Boston Dynamics

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

In the wake of their victory, they watched in amazement as the Boston Dynamics robotic bull trotted toward their garage. It squatted on the ground and shut down. The team members swarmed around the robot and opened the crate that was strapped to its back. It contained a case of champagne, brought as a congratulatory offering from the Boston Dynamics engineers in an attempt to bond the two groups of roboticists who would soon be working together on some future Google mobile robot. Several of the company’s engineers had considered doing something splashier. While planning for the Boston Dynamics demonstrations at the speedway, executives at another one of Rubin’s AI companies came up with a PR stunt to unveil at the Boston Dynamics demonstrations during both afternoons of the Robotics Challenge.

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In the initial DRC competition in 2013, the robots were almost completely teleoperated by a human reliant on the robot’s sensor data, which was sent over a wired network connection. Boston Dynamics built Atlas robots with rudimentary motor control capabilities like walking and arm movements and made them available to competing teams, but the higher-level functions that the robots would need to complete specific tasks were to be programmed independently by the original sixteen teams. Later that fall, when Boston Dynamics delivered the robots to the DRC, and also when they actually competed in a preliminary competition held in Florida at the end of the year, the robots proved to be relatively slow and clumsy.

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DARPA organized the first contest into “tracks” broken broadly into teams that supplied their own robots and teams that used the DARPA-supplied Atlas robots from Boston Dynamics. The preliminary trial turned out to be a showcase for Google’s new robot campaign. Rubin and a small entourage flew into an airport north of Miami on one of Google’s G5 corporate jets and were met by two air-conditioned buses rented for the joint operation. The contest consisted of the eight separate tasks performed over two days. The Atlas teams had a comparatively short amount of time before the event to program their robots and practice, and it showed. Compared with the nimble four-legged Boston Dynamics demonstration robots, the contestants themselves were slow and painstaking.

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

Leopoldina Fortunati, “Robotization and the Domestic Sphere,” New Media and Society 20, no. 8 (August 2018): 2673–2690; Elizabeth Broadbent, “Interactions with Robots: The Truths We Reveal about Ourselves,” Annual Review of Psychology 68 (2017): 627–652. 2. Matt Simon, “Watch Boston Dynamics’ Humanoid Robot Do Parkour,” Wired, October 11, 2018, https://www.wired.com/story/watch-boston-dynamics-humanoid-robot-do-parkour/; Marc DeAngelis, “Boston Dynamics’ Atlas Robot Is Now a Gymnast,” Engadget, September 24, 2019, https://www.engadget.com/2019/09/24/boston-dynamics-atlas-gymnast/. 3. Mark Prigg, “Google’s Terrifying Two Legged Giant Robot Taught How to CLEAN: Researchers Reveal Ian the Atlas Robot Can Now Vacuum, Sweep and Even Put the Trash Away,” Daily Mail, January 15, 2016, https://www.dailymail.co.uk/sciencetech/article-3401743/Google-s-terrifying-two-legged-giant-robot-taught-CLEAN-Researchers-reveal-Atlas-vacuum-sweep-trash-away.html. 4.

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There’s even a soft bean-shaped robot named Somnox that you can spoon to sleep as it “breathes” and makes soothing noises. Many of these sociable characters have some impressive skills, like Boston Dynamics’s humanoid Atlas. Having thrilled everyone with his parkour and backflipping abilities in 2018, Atlas impressed again in 2019 with a gymnastics routine complete with tumbles, 360-degree jumps, a handstand, and even a split leap.2 Atlas’s designers intended him to be deployed in search-and-rescue operations, though one model by Boston Dynamics called Ian might make a decent smart wife. At six feet, two inches, Ian is filmed vacuuming, sweeping, and even taking out the rubbish.3 Swoon.

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See also Siri (Apple) as Big Five member, 85 employment at, and gender, 9 Home system, 46 renewable energy commitment, 101 users’ loss of control over devices, 193–194 working conditions, 99 Apple Watch, 30 Applin, Sally, 187 Aristotle (Mattel), 196–197 Artificial intelligence (AI) biases in, 219 as component of smart wife, 2 diversity imbalance in industry, 9, 10–11, 62 ethics, 224–226 marriage between Alexa and Siri, 127, 210 need for transdisciplinary approach to, 213 Artificial life made in creator’s image, 70 Ashley Too bot (Black Mirror), 218–219 Asian internet companies, 101 Asian smart wife markets, 4 ASIMO (Honda), 49, 56–57, 58, 67 Asimov, Isaac, 172–173 Assistive robots. See Social robots Association for Computing Machinery, 172 Astro Boy, 49, 65–67, 70, 211 Atlas (Boston Dynamics), 51 Audiovisual technologies, 178 Austin Powers (film series), 14, 15 Australia classification and ratings system, 222 and ecological footprint model, 86 functionality and usability of smart wives, 31, 39 housework and gender, 6–7 RUOK Mate, 192 stalking, 199–200 Autism, 52, 74 Ava (Ex Machina), 14, 125 AYA (digital voice assistant), 221 Baidu, 4 Ballie (Samsung), 58–59 Barassi, Veronica, 196 Barber, Trudy, 117 Barbera, Joseph, 25 Barrett, Brian, 188 Bartneck, Christoph, 160 Bates, Laura, 61, 116, 134, 137–138 Beard, Mary, 167 Bechdel Test, 218, 286n35 Becoming Cliterate (Mintz), 122 Beer fridges, 35–36 Bell, Genevieve, 27, 97, 192, 213, 227 Benefits of smart wives, 9, 38–42 Berg, Anne-Jorunn, 32 Bergen, Hilary, 14, 99, 150, 152, 153, 157–158, 163, 164, 192, 193–194 Bezos, Jeff, 79, 80–81, 83–84, 97, 99, 109, 255n34 Big Brother, 175, 193 Big Five, 85, 107, 189 Big Mother, 193 Bipedal FT (efutei), 71 Birhane, Abebe, 174 Black Mirror (TV series), 218–219 Blade Runner (film), 14, 15, 64, 125 Blade Runner 2049 (film), 125 Blue Origin, 81 Body F (RealDoll), 119 Bogost, Ian, 164 Borg (Star Trek), 102–105, 106, 198 Borg, Anita, 212 Bose speakers, 185–186 Boston Dynamics, 51 Bowie, David, 210 Bowles, Nellie, 83, 200, 202 Boyhood, 204.

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

AdWords and AdSense together represented what Levy calls a “cash cow” to fund the next decade’s worth of Web projects, which included the acquisition of YouTube and the creation of the Android mobile operating system, Gmail, Google+, Blogger, the Chrome browser, Google self-driving cars, Google Glass, Waze, and its most recent roll-up of artificial intelligence companies including DeepMind, Boston Dynamics, and Nest Labs.70 More than just cracking the code on Internet profits, Google had discovered the holy grail of the information economy. In 2001, revenues were just $86 million. They rose to $347 million in 2002, then to just under a billion dollars in 2003 and to almost $2 billion in 2004, when the six-year-old company went public in a $1.67 billion offering that valued it at $23 billion.

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The distinguished Financial Times economics columnist Martin Wolf warns that intelligent machines could hollow out middle-class jobs, compound income inequality, make the wealthy “indifferent” to the fate of everyone else, and make a “mockery” of democratic citizenship.20 “The robots are coming and will terminate your jobs,”21 worries the generally cheerful economist Tim Harford in response to Google’s acquisition in December 2013 of Boston Dynamics, a producer of military robots such as Big Dog, a three-foot-long, 240-pound, four-footed beast that can carry a 340-pound load and climb snowy hiking trails. Harford suspects 2014 might be the year that computers finally become self-aware, a prospect that he understandably finds “sobering” because of its “negative impact of . . . on the job market.”22 He is particularly concerned with how increasingly intelligent technology is hollowing out middle-income jobs such as typists, clerks, travel agents, and bank tellers.

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It underpins the automation of classrooms, libraries, hospitals, shops, churches, and homes.”24 With its massive investment in the development of intelligent labor-saving technologies like self-driving cars and killer robots, Google—which has imported Ray Kurzweil, the controversial evangelist of “singularity,” to direct its artificial intelligence engineering strategy25—is already invested in the building and management of the glass cage. Not content with the acquisition of Boston Dynamics and seven other robotics companies in the second half of 2013,26 Google also made two important purchases at the beginning of 2014 to consolidate its lead in this market. It acquired the secretive British company DeepMind, “the last large independent company with a strong focus on artificial intelligence,” according to one inside source, for $500 million; and it bought Nest Labs, a leader in smart home devices such as intelligent thermostats, for $3.23 billion.

To Be a Machine: Adventures Among Cyborgs, Utopians, Hackers, and the Futurists Solving the Modest Problem of Death
by Mark O'Connell
Published 28 Feb 2017

In 2013, Google had paid half a billion dollars for Boston Dynamics, whose menagerie of uncanny creatures—BigDog, Cheetah, Sand Flea, LittleDog—had been created largely with DARPA funding, and whose Atlas robot was being used as hardware by several of the teams here in Pomona. A few hundred yards from the racetrack, in the massive hangarlike building from which the robots were directed by their engineers, a squad of Boston Dynamics technicians was also on hand to tend to the contusions and malfunctions of the Atlas humanoids. Boston Dynamics, with its weird techno-fauna, was itself a hybrid specimen of the relationship between the Pentagon and Silicon Valley; its machines were the unnatural creatures of a new military-industrial complex.

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Highlighted here were some of the organization’s major accomplishments, among the more recent of which were the 2003 launch of the X-45A, an early prototype of the Predator and Reaper drones responsible for the deaths of hundreds of Pakistani civilians and children, and a monstrous unmanned armored ground vehicle named, with admirable frankness, “The Crusher.” Further on, I passed a black quadruped robot in a glass display cabinet, a nightmarish pastiche of a Damien Hirst installation. The encased specimen was a creature known as Cheetah, developed with DARPA funding by Boston Dynamics, an industry-leading robotics laboratory that had been acquired by Google in 2013. This robot was capable of running at 28.3 miles per hour, faster than any recorded human. I had seen it in action on YouTube—itself a wholly owned subsidiary of Google—and it was somehow thrilling and abominable: this rough beast, its hour come at last, emerging at an uncanny gallop from some final merger of corporate and state power in the crucible of technology.

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Hewlett-Packard, the valley’s first major success, was a military contractor whose cofounder David Packard served as deputy secretary of defense during the Nixon administration. His most significant contribution during his term of office, Solnit points out, “was a paper about overriding the laws preventing the imposition of martial law.” I was aware that there was something unreasonable, even slightly hysterical, in my reaction to Boston Dynamics’ menagerie of humanoids and techno-animals, some half-gleeful indulgence of a paranoid tendency, but I could not on that account disregard that reaction. At a subcortical level, I rejected these creatures and what they represented; some primitive, human part of me wanted to smash them with a hammer just as the young Thomas Aquinas destroyed the automaton of Albertus Magnus.

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

Gill Pratt: For Gill Pratt’s quote on the Challenge, see: https://spectrum.ieee.org/automaton/robotics/humanoids/darpa-robotics-challenge-amazing-moments-lessons-learned-whats-next. Boston Dynamics’ robot Atlas: See: https://www.bostondynamics.com/atlas. Honda also got in on the action: Evan Ackerman, “Honda Unveils Prototype E2-D2 Disaster Response Robot,” IEEE Spectrum, October 2, 2017. Softbank: Ingrid Lunden, “Softbank Is Buying Robotics Firm Boston Dynamics and Schaft from Alphabet,” TechCrunch, June 8, 2017. After decades of rising life expectancies: See: https://www.economist.com/graphic-detail/2019/07/09/japans-pension-problems-are-a-harbinger-of-challenges-elsewhere.

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Even DARPA program manager and Robot Challenge organizer Gill Pratt couldn’t abide his own live event: “Why would anyone sit in the sun and heat, watching a machine take an hour to go through eight simple tasks that you could do in five minutes?” But progress was swift. A year later, a video released online showed off Boston Dynamics’ robot Atlas, the second place winner from the 2015 DARPA Challenge, hiking through slick, snowy woods, stacking boxes in a warehouse, even regaining his balance after getting whacked with a hockey stick. A year after that, a different video showed Atlas navigating an obstacle course that included a backflip off a wooden crate and color commentary by a sports announcer: “A 360 spin onto the pallet, backflip gainer off…” Honda also got in on the action.

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By 2017, they’d created a prototype disaster-response bot that could climb ladders, shimmy sideways, and even get down on all fours and knuckle-walk through rough terrain. In the six years since Fukushima, we’d gone from drunken droids to disaster-ready ninjas. And not to be outdone by Honda, 2017 also saw the Japanese conglomerate Softbank buy Boston Dynamics from Alphabet (who had purchased the company back in 2013). The reason? A different national disaster facing Japan—a rapidly aging population and no one to care for the elderly. After decades of rising life expectancies and falling birth rates, Japan entered the new millennium with the bulk of its population edging into retirement, and no one to take their place.

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

The agency may have been thinking about recent calamity at the Fukushima nuclear plant in Japan, where the risks from radioactivity made human presence highly risky. But those are precisely the skills needed for operating in ground close combat too. The robots failed to impress. We’ve become familiar over the last decade with seeing the increasing dexterity of robots produced by the firm Boston Dynamics. They supplied the ATLAS robots used by competitors in the Grand Challenge. ATLAS is an impressive humanoid machine, capable of bounding from one surface to another.7 It’s a small imaginative leap from seeing that to visions of Arnie’s Terminator—a relentless killing machine, that as one character says, ‘absolutely will not stop, ever, until you are dead’.

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ATLAS is an impressive humanoid machine, capable of bounding from one surface to another.7 It’s a small imaginative leap from seeing that to visions of Arnie’s Terminator—a relentless killing machine, that as one character says, ‘absolutely will not stop, ever, until you are dead’. Thankfully the comic failures of the rescue droids in DARPA’s competition suggest Judgment Day lies some way distant. Opening a door handle was too much for one machine, which overbalanced, toppled and was unable to regain its feet.8 Definitely more Shakey than Arnie. The problem is that Boston Dynamics’ machines look amazing, and indeed are impressive. But they are remote controlled. AI controlled gyroscopes keep them upright and agile, but not much more. Their capacity for autonomous decision-making is, at best, undemonstrated in these corporate video releases. And if the state of the art in the Grand Challenge is anything to go by, it won’t be that great.

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Formidable divers, they fold back their wings at the last moment, spearing down into the ocean on the sardines below, in a battle of swarm versus shoal. Could it be that the warbot of the near future will move from one domain to another? More broadly, nature has offered inspiration to roboticists for decades, whether the sinister canter of a headless Boston Dynamics quadruped, or the graceful flapping of a flock of electric blue robot butterflies. This experimental phase, where concepts and platform designs for warbots are proliferating, is a robot menagerie. The best combination of traits in our warbot army remains undetermined. Even if we allow that the swarm is a potent way of using tactical autonomous weapons, it’s not the only possibility.

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

If the AI disproportionately rejects parole for black prisoners, and yet reduces the black prison population significantly, is that a desirable outcome? The question forces consideration of the aim of policy—what counts as a better outcome—but also of the wider social system within which decision making is being delegated from humans to machines. 1. ‘Parkour Atlas’, Boston Dynamics, YouTube, https://www.youtube.com/watch?v=LikxFZZO2sk; ‘UpTown Spot’, Boston Dynamics, YouTube, https://www.youtube.com/watch?v=kHBcVlqpvZ8, accessed 18 October 2018. 2. Public goods from which people can be excluded are known as club goods. 3. With a Laspeyres index. With a Fisher ideal index it would be a conceptual basket of goods, not the actual 2018 (or 1978) basket.

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The continuing economic, social, and political transformations driven by technological change are, paradoxically, making our increasingly machine-run world ever less mechanistic and predictable. As in mediaeval maps, there are monsters in the unknown territories beyond the boundaries of current knowledge. The new monsters are symbolised by the nightmare robot creations of Boston Dynamics.1 Digital transformation of everyday life, of business and consumption, of social relations and politics, raises two questions. One is an old question requiring new answers: what kind of society do we want, and how do we measure progress towards it? The second is what makes policies effective in delivering progress in this non-linear, complex world, that is not amenable to simple causal explanations?

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J., 122, 124 BBC Reith Lectures, 77–78 BBC Trust, 83 Becker, Gary, 2, 92, 119 behavioural economics: aggregation and, 3, 40, 42, 71–72, 100–102, 106, 113, 122–23, 141, 176–77, 201–2; beliefs of tomorrow and, 22; bias and, 109, 136; Coase on, 58; cognitive science and, 35–36, 48, 51, 91–92, 118–19, 186; competition and, 45–51 (see also competition); consumers and, 22, 59–60, 92, 109; context and, 88; failures and, 55; Goodhart’s Law and, 72, 103; happiness and, 70–71, 153; incentives and, 29, 33, 35, 55, 63–64, 80, 106, 110, 160, 200; interventions and, 48, 63, 104, 106, 160, 208, 211; markets as process and, 37–45; models and, 22, 35, 47, 63, 88, 92–93, 119, 136, 154; outsider context and, 88, 92–93, 100, 103–9; performativity and, 11, 23, 30, 211; progress and, 136–37, 145, 154, 157–60; psychology and, 38, 63, 70, 92, 94; public choice theory and, 64, 106, 119, 124; public services and, 33; rationality and, 22, 35, 46–47, 59, 109, 117–19; self-referential policy advice and, 63–64; separation protocol and, 119–20, 124; special interest groups and, 64–66; technocratic dilemma and, 67–79; twenty-first-century policy and, 186, 202, 207–8; Wu study and, 8 Bell, Daniel, 67 Bernanke, Ben, 17 Berners-Lee, Tim, 195 bias: academics and, 6; artificial intelligence (AI) and, 13, 161, 165, 187; behavioural, 109, 136; causality and, 13, 105; control groups and, 105; data, 13, 101, 105, 161, 187, 209; decision making and, 13, 109, 187, 209; framing effects and, 47; gender, 6, 8; institutional, 180; market, 180, 187, 209; non-rational, 47, 109; skill-biased technical change and, 132; special interest groups and, 64–66; survey, 101; twenty-first-century policy and, 187, 209 Biden, Joe, 205 Big Bang, 16 big data, 3, 13, 40, 51, 86, 100, 203, 209 biodiversity, 39, 63, 165 Black, Fisher, 23–25, 28 blackboard economies, 99 black box solutions, 161 BlackLivesMatter, 9, 214 black markets, 43 Black-Scholes-Merton model, 24–25 Blair, Tony, 208 Blake, William, 150 Blue Books, 150 BMW, 196 Booking, 173 Borges, J., 90 Boskin Commission, 146–47 Boston Dynamics, 137 Bowles, Sam, 85, 117, 119 Bretton Woods, 192 Brexit, 1, 37, 53, 56, 70, 110, 131, 155, 213 Brown, Dan, 108 Brynjolfsson, Eric, 176 bubbles, 20, 22, 29 Buchanan, James, 33 budget constraints, 177 Bundeskartellamt, 205 Bureau of Economic Policy Analysis, 66 business cycles, 71, 81, 102, 124 calculus, 16, 33, 90, 145 Calculus of Consent, The: Logical Foundations of Constitutional Democracy (Buchanan and Tullock), 33 Camus, Albert, 87, 108, 111 capitalism: criticism of, 19–20; free market and, 19, 41, 186; globalisation and, 110, 132, 139, 154, 164, 193–94, 196, 213; inequality from, 19; progress and, 143, 149; Schumpeter on, 143; twenty-first-century policy and, 186, 190, 195 Capital (Piketty), 131 carbon emissions, 38–40, 180, 187 Carlin, Wendy, 85 Cartlidge, John, 27 Case, Anne, 131 cash for clunkers, 55, 63 causality: bias and, 13, 105; correlation and, 94; deductive approach and, 103; economically establishing, 100; empirical work and, 2, 61, 94–96, 99; feedback and, 11, 94, 96; Leamer on, 102; methodological debate over, 2; models and, 2, 94–95, 102; moral issues and, 96; outsider context and, 94–96, 99–105; progress and, 137; public responsibilities and, 61, 74; randomised control trials (RCTs) and, 93–95, 105, 109–10; reflexivity and, 11, 81; societal statistics and, 61; statistics and, 61, 95, 99, 102; two-way, 94, 96 central banks: independence of, 16; progress and, 149; public responsibilities and, 16, 32, 62, 64, 66–67, 76, 81 central planning: artificial intelligence (AI) and, 184, 186–87; competition and, 38, 41, 124, 182; failure of communist, 40, 182–88, 190; socialist calculation debate and, 182–88, 190, 209 Central Planning Bureau, 66 Chetty, Raj, 86 Chicago School, 24–25, 73, 75, 190, 193–94 Chile, 184 China, 173, 195, 206 Citadel, 27 City of London, 16, 19 climate change, 85, 148, 154 Close the Door campaign, 155–56 cloud computing, 150, 170–72, 184, 197 Coase, Ronald, 57–58, 62, 98–99 codes of conduct, 9, 206 cognitive science, 35–36, 48, 51, 91–92, 118–19, 186 Colander, David, 100 Cold War, 190 Coming of Post-Industrial Society, The (Bell), 67 common sense, 78, 127 communication, 53, 127, 168; bandwidth and, 171; compression and, 171; cost of, 196; 4G platforms and, 195; instant messaging, 171; latency and, 171; price of, 150, 171, 177; servers and, 25–26, 141, 170; smartphones and, 46, 138–39, 164, 171, 173, 177, 195, 198; SMS, 171; social media and, 52, 73, 82, 140–41, 149, 157, 163, 173, 176–77, 195; telephony and, 4, 31, 46, 98, 123, 138–39, 144, 156, 164, 171, 173–74, 177, 184, 195, 198; 3G platforms, 60, 139, 173, 195; transmission speeds and, 171 comparative advantage, 78, 97 competition: behavioural fix and, 45–51; central planning and, 38, 41, 124, 182; Chinese, 173, 195, 206; creative destruction and, 41; digital economy and, 42, 85, 165, 181, 201–6; directory numbers and, 60; empirical work and, 181, 209; envelopment and, 203–4; incumbents and, 41–42; innovation and, 28, 41, 46, 68, 85, 209; monopolies and, 20, 42; network effects and, 202, 205; opportunity cost and, 56, 58, 80, 156; outsider context and, 98, 105; Pareto criterion and, 122–23, 126–27, 129; production and, 12, 41; profit and, 33, 41–42, 105, 204; progress and, 135, 158, 165; public responsibilities and, 28, 33, 38, 41–42, 45–48, 57–69, 74, 77, 79, 85; rationality and, 117; resource, 41, 45, 117, 123, 125; separation protocol and, 120, 123–25; socialist calculation debate and, 182–83; special interest groups and, 64–66; specific studies in, 12; spectrum auctions and, 60–61; SSNIP test and, 204; twenty-first-century policy and, 182, 201–9 Competition and Markets Authority (CMA), 205 computers: AI and, 116 (see also artificial intelligence [AI]); Black-Scholes-Merton model and, 24–25; changing technology and, 169; cloud computing and, 150, 170–72, 184, 197; data sets and, 2, 13, 51–52, 60, 101, 161, 177, 201, 209; David on, 169; declining price of, 170; empirical work and, 2, 17, 52; exchange locations and, 25; feedback and, 179; Millennium Bug and, 155; Moore’s Law and, 170, 184; power of, 2, 17, 40, 58, 170, 183–84, 188; progress and, 138, 144, 155; rationality and, 116–17; servers and, 25–26, 141, 170; software and, 25, 140, 155, 171, 177–78, 186, 197, 200–201, 203; Solow on, 169; speed and, 25, 184; statistics and, 17, 52, 58, 144, 169; supercomputers, 170; twenty-first-century policy and, 183–84, 186, 188, 214; ultra-high frequency trading (HFT) and, 25–27 conservatism, 30 Consumer Price Index (CPI), 146–47, 172 consumers: bad choices and, 3; behavioural economics and, 22, 59–60, 92, 109; conspicuous consumption and, 42; digital economy and, 42, 137, 172–76, 181, 198, 200–206, 213; empirical work and, 3, 181; income and, 93 (see also income); innovation and, 28, 102, 200; Keynes and, 22; online shopping and, 173, 198; outsider context and, 92, 96, 98, 100–102, 105, 108–9; progress and, 137, 141, 144, 146–47, 151; public responsibilities and, 22, 28, 42, 59–60, 65; rationality and, 116; technology and, 28, 102, 171–76, 181, 200, 213; time spent online, 176–78; twenty-first-century policy and, 184, 198–206; welfare and, 105, 206 Cook, Eli, 150 copyright, 140 CORE’s The Economy, 85–86, 212–13 cost-benefit analysis (CBA), 56–57, 58n12, 125–26, 207 cost of living, 143–47, 172 counterfactuals, 97–98, 158, 161, 198, 208 Covid19 pandemic: body politics and, 163; financial recovery from, 88, 114; GDP growth and, 88, 165; impact of, 3, 10–11, 14, 20, 38, 43, 45, 68, 75, 88, 110, 114, 132–33, 149, 153, 155, 163–66, 181, 194, 213–15; lockdowns and, 3, 43, 45, 88, 114, 163, 198; public opinion and, 165–66 “Creating Humble Economists” (Colander), 100 creative destruction, 41 curriculum issues, 2, 4–5, 83, 85, 88 Daily Telegraph, 159 Darwin, Charles, 48 data centres, 26 data sets, 2, 13, 51–52, 60, 101, 161, 177, 201, 209 David, Paul, 169 Deaths of Despair (Case and Deaton), 131 Deaton, Angus, 128–29, 131 debt, 76, 101, 153 decision making: artificial intelligence (AI) and, 116, 186–87; bias and, 13, 109, 187, 209; Green Book and, 56, 126; normative economics and, 110, 114, 120; opportunity cost and, 56; outsider context and, 93; production and, 12, 123, 140, 196; progress and, 160, 162; rationality and, 116 (see also rationality); rules of thumb and, 47–48, 90, 117, 212; self knowledge and, 81; separation protocol and, 120 DeepMind, 115–16 Deliveroo, 173 demand management, 31, 191–92 democracy, 33, 67, 69, 79, 193 deregulation, 16, 31, 60, 68, 71, 193–94 derivative markets, 16, 18, 23–25, 28 Desrosières, Alain, 146 Dickens, Charles, 150 digital economy: AI and, 115 (see also artificial intelligence (AI)); changing nature of, 168–81; cloud computing and, 150, 170–72, 184, 197; cogs and, 6, 129, 154, 165, 179; competition and, 42, 85, 165, 181, 201–6; consumers and, 42, 137, 172–76, 181, 198, 200–206, 213; difference of, 168–76; dominance of by giant companies, 133; envelopment and, 203–4; 4G platforms, 195; GAFAM and, 173; globalisation and, 110, 132, 139, 154, 164, 193–96, 213; GPTs and, 169; Great Financial Crisis (GFC) and, 113–14; growth and, 129, 132, 140, 143, 194, 202; implications of, 176–78, 211–14; individual and, 6, 13–14, 128–29, 141, 175, 179, 181, 201; innovation and, 169–70; market changes and, 173–76; measuring online value and, 176; monsters and, 6, 154; network effects and, 127, 141, 174, 177, 185, 199–202, 205, 209; new agenda for, 179–81; online shopping and, 173, 198; Phillips machine and, 135–37, 151, 192; populism and, 211; production and, 132, 140, 142, 176, 195–97, 202, 213; progress and, 14, 137–43, 150, 153–54, 164–67; Project CyberSyn and, 184; services and, 176; software and, 25, 140, 155, 171, 177–78, 186, 197, 200–201, 203; statistics and, 113, 150, 164, 170, 172, 212; superstar features and, 173–74; 3G platforms, 60, 139, 173, 195; twenty-first-century policy and, 13, 185–88, 194–210; wealth creation and, 132–33; welfare and, 128, 134, 143, 206, 208, 212 Director, Aaron, 190 directory numbers, 60 discount rates, 147–48 diversity, 6–9, 213–14 Dow Jones, 26 Duflo, Esther, 20–21, 52, 109, 137 eBay, 175 ECO, 11 Economics Job Market Rumors, 8 Economics Observatory (ECO), 214 economies of scale: changing technology and, 174; network effects and, 127, 174, 177, 185, 199–201, 209; progress and, 142 education: derivatives and, 16; growth and, 16–17, 132; interventions and, 12; online, 177; policy on, 60; provision of basic, 30; real-world context and, 88; skills and, 88, 128, 132, 169–70; spread of higher, 151, 153 Efficient Markets Hypothesis, 17, 29 Eichengreen, Barry, 16 electricity: changing economies and, 127, 169, 191–92; progress and, 139, 142, 156, 165, 169, 191–92; regulation and, 65; supply of, 32; twenty-first-century policy and, 191–92, 200–201; warranties on goods and, 105 empirical work: behavioural economics and, 117, 159; causality and, 2, 61, 94–96, 99; competition and, 181, 209; computers and, 2, 17, 52; consumers and, 3, 181; context and, 17, 35, 61, 78, 92; correlation and, 70, 94; counterfactuals and, 97–98, 158, 161, 198, 208; data sets and, 2, 13, 51–52, 60, 101, 161, 177, 201, 209; feedback and, 11, 94–95, 155, 179, 188–89, 203, 205; growth and, 17, 61, 78, 209; macroeconomics and, 74, 100; market structures and, 35; physics envy and, 50; politics and, 3, 76, 78–79, 124, 213; populism and, 77; public responsibilities and, 17, 35, 40, 52, 61, 70, 74–81, 90, 92, 94–102, 110–11; randomised control trials (RCTs) and, 93–95, 105, 109–10; rationality and, 17; separation protocol and, 119, 124, 128; social constructs and, 13; statistics and, 17, 52, 61, 90, 95, 99; taxes and, 3; theory and, 2, 17, 52, 74, 90, 96, 99, 124, 181 endogenous growth theory, 17, 202 Enlightenment, 20 envelopment, 203–4 environmentalists, 126 equilibrium, 31, 38–39, 90–91, 123, 182 ethics, 4, 34, 39, 100, 105, 115, 119–24 Ethics and Society group, 115 ethnicity, 6–7, 9 European Commission, 67, 130, 205 European Steel and Coal Community, 190 European Union (EU), 37, 67, 195, 204 Eurozone, 67, 74 exchange rates, 118, 192 Facebook, 133, 173, 204–5 facial recognition, 165 fairness, 43, 45–46, 166 fake items, 98 Fear Index, The (Harris), 27 feedback: causality and, 11, 94–96; changing technology and, 179; political economy and, 188–89; progress and, 155; twenty-first-century policy and, 203, 205 financial intermediation services indirectly measured (FISIM), 28 Financial Times, 68–69, 97–98 Fisher Ideal index, 144n3 fixed costs, 174, 177, 179, 185–86, 200 forecasting: agent-based modeling and, 102; conditional projections and, 76; financial crises and, 17, 30, 100–101, 112–13; growth and, 37, 61; inflation and, 36; macroeconomics and, 3, 12, 36–37, 76, 101–2, 112; models and, 17, 74, 101–2, 113; self-fulfilling prophecies and, 5, 22–23, 154–55, 157; twenty-first-century policy and, 205; weather, 76 Fourastié, J., 191 4G platforms, 195 framing, 47, 130, 208 Frankenfinance, 18, 21, 25, 51–52, 165 Freakonomics, 108 free market: Brexit and, 213; capitalism and, 19, 41, 186; criticism of, 19; globalisation and, 110, 132, 139, 154, 164, 193–94, 196, 213; politics and, 30, 36, 130, 206; public responsibilities and, 19, 30–32, 35–36, 45, 54; separation protocol and, 123–24; twenty-first-century policy and, 182, 186, 191, 193, 195, 207 frictions, 22, 113, 136, 154, 182 Friedman, Ben, 16 Friedman, Milton, 16, 31, 93, 104, 121, 190 Furman, Jason, 86 GAFAM, 173 GameStop, 27 game theory, 48, 90–91, 129, 159–60, 179–80 Gelman, Andrew, 108 gender, 6–9, 93 GenZ, 166 Giavazzi, Francesco, 68 Gigerenzer, Gerd, 48 Gilded Age, 133 Giudici, Claudio, 69 Glaeser, Ed, 92 globalisation, 110, 132, 139, 154, 164, 193–96, 213 Goldman Sachs, 19 Good Economics for Hard Times (Banerjee and Duflo), 109 Goodhart’s Law, 72, 103 Google, 133, 141, 173, 201, 204–5 Gordon, Robert, 142 Gould, Stephen Jay, 49–50 Gove, Michael, 110, 149 Government Economic Service (GES), 53, 83–85 GPT, 169 Great Depression, 3, 10, 17, 20, 74, 191, 213 Great Financial Crisis (GFC): behavioural economics and, 51; consequences of, 1, 3, 11, 213; digital economy and, 113–14; dynamic stochastic general equilibrium models and, 31; forecasting, 30, 101, 112–13; Greece and, 56–58, 67; Italy and, 56–58, 67–69; models and, 31, 101, 113; outsider context and, 87–88, 101, 110, 112–14; progress and, 149, 153, 159; public responsibilities and, 16–19, 21, 29–31, 37–38, 50–51, 56, 67–68, 73–74, 79, 84; technology and, 56, 181; twenty-first-century policy and, 194 Great Moderation, 17, 73 Greece, 56, 67–68 greed, 11, 16, 29, 164 Green, Duncan, 95–96 Green Book, 56, 126 Greenspan, Alan, 101 Griliches, Zvi, 198 Gross Domestic Product (GDP), 60; Covid19 pandemic and, 88, 165; Fisher Ideal index and, 144n3; FISIM and, 28; flatlining of, 142; free market and, 130; Gross Domestic Product (GDP) and, 172–73; Gross National Product (GNP) and, 151; growth and, 28, 46, 88, 97, 138, 143–44, 159, 165, 169, 171–72; inflation and, 13, 113, 148; internet and, 97; Laspeyres index and, 144n3; macroeconomics and, 13, 101, 113, 151; progress and, 138, 142–44, 148, 151, 158–59, 165, 172–73; real, 101, 142–44, 169, 173; Sen-Stiglitz-Fitoussi Commission on the Measurement of Economic Performance and, 151; social welfare and, 134; twenty-first-century policy and, 187; Winter of Discontent and, 158, 192 Gross National Product (GNP), 151 Grove, Andy, 41 growth: changing economies and, 171–72, 212; Covid19 pandemic and, 88, 165; derivatives market and, 16, 23, 28; digital technology and, 129, 132, 140, 143, 194–210; education and, 16–17, 132; empirical work and, 17, 61, 78, 209; endogenous growth theory and, 17, 202; faster, 66, 71, 144, 159; forecasting, 37, 61; Goodhart’s Law and, 72; Gross Domestic Product (GDP) and, 28, 46, 88, 97, 138, 143–44, 159, 165, 169, 171–72; income, 70, 131, 138, 143, 164–65, 194, 207; inflation and, 12, 66, 73, 178; innovation and, 37, 41, 46, 68, 71, 194, 209; internet and, 97; living standards and, 143–47, 172, 194; outsider context and, 12, 97, 101n1, 111; political economy and, 167, 181, 188–95; progress and, 138, 140, 143–45, 152, 159, 165; public responsibilities and, 16–17, 23, 28, 37, 41, 46, 61, 66, 68–73, 76, 78; recession and, 17, 51, 73, 111, 154, 158–59; slow, 11, 72; spillovers and, 129–30; sustainability and, 11, 20, 111, 148, 152, 166; technology and, 71, 132, 140, 202; twenty-first-century policy and, 187, 191–92, 194, 202, 207, 209; velocity of money and, 71 Guardian, 159 happiness, 70–71, 153 Harberger, A.

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

The smart home cannot fold the laundry, clear the dishes, or pick up the newspaper. It really wants a physical robot to do its bidding. FIGURE 5: (left) BRETT folding towels; (right) the Boston Dynamics SpotMini robot opening a door. It may not have too long to wait. Already, robots have demonstrated many of the required skills. In the Berkeley lab of my colleague Pieter Abbeel, BRETT (the Berkeley Robot for the Elimination of Tedious Tasks) has been folding piles of towels since 2011, while the SpotMini robot from Boston Dynamics can climb stairs and open doors (figure 5). Several companies are already building cooking robots, although they require special, enclosed setups and pre-cut ingredients and won’t work in an ordinary kitchen.19 Of the three basic physical capabilities required for a useful domestic robot—perception, mobility, and dexterity—the latter is most problematic.

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There are dozens of grasp types just for rigid objects and there are thousands of distinct manipulation skills, such as shaking exactly two pills out of a bottle, peeling the label off a jam jar, spreading hard butter on soft bread, or lifting one strand of spaghetti from the pot with a fork to see if it’s ready. It seems likely that the tactile sensing and hand construction problems will be solved by 3D printing, which is already being used by Boston Dynamics for some of the more complex parts of their Atlas humanoid robot. Robot manipulation skills are advancing rapidly, thanks in part to deep reinforcement learning.20 The final push—putting all this together into something that begins to approximate the awesome physical skills of movie robots—is likely to come from the rather unromantic warehouse industry.

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A superficially quite different explanation of explanation-based learning: John Laird, Paul Rosenbloom, and Allen Newell, “Chunking in Soar: The anatomy of a general learning mechanism,” Machine Learning 1 (1986): 11–46. Image Credits Figure 2: (b) © The Sun / News Licensing; (c) Courtesy of Smithsonian Institution Archives. Figure 4: © SRI International. creativecommons.org/licenses/by/3.0/legalcode. Figure 5: (left) © Berkeley AI Research Lab; (right) © Boston Dynamics. Figure 6: © The Saul Steinberg Foundation / Artists Rights Society (ARS), New York. Figure 7: (left) © Noam Eshel, Defense Update; (right) © Future of Life Institute / Stuart Russell. Figure 10: (left) © AFP; (right) Courtesy of Henrik Sorensen. Figure 11: Elysium © 2013 MRC II Distribution Company L.P.

The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies
by Erik Brynjolfsson and Andrew McAfee
Published 20 Jan 2014

In March of 2012, Kiva was acquired by Amazon—a leader in advanced warehouse logistics—for more than $750 million in cash.31 Boston Dynamics, yet another New England startup, has tackled Moravec’s paradox head-on. The company builds robots aimed at supporting American troops in the field by, among other things, carrying heavy loads over rough terrain. Its BigDog, which looks like a giant metal mastiff with long skinny legs, can go up steep hills, recover from slips on ice, and do other very dog-like things. Balancing a heavy load on four points while moving over an uneven landscape is a truly nasty engineering problem, but Boston Dynamics has been making good progress. As a final example of recent robotic progress, consider the Double, which is about as different from the BigDog as possible.

…

It makes for compelling entertainment, and it seems more and more plausible as technology continues to advance and demonstrate human-like capabilities. Teamwork, after all, is another of these capabilities, so why wouldn’t future versions of Watson, the Google autonomous car, the BigDog robot from Boston Dynamics, drone aircraft, and lots of other smart machines decide to work together? And if they did, wouldn’t they soon realize that we humans treat our technologies pretty poorly, scrapping them without a second thought? Self-preservation alone would plausibly motivate this digital army to fight against us (perhaps using Siri as an interpreter for the enemy).

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id=USARGDPH INDEX Academically Adrift: Limited Learning on College Campuses (Arum and Roksa) Acemoglu, Daron Affinnova Aftercollege.com Agarwal, Anant Age of Spiritual Machines, The: When Computers Exceed Human Intelligence (Kurzweil) Agrarian Justice (Paine) agriculture: development of inelastic demand in Ahn, Luis von Aiden, Erez Lieberman Airbnb.com Alaska, income guarantee plan in algorithms Allegretto, Sylvia Allstate Amazon Amazon Web Services American Society of Civil Engineers (ASCE) Android animals, domestication of Apple Arthur, Brian artificial intelligence (AI) future of SLAM problem in uses of see also robots Arum, Richard ASCI Red ASIMO Asimov, Isaac Asur, Sitaram Athens, ancient ATMs Audi Australia, immigrant entrepreneurship in Autodesk automation: future of labor market effects of in manufacturing Autor, David Baker, Stephen Barnes & Noble Bartlett, Albert A. Bartlett, Bruce Bass, Carl batteries Baxter Beane, Matt Bebchuk, Lucian Beck, Andrew Bed Bath & Beyond Berners-Lee, Tim Bernstein, Jared Bezos, Jeff Bhutan BigDog Black Death Blecharczyk, Nathan Blogger books: digitization of Internet retailing of Boskin Commission Boston Dynamics Boudreaux, Donald bounty digitization and productivity growth and spread vs. Brabham, Daren Brain Gain: Rethinking U.S. Immigration Policy (West) breast cancer Bresnahan, Tim Brin, Sergey broadband Brookings Institution Brooks, Rodney browsers Brynjolfsson, Erik Buddha Bureau of Economic Analysis, U.S.

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

As a result, the company can in principle fuse together a suite of virtually hegemonic web products like GMail and the Chrome browser, the hundreds of millions of devices running the Android operating system, a high-resolution global mapping capability, the networked Nest thermostats and other home-automation systems, the Glass augmented reality visor, the Daydream VR headset, an autonomous-car initiative, the DeepMind artificial intelligence unit, the Sidewalk Labs smart-city effort, even the military robots produced by their Boston Dynamics division. There is surely something troubling, if not outright dystopian, about this particular assembly of forces and capabilities. The thought that a single entity controls all of these products and services—and is able to tap and exploit the flow of information as it courses through and between them—is more than a little unsettling.

…

It has been widely reported that the Nest team loathed founder Tony Fadell, and the division suffered from a string of embarrassing reversals during its time under the Google aegis;2 to date, the parent organization has been unable to leverage the data presumably flowing upstream from its thermostats and networked cameras. Boston Dynamics was put up for sale in March 2016, in what has been characterized as a corporate retreat from the entire field of robotics (and what was notably, again, a failure to integrate organizational cultures following an acquisition).3 The company’s autonomous car initiative has suffered a long wave of defections among senior personnel, and keeps rolling back the date at which it plans to introduce its driverless technology;4 it now estimates its vehicles will be fielded commercially no sooner than 2020.

…

,” Neurobiology of Aging, April 2009, Volume 30, Issue 4, pp. 507–14. 10Radical technologies 1.Bruce Sterling and Jon Lebkowsky, “Topic 487: State of the World 2016,” The WELL, January 3, 2016, well.com. 2.Mark Bergen, “Nest CEO Tony Fadell Went to Google’s All-Hands Meeting to Defend Nest. Here’s What He Said,” Recode, April 13, 2016. f 3.Brad Stone and Jack Clark, “Google Puts Boston Dynamics Up for Sale in Robotics Retreat,” Bloomberg Technology, March 17, 2016. 4.John Markoff, “Latest to Quit Google’s Self-Driving Car Unit: Top Roboticist,” New York Times, August 5, 2016. 5.Mark Harris, “Secretive Alphabet Division Funded by Google Aims to Fix Public Transit in US,” Guardian, June 27, 2016. 6.Siimon Reynolds, “Why Google Glass Failed: A Marketing Lesson,” Forbes, February 5, 2015. 7.Rajat Agrawal, “Why India Rejected Facebook’s ‘Free’ Version of the Internet,” Mashable, February 9, 2016. 8.Mark Zuckerberg, “The technology behind Aquila,” Facebook, July 21, 2016, facebook.com/notes/mark-zuckerberg/the-technology-behind-aquila/10153916136506634/. 9.Mari Saito, “Exclusive: Amazon Expanding Deliveries by Its ‘On-Demand’ Drivers,” Reuters, February 8, 2016. 10.Alan Boyle, “First Amazon Prime Airplane Debuts in Seattle After Secret Night Flight,” GeekWire, August 4, 2016. 11.Farhad Manjoo, “Think Amazon’s Drone Delivery Idea Is a Gimmick?

When Computers Can Think: The Artificial Intelligence Singularity
by Anthony Berglas , William Black , Samantha Thalind , Max Scratchmann and Michelle Estes
Published 28 Feb 2015

One common approach is to plan the motions in the much higher dimensional space of joint angles rather than the locations in three dimensional space. Movement and Balance One of the more difficult tasks for a humanoid robot is to maintain balance while walking over rough surfaces. The Atlas robot shown below can walk over a surface covered by unstable rocks. Atlas was developed by the Boston Dynamics company which has recently been purchased by Google. The kinodynamic processing requires very carefully measuring the current state of the robot’s balance and movement. This is then compared to the desired state so that movements can be planned that will produce the desired state. Due to the chaotic environment, these plans never quite work as expected, so new plans need to be continuously produced.

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The reader is encouraged to view the video of Atlas’s impressive performance, but it is still moves rather awkwardly, and only remains upright by flailing its weighted arms around quite vigorously. This is in stark contrast to a human that could not only walk but run over this terrain very smoothly. Atlas robot walking over rough terrain. Corporate Boston Dynamics Robocup Humanoid robocup. Corporate http://www.pbs.org/wgbh/nova/tech/soccer-playing-robots.html Some of the greatest advances in robotics are demonstrated at the annual international Robocup event, in which dozens of teams of engineers compete to build humanoid robots that can win a game of soccer against competing robots.

…

One major benefit of the DARPA challenge is that DARPA has funded a sophisticated, publicly available, open source robotic simulator named Gazebo. This makes it much easier for smaller research teams that are not part of the main challenge to do advanced robotics research. Presumably the Atlas humanoid robots being built for DARPA by Boston Dynamics will also become available at more reasonable prices. While these problems may not require the resolution of the deeper issues in artificial intelligence, they do require the solution of many simpler ones, particularly in machine vision and sensing. And building a system that actually works coherently involves much more complexity than just the sum of the parts.

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

Moflin, from Vanguard Industries, claims to be furry, emotionally responsive, and make all the right noises, and, of course, it doesn’t have bowels or need to be taken outside. Tombot is marketed as an emotional-support animal who will bark for treats and wag his tail. He’s always a puppy, and he’s always there. I prefer Spot – from Boston Dynamics – but Spot is a working dog. With a great video. * * * For anyone who can’t go out – or who is afraid to do so – a robopet can manage without a walk, though your AI dog can be programmed to actively encourage you to go out – some have timers that can be set, and the dog woofs for walkies.

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They are also excellent at carrying people. * * * Robots. One word. So many applications. A mechanical programmable device. Giant assembly-line arms. R2-D2, C-3PO, Data, the Terminator. Sophia and her family (she has an argumentative brother called Hans). A sexbot with blinking eyes and a ro-gasm. Boston Dynamics’ Spot the dog. Robots are not one thing. Not one shape. Not one job. Robots are developing all the time. The smarter AI gets, the smarter robots will get. At present there are serious technical issues to overcome. All artificial intelligence is narrow AI – programmed, specific, problem-solving that doesn’t transfer well to other domains.

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Auden, reproduced with permission of Curtis Brown Ltd; p.145 Harmony RealDoll at the 2020 AVN Adult Entertainment Expo © Ethan Miller / Getty Images; p.162 Pepper the Humanoid Robot at the Tokyo International Film Festival © Dick Thomas Johnson / Creative Commons; p.167 Moflin © Vanguard Industries ; p.168 Tombot © Tombot, Inc; p.168 Spot © Boston Dynamics; p.169 Little Sophia © Hanson Robotics; p.170 Sophia the Robot © Hanson Robotics; p.210 Wrens operating the Colossus computer © Science & Society Picture Library; p.211 Ann Moffatt and her daughter in 1968 © Ann Moffatt; p.215 Woman setting the wires of the ENIAC, 1947 © Francis Miller / Getty Images; p.249 ‘An Arundel Tomb’ is from The Whitsun Weddings by Philip Larkin, reproduced with permission of Faber and Faber Ltd Every effort has been made to trace the copyright holders and obtain permission to reproduce this material.

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

Sensor technology, speech recognition, information searches, and so forth are advancing apace. So (albeit with a more substantial lag) is physical dexterity. Robots are still clumsier than a child in moving pieces on a real chessboard, tying shoelaces, or cutting toenails. But here too there is progress. In 2017, Boston Dynamics demonstrated a fearsome-looking robot called Handel (a successor to the earlier four-legged Big Dog), with wheels as well as two legs, that is agile enough to perform back flips. But it will be a long time before machines outclass human gymnasts—or indeed interact with the real world with the agility of monkeys and squirrels that jump from tree to tree—still less achieve the overall versatility of humans.

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See also genomes bio terror, 73, 75, 77–78 bioweapons of governments, 77 black carbon, reduction of, 47 Black Death, 76, 216 black holes: in center of Milky Way, 124; crashing together, 171; Einstein’s theory applied to, 166, 186; evaporation of, 179; fears about particle accelerators and, 111–12; as simple entities, 166, 173; space telescopes with evidence of, 142 blockchain, 220 Blue Origin, 146 Borucki, Bill, 132 Boston Dynamics, 88 bottlenecks, evolutionary, 155–56, 158 Boyle, Robert, 61–63 brain: basic science needed for medical applications to, 212; chain of complexity from big bang to, 214; complexity of, 174, 176–77; computer simulations of, 190; limits to human understanding and, 189–90, 192–94; mystery of self-awareness and, 193 brain death, 71 brain implants, downloading thoughts from, 105 Breakthrough Listen, 157 Brewster, David, 126–27 Brooks, Rodney, 106 Brundtland, Gro Harlem, 26 Bruno, Giordano, 129 C4 pathway, 25 carbon capture and storage, 51, 58 carbon dioxide in atmosphere, 1, 38–44; cosmic history of carbon atoms in, 123; cutting to preindustrial level, 52; direct extraction of, 59; electric cars and, 47; predicting accelerated increase in, 57–58.

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

Enabled by a new generation of sensors and actuators, and driven by near-infinite computing and artificial intelligence, there’s a Cambrian explosion49 in robotics, with species of all sizes, shapes, and modes of mobility crawling out of the muck of the lab and onto the terra firma of the marketplace. Festo, for one example, has created a robot that flies like a bird. Boston Dynamics, for another, now makes robots that can climb, crawl, jump, and hop, and all while carrying heavy loads (some bots can manage over a hundred kilograms of weight). These “Sherpa-bots” can traverse boulder-strewn hillsides, balance on sheets of ice, and even jump from the ground to a rooftop three stories up.

…

These “Sherpa-bots” can traverse boulder-strewn hillsides, balance on sheets of ice, and even jump from the ground to a rooftop three stories up. But what has been relatively slow progress—run out of university labs and funded by government grants—took a quantum leap forward in late 2013, when Amazon announced it was going into the drone business50 and Google announced the acquisition of eight robotics companies (including Boston Dynamics).51 With the big dogs in the game, progress is coming even faster. And the resulting change will be considerable. Robots don’t unionize, don’t show up late, and don’t take lunch, yet Baxter can work an assembly line for the equivalent of $4 an hour.52 A 2013 report from the Oxford Martin School concludes that 45 percent of American jobs are at high risk of being taken by computers (AI and robots) within the next two decades.53 Good or bad, this same trend is evident around the world.

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Italic numbers refer to charts/graphs Aabar, 127 Abundance (Diamandis and Kotler), xi–xii, xv, 34, 54, 136, 137, 146, 162, 274 AbundanceHub.com, 158, 162, 210, 277 Abundance360Summit, 278 Academy of Achievement, 129 activists, xiii, 180 in crowdfunding campaigns, 201–3, 212, 230 additive manufacturing, 30, 31, 33, 41 AdhereTech, 47 AdSense, 139 Advanced Research Projects Agency Network (ARPANET), 27 advertising, 241, 242 in crowdfunding campaigns, 212–13 crowdsourcing platforms for, 151, 152–54, 158 advocates, in crowdfunding campaigns, 200–201, 205 AdWords, 241 aerospace industry, 112, 117, 133 skunk methodology used in, 71–73, 75 3–D printing and, 34, 35–37 see also space exploration affiliate marketing, 199–200, 205 Ahn, Luis von, 154, 155–56 Airbnb, 20, 21, 66 Airbus, 249 airlines, 43, 124, 125, 126, 127, 260 AI XPRIZE, 54 algorithms, 43, 51, 52, 66, 85, 220, 227 crowdsourcing projects and, 158, 159, 160, 161, 227 machine-learning, 54–55, 55, 58 Netflix Prize for, 254–56 PageRank, 135 Align Technology, 34–35 Amazon, 50, 76, 97, 128, 129–34, 157, 195, 254 drone proposal of, 61, 133–34 Amazon Web Services, 131, 132 America Online (AOL), 76, 143 Anderson, Chris, 10–11, 54, 123, 224, 229, 242 Anderson, Eric, 95, 96, 97, 98, 99, 100, 179, 202, 203–4 Andraka, Jack, 65 Andreessen, Marc, 27, 33 Android, 16, 135, 176 AngelList, 172, 173–74 Anheuser-Busch, 145 Ansari XPRIZE, 76, 96, 115, 127, 134, 246, 248–49, 253, 260, 261, 262, 263, 264, 265, 266, 267, 268 anti-aging projects, 66, 81, 136, 139 Apollo Program, 96, 100, 118, 139 Appert, Nicolas, 245 Appirio, 227–28 Apple, 18, 28, 62, 72, 111, 128 applications (apps), 13, 13, 15, 16, 28, 45, 47, 150, 158, 176 Arduino, 43 ARKYD Space Telescope campaign, 172, 174–75, 179–80, 186, 187, 188, 193, 195, 207, 209, 242 early donor engagement in, 203–5 hype created in, 205 launch of, 200, 208 recruiting of activists in, 201–3, 212 ”space selfie” reward offered in, 180, 189–90, 196, 208 Arnaout, Ramy, 227 artificial intelligence (AI), x, 22, 24, 41, 44, 52–59, 61, 62, 63, 66, 81, 135, 146, 160, 162, 216, 228, 275, 276, 295n crowdsourcing projects and, 167, 295n entrepreneurial opportunities in, 54, 56–59 Google’s development of, 24, 53, 58, 81, 138–39 Association of Space Explorers, 102 asteroids, ix–x, 180, 228–29 mining of, 95–96, 97–99, 107, 109, 179, 221, 276 Asteroid Zoo, 228 astronomy, 219–21, 228, 247, 267 Autodesk, 48–49, 51, 63, 65 automation, 47–48, 56 automobile industry, 29, 222–23 3–D printing in, 32 see also Local Motors; Tesla Motors autonomous cars, 43–44, 44, 48, 62, 66, 135, 136, 137, 262 autonomy, 79, 80, 85, 87, 92 Babson School of Business, 14 BackRub (research project), 135 Bacon, Jono, 237 Bad Girl Ventures, 19 bake-offs, in incentive competitions, 264 Barnett, Chance, 173 Barrie, Matt, 149–50, 158, 163, 165, 166, 167, 207 Barry, Dan, 35, 61, 62 Bass, Carl, 48–49, 50, 51 Baxter (robot), 60–61 Beland, Francis, 250 Bennett, Jim, 255 Berns, Gregory, 108 Beth Israel Deaconess Medical Center (Boston), 227 Better Blocks, 240–41 Bezos, Jeff, xiii, 73, 97, 115, 126, 128–34, 138, 139, 167 on risk management, 76–77 thinking-at-scale strategies of, 128, 129, 130–33 Bezos, Mark, 128 Bianchini, Gina, 217, 219, 224, 233 Big Think, 49, 121 biotechnology, 63–65 see also genomics; synthetic biology BlackBerry, 176 Blakey, Marion, 110 Blastar (video game), 117 blogs, in crowdfunding campaigns, 177, 205, 206 blue ellipticals, 219–20 Blue Origin, 97, 133 Boeing, 127, 249 Boston Dynamics, 61 Boston Globe, 227 Brand, Stewart, 26 Branson, Richard, xiii, 73, 84, 86, 99, 100, 111–12, 115, 123–28, 138, 139 space tourism projects of, 96–97, 115, 125, 127 thinking-at-scale strategies of, 125–27 Briggs, William, 47 Brin, Sergey, 81, 128, 135 British Admiralty, 267 British Airways, 124, 125, 126 British Medical Journal, 109 British Petroleum (BP), 250, 251 Brooks, Rodney A., 60 Brown, Dan, 152 brute force, 51 Burchard, Brendon, 210 Business Insider, 132 Business World, 144 buzz marketing, 240–41 Bye, Stephen, 45 Calacanis, Jason, 139 Calico, 139 Callaghan, Jon, 62 Caltech, 27 camel racing, 59–60 cameras, 3–4, 152 see also digital cameras Cameron, James, 250 campaign managers, in crowdfunding, 192, 194 Canadian Space Agency (CSA), 102 Cane, Daniel, 57 Capp, Al, 71, 72 CAPTCHA, 154, 155, 167, 295n CastingWords, 145 celebrity (the face): in crowdfunding campaigns, 192, 198, 207 in incentive competitions, 273 cell phones, 49, 135, 163 see also smartphones CFM International, 34 challenge/skills ratio, 91 “charge-coupled device” (CCD), 4–5 Chen, Michael, 35, 36, 37 China, 17, 18, 62 Chinese National Space Administration, 102 Chrome, 135, 138 Chung, Anshe, 144 Cinematch, 254, 255 Cisco, 46 Clarke, Arthur C., 52, 53, 100 cloud services, 39, 45, 50, 51, 56, 57, 63, 65, 66, 132, 216, 227–28 CNN, 48 cognitive biases, 246 cognitive surplus, 215 CoheroHealth, 47 Colgate Palmolive, 154 Comedy Central, 95 communities, online, 22, 182, 215–42, 243 building member base in early days of, 233–34 case studies of, 219–28 collaborative structures of, 217, 227, 228, 236, 237, 255 contests and competitions in building of, 224, 225–27, 232, 237, 240; see also incentive competitions DIY, see DIY communities driving growth in, 239–41 engagement strategies in, 224, 227, 235, 236–38, 239, 241 exponential, see exponential communities Law of Niches in, 221, 223, 228, 231 managing of, 238–39 monetization of, 241–42 passion as important in, 224, 225, 228, 231, 258 rate of innovation in, 216, 219, 224, 225, 228, 233, 237 rating systems in, 226, 232, 236–37 reputation economics in building of, 217–19, 230, 232, 236–37 self-organizing structures of, 217, 237 see also crowdfunding, crowdfunding campaigns; crowdsourcing Compaq, 117 computers, x, 7, 26, 72, 76, 135 see also artificial intelligence (AI); supercomputers Comsat, 102 constraints, power of, 248–49, 259 contract research and manufacturing services (CRAMS), 65 Coolest Cooler campaign, 210–13 corporate sponsorship, 246, 246 Cotichini, Christian, 257 Cotteleer, Mark, 33 Coulson, Simon, 150 Craigslist, 11, 257 creative assets, crowdsourcing of, 158 Creative Commons license, 224 Credit Suisse, 56 Cretaceous Period, ix CrossFit, 229 Crowdfunder, 172, 173, 175 crowdfunding, crowdfunding campaigns, xiii, 22, 103, 144–45, 147–48, 167, 169–213, 216, 242, 243, 247, 258, 270 advertising in, 212–13 building perfect team for, 191–94 building your audience in, 199–203 case studies in, 174–80 celebrity face of, 192, 198, 207 choosing idea for, 184–85 costs in, 195 data-driven decision making in, 207–10, 213 emergence of, 170–71, 170 engagement strategies in, 203–6, 207 feedback in, 176, 180, 182, 185, 190, 199, 200, 202, 209–10 fundraising targets in, 185–87, 191 global focus in, 209 how-to guide to, 181–213 launching with super-credibility in, 190, 199, 203, 204 length and schedule for, 187–89 pitch videos in, 177, 180, 192, 193, 195, 198–99, 203, 212 planning, materials, and resources in, 194–95 promotions and contests in, 207 reward-based, see reward-based crowdfunding setting rewards in, 189–91, 189 seven benefits of, 181–83 telling meaningful story in, 195–98 types of, 172–75 week-by-week execution plan for, 206–7 see also specific crowdfunding campaigns Crowdsortium, 162–63 crowdsourcing, xiii, 18, 22, 57, 85, 103, 143–67, 193, 223, 237, 240–41, 243, 245, 256, 275 in advertising, 151, 152–54, 158 AI as potential threat to, 167, 295n in automotive production, 223–24, 238 best practices of, 163–67 building communities for, see communities, online clear roles and communication in, 165–66 collaboration in, 144, 165–66, 167, 217, 227, 228, 236, 237, 255, 260–61 competitions and, 148, 152–54, 159, 160, 223, 224, 226–27, 232, 237, 240, 259; see also incentive competitions of creative and operational assets, 158–60 definition of, 144 in designing incentive competitions, 257–58 dual-use, 154–56 Freelancer.com case study in, 149–51, 158 growing interconnectedness and, 146–47, 147 incentive competitions and, see incentive competitions industry websites on, 162–63 of micro- vs. macrotasks, 156–58 most common uses for, 156–67 in product development, 18, 19, 223–25, 226–27 in retail and consumer products industry, 159–60 in scientific research, 145–46, 220–21, 227, 228–29 in software development, 144, 159, 161, 226–27, 236 of testing and discovery insights, 160–62 traffic data garnered by, 47 Crowdsourcing.org, 162 Csikszentmihalyi, Mihaly, 89, 92 Cube, 32 CubeSats, 36–37 Culver, Irv, 72 Cummins Engine, 222 Curiosity rover, 99 customer-centric business, 84, 116, 126, 128, 130, 131–32, 133, 138 Daily Show, 95 DARPA Grand Challenge, 262 Dartmouth Summer Research Project, 59 data mining, 42–44, 47–48, 256 AI’s role in, 55–59 behavior tracking and, 47 see also information data sets, preparing of, 164 Da Vinci Code, The (Brown), 152 debt funding, 172, 173, 174 deceptive phase, exponential, 8, 8, 9, 10, 24, 25–26, 29, 41 of AI, 59 in robotics, 60 of 3–D printing, 30, 31 Deep Learning (algorithm), 58, 59 Deepwater Horizon oil rig, 250 Defense Department, US, 71, 72 DeHart, Jacob, 143, 144 DeJulio, James, 151–52, 153, 166 Dell, 50 Deloitte Center for the Edge, 106 Deloitte Consulting, 33, 39, 159, 160, 245, 274 Deloitte University Press, 56 dematerialization, exponential, 8, 8, 10, 11–13, 14, 15, 20–21, 66 democratization, exponential, xii, 8, 10, 13–15, 21, 33, 59, 276 in bioengineering, 64–65 infinite computing and, 51–52 demonetization, exponential, 8, 8, 10–11, 14, 15, 138, 163, 167, 223 in bioengineering, 64–65 infinite computing and, 52 D.

The Myth of Capitalism: Monopolies and the Death of Competition
by Jonathan Tepper
Published 20 Nov 2018

Between Google, Amazon, Apple, Facebook, and Microsoft more than 500 companies have been bought out in the past decade.51 These giants are looking for the younger fast growers. You can see how big companies kill productivity by looking at Google and the field of robotics. In 2013 Google acquired Boston Dynamics, as well as eight other robotics companies, to create a new robotics division called Replicant, named in honor of the cyborgs in Blade Runner. The robotics industry was excited that the 800-pound gorilla in technology was throwing money at research. However, it turned into a disaster. Over time, Google shut many of the companies down and many of the top researchers left.

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Over time, Google shut many of the companies down and many of the top researchers left. Jeremy Conrad, a partner at hardware incubator Lemnos Labs, said, “These were some of the most exciting robotics companies, and they're just gone.”52 Google faced internal fears of being associated with terrifying machines that may take over human jobs, and Boston Dynamics was not part of its key search ad business.53 In June 8, 2017, Google announced the sale of the company to Japan's SoftBank Group. The phenomenon is not new. We've seen giant monopolies throw away innovation before. During the 1960s and early 1970s, Xerox had a monopoly on its copying technology, protected by its patents.

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(Grullon/Larkin/Michaely), 13 Arnold, Thurman, 145 Artisans Dwelling Act, 240 Asset management, Morganization, 203–204 Atkinson, Robert, 6, 52 AT&T innovation, 55 patent war, 19 phone market dominance, 126–127 Audretsch, 54 Autor, David, 40, 227 Axelrod, Robert, 27 Azar, José, 38, 72, 199 B Bailey, James, 180 Baker, Jonathan, 39, 225 Baker, Meredith Attwell, 7 Bank of America, market dominance, 3, 127 Bank of England, 17–18 Banks mergers, 128f oligopolies, 127, 129 owners, ranking, 200f Bannon, Stephen, 189 Baxter, William F., 158 Bayer, lobbying spending, 192 Beautiful Mind, A, (movie), 26 Beer duopolies, 122–123 mergers, impact, 43–44 Berger, David, 52 Berge, Wendell, 150 Berkshire Hathaway Buffett control, 2 “Celebration of Capitalism,” 1 Berners Lee, Tim, 101 Bessen, James, 188 Bezos, Jeff, 78, 105 Big 3 S&P500 ownership, 203f Big Business and the Third Reich (Schweitzer), 148 Big Data, Big Brother (relationship), 112 Big Is Beautiful (Atkinson/Lind), 6, 52 Birkenstock, piracy accusation, 103 Blade Runner (movies), 170–171 Blankfein, Lloyd, 189–190 Blonigen, Bruce, 40–41 Bogle, Jack, 202 Booth School of Business (University of Chicago), 163 Bork, Robert, 155, 157–159, 165 antitrust revolution, 238–239 Boston Dynamics, Google acquisition, 54 Boston Tea Party, reason, 236 Brandeis, Louis, 233, 237–238 Brands, ripoffs, 102–103 Brexit, vote, 112–113 Brown, G.R. Kinney Co. (Supreme Court merger prohibition), 154 Brown Shoe case, 153–154 Buffalo Courier-Express (business loss), 2 Buffalo Evening News (Buffett purchase), 2 Buffett, Warren, 196 billionaires, agreement, 1 investor waste, 201–202 Morgan, comparison, 198 Bunge, market dominance, 133 Burke, Edmund, 239 Burns, Arthur Robert, 145 Busch III, August, 29 Bush, George W., 161 reverse revolvers, 191–192 Businesses dynamism, decline, 46 investment level, reduction, 205 Buyback corporation, 208 Buybacks impact, 206 increase, 207f share buybacks, limitation, 247 C Cable mergers, impact, 43 monopolies/local monopolies, 116–117 Capital access, 66 ownership, worker shares, 246 perspective (Marx), 9 Capital in the Twenty-First Century (Piketty), 213–214 Capitalism problem, US/UK perception, 213 reforming, 239 Capital Returns (Marathon Asset Management), 8 Capone, Al, 22 Captured Economy, The, (Lindsey/Teles), 188 Cardinal Health, price-fixing allegations, 131 Cargill, market dominance, 133 Carlton, Dennis, 163 Carnegie, Andrew, 139, 143 Carpenter II, Dick M., 83 Cartels Chicago School perspective, 23 promotion, central bank rates (impact), 26f study, 25 Cartels: A Challenge to a Free World (Berge), 150 Castellammarese War, 21 CBS Corporation, market dominance, 133 CelebrityNetWorth, Google data theft, 89–90 Central Selling Organization, 24 CEO-to-worker compensation ratio, increase, 221f Chamberlin, Edward, 7 Chambers, Dustin, 179 Chemotherapy regulation, 167 usage, 178 Chicago School, 155–156 China, Big Data/Big Brother (relationship), 112 Chipotle, McDonald's release, 56 Christensen, Clayton, 55 Citigroup, market dominance, 127 Civil government, instituting, 191 Clayton Act of 1914, 7 Clayton Antitrust Act (1914), 144, 160, 209 Clemenceau, George, 233 Clifton, Daniel, 187 Clinton, Bill (reverse revolvers), 191 Clinton, Hillary, 189, 212 Coal Question, The, ( Jevons), 18 Cohn, Gary, 189 Collusion, impact, 32 Community Standards (Facebook), 92 Commuting zones, labor concentration (increase), 73f Companies growth phases, 52f lobbying, returns (comparison), 187f long-term returns, 204 platform companies, 97–98 self-disruption, failure, 55 synergies, 41 technology purchases, 106 Competition absence, 241 encouragement, patents (expiration), 246 Google, impact, 95 patents, impact, 175 promotion, patents/copyrights (impact), 246 reduction, mergers and acquisitions (impact), 12 restoration, Representative/Senator encouragement, 248 Competitors conflict, 31 reduction, mergers (prevention), 242 Composition, fallacy, 18 Computer operating systems, monopolies/local monopolies, 117 Concentrated industries, ranking, 33t Concrete, mergers (impact), 43 Confessions of the Pricing Man (Simon), 29 Conglomerates, purchase, 154 Connor, John, 23 Conrad, Jeremy, 54 Consumers, desires, 115 Consumer welfare, 158–159 Contract workers, hiring (fervor), 75–76 Copyrights, 246 Copyright Term Extension Act, 174 Corbyn, Jeremy (selection), 212 Corporate profits employee compensation, contrast, 223f increase, 65 Corporate trusts, control, 234 Costco workers, needs (understanding), 77 Counterfeits, impact, 102–103 Cox, Archibald, 157 CR4, 33 Creating and Restoring Equal Access to Equivalent Samples Act (CREATES), 176 Creative destruction, process, 45 Credit reporting bureaus, oligopolies, 125 Credit Suisse, Global Wealth Report issuance, 218 study, 10 Crisis of Capitalism, A, (Posner), 156 Curry, Steph, 3 Curse of Bigness, The, (Brandeis), 237 Customer lock-in (reduction), rules (creation), 246 CVS Caremark, market dominance, 130 D Dairy Farmers of America, price fixing, 119 Dalio, Ray, 229 David, Larry, 89 DaVita, Fresenius (merger), 124 Dayen, David, 96 Dean Foods, price fixing, 118–119 De Beers Consolidated Mines (cartel), 24 Decartelization Branch, 151–152 Decartelization/deconcentration policy, 150–151 Decker, Ryan, 47 Decline of Competition, The, (Burns), 145 de Loecker, Jan, 41, 226 Dent, Robert, 52 Diapers.com, Amazon predation, 106 Dickens, Charles, 18 Digital Millennium Copyright Act, 103 Digital platforms, scale, 91 Dimon, Jamie, 182 Dirlam, Jeff, 167 Disraeli, Benjamin, 240 Diversity, impact, 58–61 DNA damage, 178 Dodd-Frank Act 2010 Full Employment Act for Lawyers, Accountants, and Consultants, 182 impact, 181 passage, 184 Döttling, Robin, 56 Doubleclick, Google acquisition, 91, 118 “Double Irish” arrangement, 92–93 Dow Chemicals, DuPont (merger), 121 Dreyfus, market dominance, 133 Drugs prices, high level, 174 reformulation, 175 wholesalers, oligopolies, 131–132 Duisberg, Carl, 146–147 Duke, James Buchanan, 142 Duke, Mike, 16 Dunbar's Number, creation, 51 Duopolies, 15, 115–116, 122–125 Durant, Will, 231 Düsseldorf Agreement, 148 “Dutch Sandwich” arrangement, 92–93 E Echo Show (Amazon), 107 Economic dynamism, reduction, 37 Economic freedom, 143–144, 233, 238–239 Economic inequality, increase, 227–228 Economic model, adjustment, 41–42 Economies of scale, increase, 51 Economy advanced economies, markups (increase), 228f firms, role (decrease), 48f problems, Trump perspective, 213 Edison, Thomas, 67, 195 Eeckhout, Jan, 41, 226 Eisenhower, Dwight, 146, 148, 151 Ellenberg, Jordan, 214 Employees compensation, corporate profits (contrast), 223f perks, 75 Employers and Workmen Act, 240 Employment clauses, usage, 69 Ennis, Sean, 226 Entrepreneurship, decline, 46 Equifax, security breach, 81–82 Erhard, Ludwig, 153 Europe rebuilding, 153–154 ordoliberlism, 153 Evans, Benedict, 108 Evans, David, 106 Exchange-traded funds (ETFs), inexpensiveness, 203 Express Scripts, market dominance, 130 F Facebook church, Zuckerberg comparison, 113 Community Standards project, 92 creation, 117 Instant Articles, 102 lobbying efforts/expenses, 95–96 market dominance, 123–124 News Feed, impact, 99–100 news/information source, problems, 112–113 profitability/power, 99 Factory Act, 240 Fair Isaac's Corporations (FICO), credit-scoring formula, 125 Fast-food chains, employment clauses, 69 Federal Arbitration Act, 80, 82 Federal Express, duopoly, 3 Federal government Goldman Sachs, revolving door, 190f Monsanto, revolving door, 193f Federal Register, pages (number, increase), 181f Federal Reserve Act (1913), 209 Federal Trade Commission, 159, 163 creation, 144 Federation of British Industry, Düsseldorf Agreement, 148 Fidelity, market dominance, 135 Financial crisis (2007-2008), 25 Firms entry, reduction, 53f predatory pricing, punishment (laws), 244 role, decrease, 48f First American, market dominance, 135 Five Families, 22 Five Forces (Porter), 14–15 Fleming, Lee, 70–71 Forced arbitration, 79–81 Ford, Henry, 16 Foreign exchange traders, currency price fixing, 24 Foundem, 97 search problems, 87–88 Frankel, Jonathan, 107 Freight railroad, concentration, 119 Freireich, Emil, 176–177, 181 Friedman, Milton, 155, 179, 204, 233, 238 Funeral homes, monopolies/local monopolies, 121–122 Furman, Jason, 39 G Game theory, 26 Gates, Bill, 78 Geithner, Timothy, 190, 211 General Theory, The, (Keynes), 17 Germany German Decartelizing law (1947), 152 nationalist party, impact, 213 reconstruction, 151, 238 surrender, 151 Gerstner, Jr., Louis V., 50 Gibbons, Thomas, 137–138 Gibbons v.

Ghost Road: Beyond the Driverless Car
by Anthony M. Townsend
Published 15 Jun 2020

“automated” vehicles, 39 Autonomy (Burns), 214 autonomy, defined, 42 Autophobia (Ladd), 80 Autopilot (Tesla), 26–29 Autor, David, 150, 151, 152, 155 Baidu, 54 Bezos, Jeff, 221 Big Dog (Boston Dynamics), 79 big mobility, 239–47 bike sharing Bird Rides, 65, 66, 67 dockless bike-share, 64–65, 66, 67 docks, 64 Lime Bike, 67 microsprawl and, 202 rebalancing problem, 64–65 smartphone apps, 64 Vélib system (Paris), 63 VeoRide, 67 “white bikes,” 63 Bird Rides, 65, 66, 67 “block captain” ushers, 78 Bloomberg Philanthropies, 214 Blue Apron, 141, 145–46 Blue Gene/L (IBM), 36 Blueprint for Autonomous Urbanism, 193–94, 196, 242 Boston Dynamics, 79 Bostrom, Nick, 236–37, 238 Brooks, Rodney, 235 Brown, Joshua, 28 Burns, Larry, 214 buses bus rapid transit (BRT), 69–70, 72 CityPilot system, 72 driverless city buses, 216 platoons and platooning, 69–70, 70–71 software trains, 70–71, 70–72, 197, 200–201, 202, 204, 206 BVG (Berlin), 216 CalPers, 182–83 Calthorpe, Peter, 202 Caltrans, 170 carbon emissions AVs as tool for reducing, 19, 137 driverless shuttles and, 105 from manufacturing of clothing, 148 microsprawl and, 200, 202, 203, 204 platooning and, 68 software trains and, 72 Careem, 177 car-lite communes, 14–15, 60, 121, 244, 253, 254 Charles I (king), 161 Charlier, Frederic, 170–71 Cheetah 3 (MIT), 79 Chicago parking-meter contract, 173 Chin, Ryan, 62–63 Christine (King), 42 circular economies, 146–49, 196, 221 Citi Bike docks (New York City), 64 CityMobil2, 102–5 CityPilot system, 72 civic caravans, 73–75, 76–77, 77, 199 Clarke, Randy, 72 ClearRoad, 169–72, 216 clothing AirCloset, 148 carbon emissions from manufacturing, 148 in circular economies, 148–49 Rent the Runway, 140–41, 145 CloudKitchens, 140 coal and Jevons effect, 143–45 Coal Question, The (Jevons), 144 code and programming for AVs malleability of, 228, 245, 248 pushing code, 227 role in shaping driverless revolution, 227–28, 247–49 writing compared to coding, 226–27 see also computers and self-driving vehicles Cody (IDEO), 125 cognitive tasks and automation, 150–51, 151, 152–53 complete streets (shared streets), 208–9 computers and self-driving vehicles data exhaust, 108–12 data logged daily, 35, 108 microtransit mesh, 107–8, 111, 157 Pegasus onboard AV computer, 35–36 scan, study, and steer as basic tasks, 34–38 supercomputer location under seat, 84 vehicular variety increase, 53 see also code and programming for AVs; deep learning; reprogramming mobility computer vision, 152, 230, 231 congestion pricing at the curb, 223 electronic tolling, 169–72 mobility policy and, 182 in New York City, 165–67, 167, 168, 172–73 speculation or perverse incentives, 172–73 support for, 167–69 Uber, 179, 181 Vickrey’s study of, 165–66 weaponization by speculators, 17 see also financialization of mobility continuous delivery compared to historical shopping habits, 115–16, 120–21 and last mile logistics, 121–29 costs decline in twentieth century, 130 deskilling of delivery, 124 effect of instant delivery, 218 efficiency improvements and rebound effect, 145–47 free or cheap delivery and, 116–17, 204 freight AVs and, 125–26 fulfillment centers, 121, 123, 132, 136–37, 152, 158 impact on jobs, 155 impact on local businesses, 140–42 kippleization and, 142–43 nighttime delivery, 128–29, 130 overview, 120–21 package lockers and, 127, 130, 219, 221 piggybacking deliveries, 126–27 same-day delivery, 119, 123–24, 132–33, 138 see also e-commerce conveyors in circular economies, 148 deep learning, 57 Kiwibots, 57 last-mile deliveries, 124–25 maintenance, repair, and remote monitoring, 132 overview, 56–57, 60–61 Starship conveyors, 55–56, 57, 125, 192 Coord, 232–33 Cops (TV show), 24 Coresight Research, 117 core (urban core), 187, 188, 188–96, 194–95 Costco, 116 Could This Be You (TV show), 24 creative destruction, defined, 137 Credit Suisse, 117 cruise control, 24–25, 26 curb pricing and curb-access fees, 220–21, 222–23, 232 Curbs API, 232–33 Cushman & Wakefield, 117 Daimler, 6, 68, 69, 72, 190 Daley, Richard, 173 DARPA (Defense Advanced Research Projects Agency) Grand Challenges, 6–7, 68, 104, 133, 230 data collaboratives, 233 data exhaust, 108–12 Death and Life of Great American Cities, The (Jacobs), 57 deaths caused by motor vehicles, 9, 38, 156 deep learning advances in, 39, 42 computer power consumption, 37 conveyors, 57 fleet learning, 37 human intelligence tasks (HITs) required, 41 limits of, 235–36 neural networks, 36–37, 84, 235 occupancy grid, 37 overview, 36–37 and task model, 152 training, 37, 41, 153, 235 see also artificial intelligence; machine learning Deliveroo, 56, 124 delivery, continuous.

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And if you delegate the balancing act of a two-wheeled speed demon to software and gyroscopes, many more people might downsize to high-revving, lane-weaving hot rods. Highways could carry far more singlepassenger vehicles. Then there are the hominids, which are blurring the line between driverless vehicle and autonomous machine. Boston Dynamics’ Big Dog was built as a cargo-carrying support vehicle for troops. It can carry a 100-pound load over the roughest terrain. MIT’s Cheetah 3 is superior to many AVs, since it can operate in complete darkness or torrential rain. Instead of cameras, radar, or lidar, it navigates entirely through tactile feedback picked up by its four feet—a technique its creators call “blind locomotion.”

Architects of Intelligence
by Martin Ford
Published 16 Nov 2018

He also understood that the best way to test those models was to build real robots and to see how biological legged locomotion worked. He realized that in order to test that idea, he needed the resources of a company to actually make those things. So, that’s what led to Boston Dynamics. At this point, whether it’s Boston Dynamics or other robots, such as Rodney Brooks’ work with the Baxter Robots, we’ve seen these robots do impressive things with their bodies, like pick up objects and open doors, yet their minds and brains hardly exist at all. The Boston Dynamics robots are mostly steered by a human with a joystick, and the human mind is setting their high-level goals and plans. If we could just get something at the level of the mind of a one-and-a-half-year-old into the robotic hardware that we already have, that would be incredibly useful as a technology.

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That, to me, is the first thing to understand, and if we could build a robot that had that level of intelligence, it would be amazing. If you look at today’s robots, robotics on the hardware side is making great progress. Basic control algorithms allow robots to walk around. You only have to think about Boston Dynamics, which was founded by Mark Raibert. Have you heard about them? MARTIN FORD: Yeah. I’ve seen the videos of their robots walking and opening doors and so forth. JOSH TENENBAUM: That stuff is real, that’s biologically inspired. Mark Raibert always wanted to understand legged locomotion in animals, as well as in humans, and he was part of a field that built engineering models of how biological systems walked.

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

Having collected a large data set from this activity, the systems turn out to be better at recognising images from the ImageNet database than systems which have not had the physical training.[clii] Google's robot army – the dog that didn't bark In late 2013, Google announced the purchase of no fewer than eight robotics companies. (Since you ask, they are Boston Dynamics - purveyor of the famous Big Dog and Atlas models - Bot and Dolly, Meka, Holomni, SCHAFT, Redwood, Industrial Perception, and Autofuss.) Google also announced that the new division which owned them would be run by Andy Rubin, who created a huge global business with the Android phone platform.

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Google also announced that the new division which owned them would be run by Andy Rubin, who created a huge global business with the Android phone platform. A year later, in October 2014, Andy Rubin left Google to found a technology startup incubator, which prompted observers to remark that Google had been surprisingly quiet about its collection of robot makers. In early 2016, rumours spread that Google was considering selling Boston Dynamics, the creator of Big Dog and Atlas, two of the world’s most impressive robots. Google is an experienced acquirer of companies – by the end of 2014 it had acquired 170 of them – and it expects them to make an impact. The hurdle for potential acquisition targets is the “toothbrush test”, meaning that their services must be potentially useful to most people once or twice every day.

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

France has initiated a similar program, pledging $126.9 million to develop its industry and catch up to Germany. Sweden has similarly earmarked millions to give out to individuals and corporations through innovation awards such as Robotdalen (“robot valley”), launched in 2011. The private sector is also investing at increasingly higher levels. Google purchased Boston Dynamics, a leading robotics design company with Pentagon contracts, for an untold sum in December 2013. It also bought DeepMind, a London-based artificial intelligence company founded by wunderkind Demis Hassabis. As a kid, Hassabis was the second-highest-ranked chess player in the world under the age of 14, and while he was getting his PhD in cognitive neuroscience, he was acknowledged by Science magazine for making one of the ten most important science breakthroughs of the year after developing a new biological theory for how imagination and memory work in the brain.

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See also Amazon BGI, 67 Bitcoin: Andreessen on, 103–4, 116–17 benefits of, 102–5, 116–17 blockchain and, 101–6 CoinDesk, 167 criticism of, 111–12 establishment and, 111–15 explained, 98–100 financial system and, 99 future of, 115–17 governments and, 111–15 hacking and, 106–11 micropayments and, 105–6 mining and, 102–3 competitors and, 117–19 Songhurst on, 104 widespread use of, 98 see also blockchain blockchain: Bitcoin and, 101–6 efficiency and, 104 establishment and, 111–15 explained, 101 future of, 120 hacking and, 106, 108–9 law enforcement and, 111 as next protocol, 115–17 regulation of, 103 transaction history and, 114 see also Bitcoin Bloomberg, Michael, 167–68 Booker, Cory, 167–68 Booker T. Washington Middle School, 59 Boston Dynamics, 25 botnet attacks, 126, 134–35, 141. See also cyberattacks Brin, Sergey, 57–58, 60 Broad Institute, 48, 54 Brooks, David, 162 bucardo, 63–64 Camp, Garrett, 92 cancer: brain and, 55 breast, 72–73 detection and, 72–73 DNA and, 45 drugs and, 51–52, 59, 72 FLT3 and, 46 future of treatment, 6, 33, 185 genome sequencing and, 46–52; see also genomics liquid biopsy and, 49 mutations and, 51 ovarian, 49–50 PGDx and, 50–51 R&D investment in research for, 66 RNA and, 45–46 robots and, 33 Wartman, Lukas and, 44–47, 58, 61 Celtel, 85–86, 88 Chase.

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

While there have no doubt been many difficult challenges in replicating or making equivalents of human and animal body parts, these are largely being met, driven partially by the development of new materials and ever faster and smaller electronics. The Shadow Robot Company of London manufactures hands that demonstrate the same dexterity and range of motion as their human counterparts, with the strength to firmly grasp power tools and the delicacy to hold an egg. In 2005, Boston Dynamics revealed ‘BigDog’ – a four-legged robotic packhorse that can carry 340 pounds and traverse tricky terrain with apparent ease. Robotic vision systems can now capture images in resolutions that rival the human eye. No, it’s not the mechanics of the robot body that’s the sticking point for the autonomous intelligent robot, it’s giving it a mind.

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INDEX 23andMe 274, 297–9 42 100, 273 2001: A Space Odyssey 76, 102, 133 A Abengoa Solar 193 activated carbon 216–17 adenine 37–9, 46 aerosols 168–70 af Ekenstam, Robin 103, 104 Africa 252, 253, 302 Age of Spiritual Machines, The (Kurzweil) 274–5 agriculture 221–40, 253 Agüera y Arcas, Blaise 163 AInimals 92, 94, 96, 102–4, 105 algae 187, 210–12 Algenol Biofuels 187, 189 alleles 45, 48 Allen 83, 84 Amundsen, Roald 178 Anderson, Chris 291–5 Andrews, Lori 27 Angier, Natalie 47 Annas, George 27 Ansari X Prize for Spaceflight 135 Aquaflow Bionomic Corporation 208, 210–12 Arcadia 237–8 Arcadia (Stoppard) 281 Archer, David 177 Archon X Prize 50, 51 Aristotle 97 ARPANET 152 Art of War, The (Sun Tzu) 40–1, 51–2 artificial intelligence 73–107 Artificial Intelligence: AI 75 Asimov, Isaac 76–7 augmented reality 162–4 Augustine Commission 136 Australia climate change scepticism 168, 171 farming 221–40 Internet 157 mousepox virus 63–4 autocatalysis 270 B Bacillus subtilis 100, 273 Bacon, Francis 96–8, 99 bacteria 56–7, 61, 302 Bedau, Mark 66, 280 Bedford, James 15 Berners-Lee, Mike 169–70 Berners-Lee, Tim 154, 159 ‘Better World Shopper’ 163 Bezos, Jeff 141 BigDog 84 Bigelow, Robert 137 Billen, Abigail 31 Binney, Don 218 biochar 208–10, 212–20 biofuels 56–7, 61, 186–9, 210–12 biomass 209–10 bionics 14, 29, 301 biotechnology 35–70 bioterrorism 63–6, 68 BioTime 53–4 Birchall, Martin 20 bird flu 69–70 black carbon 169–70 Black Phantom 212–14, 219, 299, 301 Blackburn, Elizabeth 18 Blackstone Ranch 234 Blackwell, Paul 213 Blasco, Maria 18, 19 Blayney 235–7 Blenheim 210–12 blood transfusion 33 Blue Brain 90, 91 Blue Origin 141 Blundell, James 33 Bonaparte, Napoleon 146 Bongard, Josh 95 Boree Creek 237–8 Borman, Frank 135 Boston Dynamics 74–5 Bostrom, Nick 13, 17, 18, 22–31, 62, 65, 66 carbon-chauvinism 102 existential risk 63 and Kurzweil 267, 269 Bourke, Joanna 149 Brand, Stewart 108–9, 128, 270, 276 Branson, Richard 135, 141 Breazeal, Cynthia 76–82, 84–6, 90–2, 94, 101–2, 269, 277–8 Bréon, François-Marie 169 Brin, Sergey 273–4, 297 Broad Institute 40 Broecker, Wallace 173, 174, 177–86 Brooks, Rodney 76, 82, 83–4, 89, 103, 104, 105 Brown, John Seely 156, 282–3, 284–91, 292, 304 Buck, Vicki 207–8, 210–20, 288, 299 Burke, James 160, 161, 162 Burma 157 C C-3PO 76, 83, 102 cadmium 195, 196 California NanoSystems Institute 118 cancer 19, 40–1, 46–7 Candide (Voltaire) 218 carbon cycle 209 carbon dioxide (CO2) 57, 167–8, 170–1, 175–7, 186, 302 and agriculture 228–31, 233–5 biochar 209–10 biofuels 187–9 industrial uses 183–4 carbon nanotubes 110–11 carbon neutrality 243–4, 245 carbon scrubbers 179–85, 259–60, 299 Carbonscape 208, 212–20, 299, 301 carrying capacity 128–9 Castillo, Claudia 19–20, 33 Çatağay, Tolga 273 Catholic Church 106 Cave, Nick 304 Celera Genomics 36 Celsias 208 Cerf, Vint 151–64, 187, 245, 268, 283, 284, 299 Chappe, Abraham 146 Chappe, Claude 146 Chappe, René 146 charcoal 208–10, 212–20 chess 82, 83, 86 China 157, 200 Chomsky, Noam 303 chromosomes 44, 45–6 Chu, John 155 Chui, Alex 15 Church, George biofuels 57, 211 bioterrorism 63, 65–6 genome engineering 52, 56, 60–3, 64, 70, 105, 186–7, 203 genome sequencing 50–1 human genome project 35 human machines 89 IVF 106 and Lackner, Klaus 189 licensing 66–7 Personal Genome Project 36–7, 39, 41–50, 273, 299, 300, 301 Ćirković, Milan 65 cities 250, 252–3 Claramunt, Xavier 137 climate change 143, 164, 167–72, 174–7, 208 and agriculture 228–31, 233–5 Maldives 241–9, 256–62 Northwest Passage 178 Clinton, Bill 35–6 clouds 169 Cobar 231–5 Collins, Mike 135 Collins, Paul 192 Columbia University Medical Center 31 Columbus, Christopher 303 Comer, Gary 177, 178 Commercial Spaceflight Federation 138 Complete Genomics 51 Connections 160 Consortium for Polynucleotide Synthesis 68 Copenhagen Accord 256 Cornell University 93–6, 98–101, 210 couchsurfing.org 158 Coughlan, Anna 221–2, 239–40 Coughlan, Michael 221–2, 239–40 ‘Couldn’t Be Done’ (Tim Finn) 208 Crichton, Michael 122 cryonics 15–16 Cuba 157 cytosine 37–9, 46 D dance 155 De Cari, Gioia 262 de Grey, Aubrey 14, 16, 17–18, 21, 34 ‘Death Clock’ 12–13 deductive reason 97 Deep Blue 82–3 del Cardayré, Stephen 61 Desertec Industrial Initiative 193 Deutsche Bank 193 diatoms 117–18 diesel 56–7 Dijkstra, Edsger 82 DNA (Deoxyribonucleic acid) 38–9, 40, 297–8 naked 46 nanotechnology 113, 119–20 Parkinson’s disease 273–4 Door into Summer (Heinlein) 142 double helix 38 double pendulum 98–9 Dragon 136 Drexler, Eric 109–17, 125, 127–30, 286, 287, 299, 300 critics 123–4 Grey Goo 121–3 and Kurzweil 268, 269 E E. coli 56–7, 61, 64 E85 cars 188 EasyJet 20 education 284–5, 288 Egypt 157 Ehrenreich, Barbara 303 Eigler, Donald 113, 125 Einhorn, Thomas 31 Einstein, Albert 140 Eisenberger, Peter 184 electricity 285–6 Eliza 86–7 Ember, Carol 147 enhancement 26–9 Endy, Drew 66 energy 191–2, 193–5, 202, 204 fossil fuels 168, 191–2, 193, 302 solar 190–1, 192–3, 195–205, 206, 274, 295, 302 Engines of Creation (Drexler) 109, 110–11, 115, 121, 122, 123, 127–8, 300 Enlightenment 267 Enriquez, Juan 33, 278–82, 293 Eros (Asteroid) 134 Estep, Preston 16 ethanol 187 Ethiopia 199, 200 Etiwanda Station 231–5 Eureqa 101 evolution 70, 105, 279–80, 281–2 existential risk 63 Exxon Mobil 56 EZ-Rocket 142 F Falcon 9 136 farming 221–40, 253 Feynman, Richard 112, 113 Finn, Tim 208 Flannery, Tim 215 flu 64–5, 69–70 Følling’s disease 44, 58 foot-and-mouth disease 68–9 forests 253–4 Forster, E.

Nobody's Fool: Why We Get Taken in and What We Can Do About It
by Daniel Simons and Christopher Chabris
Published 10 Jul 2023

When these demos appear to work—which they almost always do—they provide a compelling signal of truth to their viewers; it’s hard to question something you’ve seen with your own eyes. Thanks to our truth bias, we trust that what we are seeing is at least a close approximation of reality and that we’re not being deliberately misled. For example, the robotics firm Boston Dynamics (once owned by Google) regularly releases videos of its humanoid robots doing incredible stunts, such as performing parkour moves, but no video can tell us whether the robot would succeed on an obstacle course it had never seen with objects it had never encountered. Maybe it would, but in the face of a compelling demo, we tend to assume that the performance we’re seeing is generalizable to similar settings even when we have no direct evidence, at least from the demo, that it does.6 The practice of developing computer systems capable of performing with apparent intelligence in highly constrained situations and either claiming or implying that they would work just as well in a broad range of contexts goes back at least fifty years.

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When psychics like Browne fail spectacularly, it’s often because they mistakenly assumed that they could get away with a specific prediction about an unsolved cold case—because nobody would ever be able to challenge it. Chris used to show his classes a video clip of Sylvia Browne getting nothing right about a caller’s dead father, but when he went back to YouTube the next year, the video was gone. 6. Boston Dynamics parkour video: “More Parkour Atlas,” September 24, 2019 [https://www.youtube.com/watch?v=_sBBaNYex3E]. 7. For an example of research on “one-pixel attacks” on deep neural networks for image recognition, see J. Su, D. V. Vargas, and K. Sakurai, “One Pixel Attack for Fooling Deep Neural Networks,” IEEE Transactions on Evolutionary Computation 23 (2019): 828–841 [doi.org/10.1109/TEVC.2019.2890858]. 8.

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

Traditional military contractors such as Northrop Grumman, Boeing, and Lockheed Martin were early entrants into the world of robotics, followed by smaller specialized firms such as Boston Dynamics and iRobot (yes, the same people who make your Roomba vacuum make the IED-disposal PackBot). But now another deeply disruptive player has entered the world of robotics: Google. The search giant is on a robo-buying binge and purchased or acquired eight separate robotics companies in a six-month period through 2014, including companies that specialize in humanoid walking robots, robotic arms, robotics software, and computer vision. Its largest and most surprising robotics acquisition, however, was the military robotics company Boston Dynamics, the same folks who make BigDog, Cheetah, Sand Flea, RiSE, and PETMAN (a biped humanoid robot that might well be the soldier of the future).

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Unmanned ground vehicles (UGVs), such as iRobot’s PackBot, routinely help with the detection and disposal of improvised explosive devices (IEDs). Foster-Miller’s TALON is a “man-portable robot which operates on small treads” like a miniature tank. It can be outfitted with machine guns, .50-caliber rifles, grenade launchers, and antitank rockets, all while being remotely controlled via joystick. Boston Dynamics’ Sand Flea weighs only eleven pounds but can jump up to thirty feet high, landing on the roof of a building or precisely leaping through an open window, capturing all it sees with its HD camera. The company has also created BigDog, a four-legged robot that can carry up to four hundred pounds of gear and weapons, easily walking over rugged terrain and obediently following its soldier master.

Elon Musk
by Walter Isaacson
Published 11 Sep 2023

“If you can create a self-driving car, which is a robot on wheels, then you can make a robot on legs as well,” Musk said. In early 2021, Musk began mentioning at his executive meetings that Tesla should get serious about building a robot, and at one point he played for them a video of the impressive ones that Boston Dynamics were designing. “Humanoid robots are going to happen, like it or not,” he said, “and we should do it so we can guide it in a good direction.” The more he talked about it, the more excited he got. “This has the potential to be the far biggest thing we ever do, even bigger than a self-driving car,” he told his chief designer, Franz von Holzhausen.

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A few days later, Tesla’s design chief, Franz von Holzhausen, convened a group to begin building the real thing: a robot that could emulate a human. Musk gave one directive: it was to be a humanoid robot. In other words, it was supposed to look like a person rather than a mechanical contraption with wheels or four legs like Boston Dynamics and others were making. Most workspaces and tools are designed to accommodate the way humans do things, so Musk believed that a robot should approximate human forms in order to operate naturally. “We want to make it as human as possible,” von Holzhausen told the ten engineers and designers seated around his conference table.

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See Tesla Autopilot project Babuschkin, Igor, 605 Babylon Bee, 419, 527, 529, 554 Baglino, Drew, 195 Autopilot project and, 246, 247 Model S and, 199 Optimus and, 498 Robotaxi and, 501, 502 Straubel departure and, 302, 303 Baker, Jim, 571 Balajadia, Jehn, 362, 513 Bankman-Fried, Sam, 460–61 Banks, Azealia, 308–9 Banks, Iain, 400 Bannon, Pete, 396 Bard, 601 Barenholtz, Jeremy, 497, 561, 562–63 Barra, Mary, 421 Bassett, Natasha, 7, 265, 451, 491 Battle of Polytopia, The, 46 Bauch, Matt, 597 Beal Aerospace, 115 Beeple, 448–49 Belsky, Scott, 520 Benioff, Marc, 430 Berland, Leslie, 465, 508, 534 Bernstein, Carl, 573 Beykpour, Kayvon, 520 Bezos, Jeff, 223, 353 Amazon HQ and, 336 competition with SpaceX, 226, 227–28, 231–32, 233–34, 354 competition with Starlink, 355–56 Inspiration4 mission and, 385 love of space travel, 224–25 management style, 166, 354–55 space tourism and, 353, 356, 383, 476 Trump and, 261 Washington Post purchase, 357 wealth of, 408 Bhattacharya, Jay, 573, 578 Biden, Hunter, 567, 577, 579 Biden, Joe, and administration, 420–23, 535, 567 Binder, Matt, 576 Birchall, Jared Austin home plans and, 473 EM’s demon mode and, 539 EM’s management of Twitter and, 543 EM’s politics and, 419, 443 Kimbal’s restaurant business and, 300–301 philanthropy and, 439 Twitter acquisition and, 442, 451, 452–53, 464, 490, 494, 512 Twitter board invitation and, 445 Blade Runner, 318, 485 Blastar, 29, 33, 425 Blue Origin, 224, 226, 227–28, 233–34, 354, 355 board games. See strategy games Boeing, 101, 113, 123, 187, 206, 348, 350 See also Lockheed-Boeing United Launch Alliance Bolden, Charlie, 206 Bolsonaro, Jair, 419 Boring Company, The, 257–59, 288, 298, 441, 472, 496, 585 Boston Dynamics, 394 Botha, Roelof, 75, 82, 86 Boucher, Claire. See Grimes Boudette, Neal, 283, 406 Bowles, Nellie, 568, 569–71, 576 Brady, Nicholas, 48 Branson, Richard, 353, 356–57, 383, 476 Brin, Sergey, 63, 126, 138, 180, 468–69 Brodie-Sangster, Thomas, 471 Brown, Jerry, 218 Brown, Mary Beth, 116 Brown, Tina, 66 Brownlee, Marques, 217 Burning Man, 103, 252, 310, 341, 378–79 Bush, George W., 101 Butterfield, Elissa, 258, 321, 329 Buzza, Tim Falcon 1 launch attempts and, 151, 184, 185, 186 Falcon 9 liftoff and, 210 on improvisation, 116–17 launch location and, 145 NASA contract and, 205 on production algorithm, 113 testing and, 115, 116 Calacanis, Jason, 523, 529, 530, 531, 576 Cameron, James, 92 Cantrell, Jim, 95–96, 98, 99–100, 101 CAPTCHA technology, 83 Challenger mission, 119, 385 Chanos, Jim, 278 Chappelle, Dave, 580 ChatGPT, 243, 593, 600–601, 606 Chinnery, Anne, 149 Christensen, Clayton, 84 Christian symbolism, 71 CitySearch, 65 Civilization, 46, 51, 425 Claassen, Kate, 511 Cleese, John, 498 Clinton, Hillary, 261, 424, 525 Clooney, George, 143 Cobra Kai, 346 Cocconi, Alan, 126 Coffin, Gage, 273 comics, 27 communications satellites.

Surveillance Valley: The Rise of the Military-Digital Complex
by Yasha Levine
Published 6 Feb 2018

It is frequently an equal partner that works side by side with government agencies, using its resources and commercial dominance to bring companies with heavy military funding to market. In 2008, it launched a private spy satellite called GeoEye-1 in partnership with the National Geospatial-Intelligence Agency.140 It bought Boston Dynamics, a DARPA-seeded robotics company that made experimental robotic pack mules for the military, only to sell it off after the Pentagon determined it would not be putting these robots into active use.141 It has invested $100 million in CrowdStrike, a major military and intelligence cyber defense contractor that, among other things, led the investigation into the alleged 2016 Russian government hacks of the Democratic National Committee.142 And it also runs JigSaw, a hybrid think tank–technology incubator aimed at leveraging Internet technology to solve thorny foreign policy problems, everything from terrorism to censorship and cyberwarfare.143 Founded in 2010 by Eric Schmidt and Jared Cohen, a twenty-nine-year-old State Department whiz kid who served under both President George W.

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And now we are on the front end of the spear that is commercializing this technology” (Brian X. Chen, “Google’s Super Satellite Captures First Image,” Wired, October 8, 2008). 141. John Markoff, “Google Adds to Its Menagerie of Robots,” New York Times, December 14, 2013; Alex Hern, “Alphabet Sells Off ‘BigDog’ Robot Maker Boston Dynamics to Softbank,” Guardian, June 9, 2017. 142. Yasha Levine, “From Russia, with Panic: Cozy Bears, Unsourced Hacks—and a Silicon Valley Shakedown,” The Baffler, March 2017, https://thebaffler.com/salvos/from-russia-with-panic-levine. 143. Started as Google Ideas in 2010, it was rebranded as JigSaw in 2016.

Wired for War: The Robotics Revolution and Conflict in the 21st Century
by P. W. Singer
Published 1 Jan 2010

As one said, “Fact of nature: There are no large land creatures with six legs and there never have been.” Designs that find their inspiration in living organisms are known as “biomimetic” (“bio” from “biology” and “mimetic” meaning to “mimic” or “copy”). Perhaps the best known of these in military circles is a four-legged robot made by Boston Dynamics. The “Big Dog” (others call it the “Robot-Ass,” but that name hasn’t stuck for marketing reasons) is designed to serve as a modern-day packhorse, following after soldiers with their backpacks and other gear. The current prototype is the shape and size of a mule. The four legs differ from a mule’s in having three joints and springs built into them that can change length, much like a tent pole.

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Such “self-transforming” or mighty “morphing” robots will range from changing slightly between a few designs like the Transformers to ones that could recast themselves into hundreds of forms like the T-1000 robot in the movie Terminator 2. At the most simple level are robots with morphing effectors that alter to allow more efficient movement in different domains. An example of this is the RHEX made by Boston Dynamics. It has legs that can transform into flippers, allowing it to walk on land or swim underwater. The Naval Postgraduate School in Monterey, California, similarly built a plane the size of a small bird that can both fly and crawl. It originally came out of a request by the special forces for robotic planes that could do such things as fly up to a windowsill and then creep inside.

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Balkan wars Ballistic Missile Early Warning System Band of Brothers (Ambrose) Barber, Mack Baroque Cycle (Stephenson) Bateman, Robert “Bob,” BAUV (Biometric Autonomous Undersea Vehicle) Beane, William “Billy,” Bear, Greg Bell, Sam Bellflower, John Bello, Louis Bennett, Andrew Berra, Yogi Best Military Science Fiction of the 20th Century, The (Turtledove and Greenberg) Big Dog (robot) bin Laden, Osama Biometric Autonomous Undersea Vehicle (BAUN) Black Hawk Down (film) Blackwater Blade Runner (film) blitzkrieg warfare Blue, Linden Blue, Neal Blue Brain project Blue Force Tracker Blue Gene (supercomputer) Boeing company Boeing X-45, Bogosh, Ted Boot, Max Border Hawk (drone) Border Patrol, U.S. Bosnia Boston Dynamics Boutelle, Steven Bowles, Erskine Bradbury, Ray Bradley, Omar BrainGate technology Brain-Interface Project Branson, Richard Brazil Brezina, Byron Brooks, Rodney Brown, Dan Bruemmer, David Buchan, Glenn Buchanan, Walter Buddha in the Robot, The (Mori) Bug’s Life, A (film) “Building Gods or Building Our Potential Exterminators?”

Survival of the Richest: Escape Fantasies of the Tech Billionaires
by Douglas Rushkoff
Published 7 Sep 2022

Is it going to turn into civil disorder?” The architects of the techno-utopian ideal now fear it will inspire a revolt of the mob that all this technology was originally invented to contain and control. Others fear AI for what people may choose to do with it. Employees protested when Google acquired military robot maker Boston Dynamics in 2013, and the company eventually shed the asset. A few years later, four thousand Googlers signed a petition and at least a dozen resigned in protest over the company’s deci sion to provide AI to Project Maven, a Pentagon program with the purpose of helping drones distinguish between targets, objects, and people.

A Generation of Sociopaths: How the Baby Boomers Betrayed America
by Bruce Cannon Gibney
Published 7 Mar 2017

Wall Street has long dismissed Google’s side projects like self-driving cars and AI as money sinks, but Google has a thoughtful plan and one you may not be fully comfortable with. Google (in the verb sense; may as well start there) “self-driving car,” “AlphaGo,” and “Android Marketshare” and you’ll get a sense for the future Google might have in mind. You can add in Boston Dynamics +Atlas +Google, and you might get a sense of Google’s terminal ambitions, even if it ultimately ditches Boston Dynamics in favor of other robotics companies. * My subject is generational; I stake little territory in the largely unhelpful and mostly pseudoscientific debate (on both sides) regarding the inherent capacities of a given group for a given subject.

The Singularity Is Nearer: When We Merge with AI
by Ray Kurzweil
Published 25 Jun 2024

The likely reason is that these latter functions have evolved over tens or hundreds of millions of years and run in the backgrounds of our brains, whereas “higher” cognition is powered by the neocortex, which is the center of our consciousness and which didn’t reach its roughly modern form until several hundred thousand years ago.[85] As AI has grown exponentially more powerful in the past several years, though, it has made amazing progress against Moravec’s paradox. In 2000, Honda’s ASIMO humanoid robot wowed experts by gingerly walking across a flat surface without falling over.[86] By 2020, Boston Dynamics’ Atlas robot could run, jump, and tumble across an obstacle course with greater agility than most humans.[87] Social robots like Sophia and Little Sophia, by Hanson Robotics, and Ameca, by Engineered Arts, can demonstrate emotion on human-looking faces.[88] Their capabilities have sometimes been exaggerated in headlines, but they nonetheless show the trajectory of progress.

…

See bubonic plague black holes, 1–2, 98 black silicon, 172–73 Blade Runner (movie), 100 Bletchley Declaration, 283 bloodstream, nanobots in. See nanobots blue goo, 277. See also nanobots Bode, Stella de, 88–89 BoJack Horseman (TV show), 221 book publishing, 53, 159–60, 212, 253 Boston Dynamics, 101 Bostrom, Nick, 41, 62, 104, 268–69, 295n bottlenecks, 60, 61 bottom-up approach to nanotech, 249–50 brain, human cerebellum of. See cerebellum computational capacity, estimates, 54, 57, 61, 62, 71–72, 246, 248, 264–65 computer interface.

Fully Automated Luxury Communism
by Aaron Bastani
Published 10 Jun 2019

But then the impossible suddenly became inevitable. Enter Atlas, the robot who learned to somersault. Atlas Somersaults If you go to YouTube and type ‘PETMAN prototype’ into the search bar, the first video that appears, posted in October 2009, is a demonstration of a biped robot developed by Massachusetts-based company Boston Dynamics. Awkward and attached to several cables, PETMAN looks like the love-child of a subwoofer and Bambi on ice. Now type in ‘What’s new, Atlas?’ On your screen will appear a video of another robot manufactured by the same company. Only this video was published in late 2017 and the robot isn’t just walking without cables, it’s doing box jumps and backflips.

The New Class Conflict
by Joel Kotkin
Published 31 Aug 2014

Claire Cain Miller and David Gelles, “After Big Bet, Google to Sell Motorola Unit,” New York Times, January 30, 2014; Dan Gallagher, “Google Still Feathers Its Nest With Big Bets,” Wall Street Journal, January 23, 2014; “Google Market Cap (GOOG),” YCharts, http://ycharts.com/companies/GOOG/market_cap. 70. Robert Sorokanich, “Google Just Bought Crazy Walking Robot Maker Boston Dynamics,” Gizmodo, December 14, 2013, http://gizmodo.com/google-just-bought-crazy-walking-robot-maker-boston-dyn-1483235880; Michael Carney, “Gruber: Nest Can Teach Google to Make Hardware. Google Can Help Nest Go Fast,” PandoDaily, January 14, 2014, http://pando.com/2014/01/14/gruber-nest-can-teach-google-to-make-hardware-google-can-help-nest-go-fast. 71.

Artificial Intelligence: A Guide for Thinking Humans
by Melanie Mitchell
Published 14 Oct 2019

I should note that a few robotics groups have actually developed dishwasher-loading robots, though none of these was trained by reinforcement learning, or any other kind of machine-learning method, as far as I know. These robots come with some impressive videos (for example, “Robotic Dog Does Dishes, Plays Fetch,” NBC New York, June 23, 2016, www.nbcnewyork.com/news/local/Boston-Dynamics-Dog-Does-Dishes-Brings-Sodas-384140021.html), but it’s clear that they are still quite limited and not yet ready to solve my family’s nightly dishwashing arguments. 15.  A. Karpathy, “AlphaGo, in Context,” Medium, May 31, 2017, medium.com/@karpathy/alphago-in-context-c47718cb95a5. 11: Words, and the Company They Keep   1.  

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

Respectful. Even tempered. Does exactly what she’s told. She’s the computer-driven Jill of all trades.” What’s more, Rosey gives sass when suitable. “Beneath the aluminum alloy core beats a battery-powered heart of pure gold.”13 We’ll get there. “The biggest problem is safety,” the former chairman of Boston Dynamics, Marc Raibert, told the Post. The company has developed agile robots that resemble animals. “The more complicated the robot, the more safety concerns. If you have a robot in close proximity to a person, and anything that goes wrong, that’s a risk to that person,” Raibert said.14 Decades ago, long before there were any actual robots, the science fiction writer Isaac Asimov proposed three laws to keep us safe from machines we create.

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

(Credit: Aethon) Figure 4.8: da Vinci surgical robot (Credit: da Vinci Surgery) The teaming of Google, whose Calico was created to solve a little problem we call death, and Johnson & Johnson, the giant of home healthcare products, is a watershed moment that will promote robots in unprecedented numbers to the operating room. One can imagine what the technologies from Google’s Boston Dynamics division, Calico, Google’s Biotech division and J&J’s incredible depth of medical device knowledge will bring. Humanoid robot surgeons will change everything, and will likely be preferred or demanded within a decade by many patients. If this seems far off, we are already moving towards a world where robots can perform surgeries without human intervention or interaction.

A World Without Work: Technology, Automation, and How We Should Respond
by Daniel Susskind
Published 14 Jan 2020

Nick Wingfield, “As Amazon Pushes Forward with Robots, Workers Find New Roles,” New York Times, 10 September 2017. 25.  For “harsh terrain,” see “Cable-Laying Drone Wires Up Remote Welsh Village,” BBC News, 30 November 2017; for “knotting ropes,” see Susskind and and Susskind, Future of the Professions, p. 99; for “backflip,” see Matt Simon, “Boston Dynamics” Atlas Robot Does Backflips Now and It’s Full-Title Insane,” Wired, 16 November 2017; and for others, see J. Susskind, Future Politics, p. 54. 26.  Data in “Robots Double Worldwide by 2020: 3 Million Industrial Robots Use by 2020,” International Federation of Robotics, 30 May 2018, https://ifr.org/ifr-press-releases/news/robots-double-worldwide-by-2020 (accessed August 2018). 2017 data from Statista, https://www.statista.com/statistics/947017/industrial-robots-global-operational-stock/ (accessed April 2019). 27.  

Blood and Oil: Mohammed Bin Salman's Ruthless Quest for Global Power
by Bradley Hope and Justin Scheck
Published 14 Sep 2020

Fox Business’s Maria Bartiromo stood up to host the panel, wearing a long, flowing white jacket. “We watch something of a revolution happening here in Saudi Arabia as the kingdom looks to growth,” she said, inviting Mohammed bin Salman onto the stage, alongside Schwarzman of Blackstone, Son, Marc Raiburt of Boston Dynamics, and Klaus Kleinfeld, the new head of the NEOM project. “If you allow me, I will speak in Arabic because a lot of Saudi audience here and I really respect them,” Mohammed said, before describing the “almost imaginary” opportunities at NEOM and squinting slightly as he listed its benefits, ticking them off on his hand.

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

(If you are thinking about Siri and its limitations, be assured that there are far more powerful programs working today in research labs.) AI is also being used to allow machines to walk and perform other movements, identifying what is an obstacle and determining what to do to get around it. Machines created by Boston Dynamics have demonstrated remarkable dexterity using AI programs to guide their decision making as they traverse real-world obstacles outside the laboratory. The field of AI gained greatest acceptance in the corporate world when it began processing the sea of data that the rest of information technology was producing.

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

Imagine turning a robot into a dream “smart husband.” In Japan, robots are being designed to help mitigate the “do-it-all” mindset that women have had to embrace. But also in the United States, we are seeing dazzling progress on the domestic front. Atlas is a humanoid robot that was designed by Boston Dynamics to be a search-and-rescue robot. It is over six feet tall and can do backflips, high jumps, split leaps, and handstands. The newer model, Ian the Atlas Robot, has been taught to clean, vacuum, and take out the trash. The reality is that women still perform the bulk of care work, rendering them significantly less mobile and flexible.

The Coming Wave: Technology, Power, and the Twenty-First Century's Greatest Dilemma
by Mustafa Suleyman
Published 4 Sep 2023

Research shows that when introduced to the topic of emerging technologies and their risks, people really do care and want to find solutions. Although many of the harms are still a way off, I believe people are perfectly capable of reading the runes here. I’ve yet to find anyone who’s watched a Boston Dynamics video of a robot dog or considered the prospect of another pandemic without a shudder of dread. Here is a huge role for popular movements. Over the last five or so years, a burgeoning civil society movement has begun to highlight these problems. The media, trade unions, philanthropic organizations, grassroots campaigns—all are getting involved, proactively looking at ways to create contained technology.

The Pentagon's Brain: An Uncensored History of DARPA, America's Top-Secret Military Research Agency
by Annie Jacobsen
Published 14 Sep 2015

(Los Alamos National Laboratory) The DARPA Modular Prosthetic Limb. The work advances robotics but is it helping warfighters who lost limbs? (U.S. Department of Defense, courtesy of Johns Hopkins University Applied Physics Laboratory) DARPA’s Atlas robot is a high-mobility humanoid robot built by Boston Dynamics. Its “articulated sensor head” has stereo cameras and a laser range finder. (Defense Advanced Research Projects Agency) Allen Macy Dulles and his sister, Joan Dulles Talley. A brain injury during the Korean War, in 1952, made it impossible for Dulles to record new memories. DARPA’s brain prosthetics program alleges to help brain-wounded warriors like Dulles, but program details remain highly classified.

Artificial Intelligence: A Modern Approach
by Stuart Russell and Peter Norvig
Published 14 Jul 2019

To his right is a pivotal position from the second game of the historic Go match between former world champion Lee Sedol and DeepMind’s ALPHAG Oprogram. Move 37 by ALPHAG Oviolated centuries of Go orthodoxy and was immediately seen by human experts as an embarrassing mistake, but it turned out to be a winning move. At top left is an Atlas humanoid robot built by Boston Dynamics. A depiction of a self-driving car sensing its environment appears between Ada Lovelace, the world’s first computer programmer, and Alan Turing, whose fundamental work defined artificial intelligence. At the bottom of the chess board are a Mars Exploration Rover robot and a statue of Aristotle, who pioneered the study of logic; his planning algorithm from De Motu Animalium appears behind the authors’ names.

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Wanderers: A Novel
by Chuck Wendig
Published 1 Jul 2019

The rules of Jenga were simple: You built a tower of wooden blocks based on the pieces provided, and then the goal was to pull pieces out, one at a time, in the hope that the tower did not fall. You competed against your opponents in the hope that the tower fell on their move, not yours. Initially, Black Swan was tested on a digital version, but later was allowed to inhabit a robotic arm with advanced, multi-articulated fingers designed by Boston Dynamics. Black Swan always won. Insofar as one could “win” Jenga, of course. The great lesson of that game was, similar to pinball, that one never truly won at Jenga. Eventually, the lesson went, the tower would fall. It could not remain standing because that was the nature of towers and time and human intervention: Just because it did not fall on your turn did not mean it would not fall.