure of the modern world, from buildings and transport to clothing and household items.

Mastering Ethereum: Building Smart Contracts and DApps
by Andreas M. Antonopoulos and Gavin Wood Ph. D.
Published 23 Dec 2018

Schedule any further necessary transactions, such as notifications, etc. A range of other schemes are also possible; for example, data can be requested from and returned directly by an EOA, removing the need for an oracle smart contract. Similarly, the request and response could be made to and from an Internet of Things–enabled hardware sensor. Therefore, oracles can be human, software, or hardware. The request–response pattern described here is commonly seen in client–server architectures. While this is a useful messaging pattern that allows applications to have a two-way conversation, it is perhaps inappropriate under certain conditions.

Tools and Weapons: The Promise and the Peril of the Digital Age
by Brad Smith and Carol Ann Browne
Published 9 Sep 2019

By May 2019, the group had more than one hundred companies from more than twenty countries, and it was putting the accord into action by endorsing practical steps to strengthen cybersecurity protection. Importantly, the need for stronger private sector collaboration found additional support around the world. To its credit, Siemens led one of the earliest efforts, creating what it called a Charter of Trust to focus on protecting the ubiquitous small devices that make up the internet of things. A number of leading European companies, including Airbus, Deutsche Telekom, Allianz, and Total, were quick to join.24 In some ways, an even more interesting reaction awaited us in Asia. While in Tokyo in July 2018, we met with senior executives at Hitachi, which wanted to be the first large Japanese signatory.

The Powerful and the Damned: Private Diaries in Turbulent Times
by Lionel Barber
Published 5 Nov 2020

FRIDAY, 22 JULY Breakfast with Masayoshi Son, the Korean-Japanese venture capitalist and founder of SoftBank, the Japanese technology powerhouse. Son is a short, balding man in his late fifties who speaks in an understated manner which gives no clue to his awesome financial power, largely based on a stake in Alibaba, the fast-growing Chinese tech giant. Son talks in vague terms about an epochal change coming with ‘the internet of things’, which explains why SoftBank has just snapped up Arm, one of Britain’s few remaining world-class technology companies. Arm, which makes advanced semiconductor chips, will play a central role in the next wave of technological changes, says Son.fn3 Memo to the editor: the FT must fix a Lunch with the FT with this man.

Makers and Takers: The Rise of Finance and the Fall of American Business
by Rana Foroohar
Published 16 May 2016

But while all this technology in Schenectady has reduced the number of machinists needed to make a battery, it has also fueled the creation of a GE global research center in San Ramon, California. The center now employs more than one thousand software engineers, data scientists, and user-experience designers who are well paid to develop the software for that kind of industrial Internet—otherwise known as the Internet of things. GE plans to hire thousands more such employees within the next half-decade. “We are probably the most competitive, on a global basis, that we’ve been in the past 30 years,” in terms of being able to make things again in the United States, says CEO Jeffrey Immelt. “Will US manufacturing go from 9 percent to 30 percent of all jobs?

The One Device: The Secret History of the iPhone
by Brian Merchant
Published 19 Jun 2017

(Which, it should be added, the company has provided in the past: Apple has reportedly opened over seventy iPhones at the behest of law enforcement, though many of those were before the Secure Enclave necessitated a novel software hack from Apple.) There may need to be a mechanism for law enforcement to access this stuff, but how we do that in the age of the Secure Enclave is an open question. For Apple, security is a question of product too. As it moves to promote Apple Pay, internet-of-things apps, and HealthKit, consumers must be confident their data can be kept safe. From a consumer’s perspective, Apple’s decision is win-win; it may be unpopular, but the message is clear: You won’t find a more secure phone anywhere. We’ll go to bat against the feds to make sure your phone is secure.

Destined for War: America, China, and Thucydides's Trap
by Graham Allison
Published 29 May 2017

Unable to determine how the hacking of one system may affect others, attackers would find it difficult to narrowly tailor the effects of their operation and avoid unintended escalation. In 2016, 180,000 Internet-connected industrial control systems were operating around the world.26 Along with the proliferation of the so-called Internet of Things, which encompasses some 10 billion devices worldwide, the number of enticing targets is growing rapidly. Collateral damage in the cyberdomain could be as deadly and disruptive as it is in traditional warfare. Hacking a military target, for example, could inadvertently disable a system used by a medical or financial complex.

Fire in the Valley: The Birth and Death of the Personal Computer
by Michael Swaine and Paul Freiberger
Published 19 Oct 2014

From a device that you can put down when you’re done with it to a device that you take off like jewelry to a device that would require outpatient surgery to disconnect, smart devices are going beyond the personal, to the intra-personal. At the same time, these devices are deeply entwined with the Internet, talking to other devices in a new “Internet of things,” bypassing their slow fleshy hosts. The two trends of smaller and more intimate devices and of an increasingly ubiquitous network are coming together to produce something that transcends either individual technology. The results will be interesting. Looking Back It’s a brave new world we are embarking on with all of our embedded personal computers that have been programmed by others who hopefully have our well being in mind.

Imaginable: How to See the Future Coming and Feel Ready for Anything―Even Things That Seem Impossible Today
by Jane McGonigal
Published 22 Mar 2022

Try to have a bit of balance in your list—at least one of the forces you pick should feel like a risk to you, and at least one should feel like an opportunity: the climate crisis post-pandemic trauma social justice movements increasing economic inequality social and political tensions caused by refugee crises and mass migration automation of work decreasing birthrates in Western countries and a “youth boom” in Africa shifting religious majorities and increasing theological diversity the global switch to renewable energy sources alternatives to capitalism and market-based economies social media–driven misinformation, disinformation, and conspiracy theories rise of authoritarianism and loss of faith in democracy widespread adoption of facial recognition and surveillance technologies digital currencies, cryptocurrency, and programmable money universal basic income and direct cash transfers internet shutdowns mandated by government or law enforcement the “right to disconnect” movement and four-day workweeks lifelong learning and “reskilling” at the workplace job guarantees regenerative design and the circular economy genomic research and CRISPR genetic modification the Internet of Things augmented and virtual reality satellite networks and space internet If there’s something on the institute’s list above that you don’t know anything about at all, this is the perfect opportunity to go find your first clue. Just do an internet search for any future force. Remember, future forces aren’t secret—they’re hiding in plain sight!

AI 2041: Ten Visions for Our Future
by Kai-Fu Lee and Qiufan Chen
Published 13 Sep 2021

Wearable devices—such as the medical ID strips in “Contactless Love”—smart rooms with sensors for temperature, smart toilets, smart beds, smart toothbrushes, smart pillows, and all kinds of invisible gadgets will regularly sample vital signs and other data and detect possible health crises. Aggregated data from these devices will accurately identify if you have a serious condition, whether it is a fever, a stroke, arrhythmia, apnea, asphyxiation, or just injuries from a fall. All this Internet of Things (IoT) data will be combined with other healthcare information such as medical history, contact-tracing records, and infection-control data, to predict and warn about future pandemics. Privacy will be an alarming issue for some users, so the system will need to anonymize the data by replacing each name with a consistent and untraceable pseudonym.

Actionable Gamification: Beyond Points, Badges and Leaderboards
by Yu-Kai Chou
Published 13 Apr 2015

Random House, New York, NY.2005.↩ Great Place to Work Institute. White Paper: “How Zappos Creates Happy Customers and Employees”, 2011.↩ SpliceToday. “Five Reasons Office Space is a Cult Classic”, 08/01/2013.↩ Aon Hewitt. “2013 Trends in Global Employee Engagement”. 2013.↩ Mckinsey Quarterly. “The Internet of Things”, 03/2010 edition.↩ Business2Community. “Deliver an Excellent Customer Experience Using Big Data”. posted 11/10/2014.↩ Chapter 5: The First Core Drive - Epic Meaning & Calling Now that we have established a foundational overview of the Octalysis Framework, it is time to dive deeper into each Core Drive and discover the power and enchantment within.

The Cult of We: WeWork, Adam Neumann, and the Great Startup Delusion
by Eliot Brown and Maureen Farrell
Published 19 Jul 2021

going to be less than $30 billion: Maureen Farrell and Eliot Brown, “WeWork Weighs Slashing Valuation by More Than Half amid IPO Skepticism,” Wall Street Journal, Sept. 5, 2019. CHAPTER 34: A SETTING SON commitments totaling $108 billion: Saheli Roy Choudhury, “SoftBank Launches New $108 Billion Fund to Invest in A.I.,” CNBC, July 25, 2019. were significantly lagging behind expectations: Parmy Olson, “SoftBank Chip-Design Unit Yet to Conquer Internet of Things,” Wall Street Journal, July 8, 2019. Saudi Arabia’s PIF was noncommittal: Phred Dvorak, Liz Hoffman, and Mayumi Negishi, “Does SoftBank Really Have $108 Billion for Its Vision Fund 2?,” Wall Street Journal, Aug. 6, 2019. Vision Fund’s profits were up 66 percent: Mayumi Negishi, “SoftBank’s Vision Fund 2 Plans to Begin Investing as Soon as Next Month,” Wall Street Journal, Aug. 7, 2019.

The Content Trap: A Strategist's Guide to Digital Change
by Bharat Anand
Published 17 Oct 2016

This book has described changes in the worlds of information goods. Similar changes are occurring in “hard” goods, too. Thermostats, refrigerators, lightbulbs, door locks, and cars are becoming “smart”—the term used to describe products that contain sensors and software to relay information, all belonging to the “Internet of Things” (or, IoT). Hard goods are beginning to resemble information goods. Manufacturing is becoming media. But if traditional content businesses teach us anything, it’s that the smart products that win will be the ones that figure out connections. Some already are. Smart homes allow refrigerators to turn off lights and lock doors.

Terms of Service: Social Media and the Price of Constant Connection
by Jacob Silverman
Published 17 Mar 2015

Given that practically everything we do now produces a digital record, this model would make all of human life part of one vast, automated dataveillance system. “Think of personal data as the digital record of ‘everything a person makes and does online and in the world,’” the WEF says. The pervasiveness of such a system will only increase with the continued development and adoption of the “Internet of things”—Internet-connected, sensor-rich devices, from clothing to appliances to security cameras to transportation infrastructure. No social or behavioral act would be immune from the long arms of neoliberal capitalism. Because everything would be tracked, everything you do would be part of some economic exchange, benefiting a powerful corporation far more than you.

Your Computer Is on Fire
by Thomas S. Mullaney , Benjamin Peters , Mar Hicks and Kavita Philip
Published 9 Mar 2021

Despite widespread tropes that portray computing and new media as immaterial and disembodied—whether through an emphasis on “virtual” reality, “telepresence,” “the Cloud,” “streaming,” the “postindustrial” economy, or otherwise—computing and new media are nothing if not entirely physical, material, and organic. They are physical machines, propelled by fire both material and metabolic. When they run, they run hot; and when they work hard, they run hotter. Data centers alone account for more than 2 percent of global energy use, energy consumption predicted to grow with the expansion of the Internet of Things.4 (Google emitted over 50 kilograms of CO2 in the time it took for you to read this sentence.) Through the studies of platforms and infrastructure, bitcoin mining, programming languages, underground cable networks, and much more, this volume drives home what is often termed the “materiality of the digital”—that is, the physicality of computational and new media technologies that are too often described in ethereal terms.

Politics on the Edge: The Instant #1 Sunday Times Bestseller From the Host of Hit Podcast the Rest Is Politics
by Rory Stewart
Published 13 Sep 2023

Boarding the train, I sent out a tweet asking if people would contribute to the campaign and received hundreds of donations within minutes – many for £5 or £10, with larger donations of over £1,000 from the owner of a shoe shop; a glass manufacturer; an art dealer; the British–French founder of a company working on the Internet of Things; and a Cumbrian farmer. A man who looked about twenty approached me and said he wanted to give me five pounds in cash, for which I was very grateful, but which later sparked a long chain of WhatsApp messages with the team about how to declare this gift. To my relief, we were now fully funded and I was able to stop taking money.

The Cryptopians: Idealism, Greed, Lies, and the Making of the First Big Cryptocurrency Craze
by Laura Shin
Published 22 Feb 2022

Vitalik felt relieved—he knew he had at least three years of runway. He did not know, however, that they were in for a bumpy ride. CHRISTOPH JENTZSCH, THE C++ developer from Mittweida, Germany, who had, before launch, created the tests that aimed to fork the network, was now working on a new venture. Slock.it was an internet-of-things company to enable a blockchain-based, decentralized sharing economy. As he had demonstrated at DevCon 1 with the electric kettle, the Slock was a device that could be unlocked with an Ethereum transaction so that, for instance, the door to a decentralized Airbnb could open as soon as the guest paid.

Corporate Finance: Theory and Practice
by Pierre Vernimmen , Pascal Quiry , Maurizio Dallocchio , Yann le Fur and Antonio Salvi
Published 16 Oct 2017

The simplest form of growth is organic volume growth, i.e. selling more and more products. That said, it is worth noting that volume growth is not always as easy as it may sound in developed countries given weak demographic growth (e.g. between −0.5% and +1% p.a. in Europe). Booming markets do exist (Internet of Things), but others are rapidly contracting (nuclear power stations, magazines) or are cyclical (transportation, paper production). At the end of the day, in mature countries, the most important type of growth is value growth. Let’s imagine that we sell a product satisfying a basic need, such as bread.

…

Finally, in terms of valuation, because it is practically impossible to come up with reliable forecasts, the usual valuation methods are not used and a hybrid method made up of the multiples and the discounted cash flows method has been developed for valuing start-ups. Questions How should an internet-of-things start-up be financed? And a pizza chain? What is an optimistic entrepreneur? What are the conclusions to be drawn? What is the counterpart of goodwill paid at the outset for a start-up? What are the advantages of the venture capital method for valuing a company that is in the process of starting up?

MacroWikinomics: Rebooting Business and the World
by Don Tapscott and Anthony D. Williams
Published 28 Sep 2010

Maria Hattar, Cisco, quoted in “Cisco: Smart grid will eclipse size of Internet,” cnet News (May 18, 2009). 14. The Digital Environment Home Energy Management System (DEHEMS). See: http://www.dehems.eu/about. 15. David Miliband, U.K. Secretary of State for Environment, quoted in “Carbon emissions: Now it’s getting personal,” New York Times (June 20, 2007). 16. Richard MacManus, “IBM and the Internet of Things,” ReadWriteWeb (July 22, 2009). 17. “World electricity: The smart grid era,” Economist (June 5, 2009). 18. “SMART 2020: Enabling the low carbon economy in the information age,” The Climate Group (2008). 19. The argument in favor of radically decentralizing energy production is also subject to the specifics of geography.

The Rise and Fall of Nations: Forces of Change in the Post-Crisis World
by Ruchir Sharma
Published 5 Jun 2016

Even with worker training and experience, these advances will do much less to raise productivity than previous innovations like electricity, the steam engine, the car, the computer, or air conditioning, which was a huge boost to human output per hour in a stuffy office setting. Optimists respond that productivity growth measurements aren’t capturing the cost and time savings produced by new technologies, ranging from artificial intelligence to increasingly powerful broadband connections and the nascent “Internet of things.” In the United States, for example, the cost of broadband Internet access has remained flat for many years, but broadband connections have grown much faster and gone mobile—a huge time savings that is not captured in the productivity growth data.9 If the optimists are right, productivity growth is considerably faster than current measurements show, and therefore so is economic growth.

Smart Grid Standards
by Takuro Sato
Published 17 Nov 2015

IEEE 802.11s networks are usually used for outdoor scenarios, where a mesh network can provide good coverage, and is deployed in the NANs for smart meter-to-smart meter communications [69]. IEEE 802.11ah is an ongoing standardization effort for the amendment of IEEE 802.11 networks with low-power, long-range communications at subgigahertz frequency bands. This emerging standard is likely to play an important role in M2M, and the Internet of Things (IoT). The key use case of IEEE 802.11ah standard is in the Smart Grid, that is, by connecting metering devices with Data Aggregation Points (DAPs) and backhaul for IEEE 802.15.4g mesh sensor networks. As subgigahertz frequency bands differ between regions and countries, it defines channelization for each region separately.

Rentier Capitalism: Who Owns the Economy, and Who Pays for It?
by Brett Christophers
Published 17 Nov 2020

Stafford, ‘All About Data: LSE Bids Show Exchanges’ New Priorities’, Financial Times, 22 October 2019. 15. Economist, ‘The London Stock Exchange Is Thriving Despite Brexit’, 9 March 2019. 16. One zettabyte = 1 trillion gigabytes, or 1021 bytes. IDC, ‘The Digital Universe of Opportunities: Rich Data and the Increasing Value of the Internet of Things’, April 2014, at emc.com. 17. Lanchester, ‘You Are the Product’. On the surveillance business of contemporary digital capitalism more broadly, see S. Zuboff, The Age of Surveillance Capitalism (London: Profile, 2019). 18. ‘Datafication’ is the term used by Jathan Sadowski in his ‘When Data Is Capital: Datafication, Accumulation, and Extraction’, Big Data & Society, doi: 10.1177/2053951718820549. 19.

The Musical Human: A History of Life on Earth
by Michael Spitzer
Published 31 Mar 2021

, https://www.musictank.co.uk/wp-content/uploads/2018/03/bpi-ai-report.pdf 13https://www.theguardian.com/artanddesign/2014/may/06/neil- harbisson-worlds-first-cyborg-artist 14https://www.abeautiful.world/stories/james-wannerton-synesthesia/ 15Nicole Santos and Maria Pulido, ‘Investigation of Sound-Gustatory Synesthesia in a Coffeehouse Setting’, in Muthu Ramachandran et al (eds.), 4th International Conference on Internet of Things, Big Data and Security (Scitepress Digital Library, 2019). https://www.academia.edu/39705707/Investigation_of_Sound-Gustatory_Synesthesia_in_a_Coffeehouse_Setting 16http://www.generativemusic.com/bloom.html 17https://ask.audio/articles/5-interesting-features-that-make-reason-an-excellent-daw-to-use 18Nick Prior, Popular Music Digital Technology and Society (London: SAGE, 2018). 19Nicholas Cook, Monique Ingalls and David Trippett (eds), The Cambridge Companion to Music in Digital Culture (Cambridge: Cambridge University Press, 2019). 20Margaret Boden, The Creative Mind: Myths and Mechanisms (London: Routledge, 2004), p. 51. 21Donald, Origins of the Modern Mind, p. 355. 22Aram Sinnreich, Mashed Up: Music, Technology, and the Rise of Configurable Culture (Amherst: University of Massachusetts Press, 2010), pp. 71–3. 23Ross Duffin, How Equal Temperament Ruined Harmony (and Why You Should Care) (New York: W.

API Design Patterns
by Jj Geewax
Published 19 Jul 2021

In other words, while this section will not provide a guaranteed right answer, it will pose a few different questions that really should be answered. No decision is likely to be purely right or wrong; however, designers must choose the best option based on what the users of the API expect. For example, a big data warehouse using an API will have very different expectations from a fleet of tiny IoT (Internet of Things) devices, as we’ll see a bit later. Let’s dive right in and start looking at the most common questions worth answering. Adding functionality The most obvious place to start is whether you want to even consider this ability to augment existing versions as a way of providing new functionality.

The Singularity Is Nearer: When We Merge with AI
by Ray Kurzweil
Published 25 Jun 2024

BACK TO NOTE REFERENCE 92 For more on GDP and marginal cost, see Tim Callen, “Gross Domestic Product: An Economy’s All,” International Monetary Fund, December 18, 2018, https://www.imf.org/external/pubs/ft/fandd/basics/gdp.htm; Alicia Tuovila, “Marginal Cost of Production,” Investopedia, September 20, 2019, https://www.investopedia.com/terms/m/marginalcostofproduction.asp; Sal Khan, “Marginal Revenue and Marginal Cost,” Khan Academy, accessed April 20, 2023, https://www.khanacademy.org/economics-finance-domain/ap-microeconomics/production-cost-and-the-perfect-competition-model-temporary/short-run-production-costs/v/marginal-revenue-and-marginal-cost; Jeremy Rifkin, The Zero Marginal Cost Society: The Internet of Things, the Collaborative Commons, and the Eclipse of Capitalism (New York: St. Martin’s, 2014). BACK TO NOTE REFERENCE 93 See the appendix for the sources used for all the cost-of-computation calculations in this book. BACK TO NOTE REFERENCE 94 “Introducing the AMD Radeon RX 7900 XT,” AMD, accessed January 30, 2023, https://www.amd.com/en/products/graphics/amd-radeon-rx-7900xt; Michael Justin Allen Sexton, “AMD Radeon RX 7900 XT Review,” PC Magazine, December 17, 2022, https://www.pcmag.com/reviews/amd-radeon-rx-7900-xt.

The Practice of Cloud System Administration: DevOps and SRE Practices for Web Services, Volume 2
by Thomas A. Limoncelli , Strata R. Chalup and Christina J. Hogan
Published 27 Aug 2014

We have more freedom to use our time to help others. Happiness multiplies love and it overflows us, leading us to share it with others. These are the early years of all this stuff whose names and definitions are still evolving: cloud computing, distributed computing, DevOps, SRE, the web, the internet of things. We are standing at the base of the mountain, looking up, wondering what the future holds. If you follow every bit of advice in this book, it will not cure all the world’s ills. It will not end poverty or make food taste better. The advice in this book is obsolete as we write it. But it is a start.

The Rust Programming Language
by Steve Klabnik and Carol Nichols
Published 14 Jun 2018

Through efforts such as this book, the Rust teams want to make systems concepts more accessible to more people, especially those new to programming. Companies Hundreds of companies, large and small, use Rust in production for a variety of tasks. Those tasks include command line tools, web services, DevOps tooling, embedded devices, audio and video analysis and transcoding, cryptocurrencies, bioinformatics, search engines, Internet of Things applications, machine learning, and even major parts of the Firefox web browser. Open Source Developers Rust is for people who want to build the Rust programming language, community, developer tools, and libraries. We’d love to have you contribute to the Rust language. People Who Value Speed and Stability Rust is for people who crave speed and stability in a language.

The Ministry for the Future: A Novel
by Kim Stanley Robinson
Published 5 Oct 2020

This meant not only that they had stopped burning carbon to a large extent— not entirely, because that would not be possible in their lifetimes— but they were also drawing carbon down from the air in significant and measurable quantities, by way of all the carbon drawdown efforts in combination. There were discussions as to how much the oceans were still serving as a sink for carbon burned into the air, but now, in the Great Internet of Things, the Quantified World, the World as Data, all these aspects of the problem were being measured, and the ocean’s uptake or drawdown was measured to within a fairly small margin of error; and the conclusion from the scientists involved was that since the ocean had already been quite saturated by the carbon it had absorbed in the previous three centuries, the drop they were seeing was only slightly explained by continuing ocean uptake.

The Rust Programming Language, 2nd Edition
by Steve Klabnik and Carol Nichols
Published 27 Feb 2023

Through efforts such as this book, the Rust teams want to make systems concepts more accessible to more people, especially those new to programming. Companies Hundreds of companies, large and small, use Rust in production for a variety of tasks, including command line tools, web services, DevOps tooling, embedded devices, audio and video analysis and transcoding, cryptocurrencies, bioinformatics, search engines, Internet of Things applications, machine learning, and even major parts of the Firefox web browser. Open Source Developers Rust is for people who want to build the Rust programming language, community, developer tools, and libraries. We’d love to have you contribute to the Rust language. People Who Value Speed and Stability Rust is for people who crave speed and stability in a language.

Building Secure and Reliable Systems: Best Practices for Designing, Implementing, and Maintaining Systems
by Heather Adkins , Betsy Beyer , Paul Blankinship , Ana Oprea , Piotr Lewandowski and Adam Stubblefield
Published 29 Mar 2020

Today, it’s not unusual for a customer to ask for a notification of a potential security problem within 24 hours (or less) of initial detection. Incident notification has become a core feature of the security domain, alongside technological advances such as easy and ubiquitous use of cloud computing, widespread adoption of “bring your own device” (BYOD) policies in the workplace, and the Internet of Things (IoT). Such advances have created new challenges for IT and security staff—for example, limited control over and visibility into all of an organization’s assets. Is It a Crisis or Not? Not every incident is a crisis. In fact, if your organization is in good shape, relatively few incidents should turn into crises.

The Patterning Instinct: A Cultural History of Humanity's Search for Meaning
by Jeremy Lent
Published 22 May 2017

There is already more machine-to-machine communication occurring over the internet than human communication, and it's estimated that, by 2020, there will be as many as fifty billion nodes linking such disparate systems as home networks, production lines, electricity grids, inventory systems, and vehicles. This begins to approach the number of neurons in the human brain, at approximately a hundred billion. Could this massive network, called the “internet of things,” ever develop its own intelligence?47 If machine intelligence became self-aware, what would that look like? For decades, the de facto standard for determining whether a machine is truly intelligent has been the Turing test: A person in a separate room engages in a written conversation with two entities.

Architecting Modern Data Platforms: A Guide to Enterprise Hadoop at Scale
by Jan Kunigk , Ian Buss , Paul Wilkinson and Lars George
Published 8 Jan 2019

There are many drivers for this transformation, but the predominant ones are: Volume The phrase big data has been used too much to retain much value, but the sheer volume of data generated by today’s enterprises, especially those with a heavy web presence—which is to say all enterprises—is staggering. The explosion of data from edge computing and Internet of Things (IoT) devices will only add to the volume. Although storing data in as granular a form as possible may not seem immediately useful, this will become increasingly important in order to derive new insights. Storage is cheap, and bad decisions that have lasting consequences are costly. Better to store in full fidelity with a modern data platform and have the option to make a new decision later.

Seeking SRE: Conversations About Running Production Systems at Scale
by David N. Blank-Edelman
Published 16 Sep 2018

Although the average SRE (hopefully) does not have to prepare for armed engagement with private militias and neo-Nazi terrorists, the parallels are nevertheless easy to draw. The success or failure of a company can depend on the ability to deliver on its promises. Failures in critical services can lead to millions or billions of dollars in productivity losses. As “Internet of Things” development continues and we integrate connected technology into spaces like healthcare, transportation, and more, it does not require a stretch of the imagination to envision an environment when reliability that deviates from the specification literally leads to life-or-death outcomes. As in activism, proper planning and preparation in the form of incident management protocols and predeveloped safeguards are necessary to be ready to address crisis situations.

Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems
by Martin Kleppmann
Published 17 Apr 2017

ISBN: 978-1-603-58055-7 550 | Chapter 12: The Future of Data Systems [93] Daniel J. Bernstein: “Listening to a ‘big data’/‘data science’ talk,” twitter.com, May 12, 2015. [94] Marc Andreessen: “Why Software Is Eating the World,” The Wall Street Journal, 20 August 2011. [95] J. M. Porup: “‘Internet of Things’ Security Is Hilariously Broken and Getting Worse,” arstechnica.com, January 23, 2016. [96] Bruce Schneier: Data and Goliath: The Hidden Battles to Collect Your Data and Control Your World. W. W. Norton, 2015. ISBN: 978-0-393-35217-7 [97] The Grugq: “Nothing to Hide,” grugq.tumblr.com, April 15, 2016. [98] Tony Beltramelli: “Deep-Spying: Spying Using Smartwatch and Deep Learning,” Masters Thesis, IT University of Copenhagen, December 2015.

MONEY Master the Game: 7 Simple Steps to Financial Freedom
by Tony Robbins
Published 18 Nov 2014

Imagine the power of that much connected and unleashed creativity across the planet! The first internet was the internet of military agencies and colleges. Then it was the dot-com internet of companies; then it was the internet of ideas; then, with social media, it was the internet of relationships. Now it’s the internet of things, of all things. Computers and sensors are embedded in everyday objects, transmitting messages back and forth to one another. Machines are connecting to other machines, which are in turn connecting to us and uniting everything in one powerful global network. And 3-D printing is how this internet will be transformed and expanded beyond our craziest dreams. 3-D PRINTING: SCIENCE FICTION TO SCIENCE FACT You know the “replicators” they use in those Star Trek movies to synthesize hamburgers and hot coffee out of thin air on the starship Enterprise?

Wanderers: A Novel
by Chuck Wendig
Published 1 Jul 2019

And now some words from our sponsors.” Everything’s connected now. It’s not just phones and tablets and cameras. It’s doorbells. It’s refrigerators. It’s sex toys! Sex toys are talking to each other! Shit, I know a fella has a trailcam, you know, for hunting? That talks to the web via a cellular signal. The Internet of Things, hell, more like the Internet of Big Brother. The goddamn Panopticon. You can be sure Hunt and her lib-witches are watching us all. Maybe even controlling us. These things talk to each other and they use them to control us. Like fluoride in the water, chemtrails in the air, we’re getting it coming and going.

Empire of Things: How We Became a World of Consumers, From the Fifteenth Century to the Twenty-First
by Frank Trentmann
Published 1 Dec 2015

The crux of the matter is that new technologies don’t automatically replace existing patterns of use. They often complement or add to them. In addition to virtual consumption, telecommunication and the internet have, arguably, reinforced physical consumption by expanding the awareness of the objects and places that exist in the world and making it easier to buy and visit them. For the internet of things to displace the empire of things, it would need to replace the existing culture of use with a radically new one. This is the hope many attach to a sharing and leasing economy. The internet, in this view, will not only bring greater efficiency but wean people off ownership and socialize them into a sharing lifestyle characterized by collaboration, long product-cycles, maintenance and reuse and a low carbon footprint.