robotic process automation

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

“An Updated Survey of Health Insurance Claims Receipt and Processing Times,” AHIP Center for Policy and Research, February 2013, http://www.ahip.org/survey/Healthcare-January 2013/. 9. Mary Lacity, Leslie Willcocks, and Andrew Craig, “Robotic Process Automation at Telefonica O2,” London School of Economics case study, April 2015, http://www.umsl.edu/~lacitym/TelefonicaOUWP022015FINAL.pdf. 10. Interview with Paul Donaldson, then of Xchanging, and Leslie Willcocks, Mary Lacity, and Andrew Craig, “Robotic Process Automation at Xchanging,” London School of Economics case study, June 2015, https://www.xchanging.com/system/files/dedicated-downloads/robotic-process-automation.pdf. 11. Jordan Novet, “South Korea’s Team KAIST Wins the 2015 DARPA Robotics Challenge,” VentureBeat, June 6, 2015, http://venturebeat.com/2015/06/06/koreas-team-kaist-wins-the-2015-darpa-robotics-challenge/. 12.

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In health insurance companies, for example, the automated processing of medical claims (known as “auto-adjudication”) went from 37 percent in 2002 to 79 percent in 2011—and it’s probably well over that figure now.8 While this sort of automated decision-making can be done with paper documents, it’s a lot easier if the information is all digitized. More recently, companies have begun to employ a technology related to business rules and BPM called “robotic process automation.” This technology has the following traits: It does not involve robots, contrary to its name; It makes use of workflow and business rules technology; It is easily configured and modified by business users; It deals with highly repetitive and transactional tasks; It doesn’t learn or improve its performance without human modification; It typically interfaces with multiple information systems as if it were a human user; this is called “presentation layer” integration.

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It is probable that many people who today hold tiny monopolies on specialized tasks they have mastered will see computers come to threaten them. Indeed, we were reminded of this in a recent conversation with Alastair Bathgate, founder of Blue Prism, the company we mentioned in Chapter 2. He sells “robotic” process automation to businesses that enables them to automate routine back-office process tasks, even where the numbers of knowledge workers performing them are not vast. We put quotes around the word “robotic” there because in fact this is software; the human’s replacement in the process has no physical embodiment.

The Globotics Upheaval: Globalisation, Robotics and the Future of Work
by Richard Baldwin
Published 10 Jan 2019

Peter Bright, “Moore’s Law Really Is Dead This Time,” ArsTechnica.com, November 2, 2016. 5. Daniel Robinson, “Moore’s Law Is Running Out—But Don’t Panic,” ComputerWeekly.com, November 19, 2017. 6. See Leslie Willcocks, Mary Lacity, and Andrew Craig, “Robotic Process Automation at Xchanging,” Outsourcing Unit Working Research Paper Series 15/03, London School of Economics and Political Science, June 2015. 7. Willcocks, Lacity, and Craig, “Robotic Process Automation at Xchanging.” 8. Quoted in Jesse Scardina, “Conversica Cloud AI Software Tackles Sales Leads,” TechTarget. com (blog), June 1, 2016. 9. Machine learning has been around for decades, but a lack of computer power and data limited the effectiveness of the algorithms it produced in the past. 10.

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Poppy is part of the new digital workforce where the “digital” refers to the worker not the work. She is a white-collar robot where the “white collar” refers to the attire of the workers she is replacing not the clothing that the robot is wearing. Poppy is an example of a new form of artificial intelligence called robotic process automation (RPA) which draws on the new capacities created by machine learning. Barnes views Poppy as a co-worker despite the fact that “she” is really just a piece of software. Indeed, it was Barnes who gave the software a name. Perhaps this naming stems from the fact that the software does exactly what Barnes used to do, and in exactly the same way.

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MEET WHITE-COLLAR AUTOMATION The sophisticated computer systems and machine learning algorithms that are behind Lex Machina and the like are very expensive and require PhD-level computer scientists to get them up and running. If these sophisticated AI platforms were restaurants, they’d have a Michelin star or two. This puts them out of the reach of the companies for which most people work, namely small-and medium-sized firms. There is, however, a “fast-food” version of white-collar robots. It’s called “robotic process automation” (RPA) software; Poppy, who we met in Chapter 4, is a good example. RPA is probably not what comes to mind when people speak of the “robot apocalypse,” but RPA will be a key part of the Globotics Transformation. It’s worth a closer look. RPAs are automating white-collar jobs in a very direct way.

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

Seth Fletcher, “How Big Data Is Taking Teachers Out of the Lecturing Business,” Scientific American, August 1, 2013, https://www.scientificamerican.com/article/how-big-data-taking-teachers-out-lecturing-business. Moving Well Beyond RPA The Virgin Trains system is a relatively advanced form of back-office automation because it can analyze and adapt to unstructured data as well as the sudden influx of data. Such applications are called “robotic process automation” (RPA). Simply put, RPA is software that performs digital office tasks that are administrative, repetitive, and mostly transactional within a workflow. In other words, it automates existing processes. But in order to reimagine processes, firms must utilize more advanced technologies—namely, AI.

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See personalization cybersecurity, 56–58, 59 Darktrace, 58 DARPA Cyber Grand Challenges, 57, 190 Dartmouth College conference, 40–41 dashboards, 169 data, 10 in AI training, 121–122 barriers to flow of, 176–177 customization and, 78–80 discovery with, 178 dynamic, real-time, 175–176 in enterprise processes, 59 exhaust, 15 in factories, 26–27, 29–30 leadership and, 180 in manufacturing, 38–39 in marketing and sales, 92, 98–99, 100 in R&D, 69–72 in reimagining processes, 154 on supply chains, 33–34 supply chains for, 12, 15 velocity of, 177–178 data hygienists, 121–122 data supply-chain officers, 179 data supply chains, 12, 15, 174–179 decision making, 109–110 about brands, 93–94 black box, 106, 125, 169 employee power to modify AI, 172–174 empowerment for, 15 explainers and, 123–126 transparency in, 213 Deep Armor, 58 deep learning, 63, 161–165 deep-learning algorithms, 125 DeepMind, 121 deep neural networks (DNN), 63 deep reinforcement learning, 21–22 demand planning, 33–34 Dennis, Jamie, 158 design at Airbus, 144 AI system, 128–129 Elbo Chair, 135–137 generative, 135–137, 139, 141 product/service, 74–77 Dickey, Roger, 52–54 digital twins, 10 at GE, 27, 29–30, 183–184, 194 disintermediation, brand, 94–95 distributed learning, 22 distribution, 19–39 Ditto Labs, 98 diversity, 52 Doctors Without Borders, 151 DoubleClick Search, 99 Dreamcatcher, 136–137, 141, 144 drones, 28, 150–151 drug interactions, 72–74 Ducati, 175 Echo, 92, 164–165 Echo Voyager, 28 Einstein, 85–86, 196 Elbo Chair, 136–137, 139 “Elephants Don’t Play Chess” (Brooks), 24 Elish, Madeleine Clare, 170–171 Ella, 198–199 embodied intelligence, 206 embodiment, 107, 139–140 in factories, 21–23 of intelligence, 206 interaction agents, 146–151 jobs with, 147–151 See also augmentation; missing middle empathy engines for health care, 97 training, 117–118, 132 employees agency of, 15, 172–174 amplification of, 138–139, 141–143 development of, 14 hiring, 51–52 job satisfaction in, 46–47 marketing and sales, 90, 92, 100–101 on-demand work and, 111 rehumanizing time and, 186–189 routine/repetitive work and, 26–27, 29–30, 46–47 training/retraining, 15 warehouse, 31–33 empowerment, 137 bot-based, 12, 195–196 in decision making, 15 of salespeople, 90, 92 workforce implications of, 137–138 enabling, 7 enterprise processes, 45–66 compliance, 47–48 determining which to change, 52–54 hiring and recruitment, 51–52 how much to change, 54–56 redefining industries with, 56–58 reimagining around people, 58–59 robotic process automation (RPA) in, 50–52 routine/repetitive, 46–47 ergonomics, 149–150 EstherBot, 199 ethical, moral, legal issues, 14–15, 108 Amazon Echo and, 164–165 explainers and, 123–126 in marketing and sales, 90, 100 moral crumple zones and, 169–172 privacy, 90 in R&D, 83 in research, 78–79 ethics compliance managers, 79, 129–130, 132–133 European Union, 124 Ewing, Robyn, 119 exhaust data, 15 definition of, 122 experimentation, 12, 14 cultures of, 161–165 in enterprise processes, 59 leadership and, 180 learning from, 71 in manufacturing, 39 in marketing and sales, 100 in process reimagining, 160–165 in R&D, 83 in reimagining processes, 154 testing and, 74–77 expert systems, 25, 41 definition of, 64 explainability strategists, 126 explaining outcomes, 107, 114–115, 179 black-box concerns and, 106, 125, 169 jobs in, 122–126 sustaining and, 130 See also missing middle extended intelligence, 206 extended reality, 66 Facebook, 78, 79, 95, 177–178 facial recognition, 65, 90 factories, 10 data flow in, 26–27, 29–30 embodiment in, 140 job losses and gains in, 19, 20 robotic arms in, 21–26 self-aware, 19–39 supply chains and, 33–34 third wave in, 38–39 traditional assembly lines and, 1–2, 4 warehouse management and, 30–33 failure, learning from, 71 fairness, 129–130 falling rule list algorithms, 124–125 Fanuc, 21–22, 128 feedback, 171–172 feedforward neural networks (FNN), 63 Feigenbaum, Ed, 41 financial trading, 167 first wave of business transformation, 5 Fletcher, Seth, 49 food production, 34–37 ForAllSecure, 57 forecasts, 33–34 Fortescue Metals Group, 28 Fraunhofer Institute of Material Flow and Logistics (IML), 26 fusion skills, 12, 181, 183–206, 210 bot-based empowerment, 12, 195–196 developing, 15–16 holistic melding, 12, 197, 200–201 intelligent interrogation, 12, 185, 193–195 judgment integration, 12, 191–193 potential of, 209 reciprocal apprenticing, 12, 201–202 rehumanizing time, 12, 186–189 relentless reimagining, 12, 203–205 responsible normalizing, 12, 189–191 training/retraining for, 211–213 Future of Work survey, 184–185 Garage, Capital One, 205 Gaudin, Sharon, 99 GE.

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See research and development (R&D) reciprocal apprenticing, 12, 201–202 recommendation systems, 65, 92, 110–111 recurrent neural networks (RNN), 63 regulations, 213 reimagining, relentless, 12, 203–205 reinforcement learning, 62 repetitive/routine work, 26–27, 29–30, 46–47 process reimagination and, 52–54 in R&D, 69–72 reimagining around people, 58–59 research and development (R&D), 10, 67–83 customization and delivery in, 77–80 ethical/legal issues in, 78–79 hypotheses in, 72–74 MELDS in, 83 observation in, 69–72 risk management and, 80–81 scientific method in, 69–77 testing in, 74–77 resource management, 74–75 retail pricing, 193–194 Rethink Robotics, 22, 24 Reverse Engineering and Forward Simulation (REFS), 72–74 Revionics, 194 Riedl, Mark O., 130 right to explanation, 124 Rio Tinto, 7–8, 109–110 risk management, 80–81 robotic arms, 21–23 learning by, 24–26 robotic process automation (RPA), 50–52 Robotics, Three Laws of (Asimov), 128–129 Robotiq, 23 Roomba, 24 Rosenblatt, Frank, 62 Round Chair, 136–137 routine work. See repetitive/routine work Royal Dutch Shell, 192 Ruh, Bill, 194–195 “Runaround” (Asimov), 128–129 Russo, Daniel, 49 safety engineers, AI, 129 safety issues, 126–129 Sagan, Carl, 135 sales.

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

Building towards this state will see data increasingly integrated into lines of business or federated across stakeholder departments, closing any gaps between the data culture and company culture through shared objectives, metrics and rewards. Fifth dimension: Data foundations Along the pathway to full maturity, a range of enabling data and technology will be required, from foundational data assets (such as new generation data platforms and data integration) to advanced solutions (such as robotic process automation and AI). Each of these is capable of yielding competitive advantage or value, through driving incremental revenue or achieving cost savings. It is important to recognise the role that these solutions play in supporting progression towards the vision, but equally to understand that they do not deliver it in their own right.

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Repetitive, low-value activities, such as reporting or data extraction, may be central to all data-driven processes. But they also consume energy and attention that could be better applied to innovative and value-creating projects. To avoid falling into the trap of being a reactive BAU machine, the data department needs to become a literal machine where possible – applying robotic process automation to routine tasks, developing ML to carry out the heavy lifting. The business case for this is significant. According to research by Microsoft in 2018, organisations applying AI to their processes outperform laggards by 5% on factors like productivity, performance and business outcomes. Among DataIQ Leaders, the range of automation projects being developed indicates the scope of the opportunity, from data wrangling and reporting through to personalisation of communications and rostering shifts for delivery drivers.

Power and Progress: Our Thousand-Year Struggle Over Technology and Prosperity
by Daron Acemoglu and Simon Johnson
Published 15 May 2023

Prediction Machines: The Simple Economics of Artificial Intelligence. Cambridge, MA: Harvard Business Review Press. Agrawal, D. P. 2007. The Indus Civilization: An Interdisciplinary Perspective. New Delhi: Aryan. AIIM (Association for Intelligent Information Management). 2022. “What Is Robotic Process Automation?” www.aiim.org/what-is-robotic-process-automation. Alexander, Magnus W. 1929. “The Economic Evolution of the United States: Its Background and Significance.” Address presented at the World Engineering Congress, Tokyo, Japan, November 1929. National Industrial Conference Board, New York. Alexopoulos, Michelle, and Jon Cohen. 2016.

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Sophisticated tax-preparation software can query users about expenses or items that look suspicious, and customers can be presented with voice-activated menus to categorize their problem (even if this often works imperfectly, ends up shifting some of the work to users, and causes longer delays as customers wait for a human to provide the necessary help). In robotic process automation (RPA), for example, software implements tasks after watching human actions in the application’s graphical user interface. RPA bots are now deployed in banking, lending decisions, e-commerce, and various software-support functions. Prominent examples include automated voice-recognition systems and chatbots that learn from remote IT-support practices.

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“[T]he challenge is…,” “improving lives…,” “creative capitalism,” and “to take on…” are from Gates (2008). On various definitions of AI, see the leading textbook, Russell and Norvig (2009), which provides several different definitions. From the Field of AI Dreams. On Jacquard’s loom, see Essinger (2004). On robotic process automation, see AIIM (2022) and Roose (2021). On RPAs’ mixed results, see Trefler (2018). On the classification of routine tasks, see Autor, Levy, and Murnane (2003) and Acemoglu and Autor (2011). The prediction that AI can perform close to 50 percent of jobs is in Frey and Osborne (2013). Further discussion can be found in Susskind (2020).

AI 2041: Ten Visions for Our Future
by Kai-Fu Lee and Qiufan Chen
Published 13 Sep 2021

But what exactly are the new jobs—and will they satisfy many humans’ desire to feel productive and useful? Who is most at risk, and how can humans flourish in the post-automation era? I’ll give my thoughts on these questions in the commentary at the end of the chapter, describing how technologies like robotics and robotic process automation will continue to evolve to take over tasks for both white-collar and blue-collar workers. IN THE DARKENED training room, Jennifer Greenwood and twelve other trainees gazed attentively at the imagery scrolling in midair before them. Accompanying the visuals was a male voice, narrating softly, like an oracle pronouncing divinations.

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The company required him to use internal software to manage client data. A semi-intelligent assistant program, powered with machine learning, would pop up from time to time, helping him crunch numbers or tidy up, fill in forms, and generate notices—junior grunt work. This system, called RPA (robotic process automation), became the last straw for her father. Slowly, he noticed this helper getting smarter and doing more, sometimes even correcting his own small human errors. When her father finally awoke to reality, it overwhelmed him. Every time the smart helper made a correction on a task, the AI noted the fix as a data point, helping it become ever smarter in the process, ever closer to replacing its human co-worker.

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How far will this job displacement go, and what industries will it hit hardest? In AI Superpowers I estimated that about 40 percent of our jobs could be accomplished mostly by AI and automation technologies by 2033. It’s not going to happen overnight, of course. Jobs will be taken over by AI gradually, just as we saw with RPA (robotic process automation) and Jennifer’s father, the underwriter, in “The Job Savior.” RPA is a “software robot” installed on workers’ computers that can watch everything the workers do. Over time, by watching millions of people at work, RPA figures out how to do employees’ routine and repetitive tasks. At some point, a company will decide it’s better off letting the robot take over a given task entirely from the human.

Super Founders: What Data Reveals About Billion-Dollar Startups
by Ali Tamaseb
Published 14 Sep 2021

Saving time and saving money were the most common needs addressed by billion-dollar companies, and startups working on those needs were more likely to become billion-dollar outcomes. Some startups can save their customers both time and money. When Romanian entrepreneurs built UiPath in 2005, they planned on outsourcing software projects for the world’s biggest companies. It wasn’t until 2012 that they realized the potential of robotic process automation (RPA) and pivoted their product to automate tasks that would typically require a human touch. RPA saves time in many ways: an insurance company can use RPA software to automate downloading a receipt from email and uploading it into a database; in the legal department of a large factory, RPA bots can help lawyers automatically send nondisclosure agreements.

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Many bootstrapped for years before later raising money from growth-stage investors or private-equity funds to accelerate growth. Atlassian—an Australian multibillion-dollar software company best known for Jira, an issue-tracking application—bootstrapped for eight years before raising a growth round from Accel partners. UiPath, a robotic process-automation company started in Romania, bootstrapped for ten years before raising venture capital. Many such companies did consulting work and provided services to bring in revenues and sustain themselves in the beginning. Some simply didn’t know about or didn’t have access to investors and had to bootstrap out of necessity.

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

General Operations Most companies have tons of repetitive digital workflows. These workflows can be tedious to complete. Employees responsible for these tasks can easily become bored and inattentive, allowing errors to creep into your operations and your data. Fortunately, these tasks are well-suited for automation by Robotic Process Automation (RPA), which are software robots programmed to perform a specified sequence of actions. Even better, RPA deployment is relatively fast and low risk, so that problematic robots can quickly be removed without detriment to existing systems. Examples of workflows at which RPAs excel include performing regular diagnostics of your software or hardware, creating and updating accounting records (such as payroll), or automatically generating and delivering periodic reports to the relevant stakeholders.

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

Fortunately, the technology hype cycle is ready to provide a stream of distractions. All too often, the word digital is conflated with whatever technology fad has made it into the colour supplements this month. Blockchain. Artificial intelligence. The Internet of Things and connected devices. Robotic Process Automation. The captains of industry, ministers and senior officials who read colour supplements during their brief periods of down time see these exciting things and commission policy papers to unpick their potential effect on the organisations they run. The papers are good. But there is a gap – sometimes a huge gap – between policy or business school smarts and technological literacy.

Futureproof: 9 Rules for Humans in the Age of Automation
by Kevin Roose
Published 9 Mar 2021

Back-office bots are the software programs that can do the kinds of menial, unsexy tasks that are necessary for any large organization to function. If you work in a big company, you can probably think of someone with a generic-sounding title like operations coordinator or benefits administrator—these are exactly the kinds of people back-office bots are designed to replace. Many of these apps fall into a category known as “robotic process automation,” or RPA. Automation Anywhere, the company whose conference was detailed in this book’s introduction, is a major RPA vendor, but there are others you’ve probably never heard of, with names like UiPath, Blue Prism, and Kryon. Collectively, these companies are worth billions of dollars, and they’ve been growing so quickly that even large tech companies have stepped into the RPA business.

Always Day One: How the Tech Titans Plan to Stay on Top Forever
by Alex Kantrowitz
Published 6 Apr 2020

“You can automate a good chunk of what it probably takes a human to do,” Venkat said, speaking with a slight hint of discomfort. “What used to take twelve days to do, in terms of processing a claim, now takes two days. It used to cost around two thousand dollars to go process something; now it costs three hundred.” UiPath is one of several “robotic process automation” companies currently surging to meet a growing demand for these capabilities. Less than two months after its Miami confab, one of UiPath’s main competitors, Automation Anywhere, raised $300 million from Softbank. And Google, for its part, isn’t the only company licensing AI decision-making power.

Leadership by Algorithm: Who Leads and Who Follows in the AI Era?
by David de Cremer
Published 25 May 2020

This is not simply a prediction for the future, it is something that is already happening today. Examples abound: IBM, for example, applies the algorithm Watson Talent to its own HR teams to promote speed, efficiency and the optimal use of their operations.⁹⁵ Another example of such automation is the use of Robotic Process Automation (RPA). RPA uses software algorithms to closely replicate repetitive tasks like moving data between two spreadsheets. And, finally, especially within the context of HR management, the employment of algorithms to conduct repetitive administrative tasks has already been proven to be effective.

Four Battlegrounds
by Paul Scharre
Published 18 Jan 2023

While Maven’s ability to move quickly, bring in commercial tech and connect it to real-world operational problems was a game-changer from a bureaucratic and cultural standpoint, many experts I spoke with who were familiar with Maven said it had not revolutionized intelligence analysis. While Maven’s tools were technically impressive, they were not (yet) delivering on AI’s hype. Shanahan said the most impactful work the JAIC had done was in “robotic process automation tools” (“I wouldn’t even call it AI,” he acknowledged). Rachael Martin, mission director for business process automation at the JAIC, explained that they were focused on using automation, analytics, and data augmentation to modernize business processes across the department to “either introduce efficiencies, or find cost savings, or find new insights, or be more predictive about the way that we conduct our business.”

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Able Archer, 287 academic espionage, 163–64 accidents, 255 ACE (Air Combat Evolution), 1–2, 222 ACLU (American Civil Liberties Union), 111, 113 Acosta, Jim, 128 Advanced Research Projects Agency, 72 Advanced Research Projects Agency-Energy, 40 adversarial examples, 239–44, 240f adversarial patches, 241–42, 242f Aether Committee, 159 Afghanistan, 45–46, 54, 255 African Union, 108 AFWERX (Air Force Works), 214 Agence France-Presse, 139 AGI (artificial general intelligence), 284 AI Global Surveillance Index, 109 AI Index, 333–34 airborne warning and control system (AWACS), 196 Air Combat Evolution (ACE), 1–2, 222 aircraft, 191, 255 aircraft availability rates, 197 aircraft carriers, 191–92 AI Research SuperCluster, 296 Air Force 480th ISR Wing, 54 Air Force Works (AFWERX), 214 airlines, 100 AI Task Force, 193–94 AI Technology and Governance conference, 177 AITHOS coalition, 136 alchemy, 232 algorithmic warfare, 53, 56, 58 Algorithmic Warfare Cross-Functional Team (AWCFT), See Project Maven algorithm(s), 288; See also machine learning computer vision, 202–3 efficiency, 51, 297–98 real world situations, vs., 230–36 in social media, 144–51 for surveillance, 82 training, 25 Alibaba, 37, 91, 212 Alibaba Cloud, 160 All-Cloud Smart Video Cloud Solution, 107 Allen, John, 280 Allen-Ebrahimian, Bethany, 82 Alphabet, 26, 296 AlphaDogfight, 1–3, 220–22, 257, 266, 272 AlphaGo, 23, 73, 180, 221, 266, 271, 274, 284, 298, 453, 454 AlphaPilot drone racing, 229–30, 250 AlphaStar, 180, 221, 269, 271, 441 AlphaZero, 267, 269–71, 284 Amazon, 32, 36, 215–16, 224 Deepfake Detection Challenge, 132 and facial recognition, 22–23 and Google-Maven controversy, 62, 66 and government regulation, 111 revenue, 297 AMD (company), 28 American Civil Liberties Union (ACLU), 111, 113 Anandkumar, Anima, 32, 120 Anduril, 66, 218, 224 Angola, 107, 108 Apollo Program, 297 Apple, 92, 95–96 application-specific integrated circuits (ASICs), 180 Applied Intuition, 224 arms race, 254, 257 Army Command College, 279 Army of None (Scharre), 196 artificial general intelligence (AGI), 284 artificial intelligence (AI) agents, 271 community, publication norms, 125 cost of, 296–97 ethics, 159 future of, 294–301 general, 284 as general-purpose enabling technology, 3–4 impact on economic productivity, 72–73 implementation, 31 indices, global, 15–17 narrowness, 233 outcomes, 299–301 regulation of, 111–13 safety, 286, 289, 304 specialized chips for, 28–29, 180, 185 “Artificial intelligence: disruptively changing the ‘rules of the game’” (Chen), 279 Artificial Intelligence Industry Alliance, 172 artificial intelligence (AI) systems future of, 294–301 humans vs., 263–75 limitations of, 229–37 roles in warfare, 273 rule-based, 230, 236 safety and security challenges of, 249–59 arXiv, 163 ASICs (application-specific integrated circuits), 180 ASML (company), 181 Associated Press, 139 Atari, 235 Atlantic, The, 173 atoms, in the universe, number of, 335 AUKUS partnership, 76 Austin, Lloyd, 292 Australia, 76, 108, 158, 182, 187 Australian Strategic Policy Institute, 82, 98, 158 Autodesk, 162 automated surveillance, 103 automatic target recognition, 56–58 automation bias, 263 autonomous cars, 23, 65 autonomous weapons, 61, 64–66, 256 autonomous weapons, lethal, 286 AWACS (airborne warning and control system), 196 AWCFT (Algorithmic Warfare Cross-Functional Team), See Project Maven Azerbaijan, 108 BAAI (Beijing Academy of Artificial Intelligence), 172, 455 backdoor poisoning attacks, 245 badnets, 246 BAE (company), 211 Baidu, 37, 92, 160, 172, 173, 212 Baise Executive Leadership Academy, 109 “Banger” (call sign), 1 Bannon, Steve, 295 Battle of Omdurman, 13 BBC, 138 BeiDou, 80 Beijing, 84, 92, 159 Beijing Academy of Artificial Intelligence (BAAI), 172, 455 Beijing AI Principles, 172, 173 Beijing Institute of Big Data Research, 157 Belt and Road Initiative, 105, 108–10 BERTLARGE, 294 Betaworks, 127–28 Bezos, Jeff, 215 biases, 234, 236 Biddle, Stephen, 219 Biden, Hunter, 131 Biden, Joe, and administration, 33–34, 147, 166–67, 184, 252, 292 big data analysis, 91 Bing, 160 Bin Salman, Mohammed, 141 biometrics, 80, 84; See also facial recognition “Bitter Lesson, The” (Sutton), 299 black box attacks, 240–41 blacklists, 99–100 BlackLivesMatter, 143, 148 “blade runner” laws, 121–22, 170 blind passes, 249 Bloomberg, 118 Bloomberg Government, 257 Boeing, 193, 216 Bolivia, 107 bots, 118, 121–22, 142, 144–49, 221 Bradford, Anu, 112 Bradshaw, Samantha, 141–42 brain drain, 31, 304 “brain scale” models, 300 Brands, Hal, 223 Brazil, 106, 107, 110 Breakfast Club, 53 Brexit referendum, 122 Bridges Supercomputer, 44 brinkmanship, 281 Brokaw, Tom, 143 Brooks, Rodney, 233 “brothers and sisters,” Han Chinese, 81 Brown, Jason, 54–55, 57, 201–3 Brown, Michael, 49, 196–97 Brown, Noam, 44, 48, 50 Bugs Bunny (fictional character), 231 Bureau of Industry and Security, 166 Burundi, 110 Buscemi, Steve, 130 Bush, George W., and administration, 68–70 ByteDance, 143 C3 AI, 196, 224 C4ISR (Command, Control, Communication, Cloud, Intelligence, Surveillance, and Reconnaissance), 107 CalFire, 201–2 California Air National Guard, 201, 203 Caltech, 32, 120 Cambridge Innovation Center, 135 cameras, surveillance, 6, 86–87, 91 Campbell, Kurt, 292 Canada, 40, 76, 158, 187 Capitol insurrection of 2021, 150 car bombs, 54–55 Carnegie Mellon University, 31–32, 45–46, 66, 193, 196, 207 Carnegie Robotics, 193 cars, self-driving, 23 Carter, Ash, 57 casualties, military, 255 CBC/Radio-Canada, 138 CCP, See Chinese Communist Party Ceaușescu, Nicolae, 345 CEIEC (China National Electronics Import and Export Corporation), 106 censorship, 175–76 centaur model, 263 Center for a New American Security, 36, 71, 222 Center for Data Innovation, 15 Center for Security and Emerging Technology, 33, 139, 162, 185, 298, 323 Center on Terrorism, Extremism, and Counterterrorism, 124 Central Military Commission, 292 Central Military Commission Science and Technology Commission, 36 central processing units (CPUs), 25 CFIUS (Committee on Foreign Investment in the United States), 179 C-5 cargo plane, 196 chance, 282 character of warfare, 280 checkers, 47 Chen Hanghui, 279 Chen Weiss, Jessica, 110 Chesney, Robert, 130 chess, 47, 267, 269, 271, 275 Chile, 107 China AI research of, 30 bots, 142 Central Military Commission Science and Technology Commission, 36 commercial tech ecosystem, 223 data privacy regulations of, 21–22 ethics standards, 171–75 High-End Foreign Expert Recruitment Program, 33 human rights abuses, 63 in industrial revolution, 12–13 internet use, 22 nuclear capabilities, 50 ranking in government strategy, 40 semiconductor imports, 29 synthetic media policies of, 140 technology ecosystem, 91–96 Thousand Talents Plan, 32 China Arms Control and Disarmament Association, 290 China Initiative, 164, 167 China National Electronics Import and Export Corporation (CEIEC), 106 China National Intellectual Property Administration (CNIPA), 353 China Security and Protection Industry Association, 91 China Telecom, 169 Chinese Academy of Sciences, 88, 158 Chinese Academy of Sciences Institute of Automation, 172 Chinese Communist Party (CCP) economic history, 85–86 human rights abuses, 79–80, 83 surveillance, 97–104, 174–77 Chinese graduate students in U.S., 31 Chinese military aggression, 76; See also People’s Liberation Army (PLA) AI dogfighting system, 257 and Google, 62–63 investments in weapons, 70 scientists in U.S., 5 and Tiananmen massacre, 68 U.S. links to, 157–58, 161, 166, 303 Chinese Ministry of Education, 162 Chinese People’s Institute of Foreign Affairs, 173 Chinese Talent Program Tracker, 33 chips, See semiconductor industry; semiconductors CHIPS and Science Act, 40, 180 Cisco, 109, 246 Citron, Danielle, 121, 130 Civil Aviation Industry Credit Management Measures, 100 Clarifai, 60–61, 63, 66, 224 Clark, Jack, 31, 117, 119–25 Clinton, Bill, and administration, 69–70, 97 CLIP (multimodal model), 295–96 cloud computing, 91, 215–16 CloudWalk, 105, 156, 389 CNIPA (China National Intellectual Property Administration), 353 COBOL (programming language), 204 cognitive revolution, 4 cognitization of military forces, 265 Colombia, 107 Command, Control, Communication, Cloud, Intelligence, Surveillance, and Reconnaissance (C4ISR), 107 command and control, 268 Commerce Department, 155–57, 166, 171, 184 Committee on Foreign Investment in the United States (CFIUS), 179 computational efficiency, 297–300 computational game theory, 47–50 compute, 25–29 control over, 27 global infrastructure, 178 hardware, 297–99 resources, size of, 294–96 trends in, 325 usage of, 26, 51 computer chips, See semiconductor industry; semiconductors Computer Science and Artificial Intelligence Laboratory (CSAIL), 156 computer vision, 55–57, 64, 224 Computer Vision and Pattern Recognition conference, 57 concentration camps, 81 confidence-building measures, 290–93 confinement, 82 content recommendations, 145 Cook, Matt, 203 cooperation, research, 303–4 Cornell University, 124 cost, of AI, 296–97 Côte d’Ivoire, 107 Cotton, Tom, 164 counter-AI techniques, 248 COVID pandemic, 74–75 CPUs (central processing units), 25 Crootof, Rebecca, 123 CrowdAI, 202, 224 CSAIL (Computer Science and Artificial Intelligence Laboratory), 156 Cukor, Drew, 57, 58–59 Customs and Border Patrol, 110–11 cyberattacks, 246 Cyber Grand Challenge, 195–96 Cybersecurity Law, 95, 174 “cyberspace,” 102 Cyberspace Administration of China, 99 cyber vulnerabilities, 238 adversarial examples, 239–44 data poisoning, 244–47 discovery, 195–96 model inversion attacks, 247 Czech Republic, 108 Dahua, 89, 156, 169, 353, 354–55, 388–89 Dalai Lama, 80 Dalian University of Technology, 212 DALL·E, 295 Darcey, Brett, 220, 249–50 DARPA (Defense Advanced Research Projects Agency), 1, 195, 210–13, 220 DARPA Squad X, 231, 233, 236 data, 18–24 explosion, 18–19 mapping, 204 open-source, 288 poisoning, 238, 244–47 privacy laws, 21–22, 111–12, 170–71, 174–77 storage, 91 usage, 51 Data Security Law, 95, 174 datasets publicly available, 139 reliance on, 323 training, see training datasets DAWNBench, 57 D-Day Invasion of Normandy, 46 dead hand, 289–90 Dead Hand, 447; See also Perimeter deception in warfare, 45 Deep Blue, 275 deepfake detection, 127, 132–33, 137–38 Deepfake Detection Challenge, 132–33 deepfake videos, 121, 130–32 deep learning, 2, 19, 31, 210, 236 Deep Learning Analytics, 209–13, 233 DeepMind, 23, 26, 32, 180, 221, 271–72, 295–96, 298–99, 441, 454 Deeptrace, 121, 130–33 defense acquisition policy, 217 Defense Advanced Research Projects Agency (DARPA), 1, 195, 210–13, 220 Defense Innovation Board, 65–66 Defense Innovation Unit (DIU), 35, 49, 57, 195–99, 214, 252 Defense One, 58 Defense Sciences Office, 231 defense start-ups, 222 Dell, 162 Deloitte, 246 Deng Xiaoping, 75, 85 Denmark, 108 Department of Defense, 35, 51–52, 56, 60–67, 70, 160, 166, 194 AI principles, 65–66 AI strategy, 249 budget, 297 contracts, 214–18 cyberattacks on, 246 innovation organizations, 198f reform, 225 Department of Energy, 246 Department of Energy’s Office of Science, 40 Department of Homeland Security, 246 Department of Justice, 164, 246 destruction, extinction-level, 282 deterrence, 51 DiCaprio, Leonardo, 130 Dick, Philip K., 81 dictator’s dilemma, 69 Didi, 92 digital devices, 18 DigitalGlobe, 204 Digital Silk Road, 110 DiResta, Renée, 139 disaster relief, 201, 204 disinformation, 117–26 AI text generation, 117–21 deepfake videos, 121 GPT-2 release, 123–24 Russian, 122 voice bots, 121–22 distributional shift, 233, 426 DIU, See Defense Innovation Unit (DIU) DNA database, 89–90 dogfighting, 1, 249–50, 272; See also Alpha Dogfight “Donald Trump neuron,” 295 Doom bots, 221 doomsday device, 282 Dota 2 (game), 26, 117, 267–72, 298 Dragonfly, 62 Drenkow, Nathan, 247 drone pilots, 223 drones, 229–30, 257, 286–87 drone video footage, 36, 53–56, 61, 65, 202–3; See also image processing; video processing drugs, 251 Dulles Airport, 110–11 Dunford, Joe, 62 Duplex, 121 Easley, Matt, 193 Eastern Foundry, 209 Economist, The, 18 Ecuador, 106 efficiency, algorithmic, 51 Egypt, 109 XVIII Airborne Corps at Fort Bragg, 194 elections, 122, 128, 129, 131, 134, 150 Elmer Fudd (fictional character), 231 Entity List, 155–57, 161, 163, 166–67, 171, 182, 184, 388–89 Environmental Protection Agency, 40 Erasmus University Medical Center, 158, 393–94 Esper, Mark, 67, 197, 205 espionage, 33, 163–64 Estonia, 108 “Ethical Norms for New Generation Artificial Intelligence,” 172 ethical use of technology, 140 ethics censorship, 175–76 Chinese standards, 171–75 data privacy, 176–77 international standards, 169–71 Ethiopia, 108 E-3 Sentry, 196 Europe AI research of, 30 in industrial revolution, 12–13 internet use, 22 and semiconductor market, 27 European Union, 76, 187 Europe Defender, 194 EUV (extreme ultraviolet lithography), 181 explainable AI, 237 export controls, 166–67, 181–86, 300 extinction-level destruction, 282 extreme ultraviolet lithography (EUV), 181 Eyes in the Sky (Michel), 54 F-35 stealth fighter jet, 254–55 Faber, Isaac, 193–94, 203 Face++, 88 Facebook account removal, 142 algorithms, 144–46 content moderation, 149 Deepfake Detection Challenge, 132 manipulated media policies of, 140 number of users, 22 and Trusted News Initiative, 139 face swapping, 121, 130–31 facial recognition attacks on, 241, 245 challenges in, 426 in China, 5–6, 80, 88–91, 103, 167 Chinese export of technology, 105–7 laws and policies for, 113, 159, 170 poor performance outside training data, 64–65 of Uighurs, 88–89, 158 in U.S., 22–23, 111, 159 fake news, 117–19, 122, 124–25 Falco (call sign), 1–2, 221, 226 Fan Hui, 298 FBI, 95–96, 164 Fedasiuk, Ryan, 162 Federal Emergency Management Agency (FEMA), 204 FedRAMP, 213 FEMA (Federal Emergency Management Agency), 204 Fidelity International, 157 field-programmable gate arrays (FPGAs), 180 “50 cent army,” 125 Fighting to Innovate (Kania), 222 filtering, of harmful content, 144 Financial Times, 157–58 Finland, 40, 187 fire perimeter mapping, 201–4 5G wireless networking, 37, 108, 182–83 Floyd, George, 143, 148 flu, H5N1 avian bird, 123 ForAllSecure, 196 Forbes magazine, 202 Ford, Harrison, 121 480th ISR Wing, 54 FPGAs (field-programmable gate arrays), 180 France, 40, 76, 108, 158, 187 Frazier, Darnella, 143 Frederick, Kara, 105 French Presidential election, 2017, 122 future, uncertainty of, 276 G7 group, 76, 187 Gab, 149 Gabon, 134 Gadot, Gal, 121 Game Changer, 206 games and gaming, 43–51, 266–73; See also specific games game trees, 47–49 GANs (generative adversarial networks), 127, 133 GAO, See Government Accountability Office (GAO) Garcia, Dominic, 203 Gates, Bill, 159 Gato, 295 GDP (gross domestic product), 69f, 85, 85f GDPR, See General Data Protection Regulation (GDPR) General Dynamics, 209, 212–13 generative adversarial networks (GANs), 127, 133 generative models, 125 genomics, 37 geopolitics, 129, 317 Germany, 12, 76, 107, 108, 158, 187 Gibson, John, 61 Gibson, William, 101, 102 Gizmodo, 120 Global AI Index, 15, 40 Global AI Vibrancy Tool, 319 go (game), 23, 47–48, 73, 180, 271, 275, 298 Golden Shield Project, 87 Goodfellow, Ian, 239 Google, 31, 32, 36, 57, 224, 294 and ASICs, 180 and Dragonfly, 339 Duplex, 121 Meena, 125 and Seven Sons of National Defense, 162 social app dominance, 143 and Trusted News Initiative, 139 work with Chinese researchers, 157, 392, 396 Google AI China Center, 62, 159, 167 Google Brain, 32, 294–96, 299 Google-Maven controversy, 22, 60–67 Google Photos, 64 Googleplex, 195 Google Translate, 234 Gorgon Stare, 53–55, 58 “Governance Principles for a New Generation of Artificial Intelligence,” 173 “Governance Principles for a New Generation of Artificial Intelligence: Develop Responsible Artificial Intelligence,” 172 Government Accountability Office (GAO), 195, 215, 217, 248 government contracting, 215–16, 222, 224–25 government-industry relationship, 95–96 government subsidies, 179–80 GPT-2 (language model), 20, 117–20, 122–25, 139, 294 GPT-3 (language model), 139, 294 GPUs (graphics processing units), 25, 28–29, 185, 296 Grace, Katja, 298 Great Britain, 191–92 Great Firewall, 62, 70, 102, 166 Great Gatsby, The (film), 130 Great Leap Forward, 85 Great Wall, 101 Greitens, Sheena, 105 Griffin, Michael, 200, 257 Guardian, The, 120, 148 Gulf War, 1991, 14, 219 HA/DR (humanitarian assistance/disaster relief), 201, 204 Hamad Bin Khalifa University, 142 Han Chinese, 81, 88 Harbin Institute of Technology, 161 hardware, computing, See compute Harvard University, 32 hashtags, 141 Hate Crimes in Cyberspace (Citron), 121 Heinrich, Martin, 37 Heritage Foundation, 105 Heron Systems in AlphaDogfight Trials, 1–2, 266, 272 background, 220–22 as defense start-up, 224 and real-world aircraft, 249–50 heuristics, 274 Hewlett Packard Enterprise, 157, 392 Hicks, Kathleen, 252 High-End Foreign Expert Recruitment Program, 33 Hikvision, 89, 91, 107, 156, 157, 353, 355, 389, 390 Hikvision Europe, 389 Himalayan border conflict, 75 Hindu, The, 139 Hinton, Geoffrey, 210 HiSilicon, 91 Hoffman, Samantha, 82, 98–99, 101, 102, 174 HoloLens, 160, 217 Honeywell, 162 Hong Kong, 75, 148, 175 Hoover Institution, 162 Horner, Chuck, 14 Howard, Philip, 141–42 Howell, Chuck, 250–51 Huawei, 29, 76, 88–89, 91, 92, 106–9, 169, 171, 182–85, 353, 354, 357, 409 Huawei France, 354 Huffman, Carter, 135–37 human cognition, 275 Human Genetics, 158 human intelligence, 284–85 humanitarian assistance/disaster relief (HA/DR), 201, 204 human-machine teaming, 263–64, 273, 276–86 human psychology, 274 human rights abuses, 63, 155, 158, 176–77 Human Rights Watch, 79, 81–82, 95, 170, 174 Hungary, 110 Hurd, Will, 39 Hurricane Dorian, 204 Husain, Amir, 66, 280 Hwang, Tim, 139, 323 hyperwar, 280 IARPA (Intelligence Advanced Research Projects Activity), 91, 246 IBM, 32, 109, 162, 215 IDG Capital, 157 IEC (International Electrotechnical Commission), 169 IEDs (improvised explosive devices), 45–46 IEEE (Institute for Electrical and Electronics Engineers), 171 iFLYTEK, 37, 91, 93–95, 104, 156, 157, 169 IJOP (Integrated Joint Operations Platform), 81–82 image classification systems, 64–65 image misclassification, 296 Imagen, 295 ImageNet, 19, 54, 210 image processing, 53–55, 58, 61 immigration policies, 33–34, 331 improvised explosive devices (IEDs), 45–46 iNaturalist, 211–12, 233 India, 75, 76, 108, 110, 187 bots, 142 in industrial revolution, 12–13 internet use, 22 industrial revolutions, 4–5, 11–13, 264–65 infant mortality, 85, 87f inference, 25, 180, 298 information processing, scale of, 269 information revolution, 14 insecure digital systems, 248 Institute for Electrical and Electronics Engineers (IEEE), 171 institutions, 35–40 Integrated Joint Operations Platform (IJOP), 81–82 Intel, 27, 29, 156, 162, 179, 181–82, 246, 390–91 intellectual property, 33, 71, 92, 163–64, 179 Intellifusion, 88, 156 intelligence, human, 284–85 intelligence, surveillance, and reconnaissance (ISR), 53–54 Intelligence Advanced Research Projects Activity (IARPA), 91, 246 intelligence analysis, 55 intelligentization of military, 37, 53, 222, 265 intelligentization of surveillance systems, 88 Intelligent Systems Center, 238, 247–48 Intelligent Trial System, 95 Intelligent UAV Swarm System Challenge, 36 international cooperation, 76 International Electrotechnical Commission (IEC), 169 International Organization for Standardization (ISO), 169 international stability, 286–93 international standard-setting, 169–71 International Telecommunication Union (ITU), 169 internet in China, 87, 92, 97, 99 data capacity of, 18 usage, 22 IP Commission, 164 iPhone encryption, 174 Iran, 142 Iraq, 45–46, 58, 253, 255–56 ISIS, 58, 63 ISO (International Organization for Standardization), 169 ISR (intelligence, surveillance, and reconnaissance), 53–54 Israel, 187, 278 IS’Vision, 156 Italy, 76, 108, 187 ITU (International Telecommunication Union), 169–70 JAIC (Joint AI Center), 35, 66, 200–208, 214, 289 jamming and anti-jamming strategies, 50 Japan, 27, 76, 108, 158, 181–82, 187 JASON scientific advisory group, 251 Javorsek, Dan “Animal,” 3, 230 jaywalking, 99 JEDI (Joint Enterprise Defense Infrastructure), 61, 214–18, 224 Jennings, Peter, 143 Johansson, Scarlett, 121, 130 Johns Hopkins University, 223 Johns Hopkins University Applied Physics Laboratory, 238, 247 Joint Enterprise Defense Infrastructure (JEDI), 61, 214–18, 224 “Joint Pledge on Artificial Intelligence Industry Self-Discipline,” 172 Jones, Marc Owen, 142 Jordan, 109 Joske, Alex, 158 Kania, Elsa, 36, 96, 222–24 Kasparov, Garry, 275 Katie Jones (fake persona), 131 Kaufhold, John, 209, 213 Kazakhstan, 108, 155–56 Keegan, John, 443 Ke Jie, 73 Kelly, Kevin, 4 Kelly, Robin, 39 Kennedy, Paul, 12, 13 Kenya, 107 Kernan, Joseph, 200 Kessel Run, 214 KFC, 92 KGB, 122 Khan, Saif, 185–86, 298 Khashoggi, Jamal, 141–42 kill chain, 263 Kim Jong-un, 131 King’s College London, 273 Kingsoft, 160 Kocher, Gabriel “Gab707,” 230 Komincz, Grzegorz “MaNa,” 270 Kovrig, Michael, 177 Krizhevsky, Alex, 210 Kuwait, 46 Lamppost-as-a-Platform, 107 language models, 20, 118–20, 124–25, 232, 234, 294; See also GPT-2; GPT-3; OpenAI Laos, 108 Laskai, Lorand, 96 Laszuk, Danika, 128, 140 Latvia, 108 Lawrence, Jennifer, 130 laws and regulations, 111–13 “blade runner,” 121–22, 170 data privacy, 21–22, 111–12, 170–71, 174–77 facial recognition, 113 and Microsoft, 111 for surveillance, 108–9 learning, unintended, 234 learning hacks, 234–35 Lebanon, 109 Lee, Kai-Fu, 22 Lee, Peter, 165, 167 legal reviews, 259 Le Monde, 108 Les, Jason, 46, 48 lethal autonomous weapons, 286 “liar’s dividend,” 130 Li Bin, 291 Libratus, 43–51, 266–67, 271 Libya, 109 Li Chijiang, 290–91 life expectancy, 85, 86f Li, Fei-Fei, 62 Lin Ji, 93–95, 104 Liu Fan, 393–94 LinkedIn, 131 lip-syncing, 130–31 lithography, extreme ultraviolet (EUV), 181 Liu He, 76 Liu Qingfeng, 156 Llorens, Ashley, 248, 249 Lockheed Martin, 1, 57, 211 London, 109 Long Kun, 291 long-term planning, 270 Lord, Ellen, 217 Lucky, Palmer, 66 Luo, Kevin, 161 Machine Intelligence Research Institute (MIRI), 298 machine learning and compute, 25–26, 32, 296–97 failure modes, 64, 232–33, 236–39, 243–44, 246–49 at Heron Systems, 220–21 opacity of algorithms, 145 and synthetic media, 127, 139 training data for, 202–5, 230 and voice synthesis, 137 at West Point, 194–95 MacroPolo, 30 Made in China 2025, 37, 183 Malaysia, 106 Management Action Group, 56 maneuver warfare, 442 Manhattan Project, 297 Mao Zedong, 85, 97 Marines, 231 marriage, coerced, 81 Martin, Rachael, 206 Martin Aspen (fake persona), 131 Massachusetts Institute of Technology (MIT), 31, 156, 157, 165, 233 Mattis, Jim, 53, 61, 197, 209, 215, 280 MAVLab (Micro Air Vehicle Lab), 250–52 Max Planck Society, 158, 393 McAulay, Daniel, 267 McCord, Brendan, 52, 56–57, 200 McKinsey, 25 McKinsey Global Institute, 72–73 McNair, Lesley, 192 McQuade, Michael, 66 media, AI-generated, 118–20 media conferences, 109 Meena, 125 Megatron-Turing NLG, 20, 294 Megvii, 88–89, 156, 160, 212, 353, 354, 357, 388 Memorandum of Understanding Regarding the Rules of Behavior for Safety of Air and Maritime Encounters, 292 Meng Wanzhou, 177 Merrill Lynch, 162 Meta, 22, 143, 296 metrics, 320 Mexico, 107 Michel, Arthur Holland, 54 Micron, 182 Microsoft, 294 China presence, 159 and computer vision, 57 and cyberattacks, 246–47 deepfake detection, 132, 138–39 and Department of Defense, 36, 62, 66, 215–17, 224–25 digital watermarks, 138 and facial recognition, 23, 111 financial backing of AI, 296–97 funding, 296 and Google-Maven controversy, 62, 66 and government regulation, 111 and ImageNet, 54 Megatron-Turing NLG, 20, 294 and OpenAI, 26 revenue, 297 and Seven Sons of National Defense, 162 and Trusted News Initiative, 139 work with Chinese researchers, 157, 393, 396 Microsoft Research, 31, 167 Microsoft Research Asia, 157–63, 165–67 Microsoft’s Asia-Pacific R&D Group, 161 Middlebury Institute, 124 military AI adoption, 35–37, 219–26 applications, 191–94 military capabilities, 47 military-civil fusion, 5, 95, 161–63 military competition, 304 military forces cognitization, 265 military organization, 278–79 military power, potential, 13 military tactics, future, 277 Ministry of Industry and Information Technology, 87 Ministry of Public Security, 87, 89–90, 158 Ministry of Public Security (MPS), 95 Ministry of Science and Technology, 172, 173 Minneapolis police, 143 minority identification technology, 88–89 “Minority Report, The” (Dick), 81 MIRI (Machine Intelligence Research Institute), 298 Missile Defense Agency, 218 MIT, See Massachusetts Institute of Technology (MIT) MITRE, 250 MIT-SenseTime Alliance on Artificial Intelligence, 156 MIT Technology Review, 93, 159 mobile devices, 18 Mock, Justin “Glock,” 2 model inversion attacks, 247 Modulate, 135–36, 138 monitoring and security checkpoints, 80 Moore’s law, 26, 28, 325 Morocco, 109 Mozur, Paul, 101, 102 MPS Key Lab of Intelligent Voice Technology, 95 MQ-9 Reaper, 53 Mulchandani, Nand, 207, 214, 217 multimodal models, 295–96 multiparty game theory, 50 mutism, 128 Mutsvangwa, Christopher, 105 NASA (National Aeronautics and Space Administration), 40, 72, 220 national AI research cloud, 32 National Artificial Intelligence Initiative Act of 2020, 32 National Artificial Intelligence Research Resource, 32 National Defense Education Act, 33 National Defense Strategy, 52 National Development and Reform Commission, 88 National Geospatial-Intelligence Agency (NGA), 56 National Institute of Standards and Technology, 40 National Institutes of Health, 40 National Instruments, 162 National Intelligence Law, 95, 174 National New Generation Artificial Intelligence Governance Expert Committee, 172 National Oceanic and Atmospheric Administration (NOAA), 40, 204 national power, 13, 318 National Robotics Engineering Center (NREC), 193 National Science Advisory Board for Biosecurity, 123 National Science Foundation, 40 National Security Agency, 216 National Security Commission on AI, 33, 39, 73, 186, 250, 252, 258 National Security Law, 95, 174 national security vulnerabilities, 239 National University of Defense Technology (NUDT), 157, 161 NATO, 287 natural language processing, 206 Nature (journal), 123 nature of war, 280–84 Naval Air Station Patuxent River, 220 Naval Research Laboratory, 162 Naval War College, 219 negative G turns, 249 Netherlands, 158, 181, 187 NetPosa, 156, 391 Neural Information Processing Systems, 232 neural networks, 19f, 25 applications, 54 badnets, 246 and Deep Learning Analytics, 210 explainability, 236–37 failure modes, 232–34, 250 and Heron Systems, 220 training, 19 NeurIPS, 30 Neuromancer (Gibson), 101 “New Generation Artificial Intelligence Development Plan,” 71, 169 New H3C Technologies, 157 “new oil,” 11–17 news articles, bot-generated, 118 new technologies, 255–56 new technologies, best use of, 191–92 New York Times, 31, 118, 125, 138, 290 NGA (National Geospatial-Intelligence Agency), 56 Nieman Journalism Lab, 145 1984 (Orwell), 97–98, 103 NIST (National Institute of Standards and Technology), 91 Nixon, Richard, and administration, 68 NOAA (National Oceanic and Atmospheric Administration), 40, 204 Nokia Bell Labs, 157 Normandy, France, 46 North Korea, 50, 117–18 Northrop Grumman, 57, 211, 216 NREC (National Robotics Engineering Center), 193 nuclear war, 288 nuclear weapons, 11, 50 NUDT (National University of Defense Technology), 157, 161 NVIDIA, 20, 28–29, 32, 120, 156, 246, 294, 390–91 Obama, Barack, and administration, 70, 71, 73, 137 object recognition and classification, 55–58 Office of Inspector General (OIG), 216 Office of Naval Research, 157 Office of Responsible AI, 159 Office of Technology Assessment, 162 OIG (Office of Inspector General), 216 oil, 20–21; See also “new oil” 160th Special Operations Aviation Regiment, 207 OpenAI, 26, 117–20, 122–25, 272, 294, 295–97, 299; See also GPT-2 (language model); GPT-3 (language model) OpenAI Five, 268, 270–71 Operation RYaN, 445; See also RYaN; VRYAN Oracle, 215–18, 224 Orwell, George, 97–98, 103 Osprey tiltrotor aircraft, 255 O’Sullivan, Liz, 60–61, 63, 65 OTA (other transaction authority), 217 outcomes of AI, 299–301 of war, 282–83 Owen, Laura Hazard, 145 Oxford Internet Institute, 141 Pakistan, 107, 142 Palantir, 109 PaLM, 294–95 Pan, Tim, 160, 161, 163 Papernot, Nicolas, 239 Pappas, Mike, 135–38, 140 Paredes, Federico, 250 Parler, 149 Partnership on AI, 132 patches, adversarial, 241–42, 242f Patrini, Giorgio, 130, 132–34, 137, 140 Patriot air and missile defense system, 253 Payne, Kenneth, 273–74 Pelosi, Nancy, 76, 128 Pence, Mike, 295 pension funds, 157 People’s Liberation Army (PLA); See also military-civil fusion affiliated companies, 166–67 and drone pilots, 222–23 researchers funded by, 158, 164 Percent Corporation, 107 Percipient.AI, 224 Perimeter, 289; See also Dead Hand Persian Gulf War, 46, 318 Personal Information Protection Law, 174, 176 pharmaceuticals, 251 phenotyping, DNA, 90 Philippines, 109 phones, 89 phone scanners, 89 photoresist, 182 phylogenic tree, 211 physical adversarial attacks, 242f, 243f, 429 Pichai, Sundar, 62 Pittsburgh, Pa., 44, 193 Pittsburgh Supercomputing Center, 44 PLA, See People’s Liberation Army Pluribus, 50, 51 poisonous animal recognition, 211 poker, 43–44, 46–48, 50, 266–67, 269–73, 335 Poland, 108 Police Audio Intelligent Service Platform, 95 Police Cloud, 89–90 policy analysis, automated, 206 Politiwatch, 124 pornography, 121, 130 Portman, Rob, 37 Poseidon, 289; See also Status-6 post-disaster assessment, 204 power metrics, 13 Prabhakar, Arati, 210 prediction systems, 287–88 predictive maintenance, 196–97, 201 Price, Colin “Farva,” 3 Primer (company), 224 Princeton University, 156, 157 Project Maven, 35–36, 52–53, 56–59, 194, 202, 205, 224; See also Google-Maven controversy Project Origin, 138 Project Voltron, 195–99 Putin, Vladimir, 9, 131, 304–5 Q*bert, 235 Quad summit, 76 Qualcomm Ventures, 157 Quantum Integrity, 132 quantum technology, 37 “rabbit hole” effect, 145 race to the bottom on safety, 286, 289, 304 radar, synthetic aperture, 210 Rahimi, Ali, 232 Raj, Devaki, 202, 207, 213, 224 Rambo (fictional character), 130 RAND Corporation, 252 ranking in government strategy, 40 Rao, Delip, 120, 123 Rather, Dan, 143 Raytheon, 211 reaction times, 272–73 real-time computer strategy games, 267–69 real-world battlefield environments, 264 situations, 230–36 Rebellion Defense, 224 Reddit, 140 reeducation, 81 Reface app, 130 reinforcement learning, 221, 232, 243, 250 repression, 81, 175–77 research and development funding, 35–39, 36f, 38f, 39f, 333–34 Research Center for AI Ethics and Safety, 172 Research Center for Brain-Inspired Intelligence, 172 research communities, 327 responsible AI guidelines, 252 Responsible Artificial Intelligence Strategy, 252 résumé-sorting model, 234 Reuters, 95, 139 Rise and Fall of the Great Powers, The (Kennedy), 12 risk, 271, 290–93 robotic nuclear delivery systems, 289 robotic process automation tools, 206 robotic vehicles, 266 robots, 92–94, 265–66, 286 Rockwell Automation, 162 Rockwell Collins, 193 Romania, 108 Root, Phil, 231 Roper, Will, 55–56, 214, 224, 225, 257 Rubik’s cube, 26 rule-based AI systems, 230, 236 Rumsfeld, Donald, 61 Russia, 12, 40, 52, 108, 110 bots, 142 cyberattacks of, 246 disinformation, 122 invasion of Ukraine, 129, 196, 219, 288 nuclear capabilities, 50 submarines, 255 Rutgers University Big Data Laboratory, 156 RYaN (computer program), 287, 445; See also Operation RYaN; VRYAN safe city technology, 107–8 safety of AI, 286, 289, 304 Samsung, 27–29, 179, 181 Sandholm, Tuomas, 43–51 Sasse, Ben, 184 satellite imagery, 56 Saudi Arabia, 40, 107, 109, 141–42 Scale AI, 224 scaling of innovation, 224 Schatz, Brian, 37 schedule pressures, 254–55 Schmidt, Eric, 39, 40, 71–73, 150, 164–65 Schumer, Chuck, 39 Science (journal), 123 Seagate, 156, 390 security applications, 110–11, 315 security dilemma, 50–51, 289 Sedol, Lee, 23, 266, 274–75, 298 self-driving cars, 23, 65 semiconductor industry; See also semiconductors in China, 178–79 chokepoints, 180–81 export controls, 181–86 global chokepoints in, 178–87 globalization of, 27–29 international strategy, 186–87 in Japan, 179 supply chains, 26, 76, 300 in U.S., 179–80 Semiconductor Manufacturing International Corporation (SMIC), 178, 181, 184 semiconductor(s) fabrication of, 32 foundries, 27–28 improvements in, 325 manufacturing equipment, 179 market, 27 as strategic asset, 300 Seminar on Cyberspace Management, 108–9 SenseNets, 91, 156, 357 SenseTime, 37, 88–89, 91, 156, 160, 169, 353–54, 357, 388 SensingTech, 88 Sensity, 130–33 Sentinel, 132 Sequoia, 157 Serbia, 107, 110 Serelay, 138 servicemember deaths, 255 Seven Sons of National Defense, 161–62 “shallow fakes,” 129 Shanahan, Jack on automated nuclear launch, 289 on international information sharing, 258, 291–92 and JAIC, 66, 201, 203, 205–6, 214 and Project Maven, 57–58 on risks, 254, 256 Sharp Eyes, 88, 91 Shenzhen, China, 37 Shield AI, 66, 196, 222, 224 shortcuts, 254–56 Silk Road, 110 SIM cards, 80, 89 Singapore, 106, 107, 158 singularity in warfare, 279–80 Skyeye, 99 Skynet, 87–88, 90, 91 Slashdot, 120 Slate, 120 smartphones, 26, 80 SMIC (Semiconductor Manufacturing International Corporation), 178, 181, 184 Smith, Brad, 159, 163, 166, 167 social app dominance, 149–50 social credit system, 99–100 social governance, 97–104 social media, 126, 141–51 socio-technical problems, 65 soft power, 317 SOFWERX (Special Operations Forces Works), 214 SolarWinds, 246 South Africa, 107 South China Sea militarization, 71, 74 South Korea, 27, 40, 182, 185, 187 Soviet Union, 287, 289, 447 Spain, 40, 107 SparkCognition, 66, 224 Spavor, Michael, 177 Special Operations Command, 218 Special Operations Forces Works (SOFWERX), 214 speech recognition, 91 “Spider-Man neuron,” 295 Springer Nature, 158 Sputnik, 33, 71–72 Stability AI, 125, 295 stability, international, 286–93 Stable Diffusion, 125, 139, 295 Stallone, Sylvester, 130 Stanford Internet Observatory, 139 Stanford University, 31, 32, 57, 162 Starbucks, 92 StarCraft, 180, 298 StarCraft II, 267, 271, 441 Status-6, 289; See also Poseidon Steadman, Kenneth A., 192 STEM talent, 30–34 sterilization and abortion, 81 Strategic Capabilities Office, 56 strategic reasoning, 49 Strategy Robot, 44–45, 49, 51 Strike Hard Campaign, 79–80 Stuxnet, 283 subsidies, government, 179–80 Sullivan, Jake, 186 Sun Tzu, 45 superhuman attentiveness, 269–70 superhuman precision, 270 superhuman reaction time, 277 superhuman speed, 269, 271 supervised learning, 232 supply chain(s), 300 attacks, 246 global, 76, 179, 183 “Surprising Creativity of Digital Evolution, The,” 235 surveillance, 79–90 cameras, 6, 86–87, 91 laws and policies for, 108–9 throughout China, 84–90 in Xinjiang, 79–83 Sutskever, Ilya, 210 Sutton, Rich, 299, 455 swarms and swarming, 277–79 autonomous systems, 50, 220 demonstrations, 257 Sweden, 108, 158, 187 Switch-C, 294 Synopsys, 162 synthetic aperture radar, 210 synthetic media, 127–34, 138–39 criminal use, 128–29 deepfake detectors, 132–33 deepfake videos, 130–32 geopolitical risks, 129–30 watermarks, digital, 138–39 Syria, 58 system integration, 91 tactics and strategies, 270 Taiwan, 27, 71, 76, 100, 175, 178, 185–86 Taiwan Semiconductor Manufacturing Company (TSMC), 27–28, 179, 181, 184 Taiwan Strait, 71, 75–76 talent, 30–34, 304 Tang Kun, 393 tanks, 192 Tanzania, 109 targeting cycle, 263 target recognition, 210 Target Recognition and Adaptation in Contested Environments (TRACE), 210–12 Tay, chatbot, 247 TDP (thermal design power), 454 TechCrunch, 120 technical standards Chinese, 171–75 international, 169–71 techno-authoritarianism, 79–110, 169 China’s tech ecosystem, 91–96 global export of, 105–10, 106f social governance, 97–104 throughout China, 83–90 in Xinjiang, 79–83 technology ecosystem, Chinese, 91–96 platforms, 35 and power, 11 transfer, 33, 163–64 Tektronix, 162 Tencent, 37, 143, 160, 169, 172 Tensor Processing Unit (TPU), 180 Terregator, 193 Tesla, 65, 180 TEVV (test and evaluation, verification and validation), 251–52 Texas Instruments, 162 text generation, 117–21, 123 text-to-image models, 125, 295 Thailand, 107, 109 thermal design power (TDP), 454 Third Offset Strategy, 53, 61 “Thirteenth Five-Year Science and Technology Military-Civil Fusion Special Projects Plan,” 73 Thousand Talents Plan, 32, 164 “Three-Year Action Plan to Promote the Development of New-Generation AI Industry,” 73 Tiananmen Square massacre, 68, 97–98, 103, 148, 160, 341, 359 tic-tac-toe, 47, 336 TikTok, 146–49 Tortoise Market Research, Inc., 15, 40 TPU (Tensor Processing Unit), 180 TRACE (Target Recognition and Adaptation in Contested Environments), 210–12 Trade and Technology Council (TTC), 187 training costs, 296–97 training datasets, 19–23 attacks on, 238–40, 244–45 of drone footage, 203 “radioactive,” 139 real world environments, vs., 58, 64, 233, 264 size of, 294–96 transistor miniaturization, 28 transparency among nations, 258–59, 288 Treasury Department, 246 Trump, Donald, and administration; See also “Donald Trump neuron” budget cuts, 39–40 and COVID pandemic, 74 and Entity List, 166 GPT-2 fictitious texts of, 117–19 graduate student visa revocation, 164 and Huawei, 182–84 and JEDI contract, 215–16 national strategy for AI, 73 relations with China, 71 and TikTok, 147 Twitter account, 150 trust, 249–53 Trusted News Initiative, 138–39 “truth,” 130 Tsinghua University, 31, 93, 173, 291 TSMC, See Taiwan Semiconductor Manufacturing Company (TSMC) TTC (Trade and Technology Council), 187 Turkey, 107, 108, 110 Turkish language, 234 Twitter, 139–40, 142, 144, 149, 247 Uganda, 108, 109 Uighurs; See also Xinjiang, China facial recognition, 88–89, 158, 353–55 genocide, 79, 304 mass detention, 74, 79–81, 102, 175 speech recognition, 94 surveillance, 82, 155–56 Ukraine, 108, 129, 196, 219, 288 United Arab Emirates, 107, 109 United Kingdom, 12, 76, 108, 122, 158, 187, 191–92 United States AI policy, 187 AI research of, 30 Chinese graduate students in, 31 competitive AI strategy, 185 United States Presidential election, 2016, 122 United States Presidential election, 2020, 128, 131, 134, 150 University of Illinois, 157 University of Richmond, 123 Uniview, 89, 355 unsupervised learning, 232 Ürümqi, 80, 84 Ürümqi Cloud Computing Center, 156 U.S.

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

(In a 2018 competition, AI software outscored experienced law professors in analyzing standard nondisclosure agreements and completed the task two hundred times faster.25) Routine forms of computer programming—the kind that is often outsourced today—are also likely to be automated. Indeed, almost anything that can be outsourced is a good candidate for automation, because outsourcing involves decomposing jobs into tasks that can be parceled up and distributed in a decontextualized form. The robot process automation industry produces software tools that achieve exactly this effect for clerical tasks performed online. As AI progresses, it is certainly possible—perhaps even likely—that within the next few decades essentially all routine physical and mental labor will be done more cheaply by machines.

Head, Hand, Heart: Why Intelligence Is Over-Rewarded, Manual Workers Matter, and Caregivers Deserve More Respect
by David Goodhart
Published 7 Sep 2020

Of course, the decline in this skill does not imply that there will be no authors, writers, or editors in the future—but as in many other occupations, some of the more basic aspects of the work will shift to machines.”40 And that bank manager whose judgment has been replaced with a loan approval algorithm is emblematic of a broader shift in lower-level financial service jobs, which is likely to have a big impact on heavily financialized economies like the United States and the United Kingdom. As the report says: “A range of back-office functions to be automated, include financial reporting, accounting, actuarial sciences, insurance claims processing, credit scoring, loan approval, and tax calculation. Computer algorithms and ‘robotic process automation’ can drastically reduce the time and manpower devoted to these activities.”41 Capitalism in the Age of Robots What does all this mean? The knowledge economy needs fewer knowledge workers than expected. The recent expansion of higher education in much of the West will stop or even go into reverse as the demand for the middling and lower-rung jobs of the knowledge economy will decline.