AlphaGo

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The Creativity Code: How AI Is Learning to Write, Paint and Think by Marcus Du Sautoy

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Rather than resting, though, Sedol stayed up till 6 a.m. the next morning analysing the games he’d lost so far with a group of fellow professional Go players. Did AlphaGo have a weakness they could exploit? The machine wasn’t the only one who could learn and evolve. Sedol felt he might learn something from his losses. Sedol played a very strong opening to game 3, forcing AlphaGo to manage a weak group of stones within his sphere of influence on the board. Commentators began to get excited. Some said Sedol had found AlphaGo’s weakness. But then, as one commentator posted: ‘Things began to get scary. As I watched the game unfold and the realisation of what was happening dawned on me, I felt physically unwell.’ Sedol pushed AlphaGo to its limits but in so doing he revealed the hidden powers that the program seemed to possess.

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Sedol and his team had stayed up all of Saturday night trying to reverse-engineer from AlphaGo’s games how it played. It seemed to work on a principle of playing moves that incrementally increase its probability of winning rather than betting on the potential outcome of a complicated single move. Sedol had witnessed this when AlphaGo preferred lazy moves to win game 3. The strategy they’d come up with was to disrupt this sensible play by playing the risky single moves. An all-or-nothing strategy might make it harder for AlphaGo to score so easily. AlphaGo seemed unfazed by this line of attack. Seventy moves into the game, commentators were already beginning to see that AlphaGo had once again gained the upper hand.

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Seventy moves into the game, commentators were already beginning to see that AlphaGo had once again gained the upper hand. This was confirmed by a set of conservative moves that were AlphaGo’s signal that it had the lead. Sedol had to come up with something special if he was going to regain the momentum. If move 37 of game 2 was AlphaGo’s moment of creative genius, move 78 of game 4 was Sedol’s retort. He’d sat there for thirty minutes staring at the board, staring at defeat, when he suddenly placed a white stone in an unusual position, between two of AlphaGo’s black stones. Michael Redmond, who was commentating on the YouTube channel, spoke for everyone: ‘It took me by surprise.

Genius Makers: The Mavericks Who Brought A. I. To Google, Facebook, and the World by Cade Metz

AI winter, air gap, Airbnb, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, AlphaGo, Amazon Robotics, artificial general intelligence, Asilomar, autonomous vehicles, backpropagation, Big Tech, British Empire, Cambridge Analytica, carbon-based life, cloud computing, company town, computer age, computer vision, deep learning, deepfake, DeepMind, Demis Hassabis, digital map, Donald Trump, driverless car, drone strike, Elon Musk, fake news, Fellow of the Royal Society, Frank Gehry, game design, Geoffrey Hinton, Google Earth, Google X / Alphabet X, Googley, Internet Archive, Isaac Newton, Jeff Hawkins, Jeffrey Epstein, job automation, John Markoff, life extension, machine translation, Mark Zuckerberg, means of production, Menlo Park, move 37, move fast and break things, Mustafa Suleyman, new economy, Nick Bostrom, nuclear winter, OpenAI, PageRank, PalmPilot, pattern recognition, Paul Graham, paypal mafia, Peter Thiel, profit motive, Richard Feynman, ride hailing / ride sharing, Ronald Reagan, Rubik’s Cube, Sam Altman, Sand Hill Road, self-driving car, side project, Silicon Valley, Silicon Valley billionaire, Silicon Valley startup, Skype, speech recognition, statistical model, stem cell, Stephen Hawking, Steve Ballmer, Steven Levy, Steven Pinker, tech worker, telemarketer, The Future of Employment, Turing test, warehouse automation, warehouse robotics, Y Combinator

said that he, too, felt a sadness: Cade Metz, “The Sadness and Beauty of Watching Google’s AI Play Go,” Wired, March 11, 2016, https://www.wired.com/2016/03/sadness-beauty-watching-googles-ai-play-go/. “There was an inflection point”: Ibid. Lee Sedol lost the third game: Metz, “What the AI Behind AlphaGo Can Teach Us About Being Human.” “I don’t know what to say today”: Cade Metz, “In Two Moves, AlphaGo and Lee Sedol Redefined the Future,” Wired, March 16, 2016, https://www.wired.com/2016/03/two-moves-alphago-lee-sedol-redefined-future/. Hassabis found himself hoping the Korean: Metz, “What the AI Behind AlphaGo Can Teach Us About Being Human.” “All the thinking that AlphaGo had done up to that point was sort of rendered useless”: Ibid. “I have improved already”: Ibid. CHAPTER 11: EXPANSION nearly 70 million people are diabetic: “Diabetes Epidemic: 98 Million People in India May Have Type 2 Diabetes by 2030,” India Today, November 22, 2018, https://www.indiatoday.in/education-today/latest-studies/story/98-million-indians-diabetes-2030-prevention-1394158-2018-11-22.

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There were more people on the Internet in China: “Number of Internet Users in China from 2017 to 2023,” Statista, https://www.statista.com/statistics/278417/number-of-internet-users-in-china/ An estimated 60 million Chinese had watched the match against Lee Sedol: “AlphaGo Computer Beats Human Champ in Hard-Fought Series,” Associated Press, March 15, 2016, https://www.cbsnews.com/news/googles-alphago-computer-beats-human-champ-in-hard-fought-series/. With a private order sent to all Chinese media in Wuzhen: Cade Metz, “Google Unleashes AlphaGo in China—But Good Luck Watching It There,” Wired, May 23, 2017, https://www.wired.com/2017/05/google-unleashes-alphago-china-good-luck-watching/. Baidu opened its first outpost in Silicon Valley: Daniela Hernandez, “‘Chinese Google’ Opens Artificial-Intelligence Lab in Silicon Valley,” Wired, April 12, 2013, https://www.wired.com/2013/04/baidu-research-lab/.

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Still, over this lunch of dumplings and kimchi and grilled meats—which he didn’t eat—Hassabis said he was “cautiously confident.” What the pundits didn’t grasp, he explained, was that AlphaGo had continued to hone its skills since the match in October. He and his team originally taught the machine to play Go by feeding 30 million moves into a deep neural network. From there, AlphaGo played game after game against itself, all the while carefully tracking which moves proved successful and which didn’t—much like the systems the lab had built to play old Atari games. In the months since beating Fan Hui, the machine had played itself several million more times. AlphaGo was continuing to teach itself the game, learning at a faster rate than any human ever could.

Artificial Intelligence: A Guide for Thinking Humans by Melanie Mitchell

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Demis Hassabis noted that “the thing that separates out top Go players [is] their intuition” and that “what we’ve done with AlphaGo is to introduce with neural networks this aspect of intuition, if you want to call it that.”26 How AlphaGo Works There have been several different versions of AlphaGo, so to keep them straight, DeepMind started naming them after the human Go champions the programs had defeated—AlphaGo Fan and AlphaGo Lee—which to me evoked the image of the skulls of vanquished enemies in the collection of a digital Viking. Not what DeepMind intended, I’m sure. In any case, AlphaGo Fan and AlphaGo Lee both used an intricate mix of deep Q-learning, “Monte Carlo tree search,” supervised learning, and specialized Go knowledge.

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Johnson quoted one Go enthusiast’s prediction: “It may be a hundred years before a computer beats humans at Go—maybe even longer.” A mere twenty years later, AlphaGo, which learned to play Go via deep Q-learning, beat Lee Sedol in a five-game match. AlphaGo Versus Lee Sedol Before I explain how AlphaGo works, let’s first commemorate its spectacular wins against Lee Sedol, one of the world’s best Go players. Even after watching AlphaGo defeat the then European Go champion Fan Hui half a year earlier, Lee remained confident that he would prevail: “I think [AlphaGo’s] level doesn’t match mine.… Of course, there would have been many updates in the last four or five months, but that isn’t enough time to challenge me.”21 Perhaps you were one of the more than two hundred million people who watched some part of the AlphaGo-Lee match online in March 2016.

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This newer version is called AlphaGo Zero because, unlike its predecessor, it started off with “zero” knowledge of Go besides the rules.27 In a hundred games of AlphaGo Lee versus AlphaGo Zero, the latter won every single game. Moreover, DeepMind applied the same methods (though with different networks and different built-in game rules) to learn to play both chess and shogi (also known as Japanese chess).28 The authors called the collection of these methods AlphaZero. In this section, I’ll describe how AlphaGo Zero worked, but for conciseness I’ll simply refer to this version as AlphaGo. FIGURE 31: An illustration of Monte Carlo tree search The word intuition has an aura of mystery, but AlphaGo’s intuition (if you want to call it that) arises from its combination of deep Q-learning with a clever method called “Monte Carlo tree search.”

Human Compatible: Artificial Intelligence and the Problem of Control by Stuart Russell

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Let’s unpack this last concern a bit. Consider AlphaGo: What purpose does it have? That’s easy, one might think: AlphaGo has the purpose of winning at Go. Or does it? It’s certainly not the case that AlphaGo always makes moves that are guaranteed to win. (In fact, it nearly always loses to AlphaZero.) It’s true that when it’s only a few moves from the end of the game, AlphaGo will pick the winning move if there is one. On the other hand, when no move is guaranteed to win—in other words, when AlphaGo sees that the opponent has a winning strategy no matter what AlphaGo does—then AlphaGo will pick moves more or less at random.

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Could something similar happen to machines that are running reinforcement learning algorithms, such as AlphaGo? Initially, one might think this is impossible, because the only way that AlphaGo can gain its +1 reward for winning is actually to win the simulated Go games that it is playing. Unfortunately, this is true only because of an enforced and artificial separation between AlphaGo and its external environment and the fact that AlphaGo is not very intelligent. Let me explain these two points in more detail, because they are important for understanding some of the ways that superintelligence can go wrong. AlphaGo’s world consists only of the simulated Go board, composed of 361 locations that can be empty or contain a black or white stone.

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This setup corresponds to the abstract mathematical model of reinforcement learning, in which the reward signal arrives from outside the universe. Nothing AlphaGo can do, as far as it knows, has any effect on the code that generates the reward signal, so AlphaGo cannot indulge in wireheading. Life for AlphaGo during the training period must be quite frustrating: the better it gets, the better its opponent gets—because its opponent is a near-exact copy of itself. Its win percentage hovers around 50 percent, no matter how good it becomes. If it were more intelligent—if it had a design closer to what one might expect of a human-level AI system—it would be able to fix this problem. This AlphaGo++ would not assume that the world is just the Go board, because that hypothesis leaves a lot of things unexplained.

The Deep Learning Revolution (The MIT Press) by Terrence J. Sejnowski

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Some thought this was cheating— an autonomous AI program should be able to learn how to play Go without human knowledge. In October, 2017, a new version, called AlphaGo Zero, was revealed that learned to play Go starting with only the rules of the game, and trounced AlphaGo Master, the version that beat Kie Jie, winning 100 games to none.35 Moreover, AlphaGo Zero learned 100 times faster and with 10 times less compute power than AlphaGo Master. By completely ignoring human knowledge, AlphaGo Zero became super-superhuman. 20 Chapter 1 There is no known limit to how much better AlphaGo might become as machine learning algorithms continue to improve. AlphaGo Zero had dispensed with human play, but there was still a lot of Go knowledge handcrafted into the features that the program used to represent the board.

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Even DeepMind, the company that had developed AlphaGo, did not know how strong their deep learning program was. Since its last match, AlphaGo had played millions of games with several versions of itself and there was no way to benchmark how good it was. It came as a shock to many when AlphaGo won the first three of five games, exhibiting an unexpectedly high level of play. This was riveting viewing in South Korea, where all the major television stations had a running commentary on the games. Some of the moves made by AlphaGo were revolutionary. On the thirty-eighth move in the match’s second game, AlphaGo made a brilliantly creative play that surprised Lee Sedol, who took nearly ten minutes to respond.

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AlphaGo used the same learning algorithm that the basal ganglia evolved to evaluate sequences of The Rise of Machine Learning 17 Figure 1.8 Go board during play in the five-game match that pitted Korean Go champion Lee Sedol against AlphaGo, a deep learning neural network that had learned how to play Go by playing itself. actions to maximize future rewards (a process that will be explained in chapter 10). AlphaGo learned by playing itself—many, many times The Go match that pitted AlphaGo against Lee Sedol had a large following in Asia, where Go champions are national figures and treated like rock stars. AlphaGo had earlier defeated a European Go champion, but the level of play was considerably below the highest levels of play in Asia, and Lee Sedol was not expecting a strong match.

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

"Friedman doctrine" OR "shareholder theory", Ada Lovelace, AI winter, air gap, Airbnb, airport security, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, algorithmic bias, AlphaGo, Andy Rubin, artificial general intelligence, Asilomar, autonomous vehicles, backpropagation, Bayesian statistics, behavioural economics, Bernie Sanders, Big Tech, bioinformatics, Black Lives Matter, blockchain, Bretton Woods, business intelligence, Cambridge Analytica, Cass Sunstein, Charles Babbage, Claude Shannon: information theory, cloud computing, cognitive bias, complexity theory, computer vision, Computing Machinery and Intelligence, CRISPR, cross-border payments, crowdsourcing, cryptocurrency, Daniel Kahneman / Amos Tversky, data science, deep learning, DeepMind, Demis Hassabis, Deng Xiaoping, disinformation, distributed ledger, don't be evil, Donald Trump, Elon Musk, fail fast, fake news, Filter Bubble, Flynn Effect, Geoffrey Hinton, gig economy, Google Glasses, Grace Hopper, Gödel, Escher, Bach, Herman Kahn, high-speed rail, Inbox Zero, Internet of things, Jacques de Vaucanson, Jeff Bezos, Joan Didion, job automation, John von Neumann, knowledge worker, Lyft, machine translation, Mark Zuckerberg, Menlo Park, move fast and break things, Mustafa Suleyman, natural language processing, New Urbanism, Nick Bostrom, one-China policy, optical character recognition, packet switching, paperclip maximiser, pattern recognition, personalized medicine, RAND corporation, Ray Kurzweil, Recombinant DNA, ride hailing / ride sharing, Rodney Brooks, Rubik’s Cube, Salesforce, Sand Hill Road, Second Machine Age, self-driving car, seminal paper, SETI@home, side project, Silicon Valley, Silicon Valley startup, skunkworks, Skype, smart cities, South China Sea, sovereign wealth fund, speech recognition, Stephen Hawking, strong AI, superintelligent machines, surveillance capitalism, technological singularity, The Coming Technological Singularity, the long tail, theory of mind, Tim Cook: Apple, trade route, Turing machine, Turing test, uber lyft, Von Neumann architecture, Watson beat the top human players on Jeopardy!, zero day

One early tell: the AI would not play aggressively unless it was behind. It was a tight first match. AlphaGo earned a very narrow victory, by just 1.5 points. Hui used that information going into the second game. If AlphaGo wasn’t going to play aggressively, then Hui decided that he’d fight early. But then AlphaGo started playing more quickly. Hui mentioned that perhaps he needed a bit more time to think between turns. On move 147, Hui tried to prevent AlphaGo from claiming a big territory in the center of the board, but the move misfired, and he was forced to resign. By game three, Hui’s moves were more aggressive, and AlphaGo followed suit. Halfway through, Hui made a catastrophic overplay, which AlphaGo punished, and then another big mistake, which rendered the game effectively over.

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Ari Goldfarb and Daniel Trefler, “AI and International Trade,” The National Bureau of Economic Research, January 2018, http://www.nber.org/papers/w24254.pdf. 36. Toby Manning, “AlphaGo,” British Go Journal 174 (Winter 2015–2016): 15, https://www.britgo.org/files/2016/deepmind/BGJ174-AlphaGo.pdf. 37. Sam Byford, “AlphaGo Retires from Competitive Go after Defeating World Number One 3-0,” Verge, May 27, 2017, https://www.theverge.com/2017/5/27/15704088/alphago-ke-jie-game-3-result-retires-future. 38. David Silver et al., “Mastering the Game of Go Without Human Knowledge,” Nature 550 (October 19, 2017): 354–359, https://deepmind.com/documents/119/agz_unformatted_nature.pdf. 39.

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See also 2001: A Space Odyssey La Mettrie, Julien Offray de, 22 Law enforcement: in optimistic scenario of future, 176; in pragmatic scenario of future, 199; social policing in China, 80 Leadership, need for courageous, 246 Learned helplessness: in pragmatic scenario of future, 190–201 Learning. See AlphaGo; AlphaGo Zero; Learning machines; Machine learning; Watson Learning machines, 30, 31–32. See also AlphaGo; AlphaGo Zero; Watson Lecun, Yann, 41, 42, 59 Lee, Peter, 122 Legg, Shane, 43 Leibniz, Gottfried Wilhelm von, 20, 21, 27; step reckoner, 21, 24 Levesque, Hector, 50 Li, Fei-Fei, 65 Li, Robin, 67, 82, 129 Li Deng, 43 LibriSpeech, 181 Life expectancy: in catastrophic scenario of future, 225 Lighthill, James, 37, 38 Liu Guozhi, 79 Logic Theorist program, 30, 34 Lovelace, Ada, 23, 259; Analytical Engine, 23, 24; Difference Engine, 23 Lyft, BAT investment in, 72 Ma, Jack, 68, 69, 70 Ma Huateng, 70 Machine learning, 29, 31, 32–33, 36, 41, 77, 93, 114; algorithms, 123, 183, 237; machines playing games, 38–50; models, 49, 91; platforms, 91; systems, 182, 184, 257; techniques, 253; technologies, 110.

Whiplash: How to Survive Our Faster Future by Joi Ito, Jeff Howe

3D printing, air gap, Albert Michelson, AlphaGo, Amazon Web Services, artificial general intelligence, basic income, Bernie Sanders, Big Tech, bitcoin, Black Lives Matter, Black Swan, Bletchley Park, blockchain, Burning Man, business logic, buy low sell high, Claude Shannon: information theory, cloud computing, commons-based peer production, Computer Numeric Control, conceptual framework, CRISPR, crowdsourcing, cryptocurrency, data acquisition, deep learning, DeepMind, Demis Hassabis, digital rights, disruptive innovation, Donald Trump, double helix, Edward Snowden, Elon Musk, Ferguson, Missouri, fiat currency, financial innovation, Flash crash, Ford Model T, frictionless, game design, Gerolamo Cardano, informal economy, information security, interchangeable parts, Internet Archive, Internet of things, Isaac Newton, Jeff Bezos, John Harrison: Longitude, Joi Ito, Khan Academy, Kickstarter, Mark Zuckerberg, microbiome, move 37, Nate Silver, Network effects, neurotypical, Oculus Rift, off-the-grid, One Laptop per Child (OLPC), PalmPilot, pattern recognition, peer-to-peer, pirate software, power law, pre–internet, prisoner's dilemma, Productivity paradox, quantum cryptography, race to the bottom, RAND corporation, random walk, Ray Kurzweil, Ronald Coase, Ross Ulbricht, Satoshi Nakamoto, self-driving car, SETI@home, side project, Silicon Valley, Silicon Valley startup, Simon Singh, Singularitarianism, Skype, slashdot, smart contracts, Steve Ballmer, Steve Jobs, Steven Levy, Stewart Brand, Stuxnet, supply-chain management, synthetic biology, technological singularity, technoutopianism, TED Talk, The Nature of the Firm, the scientific method, The Signal and the Noise by Nate Silver, the strength of weak ties, There's no reason for any individual to have a computer in his home - Ken Olsen, Thomas Kuhn: the structure of scientific revolutions, Two Sigma, universal basic income, unpaid internship, uranium enrichment, urban planning, warehouse automation, warehouse robotics, Wayback Machine, WikiLeaks, Yochai Benkler

Another AI researcher, Jonathan Schaeffer, noted that Deep Blue was regularly beating chess grandmasters by 1989, but it had taken another eight years for it to become good enough to beat Garry Kasparov. AlphaGo was about to receive its Kasparov moment. In March, Nature revealed, the software would play Lee Sedol, commonly regarded as the greatest living master, or sensei, of the game. “No offence to the AlphaGo team, but I would put my money on the human,” Schaeffer told Nature News. “Think of AlphaGo as a child prodigy. All of a sudden it has learned to play really good Go, very quickly. But it doesn’t have a lot of experience. What we saw in chess and checkers is that experience counts for a lot.”7 Not everyone has cheered on the machine’s inexorable invasion of all aspects of our lives.

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It wasn’t designed to wow the 280 million people who would eventually watch the series, but from someone of Sedol’s rank, it constituted nearly unbeatable play, and Sedol exuded a quiet but unmistakeable confidence. Then, as the game began to enter its middle phase, AlphaGo did something unusual: it instructed its human attendant to place a black stone in a largely unoccupied area to the right of the board. This might have made sense in another context, but on that board at that moment AlphaGo seemed to be abandoning the developing play in the lower half of the board. This historic move was something that no human would have feasibly played—AlphaGo calculated the probability that a human would play that move at 1 in 10,000.9 It produced instant shock and confusion among the spectators.

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Having so handily defeated Sedol at his ingenious best, AlphaGo seemed fated to execute a clean sweep in the last two games. And nothing during the first half of game four seemed to indicate the contrary. But then Sedol did something radical and unexpected—he played a “wedge” move in the middle of the board. AlphaGo, it suddenly became clear to millions of people around the world, had no idea how to respond. It made several clumsy plays and then conceded. Sedol, commentators noted, had created a masterpiece—a potential myoshu all his own. AlphaGo ended up winning four out of the five matches. One could imagine that a computer beating a historically legendary Go champion might diminish interest in Go for humans or make it less interesting to play.

Four Battlegrounds by Paul Scharre

2021 United States Capitol attack, 3D printing, active measures, activist lawyer, AI winter, AlphaGo, amateurs talk tactics, professionals talk logistics, artificial general intelligence, ASML, augmented reality, Automated Insights, autonomous vehicles, barriers to entry, Berlin Wall, Big Tech, bitcoin, Black Lives Matter, Boeing 737 MAX, Boris Johnson, Brexit referendum, business continuity plan, business process, carbon footprint, chief data officer, Citizen Lab, clean water, cloud computing, commoditize, computer vision, coronavirus, COVID-19, crisis actor, crowdsourcing, DALL-E, data is not the new oil, data is the new oil, data science, deep learning, deepfake, DeepMind, Demis Hassabis, Deng Xiaoping, digital map, digital rights, disinformation, Donald Trump, drone strike, dual-use technology, Elon Musk, en.wikipedia.org, endowment effect, fake news, Francis Fukuyama: the end of history, future of journalism, future of work, game design, general purpose technology, Geoffrey Hinton, geopolitical risk, George Floyd, global supply chain, GPT-3, Great Leap Forward, hive mind, hustle culture, ImageNet competition, immigration reform, income per capita, interchangeable parts, Internet Archive, Internet of things, iterative process, Jeff Bezos, job automation, Kevin Kelly, Kevin Roose, large language model, lockdown, Mark Zuckerberg, military-industrial complex, move fast and break things, Nate Silver, natural language processing, new economy, Nick Bostrom, one-China policy, Open Library, OpenAI, PalmPilot, Parler "social media", pattern recognition, phenotype, post-truth, purchasing power parity, QAnon, QR code, race to the bottom, RAND corporation, recommendation engine, reshoring, ride hailing / ride sharing, robotic process automation, Rodney Brooks, Rubik’s Cube, self-driving car, Shoshana Zuboff, side project, Silicon Valley, slashdot, smart cities, smart meter, Snapchat, social software, sorting algorithm, South China Sea, sparse data, speech recognition, Steve Bannon, Steven Levy, Stuxnet, supply-chain attack, surveillance capitalism, systems thinking, tech worker, techlash, telemarketer, The Brussels Effect, The Signal and the Noise by Nate Silver, TikTok, trade route, TSMC

Reg. 3967 (February 14, 2019), https://www.federalregister.gov/documents/2019/02/14/2019-02544/maintaining-american-leadership-in-artificial-intelligence. 73updated R&D plan: The National Artificial Intelligence Research and Development Strategic Plan: 2019 Update (Select Committee on Artificial Intelligence, National Science & Technology Council, June 2019), https://www.nitrd.gov/pubs/National-AI-RD-Strategy-2019.pdf. 73Chinese leaders issued a series of implementation plans: “AI in China,” OECD.AI Policy Observatory, updated September 21, 2021, https://oecd.ai/dashboards/countries/China. 73“Three-Year Action Plan”: “工业和信息化部发布《促进新一代人工智能产业发展三年行动计划（2018-2020年）》[The Ministry of Industry and Information Technology issued the ‘Three-Year Action Plan (2018-2020) for Promoting the Development of the New Generation Artificial Intelligence Industry’],” Ministry of Industry and Information Technology of the People’s Republic of China, December 14, 2017, http://www.miit.gov.cn/n1146290/n4388791/c5960863/content.html (page discontinued), https://web.archive.org/web/20180821120845/http://www.miit.gov.cn/n1146290/n4388791/c5960863/content.html; Paul Triolo, Elsa Kania, and Graham Webster, “Translation: Chinese Government Outlines AI Ambitions through 2020,” New America Blog, January 26, 2018, https://www.newamerica.org/cybersecurity-initiative/digichina/blog/translation-chinese-government-outlines-ai-ambitions-through-2020/. 73“Thirteenth Five-Year Science and Technology Military-Civil Fusion Special Projects Plan”: PRC Ministry of Science and Technology, “The ‘13th Five-Year’ Special Plan for S&T Military-Civil Fusion Development,” translated by Etcetera Language Group, Center for Security and Emerging Technology, June 10, 2020, https://cset.georgetown.edu/wp-content/uploads/t0163_13th_5YP_mil_civ_fusion_EN.pdf. 73“Accelerating the development of a new generation of AI”: Elsa Kania and Rogier Creemers, “Xi Jinping Calls for ‘Healthy Development’ of AI (Translation),” New America Blog, November 5, 2018, https://www.newamerica.org/cybersecurity-initiative/digichina/blog/xi-jinping-calls-for-healthy-development-of-ai-translation/. 73“There’s no question that there was a Sputnik moment”: Eric Schmidt, interview by author, June 9, 2020. 73DeepMind’s AlphaGo: “AlphaGo,” DeepMind, n.d., https://deepmind.com/research/case-studies/alphago-the-story-so-far; Alex Hern, “China Censored Google’s AlphaGo Match against World’s Best Go Player,” The Guardian, May 24, 2017, https://www.theguardian.com/technology/2017/may/24/china-censored-googles-alphago-match-against-worlds-best-go-player; “AlphaGo China,” DeepMind, 2017, https://deepmind.com/alphago-china. 73“not only was it notable, but they also censored”: Schmidt, interview. 73Go is an ancient strategy game: “A Brief History of Go,” American Go Association, n.d., https://www.usgo.org/brief-history-go; Peter Shotwell, The Game of Go: Speculations on Its Origins and Symbolism in Ancient China (American Go Association, updated February 2008), https://www.usgo.org/sites/default/files/bh_library/originsofgo.pdf. 73“I am not a person who believes that we are adversaries with China”: Schmidt, interview. 74a “strategic competitor”: Summary of the 2018 National Defense Strategy of the United States of America: Sharpening the American Military’s Competitive Edge (U.S.

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., https://cloud.google.com/tpu/docs/tpus. 298reduced energy consumption: The metric DeepMind used to compare AlphaGo versions, thermal design power (TDP), is not a direct measure of energy consumption. It is a rough first-order proxy, however, for power consumption. David Silver and Demis Hassabis, “AlphaGo Zero: Starting From Scratch,” DeepMind Blog, October 18, 2017, https://deepmind.com/blog/article/alphago-zero-starting-scratch. 298reduced compute usage to only 4 TPUs: Silver and Hassabis, “AlphaGo Zero: Starting From Scratch”; “AlphaGo,” DeepMind, n.d., https://deepmind.com/research/case-studies/alphago-the-story-so-far; David Silver et al., “Mastering the Game of Go without Human Knowledge,” Nature 550 (October 19 2017), 354–355, https://www.nature.com/articles/nature24270.epdf. 298reduced the compute needed for training by a factor of eight: Hernandez and Brown, Measuring the Algorithmic Efficiency of Neural Networks, 18. 298may make AI models available: Desislavov et al., Compute and Energy Consumption Trends in Deep Learning Inference; Sharir et al., The Cost of Training NLP Models, 3. 298AI training costs could be as much as thirty times higher: Khan and Mann, AI Chips, 26. 299costly and locks out university researchers: Rodney Brooks, “A Better Lesson,” Rodney Brooks (personal website), March 19, 2019, https://rodneybrooks.com/a-better-lesson/; Kevin Vu, “Compute Goes Brrr: Revisiting Sutton’s Bitter Lesson for Artificial Intelligence,” DZone.com, March 11, 2021, https://dzone.com/articles/compute-goes-brrr-revisiting-suttons-bitter-lesson; Bommasani et al., On the Opportunities and Risks of Foundation Models. 299contributes to carbon emissions: “On the Dangers of Stochastic Parrots”; Brooks, “A Better Lesson”; Vu, “Compute Goes Brrr”; Lasse F.

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Heron Systems’ AI in the AlphaDogfight competition employed high-precision, split-second gunshots, demonstrating a “superhuman capability” making shots that were “almost impossible” for humans, as one fighter pilot explained. During AlphaGo’s celebrated victory over Lee Sedol, it made a move that so stunned Lee that he got up from the table and left the room. AlphaGo calculated the odds that a human would have made that move (based on its database of 30 million expert human moves) as 1 in 10,000. AlphaGo’s move wasn’t just better. It was inhuman. AlphaGo’s unusual move wasn’t a fluke. AlphaGo plays differently than humans in a number of ways. It will carry out multiple simultaneous attacks on different parts of the board, whereas human players tend to focus on one region.

Army of None: Autonomous Weapons and the Future of War by Paul Scharre

"World Economic Forum" Davos, active measures, Air France Flight 447, air gap, algorithmic trading, AlphaGo, Apollo 13, artificial general intelligence, augmented reality, automated trading system, autonomous vehicles, basic income, Black Monday: stock market crash in 1987, brain emulation, Brian Krebs, cognitive bias, computer vision, cuban missile crisis, dark matter, DARPA: Urban Challenge, data science, deep learning, DeepMind, DevOps, Dr. Strangelove, drone strike, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, facts on the ground, fail fast, fault tolerance, Flash crash, Freestyle chess, friendly fire, Herman Kahn, IFF: identification friend or foe, ImageNet competition, information security, Internet of things, Jeff Hawkins, Johann Wolfgang von Goethe, John Markoff, Kevin Kelly, Korean Air Lines Flight 007, Loebner Prize, loose coupling, Mark Zuckerberg, military-industrial complex, moral hazard, move 37, mutually assured destruction, Nate Silver, Nick Bostrom, PalmPilot, paperclip maximiser, pattern recognition, Rodney Brooks, Rubik’s Cube, self-driving car, sensor fusion, South China Sea, speech recognition, Stanislav Petrov, Stephen Hawking, Steve Ballmer, Steve Wozniak, Strategic Defense Initiative, Stuxnet, superintelligent machines, Tesla Model S, The Signal and the Noise by Nate Silver, theory of mind, Turing test, Tyler Cowen, universal basic income, Valery Gerasimov, Wall-E, warehouse robotics, William Langewiesche, Y2K, zero day

v=JNrXgpSEEIE. 126 “I thought it was a mistake”: Ibid. 126 “It’s not a human move”: Cade Metz, “The Sadness and Beauty of Watching Google’s AI Play Go,” WIRED, March 11, 2016, https://www.wired.com/2016/03/sadness-beauty-watching-googles-ai-play-go/. 126 1 in 10,000: Cade Metz, “In Two Moves, AlphaGo and Lee Sedol Redefined the Future,” WIRED, accessed June 7, 2017, https://www.wired.com/2016/03/two-moves-alphago-lee-sedol-redefined-future/. 126 “I kind of felt powerless”: Moyer, “How Google’s AlphaGo Beat a Go World Champion.” 126 “AlphaGo isn’t just an ‘expert’ system”: “AlphaGo,” January 27, 2016. 127 AlphaGo Zero: “AlphaGo Zero: Learning from Scratch,” DeepMind, accessed October 22, 2017, https://deepmind.com/blog/alphago=zero=learning=scratch/. 127 neural network to play Atari games: Volodymyr Mnih et al., “Human-Level Control through Deep Reinforcement Learning,” Nature 518, no. 7540 (February 26, 2015): 529–33. 127 deep neural network: JASON, “Perspectives on Research in Artificial Intelligence and Artificial General Intelligence Relevant to DoD.” 129 Inception-v3: Inception-v3 is trained for the Large Scale Visual Recognition Challenge (LSVRC) using the 2012 data.

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Not only did the move feel like a move no human player would never make, it was a move no human player probably would never make. AlphaGo rated the odds that a human would have made that move as 1 in 10,000. Yet AlphaGo made the move anyway. AlphaGo went on to win game 2 and afterward Lee Sedol said, “I really feel that AlphaGo played the near perfect game.” After losing game 3, thus giving AlphaGo the win for the match, Lee Sedol told the audience at a press conference, “I kind of felt powerless.” AlphaGo’s triumph over Lee Sedol has implications far beyond the game of go. More than just another realm of competition in which AIs now top humans, the way DeepMind trained AlphaGo is what really matters.

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Connecticut teenager: Rick Stella, “Update: FAA Launches Investigation into Teenager’s Gun-Wielding Drone Video,” Digital Trends, July 22, 2015, https://www.digitaltrends.com/cool-tech/man-illegally-straps-handgun-to-a-drone/. 119 For under $500: “Spark,” DJI.com. 122 Shield AI: “Shield AI,” http://shieldai.com/ 122 grant from the U.S. military: Mark Prigg, “Special Forces developing ‘AI in the sky’ drones that can create 3D maps of enemy lairs: Pentagon reveals $1m secretive ‘autonomous tactical airborne drone’ project,” DailyMail.com, http://www.dailymail.co.uk/sciencetech/article-3776601/Special-Forces-developing-AI-sky-drones-create-3D-maps-enemy-lairs-Pentagon-reveals-1m-secretive-autonomous-tactical-airborne-drone-project.html. 123 “Robotics and artificial intelligence are”: Brandon Tseng, email to author, June 17, 2016. 124 “fully automated combat module”: “Kalashnikov Gunmaker Develops Combat Module based on Artificial Intelligence.” 125 more possible positions in go: “AlphaGo,” DeepMind, accessed June 7, 2017, https://deepmind.com/research/alphago/. 125 “Our goal is to beat the best human players”: “AlphaGo: Using Machine Learning to Master the Ancient Game of Go,” Google, January 27, 2016, http://blog.google:443/topics/machine-learning/alphago-machine-learning-game-go/. 126 game 2, on move 37: Daniel Estrada, “Move 37!! Lee Sedol vs AlphaGo Match 2” video, https://www.youtube.com/watch?v=JNrXgpSEEIE. 126 “I thought it was a mistake”: Ibid. 126 “It’s not a human move”: Cade Metz, “The Sadness and Beauty of Watching Google’s AI Play Go,” WIRED, March 11, 2016, https://www.wired.com/2016/03/sadness-beauty-watching-googles-ai-play-go/. 126 1 in 10,000: Cade Metz, “In Two Moves, AlphaGo and Lee Sedol Redefined the Future,” WIRED, accessed June 7, 2017, https://www.wired.com/2016/03/two-moves-alphago-lee-sedol-redefined-future/. 126 “I kind of felt powerless”: Moyer, “How Google’s AlphaGo Beat a Go World Champion.” 126 “AlphaGo isn’t just an ‘expert’ system”: “AlphaGo,” January 27, 2016. 127 AlphaGo Zero: “AlphaGo Zero: Learning from Scratch,” DeepMind, accessed October 22, 2017, https://deepmind.com/blog/alphago=zero=learning=scratch/. 127 neural network to play Atari games: Volodymyr Mnih et al., “Human-Level Control through Deep Reinforcement Learning,” Nature 518, no. 7540 (February 26, 2015): 529–33. 127 deep neural network: JASON, “Perspectives on Research in Artificial Intelligence and Artificial General Intelligence Relevant to DoD.” 129 Inception-v3: Inception-v3 is trained for the Large Scale Visual Recognition Challenge (LSVRC) using the 2012 data.

Robot Rules: Regulating Artificial Intelligence by Jacob Turner

"World Economic Forum" Davos, Ada Lovelace, Affordable Care Act / Obamacare, AI winter, algorithmic bias, algorithmic trading, AlphaGo, artificial general intelligence, Asilomar, Asilomar Conference on Recombinant DNA, autonomous vehicles, backpropagation, Basel III, bitcoin, Black Monday: stock market crash in 1987, blockchain, brain emulation, Brexit referendum, Cambridge Analytica, Charles Babbage, Clapham omnibus, cognitive dissonance, Computing Machinery and Intelligence, corporate governance, corporate social responsibility, correlation does not imply causation, crowdsourcing, data science, deep learning, DeepMind, Demis Hassabis, distributed ledger, don't be evil, Donald Trump, driverless car, easy for humans, difficult for computers, effective altruism, Elon Musk, financial exclusion, financial innovation, friendly fire, future of work, hallucination problem, hive mind, Internet of things, iterative process, job automation, John Markoff, John von Neumann, Loebner Prize, machine readable, machine translation, medical malpractice, Nate Silver, natural language processing, Nick Bostrom, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, nudge unit, obamacare, off grid, OpenAI, paperclip maximiser, pattern recognition, Peace of Westphalia, Philippa Foot, race to the bottom, Ray Kurzweil, Recombinant DNA, Rodney Brooks, self-driving car, Silicon Valley, Stanislav Petrov, Stephen Hawking, Steve Wozniak, strong AI, technological singularity, Tesla Model S, The Coming Technological Singularity, The Future of Employment, The Signal and the Noise by Nate Silver, trolley problem, Turing test, Vernor Vinge

See “Explore the AlphaGo Master series”, DeepMind Website, https://​deepmind.​com/​research/​alphago/​match-archive/​master/​, accessed 16 August 2018. DeepMind, promptly announced AlphaGo’s retirement from the game to pursue other interests. See Jon Russell, “After Beating the World’s Elite Go Players, Google’s AlphaGo AI Is Retiring”, Tech Crunch, 27 May 2017, https://​techcrunch.​com/​2017/​05/​27/​googles-alphago-ai-is-retiring/​ accessed 1 June 2018. Rather like a champion boxer tempted out of retirement for one more fight, AlphaGo (or at least a new program bearing a similar name, AlphaGo Zero) returned a year later to face a new challenge: AlphaGo Zero.

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, Quora, 28 July 2016, https://​www.​quora.​com/​What-are-some-recent-and-potentially-upcoming-breakthroughs-in-deep-learning, accessed 16 August 2018. 128Andrea Bertolini, “Robots as Products: The Case for a Realistic Analysis of Robotic Applications and Liability Rules”, Law Innovation and Technology, Vol. 5, No. 2 (2013), 214–247, 234–235. 129See Chapter 1 at s. 5 and FN 111. A subsequent iteration of AlphaGo, “AlphaGo Master” beat Ke Jie, at the time the world’s top-ranked human player, by three games to nil in May 2017. See “AlphaGo at The Future of Go Summit, 23–27 May 2017”, DeepMind Website, https://​deepmind.​com/​research/​alphago/​alphago-china/​, accessed 16 August 2018. 130Silver et al., “AlphaGo Zero: Learning from Scratch”, DeepMind Website, 18 October 2017, https://​deepmind.​com/​blog/​alphago-zero-learning-scratch/​, accessed 1 June 2018. See also the paper published by the DeepMind team: David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, Yutian Chen, Timothy Lillicrap, Fan Hui, Laurent Sifre, George van den Driessche, Thore Graepel, and Demis Hassabis, “Mastering the Game of Go Without Human Knowledge”, Nature, Vol. 550 (19 October 2017), 354–359, https://​doi.​org/​10.​1038/​nature24270, accessed 1 June 2018. 131Silver et al., “AlphaGo Zero: Learning from Scratch”, DeepMind Website, 18 October 2017, https://​deepmind.​com/​blog/​alphago-zero-learning-scratch/​, accessed 1 June 2018. 132Matej Balog, Alexander L.

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For an account, see Chris Baraniuk, “The Cyborg Chess Player Who Can’t Be Beaten”, BBC Website, 4 December 2015, http://​www.​bbc.​com/​future/​story/​20151201-the-cyborg-chess-players-that-cant-be-beaten, accessed 1 June 2018. 109The situation is somewhat complicated in that Kasparov had held the Fédération Internationale des Échecs (FIDE) world title until 1993, when a dispute with FIDE led him to set up a rival organization, the Professional Chess Association. 110Nick Bostrom, Superintelligence : Paths, Dangers and Strategies (Oxford: Oxford University Press, 2014), 16. 111In May 2017, a subsequent version of the program, “AlphaGo Master”, defeated the world champion Go player, Ke Jie by three games to nil. See “AlphaGo at The Future of Go Summit, 23–27 May 2017”, DeepMind Website, https://​deepmind.​com/​research/​alphago/​alphago-china/​, accessed 16 August 2018. Perhaps as a control against accusations that top players were being beaten psychologically by the prospect of playing an AI system rather than on the basis of skill, DeepMind had initially deployed AlphaGo Master in secret, during which period it beat 50 of the world’s top players online, playing under the pseudonym “Master”.

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

"World Economic Forum" Davos, AI winter, Airbnb, Albert Einstein, algorithmic bias, algorithmic trading, Alignment Problem, AlphaGo, artificial general intelligence, autonomous vehicles, barriers to entry, basic income, bike sharing, business cycle, Cambridge Analytica, cloud computing, commoditize, computer vision, corporate social responsibility, cotton gin, creative destruction, crony capitalism, data science, deep learning, DeepMind, Demis Hassabis, Deng Xiaoping, deskilling, Didi Chuxing, Donald Trump, driverless car, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, fake news, full employment, future of work, general purpose technology, Geoffrey Hinton, gig economy, Google Chrome, Hans Moravec, happiness index / gross national happiness, high-speed rail, if you build it, they will come, ImageNet competition, impact investing, income inequality, informal economy, Internet of things, invention of the telegraph, Jeff Bezos, job automation, John Markoff, Kickstarter, knowledge worker, Lean Startup, low skilled workers, Lyft, machine translation, mandatory minimum, Mark Zuckerberg, Menlo Park, minimum viable product, natural language processing, Neil Armstrong, new economy, Nick Bostrom, OpenAI, pattern recognition, pirate software, profit maximization, QR code, Ray Kurzweil, recommendation engine, ride hailing / ride sharing, risk tolerance, Robert Mercer, Rodney Brooks, Rubik’s Cube, Sam Altman, Second Machine Age, self-driving car, sentiment analysis, sharing economy, Silicon Valley, Silicon Valley ideology, Silicon Valley startup, Skype, SoftBank, Solyndra, special economic zone, speech recognition, Stephen Hawking, Steve Jobs, strong AI, TED Talk, The Future of Employment, Travis Kalanick, Uber and Lyft, uber lyft, universal basic income, urban planning, vertical integration, Vision Fund, warehouse robotics, Y Combinator

The engineers simply thought the board offered too many possibilities for a computer to evaluate. But on this day AlphaGo wasn’t just beating Ke Jie—it was systematically dismantling him. Over the course of three marathon matches of more than three hours each, Ke had thrown everything he had at the computer program. He tested it with different approaches: conservative, aggressive, defensive, and unpredictable. Nothing seemed to work. AlphaGo gave Ke no openings. Instead, it slowly tightened its vise around him. THE VIEW FROM BEIJING What you saw in this match depended on where you watched it from. To some observers in the United States, AlphaGo’s victories signaled not just the triumph of machine over man but also of Western technology companies over the rest of the world.

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Remove Deep Blue from the geometric simplicity of an eight-by-eight-square chessboard and it wouldn’t seem very intelligent at all. In the end, the only job it was threatening to take was that of the world chess champion. This time, things are different. The Ke Jie versus AlphaGo match was played within the constraints of a Go board, but it is intimately tied up with dramatic changes in the real world. Those changes include the Chinese AI frenzy that AlphaGo’s matches sparked amid the underlying technology that powered it to victory. AlphaGo runs on deep learning, a groundbreaking approach to artificial intelligence that has turbocharged the cognitive capabilities of machines. Deep-learning-based programs can now do a better job than humans at identifying faces, recognizing speech, and issuing loans.

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These internet juggernauts had given the United States a dominance of the digital world that matched its military and economic power in the real world. With AlphaGo—a product of the British AI startup DeepMind, which had been acquired by Google in 2014—the West appeared poised to continue that dominance into the age of artificial intelligence. But looking out my office window during the Ke Jie match, I saw something far different. The headquarters of my venture-capital fund is located in Beijing’s Zhongguancun (pronounced “jong-gwan-soon”) neighborhood, an area often referred to as “the Silicon Valley of China.” Today, Zhongguancun is the beating heart of China’s AI movement. To people here, AlphaGo’s victories were both a challenge and an inspiration.

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

"World Economic Forum" Davos, 23andMe, 3D printing, active measures, Ada Lovelace, additive manufacturing, agricultural Revolution, AI winter, air gap, Airbnb, Alan Greenspan, algorithmic bias, Alignment Problem, AlphaGo, Alvin Toffler, Amazon Web Services, Anthropocene, artificial general intelligence, Asilomar, Asilomar Conference on Recombinant DNA, ASML, autonomous vehicles, backpropagation, barriers to entry, basic income, benefit corporation, Big Tech, biodiversity loss, bioinformatics, Bletchley Park, Blitzscaling, Boston Dynamics, business process, business process outsourcing, call centre, Capital in the Twenty-First Century by Thomas Piketty, ChatGPT, choice architecture, circular economy, classic study, clean tech, cloud computing, commoditize, computer vision, coronavirus, corporate governance, correlation does not imply causation, COVID-19, creative destruction, CRISPR, critical race theory, crowdsourcing, cryptocurrency, cuban missile crisis, data science, decarbonisation, deep learning, deepfake, DeepMind, deindustrialization, dematerialisation, Demis Hassabis, disinformation, drone strike, drop ship, dual-use technology, Easter island, Edward Snowden, effective altruism, energy transition, epigenetics, Erik Brynjolfsson, Ernest Rutherford, Extinction Rebellion, facts on the ground, failed state, Fairchild Semiconductor, fear of failure, flying shuttle, Ford Model T, future of work, general purpose technology, Geoffrey Hinton, global pandemic, GPT-3, GPT-4, hallucination problem, hive mind, hype cycle, Intergovernmental Panel on Climate Change (IPCC), Internet Archive, Internet of things, invention of the wheel, job automation, John Maynard Keynes: technological unemployment, John von Neumann, Joi Ito, Joseph Schumpeter, Kickstarter, lab leak, large language model, Law of Accelerating Returns, Lewis Mumford, license plate recognition, lockdown, machine readable, Marc Andreessen, meta-analysis, microcredit, move 37, Mustafa Suleyman, mutually assured destruction, new economy, Nick Bostrom, Nikolai Kondratiev, off grid, OpenAI, paperclip maximiser, personalized medicine, Peter Thiel, planetary scale, plutocrats, precautionary principle, profit motive, prompt engineering, QAnon, quantum entanglement, ransomware, Ray Kurzweil, Recombinant DNA, Richard Feynman, Robert Gordon, Ronald Reagan, Sam Altman, Sand Hill Road, satellite internet, Silicon Valley, smart cities, South China Sea, space junk, SpaceX Starlink, stealth mode startup, stem cell, Stephen Fry, Steven Levy, strong AI, synthetic biology, tacit knowledge, tail risk, techlash, techno-determinism, technoutopianism, Ted Kaczynski, the long tail, The Rise and Fall of American Growth, Thomas Malthus, TikTok, TSMC, Turing test, Tyler Cowen, Tyler Cowen: Great Stagnation, universal basic income, uranium enrichment, warehouse robotics, William MacAskill, working-age population, world market for maybe five computers, zero day

Google’s co-founder Sergey Brin encouraged us to tackle it, arguing that any progress would be impressive enough. AlphaGo initially learned by watching 150,000 games played by human experts. Once we were satisfied with its initial performance, the key next step was creating lots of copies of AlphaGo and getting it to play against itself over and over. This meant the algorithm was able to simulate millions of new games, trying out combinations of moves that had never been played before, and therefore efficiently explore a huge range of possibilities, learning new strategies in the process. Then, in March 2016, we organized a tournament in South Korea. AlphaGo was pitted against Lee Sedol, a virtuoso world champion.

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Within the AI community, it represented a first high-profile public test of deep reinforcement learning and one of the first research uses of a very large cluster of GPU computation. In the press the matchup between AlphaGo and Lee Sedol was presented as an epic battle: human versus machine; humanity’s best and brightest against the cold, lifeless force of a computer. Cue all the tired tropes of Terminators and robot overlords. But under the surface, another, more important dimension was becoming clear, a tension I’d dimly worried about ahead of the contest, but the contours of which emerged more starkly as the event unfolded. AlphaGo wasn’t just human versus machine. As Lee Sedol squared up against AlphaGo, DeepMind was represented by the Union Jack, while the Sedol camp flew the taegeukgi, South Korea’s unmistakable flag.

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AlphaGo was pitted against Lee Sedol, a virtuoso world champion. It was far from clear who would win. Most commentators backed Sedol going into round one. But AlphaGo won the first game, much to our shock and delight. In the second game came move number 37, a move now famous in the annals of both AI and Go. It made no sense. AlphaGo had apparently blown it, blindly following a losing strategy no professional player would ever pursue. The live match commentators, both professionals of the highest ranking, said it was a “very strange move” and thought it was “a mistake.” It was so unusual that Sedol took fifteen minutes to respond and even got up from the board to take a walk outside.

Rage Inside the Machine: The Prejudice of Algorithms, and How to Stop the Internet Making Bigots of Us All by Robert Elliott Smith

"World Economic Forum" Davos, Ada Lovelace, adjacent possible, affirmative action, AI winter, Alfred Russel Wallace, algorithmic bias, algorithmic management, AlphaGo, Amazon Mechanical Turk, animal electricity, autonomous vehicles, behavioural economics, Black Swan, Brexit referendum, British Empire, Cambridge Analytica, cellular automata, Charles Babbage, citizen journalism, Claude Shannon: information theory, combinatorial explosion, Computing Machinery and Intelligence, corporate personhood, correlation coefficient, crowdsourcing, Daniel Kahneman / Amos Tversky, data science, deep learning, DeepMind, desegregation, discovery of DNA, disinformation, Douglas Hofstadter, Elon Musk, fake news, Fellow of the Royal Society, feminist movement, Filter Bubble, Flash crash, Geoffrey Hinton, Gerolamo Cardano, gig economy, Gödel, Escher, Bach, invention of the wheel, invisible hand, Jacquard loom, Jacques de Vaucanson, John Harrison: Longitude, John von Neumann, Kenneth Arrow, Linda problem, low skilled workers, Mark Zuckerberg, mass immigration, meta-analysis, mutually assured destruction, natural language processing, new economy, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, On the Economy of Machinery and Manufactures, p-value, pattern recognition, Paul Samuelson, performance metric, Pierre-Simon Laplace, post-truth, precariat, profit maximization, profit motive, Silicon Valley, social intelligence, statistical model, Stephen Hawking, stochastic process, Stuart Kauffman, telemarketer, The Bell Curve by Richard Herrnstein and Charles Murray, The Future of Employment, the scientific method, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, theory of mind, Thomas Bayes, Thomas Malthus, traveling salesman, Turing machine, Turing test, twin studies, Vilfredo Pareto, Von Neumann architecture, warehouse robotics, women in the workforce, Yochai Benkler

Not only is this subtle, human word warped by its wishfully mnemonic application to this program, it even seems wishful to say that AlphaGo plays Go at all, if one considers the human definition of the word ‘play’: ‘to engage in activity for enjoyment and recreation rather than a serious or practical purpose’. In that sense, AlphaGo does not play Go so much as it reduces the game to the maximization of a value function. There is no psychological evidence to suggest that any human being plays Go in this way, or that human intuition has anything in common with any given aspect of AlphaGo’s processing. In fact, given that AlphaGo has reduced the game to a mathematical optimization problem, examining more moves in its training and search than any human master is ever likely to play, and utilizing up to 1378 computer CPUs in that process, the dramatic triumph is that a human is able to win even one round against AlphaGo.

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Maddison, et al. (2016), Mastering the Game of Go with Deep Neural Networks and Tree Search. Nature, 529, 484–48,. doi: 10.1038/nature16961 15Steven Borowiec, 2016, AlphaGo Seals 4-1 Victory Over Go Grandmaster Lee Sedol. Guardian, www.theguardian.com/technology/2016/mar/15/googles-alphago-seals-4-1-victory-over-grandmaster-lee-sedol 16Adrian Lee, 2016, The Meaning of AlphaGo, The AI Program that Beat a Go Champ. MacLean’s, www.macleans.ca/society/science/the-meaning-of-alphago-the-ai-program-that-beat-a-go-champ/ 17The emphases on the words ‘think’ and ‘intuitively’ are mine. 18Galang Lufityanto, Chris Donkin and Joel Pearson, 2014, Measuring Intuition: Unconscious Emotional Information Boost Decision-Making Accuracy and Confidence. 18th Association for the Scientific Study of Consciousness , Psychological Science 27(5), www.researchgate.net/publication/265165687_Measuring_Intuition_Unconscious_Emotional_Information_Boost_Decision-Making_Accuracy_and_Confidence 19Association for Psychological Science, 2016, Intuition – It’s More than a Feeling, www.psychologicalscience.org/news/minds-business/intuition-its-more-than-a-feeling.html 20Ariadna Matamoros-Fernández, 2018, Inciting Anger through Facebook Reactions in Belgium: The Use of Emoji and Related Vernacular Expressions in Racist Discourse.

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The neural networks provide you with good intuitions, and that’s what the other programs were lacking, and that’s what people didn’t really understand computers could do. If you think that sounds quite incredible, it is, in the true meaning of that word. The implication is that AlphaGo is a computer program that can think about all the possible alternatives and then intuitively decide on the most strategic move in one of the most challenging human games. But given what we know of wishful mnemonics, it’s important to consider how AlphaGo actually works in order to discover whether it is thinking and intuiting. AlphaGo’s system consists of two deep-learning neural networks (of the type discussed in the previous chapter); that is to say, two deeply layered sets of massive, nested mathematical functions.

The Alignment Problem: Machine Learning and Human Values by Brian Christian

Albert Einstein, algorithmic bias, Alignment Problem, AlphaGo, Amazon Mechanical Turk, artificial general intelligence, augmented reality, autonomous vehicles, backpropagation, butterfly effect, Cambridge Analytica, Cass Sunstein, Claude Shannon: information theory, computer vision, Computing Machinery and Intelligence, data science, deep learning, DeepMind, Donald Knuth, Douglas Hofstadter, effective altruism, Elaine Herzberg, Elon Musk, Frances Oldham Kelsey, game design, gamification, Geoffrey Hinton, Goodhart's law, Google Chrome, Google Glasses, Google X / Alphabet X, Gödel, Escher, Bach, Hans Moravec, hedonic treadmill, ImageNet competition, industrial robot, Internet Archive, John von Neumann, Joi Ito, Kenneth Arrow, language acquisition, longitudinal study, machine translation, mandatory minimum, mass incarceration, multi-armed bandit, natural language processing, Nick Bostrom, Norbert Wiener, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, OpenAI, Panopticon Jeremy Bentham, pattern recognition, Peter Singer: altruism, Peter Thiel, precautionary principle, premature optimization, RAND corporation, recommendation engine, Richard Feynman, Rodney Brooks, Saturday Night Live, selection bias, self-driving car, seminal paper, side project, Silicon Valley, Skinner box, sparse data, speech recognition, Stanislav Petrov, statistical model, Steve Jobs, strong AI, the map is not the territory, theory of mind, Tim Cook: Apple, W. E. B. Du Bois, Wayback Machine, zero-sum game

But it had also made it possible. No sooner had AlphaGo reached the pinnacle of the game of Go, however, than it was, in 2017, summarily dethroned, by an even stronger program called AlphaGo Zero.86 The biggest difference between the original AlphaGo and AlphaGo Zero was in how much human data the latter had been fed to imitate: zero. From a completely random initialization, tabula rasa, it simply learned by playing against itself, again and again and again and again. Incredibly, after just thirty-six hours of self-play, it was as good as the original AlphaGo, which had beaten Lee Sedol. After seventy-two hours, the DeepMind team set up a match between the two, using the exact same two-hour time controls and the exact version of the original AlphaGo system that had beaten Lee.

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Recent research has looked into ways to automatically identify tasks of appropriate difficulty, and examples that can maximally promote learning in the network. The early results in this vein are promising, and work is ongoing.30 Perhaps the single most impressive achievement in automated curriculum design, however, is DeepMind’s board game–dominating work with AlphaGo and its successors AlphaGo Zero and AlphaZero. “AlphaGo always has an opponent at just the right level,” explains lead researcher David Silver.31 “It starts off extremely naïve; it starts off with completely random play. And yet at every step of the learning process, it has an opponent—a sparring partner, if you like—that’s exactly calibrated to its current level of performance.”

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After seventy-two hours, the DeepMind team set up a match between the two, using the exact same two-hour time controls and the exact version of the original AlphaGo system that had beaten Lee. AlphaGo Zero, which consumed a tenth of the power of the original system, and which seventy-two hours earlier had never played a single game, won the hundred-game series—100 games to 0. As the DeepMind research team wrote in their accompanying Nature paper, “Humankind has accumulated Go knowledge from millions of games played over thousands of years, collectively distilled into patterns, proverbs and books.”87 AlphaGo Zero discovered it all and more in seventy-two hours. But there was something very interesting, and very instructive, going on under the hood.

Rule of the Robots: How Artificial Intelligence Will Transform Everything by Martin Ford

AI winter, Airbnb, algorithmic bias, algorithmic trading, Alignment Problem, AlphaGo, Amazon Mechanical Turk, Amazon Web Services, artificial general intelligence, Automated Insights, autonomous vehicles, backpropagation, basic income, Big Tech, big-box store, call centre, carbon footprint, Chris Urmson, Claude Shannon: information theory, clean water, cloud computing, commoditize, computer age, computer vision, Computing Machinery and Intelligence, coronavirus, correlation does not imply causation, COVID-19, crowdsourcing, data is the new oil, data science, deep learning, deepfake, DeepMind, Demis Hassabis, deskilling, disruptive innovation, Donald Trump, Elon Musk, factory automation, fake news, fulfillment center, full employment, future of work, general purpose technology, Geoffrey Hinton, George Floyd, gig economy, Gini coefficient, global pandemic, Googley, GPT-3, high-speed rail, hype cycle, ImageNet competition, income inequality, independent contractor, industrial robot, informal economy, information retrieval, Intergovernmental Panel on Climate Change (IPCC), Internet of things, Jeff Bezos, job automation, John Markoff, Kiva Systems, knowledge worker, labor-force participation, Law of Accelerating Returns, license plate recognition, low interest rates, low-wage service sector, Lyft, machine readable, machine translation, Mark Zuckerberg, Mitch Kapor, natural language processing, Nick Bostrom, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, Ocado, OpenAI, opioid epidemic / opioid crisis, passive income, pattern recognition, Peter Thiel, Phillips curve, post scarcity, public intellectual, Ray Kurzweil, recommendation engine, remote working, RFID, ride hailing / ride sharing, Robert Gordon, Rodney Brooks, Rubik’s Cube, Sam Altman, self-driving car, Silicon Valley, Silicon Valley startup, social distancing, SoftBank, South of Market, San Francisco, special economic zone, speech recognition, stealth mode startup, Stephen Hawking, superintelligent machines, TED Talk, The Future of Employment, The Rise and Fall of American Growth, the scientific method, Turing machine, Turing test, Tyler Cowen, Tyler Cowen: Great Stagnation, Uber and Lyft, uber lyft, universal basic income, very high income, warehouse automation, warehouse robotics, Watson beat the top human players on Jeopardy!, WikiLeaks, women in the workforce, Y Combinator

The DeepMind team began by using a supervised learning technique to train AlphaGo’s neural networks on thirty million moves extracted from detailed records of games played by the best human players. It then turned to reinforcement learning, essentially turning the system loose to play against itself. Over the course of thousands of simulated practice games, and under the relentless pressure of a reward-based drive to improve, AlphaGo’s deep neural networks gradually progressed toward superhuman proficiency.7 The triumph of AlphaGo over Lee Sedol in 2016, and then over the world’s top-ranked player, Ke Jie, a year later, once again sent shock waves through the AI research community.

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However, China’s recent rapid progress in artificial intelligence has been significantly accelerated and orchestrated by an explicit industrial policy articulated by the central government. Many observers believe that the catalyst for the sudden surge of interest in AI on the part of the Chinese Communist Party was the highly touted contest between DeepMind’s AlphaGo system and Go champion Lee Sedol that took place in March 2016. The game of Go originated in China at least 2,500 years ago and is wildly popular and revered among the Chinese public. AlphaGo’s 4–1 triumph, which took place over seven days in Seoul, South Korea, was viewed live by more than 280 million people in China—nearly three times the audience that tunes in for a few hours to watch a typical Super Bowl.

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The specter of a computer defeating a top human player at an intellectual pursuit so deeply rooted in Chinese history and culture made an indelible impression on the public as well as on Chinese academics, technologists and government bureaucrats. Kai-Fu Lee, a Beijing-based venture capitalist and author, calls the AlphaGo–Lee Sedol match “China’s Sputnik moment,” in reference to the Soviet satellite that galvanized public support for the U.S. space program in the 1950s.6 Just over a year later, a second contest was held in Wuzhen, China. In a three-game match carrying a $1.5 million prize for the winner, AlphaGo defeated the Chinese player Ke Jie, who was then ranked number one in the world, by prevailing in three straight games. This time around, however, there was no live audience.

The Road to Conscious Machines by Michael Wooldridge

Ada Lovelace, AI winter, algorithmic bias, AlphaGo, Andrew Wiles, Anthropocene, artificial general intelligence, Asilomar, augmented reality, autonomous vehicles, backpropagation, basic income, Bletchley Park, Boeing 747, British Empire, call centre, Charles Babbage, combinatorial explosion, computer vision, Computing Machinery and Intelligence, DARPA: Urban Challenge, deep learning, deepfake, DeepMind, Demis Hassabis, don't be evil, Donald Trump, driverless car, Elaine Herzberg, Elon Musk, Eratosthenes, factory automation, fake news, future of work, gamification, general purpose technology, Geoffrey Hinton, gig economy, Google Glasses, intangible asset, James Watt: steam engine, job automation, John von Neumann, Loebner Prize, Minecraft, Mustafa Suleyman, Nash equilibrium, Nick Bostrom, Norbert Wiener, NP-complete, P = NP, P vs NP, paperclip maximiser, pattern recognition, Philippa Foot, RAND corporation, Ray Kurzweil, Rodney Brooks, self-driving car, Silicon Valley, Stephen Hawking, Steven Pinker, strong AI, technological singularity, telemarketer, Tesla Model S, The Coming Technological Singularity, The Future of Employment, the scientific method, theory of mind, Thomas Bayes, Thomas Kuhn: the structure of scientific revolutions, traveling salesman, trolley problem, Turing machine, Turing test, universal basic income, Von Neumann architecture, warehouse robotics

Trying to understand AlphaGo in this way is pointless: AlphaGo is a program that was optimized to do one single thing – to play the game of Go. We want to attribute motives and reasoning and strategy to the program, but there are none of these: AlphaGo’s extraordinary capability is captured in the weightings in its neural nets. These neural nets are nothing more than very long lists of numbers, and we have no way of extracting or rationalizing the expertise that they embody. AlphaGo can’t tell us why it made its moves, and this, as we will see, is one of the key challenges with deep learning. AlphaGo was widely touted as a triumph for the new AI of deep learning and big data – and indeed it was.

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But if you dig beneath the surface, you will find that an awful lot of the clever engineering in AlphaGo is classic AI search. Arthur Samuel, who in the 1950s developed the checkers-playing program we discussed in Chapter 2, would have had no difficulty in understanding the search techniques used in AlphaGo: there is an unbroken thread from his work in the 1950s through to the most celebrated AI system of the modern era. One might think that two landmark achievements were enough, but, just 18 months later, DeepMind were in the news again, this time with a generalization of AlphaGo called AlphaGo Zero. The extraordinary thing about AlphaGo Zero is that it learned how to play to a super-human level without any human supervision at all: it just played against itself.16 To be fair, it had to play itself a lot, but nevertheless it was a striking result, and it was further generalized in another follow-up system called AlphaZero, which learned to play a range of other games, including chess: after just nine hours of self-play, AlphaZero was able to consistently beat or draw against Stockfish, one of the world’s leading dedicated chess-playing programs.

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Before the system was announced, DeepMind hired Fan Hui, a European Go champion, to play against AlphaGo: the system beat him five games to zero. This was the first time a Go program had beaten a human champion player in a full game. Shortly after, DeepMind announced that AlphaGo was going to be pitted against Lee Sedol, a world champion Go player, in a five-match competition to be held in Seoul, Korea, in March 2016. The science of AlphaGo is fascinating, and AI researchers – myself included – were intrigued to see what would happen. (For the record, my guess was that AlphaGo might win one or two matches at most, but that Sedol would decisively win the competition overall.)

Scary Smart: The Future of Artificial Intelligence and How You Can Save Our World by Mo Gawdat

3D printing, accounting loophole / creative accounting, AI winter, AlphaGo, anthropic principle, artificial general intelligence, autonomous vehicles, basic income, Big Tech, Black Lives Matter, Black Monday: stock market crash in 1987, butterfly effect, call centre, carbon footprint, cloud computing, computer vision, coronavirus, COVID-19, CRISPR, cryptocurrency, deep learning, deepfake, DeepMind, Demis Hassabis, digital divide, digital map, Donald Trump, Elon Musk, fake news, fulfillment center, game design, George Floyd, global pandemic, Google Glasses, Google X / Alphabet X, Law of Accelerating Returns, lockdown, microplastics / micro fibres, Nick Bostrom, off-the-grid, OpenAI, optical character recognition, out of africa, pattern recognition, Ponzi scheme, Ray Kurzweil, recommendation engine, self-driving car, Silicon Valley, smart contracts, Stanislav Petrov, Stephen Hawking, subprime mortgage crisis, superintelligent machines, TED Talk, TikTok, Turing machine, Turing test, universal basic income, Watson beat the top human players on Jeopardy!, Y2K

In March 2016, as much as ten years before even the most optimistic AI analysts predicted it would happen, AlphaGo beat champion Lee Sedol, then ranked second worldwide in Go, in a five-game match. Then, in 2017, at the ‘Future of Go’ summit, its successor, AlphaGo Master, beat Ke Jie, the world’s number-one-ranked player at the time, in a three-game match. So AlphaGo Master officially became the world champion. With no humans left to beat, DeepMind developed a new AI from scratch – AlphaGo Zero – to play against AlphaGo Master. After just a short period of training, AlphaGo Zero achieved a 100–0 victory against the champion, AlphaGo Master. Its successor, the self-taught AlphaZero, is currently perceived as the world champion of Go.

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But within nineteen hours, this had changed. AlphaGo Zero had learned by then the fundamentals of Go strategies, such as life-and-death, influence and territory. Within seventy hours it was playing at superhuman level and had surpassed the abilities of AlphaGo, the version that beat world champion Lee Sedol. After twenty-one days it had reached the level of AlphaGo Master, the version that defeated sixty top professionals online and the world champion, Ke Jie, in a three-out-of-three game. By day forty, AlphaGo Zero surpassed all other versions of AlphaGo and, arguably, this newly born intelligent being had already become the smartest being in existence on the task it had set out to learn.

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How far-reaching would a delay of forty-five days be in response to the first AI threat warning? If the rate at which AlphaGo Zero learned to master humanity’s most challenging strategy game is any indication, then by the end of forty-five days, humanity would be toast. Let me explain. After beating the world champion in Go, DeepMind, who created the AI, started from scratch with AlphaGo Zero. On Day Zero, AlphaGo Zero had no prior knowledge of the game Go and was only given the basic rules as input. Three hours later, AlphaGo Zero was already playing like a beginner, forgoing long-term strategy to focus on greedily capturing as many stones as possible.

Machine, Platform, Crowd: Harnessing Our Digital Future by Andrew McAfee, Erik Brynjolfsson

"World Economic Forum" Davos, 3D printing, additive manufacturing, AI winter, Airbnb, airline deregulation, airport security, Albert Einstein, algorithmic bias, AlphaGo, Amazon Mechanical Turk, Amazon Web Services, Andy Rubin, AOL-Time Warner, artificial general intelligence, asset light, augmented reality, autism spectrum disorder, autonomous vehicles, backpropagation, backtesting, barriers to entry, behavioural economics, bitcoin, blockchain, blood diamond, British Empire, business cycle, business process, carbon footprint, Cass Sunstein, centralized clearinghouse, Chris Urmson, cloud computing, cognitive bias, commoditize, complexity theory, computer age, creative destruction, CRISPR, crony capitalism, crowdsourcing, cryptocurrency, Daniel Kahneman / Amos Tversky, data science, Dean Kamen, deep learning, DeepMind, Demis Hassabis, discovery of DNA, disintermediation, disruptive innovation, distributed ledger, double helix, driverless car, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Ethereum, ethereum blockchain, everywhere but in the productivity statistics, Evgeny Morozov, fake news, family office, fiat currency, financial innovation, general purpose technology, Geoffrey Hinton, George Akerlof, global supply chain, Great Leap Forward, Gregor Mendel, Hernando de Soto, hive mind, independent contractor, information asymmetry, Internet of things, inventory management, iterative process, Jean Tirole, Jeff Bezos, Jim Simons, jimmy wales, John Markoff, joint-stock company, Joseph Schumpeter, Kickstarter, Kiva Systems, law of one price, longitudinal study, low interest rates, Lyft, Machine translation of "The spirit is willing, but the flesh is weak." to Russian and back, Marc Andreessen, Marc Benioff, Mark Zuckerberg, meta-analysis, Mitch Kapor, moral hazard, multi-sided market, Mustafa Suleyman, Myron Scholes, natural language processing, Network effects, new economy, Norbert Wiener, Oculus Rift, PageRank, pattern recognition, peer-to-peer lending, performance metric, plutocrats, precision agriculture, prediction markets, pre–internet, price stability, principal–agent problem, Project Xanadu, radical decentralization, Ray Kurzweil, Renaissance Technologies, Richard Stallman, ride hailing / ride sharing, risk tolerance, Robert Solow, Ronald Coase, Salesforce, Satoshi Nakamoto, Second Machine Age, self-driving car, sharing economy, Silicon Valley, Skype, slashdot, smart contracts, Snapchat, speech recognition, statistical model, Steve Ballmer, Steve Jobs, Steven Pinker, supply-chain management, synthetic biology, tacit knowledge, TaskRabbit, Ted Nelson, TED Talk, the Cathedral and the Bazaar, The Market for Lemons, The Nature of the Firm, the strength of weak ties, Thomas Davenport, Thomas L Friedman, too big to fail, transaction costs, transportation-network company, traveling salesman, Travis Kalanick, Two Sigma, two-sided market, Tyler Cowen, Uber and Lyft, Uber for X, uber lyft, ubercab, Vitalik Buterin, warehouse robotics, Watson beat the top human players on Jeopardy!, winner-take-all economy, yield management, zero day

He predicted he would win at least four games out of five, saying, “Looking at the match in October, I think (AlphaGo’s) level doesn’t match mine.” The games between Sedol and AlphaGo attracted intense interest throughout Korea and other East Asian countries. AlphaGo won the first three games, ensuring itself of victory overall in the best-of-five match. Sedol came back to win the fourth game. His victory gave some observers hope that human cleverness had discerned flaws in a digital opponent, ones that Sedol could continue to exploit. If so, they were not big enough to make a difference in the next game. AlphaGo won again, completing a convincing 4–1 victory in the match.

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A team at Google DeepMind, a London-based company specializing in machine learning (a branch of artificial intelligence we’ll discuss more in Chapter 3), published “Mastering the Game of Go with Deep Neural Networks and Tree Search,” and the prestigious journal Nature made it the cover story. The article described AlphaGo, a Go-playing application that had found a way around Polanyi’s Paradox. The humans who built AlphaGo didn’t try to program it with superior Go strategies and heuristics. Instead, they created a system that could learn them on its own. It did this by studying lots of board positions in lots of games. AlphaGo was built to discern the subtle patterns present in large amounts of data, and to link actions (like playing a stone in a particular spot on the board) to outcomes (like winning a game of Go).§ The software was given access to 30 million board positions from an online repository of games and essentially told, “Use these to figure out how to win.”

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.§ The software was given access to 30 million board positions from an online repository of games and essentially told, “Use these to figure out how to win.” AlphaGo also played many games against itself, generating another 30 million positions, which it then analyzed. The system did conduct simulations during games, but only highly focused ones; it used the learning accumulated from studying millions of positions to simulate only those moves it thought most likely to lead to victory. Work on AlphaGo began in 2014. By October of 2015, it was ready for a test. In secret, AlphaGo played a five-game match against Fan Hui, who was then the European Go champion. The machine won 5–0.

Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again by Eric Topol

"World Economic Forum" Davos, 23andMe, Affordable Care Act / Obamacare, AI winter, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, algorithmic bias, AlphaGo, Apollo 11, artificial general intelligence, augmented reality, autism spectrum disorder, autonomous vehicles, backpropagation, Big Tech, bioinformatics, blockchain, Cambridge Analytica, cloud computing, cognitive bias, Colonization of Mars, computer age, computer vision, Computing Machinery and Intelligence, conceptual framework, creative destruction, CRISPR, crowdsourcing, Daniel Kahneman / Amos Tversky, dark matter, data science, David Brooks, deep learning, DeepMind, Demis Hassabis, digital twin, driverless car, Elon Musk, en.wikipedia.org, epigenetics, Erik Brynjolfsson, fake news, fault tolerance, gamification, general purpose technology, Geoffrey Hinton, George Santayana, Google Glasses, ImageNet competition, Jeff Bezos, job automation, job satisfaction, Joi Ito, machine translation, Mark Zuckerberg, medical residency, meta-analysis, microbiome, move 37, natural language processing, new economy, Nicholas Carr, Nick Bostrom, nudge unit, OpenAI, opioid epidemic / opioid crisis, pattern recognition, performance metric, personalized medicine, phenotype, placebo effect, post-truth, randomized controlled trial, recommendation engine, Rubik’s Cube, Sam Altman, self-driving car, Silicon Valley, Skinner box, speech recognition, Stephen Hawking, techlash, TED Talk, text mining, the scientific method, Tim Cook: Apple, traumatic brain injury, trolley problem, War on Poverty, Watson beat the top human players on Jeopardy!, working-age population

But it didn’t take long before even that achievement was superseded. In the fall of 2017, AlphaGo Zero, the next iteration of algorithm beyond AlphaGo, took the game world by storm.32 AlphaGo Zero played millions of games against itself, just starting from random moves. In the Nature paper, “Mastering the Game of Go Without Human Knowledge,” the researchers concluded that “it is possible [for an algorithm] to train to superhuman level, without human examples or guidance, given no knowledge of the domain beyond basic rules.” It was also a stunning example of doing more with less: AlphaGo Zero, in contrast to AlphaGo, had fewer than 5 million training games compared with 30 million, three days of training instead of several months, a single neural network compared with two separate ones, and it performed via a single tensor processing unit (TPU) chip compared with forty-eight TPUs and multiple machines.33 If that wasn’t enough, just a few months later a preprint was published that this same AlphaGo Zero algorithm, with only basic rules as input and no prior knowledge of chess, played at a champion level after teaching itself for only four hours.34 This was presumably yet another “holy shit” moment for Tegmark, who tweeted, “In contrast to AlphaGo, the shocking AI news here isn’t the ease with which AlphaGo Zero crushed human players, but the ease with which it crushed human AI researchers, who’d spent decades hand-crafting ever better chess software.”35 AI has also progressed to superhuman performance on a similarly hyper-accelerated course in the game of Texas hold’em, the most popular form of poker.

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It was also a stunning example of doing more with less: AlphaGo Zero, in contrast to AlphaGo, had fewer than 5 million training games compared with 30 million, three days of training instead of several months, a single neural network compared with two separate ones, and it performed via a single tensor processing unit (TPU) chip compared with forty-eight TPUs and multiple machines.33 If that wasn’t enough, just a few months later a preprint was published that this same AlphaGo Zero algorithm, with only basic rules as input and no prior knowledge of chess, played at a champion level after teaching itself for only four hours.34 This was presumably yet another “holy shit” moment for Tegmark, who tweeted, “In contrast to AlphaGo, the shocking AI news here isn’t the ease with which AlphaGo Zero crushed human players, but the ease with which it crushed human AI researchers, who’d spent decades hand-crafting ever better chess software.”35 AI has also progressed to superhuman performance on a similarly hyper-accelerated course in the game of Texas hold’em, the most popular form of poker.

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champions 2011—Speech recognition NN (Microsoft) 2012—University of Toronto ImageNet classification and cat video recognition (Google Brain, Andrew Ng, Jeff Dean) 2014—DeepFace facial recognition (Facebook) 2015—DeepMind vs. Atari (David Silver, Demis Hassabis) 2015—First AI risk conference (Max Tegmark) 2016—AlphaGo vs. Go (Silver, Demis Hassabis) 2017—AlphaGo Zero vs. Go (Silver, Demis Hassabis) 2017—Libratus vs. poker (Noam Brown, Tuomas Sandholm) 2017—AI Now Institute launched TABLE 4.2: The AI timeline. Kasparov’s book, Deep Thinking, which came out two decades later, provides remarkable personal insights about that pivotal AI turning point.

The Perfect Police State: An Undercover Odyssey Into China's Terrifying Surveillance Dystopia of the Future by Geoffrey Cain

airport security, Alan Greenspan, AlphaGo, anti-communist, Bellingcat, Berlin Wall, Black Lives Matter, Citizen Lab, cloud computing, commoditize, computer vision, coronavirus, COVID-19, deep learning, DeepMind, Deng Xiaoping, Edward Snowden, European colonialism, fake news, Geoffrey Hinton, George Floyd, ghettoisation, global supply chain, Kickstarter, land reform, lockdown, mass immigration, military-industrial complex, Nelson Mandela, Panopticon Jeremy Bentham, pattern recognition, phenotype, pirate software, post-truth, purchasing power parity, QR code, RAND corporation, Ray Kurzweil, ride hailing / ride sharing, Right to Buy, self-driving car, sharing economy, Silicon Valley, Skype, smart cities, South China Sea, speech recognition, TikTok, Tim Cook: Apple, trade liberalization, trade route, undersea cable, WikiLeaks

And DeepMind, founded by three brilliant technologists including a child chess prodigy, made an AI software program called AlphaGo.33 Its programmers wanted to see if AlphaGo could learn to play this incredibly complex game on its own, without a human hand. So they developed a new AlphaGo program that didn’t need any data inputs whatsoever. It would learn the game all by itself, and then go head-to-head with world champions. What happened was startling. After only seventy hours of playing matches with itself, the new AlphaGo program reached a level capable of beating top human players. Then, after a reboot, it took AlphaGo only forty days to learn the sum of humans’ knowledge of Go.

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After that it beat the previous, most advanced, version of AlphaGo 90 percent of the time. The developers were puzzled. Even they had little idea how the new AlphaGo got so smart in such a short time. These discoveries potentially had enormous implications. If similar levels of AI were applied to other areas, they could upend the way we worked, lived, drove, went shopping, ate, and even conducted diplomacy and fought wars. AlphaGo’s AI system, after all, closely mimicked the thinking behind warfare and could have battlefield applications. In China, few were paying attention until AlphaGo was put to the test against Go grandmasters, most of whom were based in East Asia.

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Everyone was expanding so fast and so they had debts to pay off. We were changing our direction several times a year, and replacing a lot of employees. It was a hard time. But we managed to survive for one reason: AlphaGo.”3 Just over a year after beating South Korean Lee Sedol, AlphaGo was ready to take on the world champion, the Chinese player Ke Jie, who agreed to compete against AlphaGo over five matches. After three grueling days and on his third and final game, Ke Jie capitulated. He had lost them all.4 “No human on earth could do this better than Ke Jie,” wrote AI expert and investor Kai-fu Lee, “but today he was pitted against a Go player on a level no one had seen before.”5 The match was a turning point.

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

"World Economic Forum" Davos, 3D printing, agricultural Revolution, AI winter, Airbnb, Albert Einstein, algorithmic trading, AlphaGo, artificial general intelligence, autonomous vehicles, basic income, Bertrand Russell: In Praise of Idleness, Big Tech, blue-collar work, Boston Dynamics, British Empire, Capital in the Twenty-First Century by Thomas Piketty, cloud computing, computer age, computer vision, computerized trading, creative destruction, David Graeber, David Ricardo: comparative advantage, deep learning, DeepMind, Demis Hassabis, demographic transition, deskilling, disruptive innovation, Donald Trump, Douglas Hofstadter, driverless car, drone strike, Edward Glaeser, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, fake news, financial innovation, flying shuttle, Ford Model T, fulfillment center, future of work, gig economy, Gini coefficient, Google Glasses, Gödel, Escher, Bach, Hans Moravec, income inequality, income per capita, industrial robot, interchangeable parts, invisible hand, Isaac Newton, Jacques de Vaucanson, James Hargreaves, job automation, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, Joi Ito, Joseph Schumpeter, Kenneth Arrow, Kevin Roose, Khan Academy, Kickstarter, Larry Ellison, low skilled workers, lump of labour, machine translation, Marc Andreessen, Mark Zuckerberg, means of production, Metcalfe’s law, natural language processing, Neil Armstrong, Network effects, Nick Bostrom, Occupy movement, offshore financial centre, Paul Samuelson, Peter Thiel, pink-collar, precariat, purchasing power parity, Ray Kurzweil, ride hailing / ride sharing, road to serfdom, Robert Gordon, Sam Altman, Second Machine Age, self-driving car, shareholder value, sharing economy, Silicon Valley, Snapchat, social intelligence, software is eating the world, sovereign wealth fund, spinning jenny, Stephen Hawking, Steve Jobs, strong AI, tacit knowledge, technological solutionism, TED Talk, telemarketer, The Future of Employment, The Rise and Fall of American Growth, the scientific method, The Theory of the Leisure Class by Thorstein Veblen, The Wealth of Nations by Adam Smith, Thorstein Veblen, Travis Kalanick, Turing test, Two Sigma, Tyler Cowen, Tyler Cowen: Great Stagnation, universal basic income, upwardly mobile, warehouse robotics, Watson beat the top human players on Jeopardy!, We are the 99%, wealth creators, working poor, working-age population, Y Combinator

That’s 230 million times as many possibilities as in chess at that same early point in the game.29 In chess, Deep Blue’s victory came in part from its ability to use brute-force processing power to calculate further ahead in a game than Kasparov could. But because of go’s complexity, that strategy would not work for AlphaGo. Instead, it took a different approach. First it reviewed 30 million moves from games played by the best human experts. Then it learned from playing repeatedly against itself, crunching through thousands of games and drawing insights from those, too. In this way, AlphaGo was able to win while evaluating far fewer positions than Deep Blue had done in its matches. In 2017, a yet more sophisticated version of the program was unveiled, called AlphaGo Zero. What made this system so remarkable is that it had wrung itself dry of any residual role for human intelligence altogether.

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Buried within Deep Blue’s code were still a few clever strategies that chess champions had worked out for it to follow in advance.30 And in studying that vast collection of past games by great human players, AlphaGo was in a sense relying on them for much of its difficult computational work. But AlphaGo Zero required none of this. It did not need to know anything about the play of human experts; it did not need to try to mimic human intelligence at all. All it needed was the rules of the game. Given nothing more than those, it played itself for three days to generate its own data—and it returned to thrash its older cousin, AlphaGo.31 Other systems are using similar techniques to engage in pursuits that more closely resemble the messiness of real life.

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Another good example of this is AlphaGo, the go-playing machine that beat the world champion Lee Sedol. Almost as remarkable as its overall victory was a particular move that AlphaGo made—the thirty-seventh move in the second game—and the reaction of those watching. The commentators were shocked. They had never seen a move like it. Lee Sedol himself appeared deeply unsettled. Thousands of years of human play had forged a rule of thumb known even to beginners: early in the game, avoid placing stones on the fifth line from the edge. And yet, this is exactly what AlphaGo did in that move.31 The system had not discovered an existing but hitherto unarticulated human rule.

The Price of Tomorrow: Why Deflation Is the Key to an Abundant Future by Jeff Booth

3D printing, Abraham Maslow, activist fund / activist shareholder / activist investor, additive manufacturing, AI winter, Airbnb, Albert Einstein, AlphaGo, Amazon Web Services, artificial general intelligence, augmented reality, autonomous vehicles, basic income, bitcoin, blockchain, Bretton Woods, business intelligence, butterfly effect, Charles Babbage, Claude Shannon: information theory, clean water, cloud computing, cognitive bias, collapse of Lehman Brothers, Computing Machinery and Intelligence, corporate raider, creative destruction, crony capitalism, crowdsourcing, cryptocurrency, currency manipulation / currency intervention, dark matter, deep learning, DeepMind, deliberate practice, digital twin, distributed ledger, Donald Trump, Elon Musk, fiat currency, Filter Bubble, financial engineering, full employment, future of work, game design, gamification, general purpose technology, Geoffrey Hinton, Gordon Gekko, Great Leap Forward, Hyman Minsky, hype cycle, income inequality, inflation targeting, information asymmetry, invention of movable type, Isaac Newton, Jeff Bezos, John Maynard Keynes: Economic Possibilities for our Grandchildren, John von Neumann, Joseph Schumpeter, late fees, low interest rates, Lyft, Maslow's hierarchy, Milgram experiment, Minsky moment, Modern Monetary Theory, moral hazard, Nelson Mandela, Network effects, Nick Bostrom, oil shock, OpenAI, pattern recognition, Ponzi scheme, quantitative easing, race to the bottom, ride hailing / ride sharing, self-driving car, software as a service, technoutopianism, TED Talk, the long tail, the scientific method, Thomas Bayes, Turing test, Uber and Lyft, uber lyft, universal basic income, winner-take-all economy, X Prize, zero-sum game

Top players in the world commentating first dismissed the move as a mistake by the AI, but then realized it was no mistake. The move was brilliant, and AlphaGo went on to beat Sedol in the game and win the five-game match 4–1. Later, pundits would say how creative the move was. It was the first time that an AI was ever said to be creative, a domain always thought to be owned solely by humans. Just one year later, in 2017, Google launched a newer version called AlphaGo Zero that beat AlphaGo 100 games to zero. Not only was that version much more powerful than its predecessor, It also didn’t require any “training” from human games. Understanding only the rules of the game, AlphaGo Zero became its own teacher, playing itself millions of times and through deep reinforcement learning getting stronger with each game.

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Until 2014, even top AI researchers believed top human competitors would beat computers for years to come because of the complexity of the game and the fact that algorithms had to compare every move, which required enormous compute power. But in 2016, Google’s DeepMind program AlphaGo beat one of the top players in the world, Lee Sedol, in a match that made history. AlphaGo’s program was based on deep learning, which was “trained” using thousands of human amateur and professional games. It made history not only because it was the first time a computer beat a top Go master, but also because of the way it did so. In game 2 and the thirty-seventh move, the computer made a move that defied logic, placing a black stone in the middle of an open area—away from the other stones.

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While I agree with the prognosis that in the short term humans are needed to help train and error correct artificial intelligence, it does not appear to me that this is any more than a transition step. We will error correct the machines until they are more “intelligent” than us. So for a short term, there might be more jobs, but then those “training the AI” jobs fall away as AI takes knowledge to the next level. Remember that just a year after AlphaGo’s release, AlphaGo Zero came out, not needing people, and winning 100 games to zero. It’s a potent example of what is possible. The AI race But it’s not just about increases in compute power. We are at an inflection point where it is about gathering the right data in data sets that can be analyzed by machines and then helping train those data sets.

I, Warbot: The Dawn of Artificially Intelligent Conflict by Kenneth Payne

Abraham Maslow, AI winter, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, AlphaGo, anti-communist, Any sufficiently advanced technology is indistinguishable from magic, artificial general intelligence, Asperger Syndrome, augmented reality, Automated Insights, autonomous vehicles, backpropagation, Black Lives Matter, Bletchley Park, Boston Dynamics, classic study, combinatorial explosion, computer age, computer vision, Computing Machinery and Intelligence, coronavirus, COVID-19, CRISPR, cuban missile crisis, data science, deep learning, deepfake, DeepMind, delayed gratification, Demis Hassabis, disinformation, driverless car, drone strike, dual-use technology, Elon Musk, functional programming, Geoffrey Hinton, Google X / Alphabet X, Internet of things, job automation, John Nash: game theory, John von Neumann, Kickstarter, language acquisition, loss aversion, machine translation, military-industrial complex, move 37, mutually assured destruction, Nash equilibrium, natural language processing, Nick Bostrom, Norbert Wiener, nuclear taboo, nuclear winter, OpenAI, paperclip maximiser, pattern recognition, RAND corporation, ransomware, risk tolerance, Ronald Reagan, self-driving car, semantic web, side project, Silicon Valley, South China Sea, speech recognition, Stanislav Petrov, stem cell, Stephen Hawking, Steve Jobs, strong AI, Stuxnet, technological determinism, TED Talk, theory of mind, TikTok, Turing machine, Turing test, uranium enrichment, urban sprawl, V2 rocket, Von Neumann architecture, Wall-E, zero-sum game

In Go, as is often said, there are more possible moves than atoms in the universe. Surely, observers thought, the raw computer power required to search deeply into that domain would be beyond modern AI? Not so. In 2016, DeepMind’s AlphaGo demonstrated that computation trumps human skill, even in a truly vast search space. In deposing Lee Sedol, the world champion, AlphaGo’s limited version of creativity overwhelmed the richer human variety.5 Go was a formidable computer science problem, but still just a board game. Nonetheless, when all was done and Sedol defeated, the match clarified some big things that have relevance for our study of AI strategists.

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At move 37 in game 2, the computer stunned onlookers and Sedol by making a radical move, one vanishingly unlikely to have been played by an expert human. There were gasps from the commentators and a startled Sedol left the table to ponder his reply. It was a game-winning move, which aficionados eagerly attributed to AlphaGo’s ingenuity, or creativity. But was the machine really ‘inspired’, or were the onlookers just anthropomorphising? In Boden’s terms, the move was new, surprising and valuable—and so genuinely creative. But it was simply exploratory creativity on steroids—searching in the available universe of possible moves for a new angle that would bring a marginally better probability of success, many moves further on.

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But it was simply exploratory creativity on steroids—searching in the available universe of possible moves for a new angle that would bring a marginally better probability of success, many moves further on. Because humans, even expert ones, don’t search so extensively in pursuit of marginal gains, the move looked highly novel. Sedol was stunned too, returning to the table to think for many minutes, before going down rapidly to defeat. Yet something was lacking. If it was creative, AlphaGo certainly wasn’t creative in the sense that Sedol was. Like other genius players, Sedol didn’t see the board as an exercise in number crunching, but as a combination of visionary strategic play and short-range tactics. And there was an intensely psychological dimension to his approach. Sedol described looking across the board, as you would in playing a human.

Artificial Unintelligence: How Computers Misunderstand the World by Meredith Broussard

"Susan Fowler" uber, 1960s counterculture, A Declaration of the Independence of Cyberspace, Ada Lovelace, AI winter, Airbnb, algorithmic bias, AlphaGo, Amazon Web Services, autonomous vehicles, availability heuristic, barriers to entry, Bernie Sanders, Big Tech, bitcoin, Buckminster Fuller, Charles Babbage, Chris Urmson, Clayton Christensen, cloud computing, cognitive bias, complexity theory, computer vision, Computing Machinery and Intelligence, crowdsourcing, Danny Hillis, DARPA: Urban Challenge, data science, deep learning, Dennis Ritchie, digital map, disruptive innovation, Donald Trump, Douglas Engelbart, driverless car, easy for humans, difficult for computers, Electric Kool-Aid Acid Test, Elon Musk, fake news, Firefox, gamification, gig economy, global supply chain, Google Glasses, Google X / Alphabet X, Greyball, Hacker Ethic, independent contractor, Jaron Lanier, Jeff Bezos, Jeremy Corbyn, John Perry Barlow, John von Neumann, Joi Ito, Joseph-Marie Jacquard, life extension, Lyft, machine translation, Mark Zuckerberg, mass incarceration, Minecraft, minimum viable product, Mother of all demos, move fast and break things, Nate Silver, natural language processing, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, One Laptop per Child (OLPC), opioid epidemic / opioid crisis, PageRank, Paradox of Choice, payday loans, paypal mafia, performance metric, Peter Thiel, price discrimination, Ray Kurzweil, ride hailing / ride sharing, Ross Ulbricht, Saturday Night Live, school choice, self-driving car, Silicon Valley, Silicon Valley billionaire, speech recognition, statistical model, Steve Jobs, Steven Levy, Stewart Brand, TechCrunch disrupt, Tesla Model S, the High Line, The Signal and the Noise by Nate Silver, theory of mind, traumatic brain injury, Travis Kalanick, trolley problem, Turing test, Uber for X, uber lyft, Watson beat the top human players on Jeopardy!, We are as Gods, Whole Earth Catalog, women in the workforce, work culture , yottabyte

Eventually, these batches were pooled, resulting in the thirty million games collected by the AlphaGo team. The programmers used the thirty million games to “train” the model that they named AlphaGo. What you must remember is that people who play Go professionally spend ages playing Computer Go. It’s how they train. Therefore, the thirty million games recorded included data from the world’s greatest Go players. Millions of hours of human labor went into creating the training data—yet most versions of the AlphaGo story focus on the magic of the algorithms, not the humans who invisibly and over the course of years worked (without compensation) to create the training data.

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AlphaGo is a remarkable mathematical achievement that was made possible by equally remarkable advances in computing hardware and software. AlphaGo’s team of designers deserves praise for this outstanding technical achievement. AlphaGo is not an intelligent machine, however. It has no consciousness. It does only one thing: plays a computer game. It contains data from thirty million games played by amateurs and by the world’s most talented players. On some level, AlphaGo is supremely dumb. It uses brute force and the combined effort of many, many humans to defeat a single Go master. The program and its underlying computational methods will likely be deployed for other useful tasks involving massive number-crunching, and that’s good for the world—but not everything in the world is a calculation.

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Half a century has been spent trying to make a machine that could beat a human chess master. Finally, IBM’s Deep Blue defeated chess champion Garry Kasparov in 1997. AlphaGo, the AI program that won three of three games against Go world champion Ke Jie in 2017, is often cited as an example of a program that proves general AI is just a few years in the future. Looking closely at the program and its cultural context reveals a different story, however. AlphaGo is a human-constructed program running on top of hardware, just like the “Hello, world” program you wrote in chapter two. Its developers explain how it works in a 2016 paper published in Nature, the international journal of science.1 The opening lines of the paper read: “All games of perfect information have an optimal value function, v*(s), which determines the outcome of the game, from every board position or state s, under perfect play by all players.

Ghost Work: How to Stop Silicon Valley From Building a New Global Underclass by Mary L. Gray, Siddharth Suri

"World Economic Forum" Davos, Affordable Care Act / Obamacare, AlphaGo, Amazon Mechanical Turk, Apollo 13, augmented reality, autonomous vehicles, barriers to entry, basic income, benefit corporation, Big Tech, big-box store, bitcoin, blue-collar work, business process, business process outsourcing, call centre, Capital in the Twenty-First Century by Thomas Piketty, cloud computing, cognitive load, collaborative consumption, collective bargaining, computer vision, corporate social responsibility, cotton gin, crowdsourcing, data is the new oil, data science, deep learning, DeepMind, deindustrialization, deskilling, digital divide, do well by doing good, do what you love, don't be evil, Donald Trump, Elon Musk, employer provided health coverage, en.wikipedia.org, equal pay for equal work, Erik Brynjolfsson, fake news, financial independence, Frank Levy and Richard Murnane: The New Division of Labor, fulfillment center, future of work, gig economy, glass ceiling, global supply chain, hiring and firing, ImageNet competition, independent contractor, industrial robot, informal economy, information asymmetry, Jeff Bezos, job automation, knowledge economy, low skilled workers, low-wage service sector, machine translation, market friction, Mars Rover, natural language processing, new economy, operational security, passive income, pattern recognition, post-materialism, post-work, power law, race to the bottom, Rana Plaza, recommendation engine, ride hailing / ride sharing, Ronald Coase, scientific management, search costs, Second Machine Age, sentiment analysis, sharing economy, Shoshana Zuboff, side project, Silicon Valley, Silicon Valley startup, Skype, software as a service, speech recognition, spinning jenny, Stephen Hawking, TED Talk, The Future of Employment, The Nature of the Firm, Tragedy of the Commons, transaction costs, two-sided market, union organizing, universal basic income, Vilfredo Pareto, Wayback Machine, women in the workforce, work culture , Works Progress Administration, Y Combinator, Yochai Benkler

Take, for example, the celebrated accomplishments of the AI powering AlphaGo, most recently chronicled in technologist Scott Hartley’s book The Fuzzy and the Techie.14 In May 2017, AlphaGo became the first computer program to beat Ke Jie, the reigning world champion of the ancient Chinese board game go. Five months later, AlphaGo fell to its progeny, AlphaGo Zero. But, lest we be too impressed, it’s important to keep in mind that the rules of go are fixed and fully formalized and it is played in a closed environment where only the two players’ actions determine the outcome. AlphaGo and AlphaGo Zero’s human programmers at the Google-backed company DeepMind gave the programs clear definitions of winning versus losing.

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[back] 14. Scott Hartley, The Fuzzy and the Techie: Why the Liberal Arts Will Rule the Digital World (Boston: Houghton Mifflin Harcourt, 2017). Hartley focuses on the case of AlphaGo. Both AlphaGo and AlphaGo Zero were the brainchildren of DeepMind, a London-based research lab acquired by Google in 2014. [back] 15. Tom Dietterich, personal conversation, April 13, 2018. Noted AI researcher Dietterich put it this way: the version of AlphaGo that defeated Ke Jie was “told” the rules of go (in the sense that it could invoke code to compute all legal moves for any board state and it was given the definitions of winning and losing).

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AlphaGo and AlphaGo Zero’s human programmers at the Google-backed company DeepMind gave the programs clear definitions of winning versus losing. Winning go is about foreseeing the long-term consequences of one’s actions as one plays them out against those of an opponent.15 So AlphaGo was trained on billions of board positions using a large database of games between human experts, as well as games against itself, allowing it to learn what constitutes a better move or a stronger board position.16 AlphaGo Zero was then steeped in all of those prior experiences by playing against AlphaGo, a mirror image of self. But, as Tom Dietterich, a noted expert in artificial intelligence research, suggests, “we must rely on humans to backfill with their broad knowledge of the world” to accomplish most day-to-day tasks.

Architects of Intelligence by Martin Ford

3D printing, agricultural Revolution, AI winter, algorithmic bias, Alignment Problem, AlphaGo, Apple II, artificial general intelligence, Asilomar, augmented reality, autonomous vehicles, backpropagation, barriers to entry, basic income, Baxter: Rethink Robotics, Bayesian statistics, Big Tech, bitcoin, Boeing 747, Boston Dynamics, business intelligence, business process, call centre, Cambridge Analytica, cloud computing, cognitive bias, Colonization of Mars, computer vision, Computing Machinery and Intelligence, correlation does not imply causation, CRISPR, crowdsourcing, DARPA: Urban Challenge, data science, deep learning, DeepMind, Demis Hassabis, deskilling, disruptive innovation, Donald Trump, Douglas Hofstadter, driverless car, Elon Musk, Erik Brynjolfsson, Ernest Rutherford, fake news, Fellow of the Royal Society, Flash crash, future of work, general purpose technology, Geoffrey Hinton, gig economy, Google X / Alphabet X, Gödel, Escher, Bach, Hans Moravec, Hans Rosling, hype cycle, ImageNet competition, income inequality, industrial research laboratory, industrial robot, information retrieval, job automation, John von Neumann, Large Hadron Collider, Law of Accelerating Returns, life extension, Loebner Prize, machine translation, Mark Zuckerberg, Mars Rover, means of production, Mitch Kapor, Mustafa Suleyman, natural language processing, new economy, Nick Bostrom, OpenAI, opioid epidemic / opioid crisis, optical character recognition, paperclip maximiser, pattern recognition, phenotype, Productivity paradox, radical life extension, Ray Kurzweil, recommendation engine, Robert Gordon, Rodney Brooks, Sam Altman, self-driving car, seminal paper, sensor fusion, sentiment analysis, Silicon Valley, smart cities, social intelligence, sparse data, speech recognition, statistical model, stealth mode startup, stem cell, Stephen Hawking, Steve Jobs, Steve Wozniak, Steven Pinker, strong AI, superintelligent machines, synthetic biology, systems thinking, Ted Kaczynski, TED Talk, The Rise and Fall of American Growth, theory of mind, Thomas Bayes, Travis Kalanick, Turing test, universal basic income, Wall-E, Watson beat the top human players on Jeopardy!, women in the workforce, working-age population, workplace surveillance , zero-sum game, Zipcar

That could also mean learning how to reason better by experience—such as discovering which reasoning steps turn out to be useful for solving a problem, and which reasoning steps turn out to be less useful. AlphaGo, for example, is a modern AI Go program that recently beat the best human world-champion players, and it really does learn. It learns how to reason better from experience. As well as learning to evaluate positions, AlphaGo learns how to control its own deliberations so that it more effectively reaches high decision-quality moves more quickly, with less computation. MARTIN FORD: Can you also define neural networks and deep learning?

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Perception and image recognition are both important aspects of operating successfully in the real world, but deep learning is only one part of the picture. AlphaGo, and its successor AlphaZero, created a lot of media attention around deep learning with stunning advances in Go and Chess, but they’re really a hybrid of classical search-based AI and a deep learning algorithm that evaluates each game position that the classical AI system searches through. While the ability to distinguish between good and bad positions is central to AlphaGo, it cannot play world-champion-level Go just by deep learning. Self-driving car systems also use a hybrid of classical search-based AI and deep learning.

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He was elected as a Fellow of the Royal Society, has been a recipient of the Society’s Mullard Award, and was also awarded an Honorary Doctorate by Imperial College London. Demis co-founded DeepMind along with Shane Legg and Mustafa Suleyman in 2010. DeepMind was acquired by Google in 2014 and is now part of Alphabet. In 2016 DeepMind’s AlphaGo system defeated Lee Sedol, arguably the world’s best player of the ancient game of Go. That match is chronicled in the documentary film AlphaGo (https://www.alphagomovie.com/). Chapter 9. ANDREW NG The rise of supervised learning has created a lot of opportunities in probably every major industry. Supervised learning is incredibly valuable and will transform multiple industries, but I think there is a lot of room for something even better to be invented.

On the Future: Prospects for Humanity by Martin J. Rees

23andMe, 3D printing, air freight, Alfred Russel Wallace, AlphaGo, Anthropocene, Asilomar, autonomous vehicles, Benoit Mandelbrot, biodiversity loss, blockchain, Boston Dynamics, carbon tax, circular economy, CRISPR, cryptocurrency, cuban missile crisis, dark matter, decarbonisation, DeepMind, Demis Hassabis, demographic transition, Dennis Tito, distributed ledger, double helix, driverless car, effective altruism, Elon Musk, en.wikipedia.org, Geoffrey Hinton, global village, Great Leap Forward, Higgs boson, Hyperloop, Intergovernmental Panel on Climate Change (IPCC), Internet of things, James Webb Space Telescope, Jeff Bezos, job automation, Johannes Kepler, John Conway, Large Hadron Collider, life extension, mandelbrot fractal, mass immigration, megacity, Neil Armstrong, Nick Bostrom, nuclear winter, ocean acidification, off-the-grid, pattern recognition, precautionary principle, quantitative hedge fund, Ray Kurzweil, Recombinant DNA, Rodney Brooks, Search for Extraterrestrial Intelligence, sharing economy, Silicon Valley, smart grid, speech recognition, Stanford marshmallow experiment, Stanislav Petrov, stem cell, Stephen Hawking, Steven Pinker, Stuxnet, supervolcano, technological singularity, the scientific method, Tunguska event, uranium enrichment, Walter Mischel, William MacAskill, Yogi Berra

This may not seem a ‘big deal’ because it’s been more than twenty years since IBM’s supercomputer Deep Blue beat Garry Kasparov, the world chess champion. But it was a ‘game change’ in the colloquial as well as literal sense. Deep Blue had been programmed by expert players. In contrast, the AlphaGo machine gained expertise by absorbing huge numbers of games and playing itself. Its designers don’t know how the machine makes its decisions. And in 2017 AlphaGo Zero went a step further; it was just given the rules—no actual games—and learned completely from scratch, becoming world-class within a day. This is astonishing. The scientific paper describing the feat concluded with the thought that humankind has accumulated Go knowledge from millions of games played over thousands of years, collectively distilled into patterns, proverbs and books.

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It excels in optimising elaborate networks, like the electricity grid or city traffic. When the energy management of its large data farms was handed over to a machine, Google claimed energy savings of 40 percent. But there are still limitations. The hardware underlying AlphaGo used hundreds of kilowatts of power. In contrast, the brain of Lee Sedol, AlphaGo’s Korean challenger, consumes about thirty watts (like a lightbulb) and can do many other things apart from play board games. Sensor technology, speech recognition, information searches, and so forth are advancing apace. So (albeit with a more substantial lag) is physical dexterity.

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But it’s becoming possible to calculate the properties of materials, and to do this so fast that millions of alternatives can be computed, far more quickly than actual experiments could be performed. Suppose that a machine came up with a unique and successful recipe. It might have succeeded in the same way as AlphaGo. But it would have achieved something that would earn a scientist a Nobel prize. It would have behaved as though it had insight and imagination within its rather specialised universe—just as AlphaGo flummoxed and impressed human champions with some of its moves. Likewise, searches for the optimal chemical composition for new drugs will increasingly be done by computers rather than by real experiments, just as for many years aeronautical engineers have simulated air flow over wings by computer calculations rather than depending on wind-tunnel experiments.

Coders: The Making of a New Tribe and the Remaking of the World by Clive Thompson

"Margaret Hamilton" Apollo, "Susan Fowler" uber, 2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, 4chan, 8-hour work day, Aaron Swartz, Ada Lovelace, AI winter, air gap, Airbnb, algorithmic bias, AlphaGo, Amazon Web Services, Andy Rubin, Asperger Syndrome, augmented reality, Ayatollah Khomeini, backpropagation, barriers to entry, basic income, behavioural economics, Bernie Sanders, Big Tech, bitcoin, Bletchley Park, blockchain, blue-collar work, Brewster Kahle, Brian Krebs, Broken windows theory, call centre, Cambridge Analytica, cellular automata, Charles Babbage, Chelsea Manning, Citizen Lab, clean water, cloud computing, cognitive dissonance, computer vision, Conway's Game of Life, crisis actor, crowdsourcing, cryptocurrency, Danny Hillis, data science, David Heinemeier Hansson, deep learning, DeepMind, Demis Hassabis, disinformation, don't be evil, don't repeat yourself, Donald Trump, driverless car, dumpster diving, Edward Snowden, Elon Musk, Erik Brynjolfsson, Ernest Rutherford, Ethereum, ethereum blockchain, fake news, false flag, Firefox, Frederick Winslow Taylor, Free Software Foundation, Gabriella Coleman, game design, Geoffrey Hinton, glass ceiling, Golden Gate Park, Google Hangouts, Google X / Alphabet X, Grace Hopper, growth hacking, Guido van Rossum, Hacker Ethic, hockey-stick growth, HyperCard, Ian Bogost, illegal immigration, ImageNet competition, information security, Internet Archive, Internet of things, Jane Jacobs, John Markoff, Jony Ive, Julian Assange, Ken Thompson, Kickstarter, Larry Wall, lone genius, Lyft, Marc Andreessen, Mark Shuttleworth, Mark Zuckerberg, Max Levchin, Menlo Park, meritocracy, microdosing, microservices, Minecraft, move 37, move fast and break things, Nate Silver, Network effects, neurotypical, Nicholas Carr, Nick Bostrom, no silver bullet, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, Oculus Rift, off-the-grid, OpenAI, operational security, opioid epidemic / opioid crisis, PageRank, PalmPilot, paperclip maximiser, pattern recognition, Paul Graham, paypal mafia, Peter Thiel, pink-collar, planetary scale, profit motive, ransomware, recommendation engine, Richard Stallman, ride hailing / ride sharing, Rubik’s Cube, Ruby on Rails, Sam Altman, Satoshi Nakamoto, Saturday Night Live, scientific management, self-driving car, side project, Silicon Valley, Silicon Valley ideology, Silicon Valley startup, single-payer health, Skype, smart contracts, Snapchat, social software, software is eating the world, sorting algorithm, South of Market, San Francisco, speech recognition, Steve Wozniak, Steven Levy, systems thinking, TaskRabbit, tech worker, techlash, TED Talk, the High Line, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, universal basic income, urban planning, Wall-E, Watson beat the top human players on Jeopardy!, WeWork, WikiLeaks, women in the workforce, Y Combinator, Zimmermann PGP, éminence grise

In the fall of 2015, we had another one of those Skynet-like moments when a form of artificial intelligence utterly destroys a human. In this case, it involved “AlphaGo”—software designed by DeepMind, a subsidiary of the Google empire—playing a wickedly great game of Go. To test their AI, DeepMind had arranged for it to play against Fan Hui, the European Go champion. It was no contest: The computer won 5 games out of 5. A few months later, AlphaGo fought Lee Sedol, an even more elite player—and again, AlphaGo dominated, 4 to 1. AlphaGo was so good at the game partly because it incorporated “deep learning,” a hot new neural-net technique that let the computer analyze millions of Go games and, on its own, build up a model of how the game worked; feed any board with Go positions into the model, and it could, in conjunction with a more traditional “Monte Carlo” algorithm, then predict a future move.

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There are considerably more possible Go games than there are atoms in the universe. So AlphaGo’s creators didn’t go that route. They didn’t sit around writing logic rules, like traditional programmers. Instead deep learning allowed AlphaGo to analyze 30 million positions from preexisting games and build up an extraordinarily sophisticated model of the game—so dense and convoluted the creators themselves could not tell you precisely how it works. But work it did. AlphaGo was a master at the game, albeit in a somewhat alien fashion. It sometimes pulled off moves no human had ever before executed. In the second game against Sedol, during move 37, AlphaGo made a play that at first flummoxed the Go experts who observed the game, as Wired reported.

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atoms in the universe: Alan Levinovitz, “The Mystery of Go, the Ancient Game That Computers Still Can’t Win,” Wired, May 12, 2014, accessed August 19, 2018, https://www.wired.com/2014/05/the-world-of-computer-go; David Silver and Demis Hassabis, “AlphaGo: Mastering the Ancient Game of Go with Machine Learning,” Google AI Blog, January 27, 2016, accessed August 19, 2018, https://ai.googleblog.com/2016/01/alphago-mastering-ancient-game-of-go.html. model of the game: Silver and Hassabis, “AlphaGo.” tales about AlphaGo: Cade Metz, “The Sadness and Beauty of Watching Google’s AI Play Go,” Wired, March 11, 2016, accessed August 19, 2018, https://www.wired.com/2016/03/sadness-beauty-watching-googles-ai-play-go.

Radical Technologies: The Design of Everyday Life by Adam Greenfield

3D printing, Airbnb, algorithmic bias, algorithmic management, AlphaGo, augmented reality, autonomous vehicles, bank run, barriers to entry, basic income, bitcoin, Black Lives Matter, blockchain, Boston Dynamics, business intelligence, business process, Californian Ideology, call centre, cellular automata, centralized clearinghouse, centre right, Chuck Templeton: OpenTable:, circular economy, cloud computing, Cody Wilson, collective bargaining, combinatorial explosion, Computer Numeric Control, computer vision, Conway's Game of Life, CRISPR, cryptocurrency, David Graeber, deep learning, DeepMind, dematerialisation, digital map, disruptive innovation, distributed ledger, driverless car, drone strike, Elon Musk, Ethereum, ethereum blockchain, facts on the ground, fiat currency, fulfillment center, gentrification, global supply chain, global village, Goodhart's law, Google Glasses, Herman Kahn, Ian Bogost, IBM and the Holocaust, industrial robot, informal economy, information retrieval, Internet of things, Jacob Silverman, James Watt: steam engine, Jane Jacobs, Jeff Bezos, Jeff Hawkins, job automation, jobs below the API, John Conway, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John Perry Barlow, John von Neumann, joint-stock company, Kevin Kelly, Kickstarter, Kiva Systems, late capitalism, Leo Hollis, license plate recognition, lifelogging, M-Pesa, Mark Zuckerberg, means of production, megacity, megastructure, minimum viable product, money: store of value / unit of account / medium of exchange, natural language processing, Network effects, New Urbanism, Nick Bostrom, Occupy movement, Oculus Rift, off-the-grid, PalmPilot, Pareto efficiency, pattern recognition, Pearl River Delta, performance metric, Peter Eisenman, Peter Thiel, planetary scale, Ponzi scheme, post scarcity, post-work, printed gun, proprietary trading, RAND corporation, recommendation engine, RFID, rolodex, Rutger Bregman, Satoshi Nakamoto, self-driving car, sentiment analysis, shareholder value, sharing economy, Shenzhen special economic zone , Sidewalk Labs, Silicon Valley, smart cities, smart contracts, social intelligence, sorting algorithm, special economic zone, speech recognition, stakhanovite, statistical model, stem cell, technoutopianism, Tesla Model S, the built environment, The Death and Life of Great American Cities, The Future of Employment, Tony Fadell, transaction costs, Uber for X, undersea cable, universal basic income, urban planning, urban sprawl, vertical integration, Vitalik Buterin, warehouse robotics, When a measure becomes a target, Whole Earth Review, WikiLeaks, women in the workforce

A book analyzing his games against Chinese “master of masters” Gu Li is simply titled Relentless.4 In Seoul Lee fell swiftly, losing to AlphaGo by four matches to one. Here is DeepMind lead developer David Silver, recounting the advantages AlphaGo has over Lee, or any other human player: “Humans have weaknesses. They get tired when they play a very long match; they can play mistakes. They are not able to make the precise, tree-based computation that a computer can actually perform. And perhaps even more importantly, humans have a limitation in terms of the actual number of go games that they’re able to process in a lifetime. A human can perhaps play a thousand games a year; AlphaGo can play through millions of games every single day.”5 Understand that here Silver is giving AlphaGo considerably short shrift.

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A human can perhaps play a thousand games a year; AlphaGo can play through millions of games every single day.”5 Understand that here Silver is giving AlphaGo considerably short shrift. A great deal of what he describes—that it doesn’t tire, that it can delve a deep tree, that it can review and learn from a very large number of prior games—is simply brute force. That may well have been how Deep Blue beat Kasparov. It is not how AlphaGo defeated Lee Sedol. For many, I suspect, Next Rembrandt will feel like a more ominous development than AlphaGo. The profound sense of recognition we experience in the presence of a Rembrandt is somehow more accessible than anything that might appear in the austere and highly abstract territorial maneuvering of go.

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But this was the quality that made it irresistible to artificial intelligence researchers, some of the brightest of whom took it up on a professional level simply so they could get a better sense for its dynamics. A few of the most dedicated wound up working together at a London-based subsidiary of Google called DeepMind, where they succeeded in developing a program named AlphaGo.3 AlphaGo isn’t just one thing, but a stack of multiple kinds of neural network and learning algorithm laminated together. Its two primary tools are a “policy network,” trained to predict and select the moves that the most expert human players would make from any given position on the board, and a “value network,” which plays each of the moves identified by the policy network forward to a depth of around thirty turns, and evaluates where Black and White stand in relation to one another at that juncture.

Artificial Whiteness by Yarden Katz

affirmative action, AI winter, algorithmic bias, AlphaGo, Amazon Mechanical Turk, autonomous vehicles, benefit corporation, Black Lives Matter, blue-collar work, Californian Ideology, Cambridge Analytica, cellular automata, Charles Babbage, cloud computing, colonial rule, computer vision, conceptual framework, Danny Hillis, data science, David Graeber, deep learning, DeepMind, desegregation, Donald Trump, Dr. Strangelove, driverless car, Edward Snowden, Elon Musk, Erik Brynjolfsson, European colonialism, fake news, Ferguson, Missouri, general purpose technology, gentrification, Hans Moravec, housing crisis, income inequality, information retrieval, invisible hand, Jeff Bezos, Kevin Kelly, knowledge worker, machine readable, Mark Zuckerberg, mass incarceration, Menlo Park, military-industrial complex, Nate Silver, natural language processing, Nick Bostrom, Norbert Wiener, pattern recognition, phenotype, Philip Mirowski, RAND corporation, recommendation engine, rent control, Rodney Brooks, Ronald Reagan, Salesforce, Seymour Hersh, Shoshana Zuboff, Silicon Valley, Silicon Valley billionaire, Silicon Valley ideology, Skype, speech recognition, statistical model, Stephen Hawking, Stewart Brand, Strategic Defense Initiative, surveillance capitalism, talking drums, telemarketer, The Signal and the Noise by Nate Silver, W. E. B. Du Bois, Whole Earth Catalog, WikiLeaks

John Brockman (New York: Penguin, 2019), 80.   26.   DeepMind has developed two major systems that play Go: AlphaGo and its successor AlphaGo Zero. I will simply refer to “AlphaGo” since my comments apply to both systems.   27.   DeepMind, “AlphaGo Zero: Discovering New Knowledge,” October 18, 2017, https://www.youtube.com/watch?v=WXHFqTvfFSw.   28.   Brenden M. Lake et al., “Building Machines That Learn and Think Like People,” Behavioral and Brain Sciences 40, no. E253 (2016): 1–101.   29.   Lake et al., 8.   30.   DeepMind, “AlphaGo Zero.”   31.   Microsoft COCO is described in Tsung-Yi Lin et al., “Microsoft COCO: Common Objects in Context,” in Proceedings of the European Conference on Computer Vision, 2014, 740–55.

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People, by contrast, can flexibly adopt different goals and styles of play: if asked to play with a different goal, such as losing as quickly as possible, or reaching the next level in the game but just barely, many people have little difficulty doing so. The AlphaGo system suffers from similar limitations. It is highly tuned to the configuration of the Go game on which it was trained. If the board size were to change, for example, there would be little reason to expect AlphaGo to work without retraining. AlphaGo also reveals that these deep learning systems are not as radically empiricist as advertised. The rules of Go are built into AlphaGo, a fact that is typically glossed over. This is hard-coded, symbolic knowledge, not the blank slate that was trumpeted.

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“Kissinger himself has become the demonstrative effect,” Grandin writes, “whatever substance there was eroded by the constant confusion of ends and means, the churn of power to create purpose and purpose defined as the ability to project power.”118 It is easier to see in this light why Kissinger is captivated by a system like Google’s AlphaGo, which learns to “dominate” humans at the game of Go by action—merely adapting to game wins and defeats—and making “strategically unprecedented moves,” as Kissinger put it, apparently without any preset notion of meaning. In AI, Kissinger found a reflection of his own imperialist project: an endeavor molded by power, circular and empty

Industry 4.0: The Industrial Internet of Things by Alasdair Gilchrist

3D printing, additive manufacturing, air gap, AlphaGo, Amazon Web Services, augmented reality, autonomous vehicles, barriers to entry, business intelligence, business logic, business process, chief data officer, cloud computing, connected car, cyber-physical system, data science, deep learning, DeepMind, deindustrialization, DevOps, digital twin, fault tolerance, fulfillment center, global value chain, Google Glasses, hiring and firing, industrial robot, inflight wifi, Infrastructure as a Service, Internet of things, inventory management, job automation, low cost airline, low skilled workers, microservices, millennium bug, OSI model, pattern recognition, peer-to-peer, platform as a service, pre–internet, race to the bottom, RFID, Salesforce, Skype, smart cities, smart grid, smart meter, smart transportation, software as a service, stealth mode startup, supply-chain management, The future is already here, trade route, undersea cable, vertical integration, warehouse robotics, web application, WebRTC, Y2K

In August 2015, IBM announced it had offered $1 billion to acquire medical imaging company, Merge Healthcare, which in conjunction with Watson will provide the means for machine learning. Astonishingly, Google’s AlphaGo beat the world champion Lee Sodol at the board game GO, which is a hugely complex game with a best of five win. What was strange is that both Lee Sodol and the European champion (who had been beat previously by AlphaGo) could not understand Google’s AlphaGo’s logic. Seemingly, AlphaGo played a move no human could understand; indeed all the top players in the world believed that AlphaGo had made a huge mistake. Even its challenger the world champion Lee Sodol thought it was a mistake; indeed he was so shocked by AlphaGo’s move he took a break to consider it, until it dawned on him the absolutely brilliance of the move.

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“It was not a human move … in fact I have never seen a human make this move”. Needless to say, Google’s AlphaGo went on to win the game. Why did AlphaGo beat the brilliant Lee Sodol? Is it simply because as a machine AlphaGo can play games 63 64 Chapter 3 |TheTechnical and Business Innovators of the Industrial Internet against itself, and replay all known human games, building up such a memory of possible moves as a process of 24/7 learning that it keep can continuously keep improving its strategic game. Google’s team analyzed the victory and realized that AlphaGo did something very strange—it calculated a move, based on its millions of known human play training movements that a human player would only have had a one in ten thousand chance of recognizing and countering that seemingly crazy move.

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Google’s team analyzed the victory and realized that AlphaGo did something very strange—it calculated a move, based on its millions of known human play training movements that a human player would only have had a one in ten thousand chance of recognizing and countering that seemingly crazy move. In fairness to the great Lee Sodol, he did manage to outwit AlphaGo to win one of the best of five games, and that appears to be an amazing achievement. References http://www.wired.com/2016/03/googles-ai-viewed-move-no-human-understand/ www.ibm.com/smarterplanet/us/en/ibmwatson http://www.wired.com/2016/03/googles-ai-viewed-move-no-human-understand/ http://3dprinting.com/what-is-3d-printing/ http://www.ptc.com/augmentedreality https://www.sdxcentral.com/articles/contributed/nfv-and-sdn-whatsthe-difference/2013/03/ CHAPTER 4 IIoT Reference Architecture The Industrial Internet is reliant on the structure of M2M technology.

The Globotics Upheaval: Globalisation, Robotics and the Future of Work by Richard Baldwin

agricultural Revolution, Airbnb, AlphaGo, AltaVista, Amazon Web Services, Apollo 11, augmented reality, autonomous vehicles, basic income, Big Tech, bread and circuses, business process, business process outsourcing, call centre, Capital in the Twenty-First Century by Thomas Piketty, Cass Sunstein, commoditize, computer vision, Corn Laws, correlation does not imply causation, Credit Default Swap, data science, David Ricardo: comparative advantage, declining real wages, deep learning, DeepMind, deindustrialization, deskilling, Donald Trump, Douglas Hofstadter, Downton Abbey, Elon Musk, Erik Brynjolfsson, facts on the ground, Fairchild Semiconductor, future of journalism, future of work, George Gilder, Google Glasses, Google Hangouts, Hans Moravec, hiring and firing, hype cycle, impulse control, income inequality, industrial robot, intangible asset, Internet of things, invisible hand, James Watt: steam engine, Jeff Bezos, job automation, Kevin Roose, knowledge worker, laissez-faire capitalism, Les Trente Glorieuses, low skilled workers, machine translation, Machine translation of "The spirit is willing, but the flesh is weak." to Russian and back, manufacturing employment, Mark Zuckerberg, mass immigration, mass incarceration, Metcalfe’s law, mirror neurons, new economy, optical character recognition, pattern recognition, Ponzi scheme, post-industrial society, post-work, profit motive, remote working, reshoring, ride hailing / ride sharing, Robert Gordon, Robert Metcalfe, robotic process automation, Ronald Reagan, Salesforce, San Francisco homelessness, Second Machine Age, self-driving car, side project, Silicon Valley, Skype, Snapchat, social intelligence, sovereign wealth fund, standardized shipping container, statistical model, Stephen Hawking, Steve Jobs, supply-chain management, systems thinking, TaskRabbit, telepresence, telepresence robot, telerobotics, Thomas Malthus, trade liberalization, universal basic income, warehouse automation

But that’s not the end of the amazing part. In a classic example of AI’s inhuman speed, the owner of AlphaGo Master developed a new version of AlphaGo that skipped the “learning from human games” part and just let it learn from playing itself from scratch. All it started with were the rules. Since computing power had increased so much since AlphaGo Master was “trained,” the results were astounding. In just 40 days of playing itself, the new version, AlphaGo Zero, beat the world’s best Go player, which, at the time was AlphaGo Master. The victory came just six months after AlphaGo Master’s astounding victory over the best human player. But machine learning is not just fun and games.

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That’s when a computer program, called AlphaGo Master, used machine learning techniques to beat the world’s best Go player.10 The how is as amazing as the what. AlphaGo Master, owned by the leading AI company DeepMind, learned the ropes by studying 30 million board positions from 160,000 actual games. This is a bit intimidating. There are only about 26 million minutes in a human working life, so AlphaGo Master started with more than a lifetime of experience. But then things got even more daunting for human players hoping to compete with this technology. To learn from experience, AlphaGo Master played more games against itself in six months than a human could play in six decades.

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Likewise, software robots aren’t very good when the nature of the problem and the nature of the solution are just intrinsically vague. That’s the case when identifying new patterns: the whole idea is that the pattern is new, so there cannot be a big dataset by definition. For example, a human Go master could presumable do fairly well on a slightly different-sized board, but AI couldn’t. At a 2017 conference, the AlphaGo Master team admitted that the AI-software would be useless if the game was played on an even slightly altered board—say one that was twenty-nine-by-twenty-nine squares instead of the standard nineteen by nineteen.6 Table 6.3 CAPABILITIES OF AI IN SOCIAL SKILLS Social Skill Description AI Skill vs.

Money: Vintage Minis by Yuval Noah Harari

23andMe, agricultural Revolution, algorithmic trading, AlphaGo, Anne Wojcicki, autonomous vehicles, British Empire, call centre, credit crunch, DeepMind, European colonialism, Flash crash, Ford Model T, greed is good, job automation, joint-stock company, joint-stock limited liability company, lifelogging, low interest rates, Nick Bostrom, pattern recognition, peak-end rule, Ponzi scheme, self-driving car, Suez canal 1869, telemarketer, The future is already here, The Future of Employment, The Wealth of Nations by Adam Smith, trade route, transatlantic slave trade, Watson beat the top human players on Jeopardy!, zero-sum game

Shortly afterwards AI scored an even more sensational success, when Google’s AlphaGo software taught itself how to play Go, an ancient Chinese strategy board game significantly more complex than chess. Go’s intricacies were long considered far beyond the reach of AI programs. In March 2016 a match was held in Seoul between AlphaGo and the South Korean Go champion, Lee Sedol. AlphaGo trounced Lee 4–1 by employing unorthodox moves and original strategies that stunned the experts. Whereas prior to the match most professional Go players were certain that Lee would win, after analysing AlphaGo’s moves most concluded that the game was up and that humans no longer had any hope of beating AlphaGo and its progeny.

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Whereas prior to the match most professional Go players were certain that Lee would win, after analysing AlphaGo’s moves most concluded that the game was up and that humans no longer had any hope of beating AlphaGo and its progeny. Computer algorithms have recently proven their worth in ball games, too. For many decades, baseball teams used the wisdom, experience and gut instincts of professional scouts and managers to pick players. The best players fetched millions of dollars, and naturally enough the rich teams grabbed the cream of the crop, whereas poorer teams had to settle for the scraps. In 2002 Billy Beane, the manager of the low-budget Oakland Athletics, decided to beat the system. He relied on an arcane computer algorithm developed by economists and computer geeks to create a winning team from players whom human scouts had overlooked or undervalued.

New Dark Age: Technology and the End of the Future by James Bridle

AI winter, Airbnb, Alfred Russel Wallace, AlphaGo, Anthropocene, Automated Insights, autonomous vehicles, back-to-the-land, Benoit Mandelbrot, Bernie Sanders, bitcoin, Boeing 747, British Empire, Brownian motion, Buckminster Fuller, Cambridge Analytica, Capital in the Twenty-First Century by Thomas Piketty, carbon footprint, coastline paradox / Richardson effect, cognitive bias, cognitive dissonance, combinatorial explosion, computer vision, congestion charging, cryptocurrency, data is the new oil, disinformation, Donald Trump, Douglas Engelbart, Douglas Engelbart, Douglas Hofstadter, Dr. Strangelove, drone strike, Edward Snowden, Eyjafjallajökull, Fairchild Semiconductor, fake news, fear of failure, Flash crash, fulfillment center, Google Earth, Greyball, Haber-Bosch Process, Higgs boson, hive mind, income inequality, informal economy, Internet of things, Isaac Newton, ITER tokamak, James Bridle, John von Neumann, Julian Assange, Kickstarter, Kim Stanley Robinson, Large Hadron Collider, late capitalism, Laura Poitras, Leo Hollis, lone genius, machine translation, mandelbrot fractal, meta-analysis, Minecraft, mutually assured destruction, natural language processing, Network effects, oil shock, p-value, pattern recognition, peak oil, recommendation engine, road to serfdom, Robert Mercer, Ronald Reagan, security theater, self-driving car, Seymour Hersh, Silicon Valley, Silicon Valley ideology, Skype, social graph, sorting algorithm, South China Sea, speech recognition, Spread Networks laid a new fibre optics cable between New York and Chicago, stem cell, Stuxnet, technoutopianism, the built environment, the scientific method, Uber for X, undersea cable, University of East Anglia, uranium enrichment, Vannevar Bush, warehouse robotics, WikiLeaks

And he added, ‘So beautiful.’26 In the history of the 2,500-year-old game, nobody had ever played like this. AlphaGo went on to win the game, and the series. AlphaGo’s engineers developed its software by feeding a neural network millions of moves by expert Go players, and then getting it to play itself millions of times more, developing strategies that outstripped those of human players. But its own representation of those strategies is illegible: we can see the moves it made, but not how it decided to make them. The sophistication of the moves that must have been played in those games between the shards of AlphaGo is beyond imagination, too, but we are unlikely to ever see and appreciate them; there’s no way to quantify sophistication, only winning instinct.

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At the time of the match, it was the 259th most powerful computer on the planet, and it was dedicated purely to chess. It could simply hold more outcomes in mind when choosing where to play next. Kasparov was not outthought, merely outgunned. By contrast, when the Google Brain–powered AlphaGo software defeated the Korean Go professional Lee Sedol, one of the highest-rated players in the world, something had changed. In the second of five games, AlphaGo played a move that stunned Sedol and spectators alike, placing one of its stones on the far side of the board, and seeming to abandon the battle in progress. ‘That’s a very strange move,’ said one commentator. ‘I thought it was a mistake,’ said the other.

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But this is as close as we shall ever get, for once again, we are peering through the window of Infinite Fun Land – an arcade we will never get to visit. Compounding this error, in 2016 a pair of researchers at Google Brain decided to see if neural networks could keep secrets.34 The idea stemmed from that of the adversary: an increasingly common component of neural network designs, and one that would no doubt have pleased Friedrich Hayek. Both AlphaGo and Facebook’s bedroom generator were trained adversarially; that is, they consisted not of a single component that generated new moves or places, but of two competing components that continually attempted to outperform and outguess the other, driving further improvement. Taking the idea of an adversary to its logical conclusion, the researchers set up three networks called, in the tradition of cryptographic experiments, Alice, Bob, and Eve.

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

Ada Lovelace, additive manufacturing, Airbnb, Albert Einstein, AlphaGo, Amazon Mechanical Turk, Amazon Robotics, augmented reality, autonomous vehicles, barriers to entry, Big Tech, biodiversity loss, bitcoin, blockchain, blood diamond, Boston Dynamics, Burning Man, call centre, cashless society, Charles Babbage, Charles Lindbergh, Clayton Christensen, clean water, cloud computing, Colonization of Mars, computer vision, creative destruction, CRISPR, crowdsourcing, cryptocurrency, data science, Dean Kamen, deep learning, deepfake, DeepMind, delayed gratification, dematerialisation, digital twin, disruptive innovation, Donald Shoup, driverless car, Easter island, Edward Glaeser, Edward Lloyd's coffeehouse, Elon Musk, en.wikipedia.org, epigenetics, Erik Brynjolfsson, Ethereum, ethereum blockchain, experimental economics, fake news, food miles, Ford Model T, fulfillment center, game design, Geoffrey West, Santa Fe Institute, gig economy, gigafactory, Google X / Alphabet X, gravity well, hive mind, housing crisis, Hyperloop, impact investing, indoor plumbing, industrial robot, informal economy, initial coin offering, intentional community, Intergovernmental Panel on Climate Change (IPCC), Internet of things, invention of the telegraph, Isaac Newton, Jaron Lanier, Jeff Bezos, job automation, Joseph Schumpeter, Kevin Kelly, Kickstarter, Kiva Systems, late fees, Law of Accelerating Returns, life extension, lifelogging, loss aversion, Lyft, M-Pesa, Mary Lou Jepsen, Masayoshi Son, mass immigration, megacity, meta-analysis, microbiome, microdosing, mobile money, multiplanetary species, Narrative Science, natural language processing, Neal Stephenson, Neil Armstrong, Network effects, new economy, New Urbanism, Nick Bostrom, Oculus Rift, One Laptop per Child (OLPC), out of africa, packet switching, peer-to-peer lending, Peter H. Diamandis: Planetary Resources, Peter Thiel, planned obsolescence, QR code, RAND corporation, Ray Kurzweil, RFID, Richard Feynman, Richard Florida, ride hailing / ride sharing, risk tolerance, robo advisor, Satoshi Nakamoto, Second Machine Age, self-driving car, Sidewalk Labs, Silicon Valley, Skype, smart cities, smart contracts, smart grid, Snapchat, SoftBank, sovereign wealth fund, special economic zone, stealth mode startup, stem cell, Stephen Hawking, Steve Jobs, Steve Jurvetson, Steven Pinker, Stewart Brand, supercomputer in your pocket, supply-chain management, tech billionaire, technoutopianism, TED Talk, Tesla Model S, Tim Cook: Apple, transaction costs, Uber and Lyft, uber lyft, unbanked and underbanked, underbanked, urban planning, Vision Fund, VTOL, warehouse robotics, Watson beat the top human players on Jeopardy!, We wanted flying cars, instead we got 140 characters, X Prize

Typically, the game tree complexity of chess is about 1040—which means, essentially, if every one of the 7 plus billion people on Earth paired up and started playing chess, it would take them trillions and trillions of years to play every single variation of the game. Yet, in 2017, Google’s AlphaGo defeated the world Go champion, Lee Sedol. Go has a game tree complexity of 10360—it’s chess for superheroes. Put differently, we humans are the only species known to have the cognitive capacity to play Go. It only took a couple hundred thousand years of evolution to develop that capability. AI, meanwhile, got there in less than two decades. Still, AI wasn’t done. A few months after that victory, Google upgraded AlphaGo to AlphaGo Zero by updating their training style. AlphaGo was educated via machine learning, essentially fed thousands of games previously played by humans, and taught the proper move and countermove for every possible position.

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AlphaGo was educated via machine learning, essentially fed thousands of games previously played by humans, and taught the proper move and countermove for every possible position. AlphaGo Zero, meanwhile, required zero data. Instead, it relies on “reinforcement learning”—it learns by playing itself. Starting with little more than a few simple rules, AlphaGo Zero took three days to beat its parent, AlphaGo, the same system that beat Lee Sedol. Three weeks later, it trounced the sixty best players in the world. In total, it took forty days for AlphaGo Zero to become the undisputed best Go player on Earth. And if that wasn’t strange enough, in May of 2017, Google used the same kind of reinforcement learning to have an AI build another AI.

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Abe, Shinzo, 47 Ablow, Keith, 247 Abu Dhabi, 217 abundance, exponential technologies and, 261–63 Abundance (Diamandis and Kotler), xi, 7, 78, 82, 99, 145, 163, 204, 213, 261–62 Abundance Digital, 264, 265 Abundance360, xii, 264, 265 Advano, Aman, 108–9 advertising: AI assistants and, 123–24 big data and, 118 Spatial Web and, 118–20 technological change and, 117–24 aerial ridesharing, 4 AeroFarms, 205 aeroponics, 204, 205 Affectiva, 137 affective computing, 136–38 Affective Computing Group, 137 aging, 170–72 as programmed process, 88–89, 169–70 see also longevity agriculture, reinvention of, 225–26 AI assistants, 35, 37, 132, 135–36, 138, 198 advertising and, 123–24 shopping and, 100–102, 113, 123–24 AI personas, 132 Airbnb, 84, 234 Akonia Holographics, 52 Aleph Farms, 208 Alexa, 100 algorithms, 87, 88 Alibaba, 99, 100, 107, 114 Alipay, 192 Alkahest, 178 Allen, Mark, 178 Allen, Paul, 176 All Nippon Airways (ANA), 26 Alphabet, 46, 89, 162, 235 Project Loon of, 39–40 Verily Life Sciences of, 157 see also Google AlphaFold, 167 AlphaGo, 36 AlphaGo Zero, 36, 37 Alzheimer’s disease, 82, 178 Amarasiriwardena, Gihan, 108–9 Amazon, 4, 21, 47, 100, 107, 108, 114, 119, 127 disruptive business model of, 98–99 Echo of, 35, 101, 132 Project Kuiper and, 40 Amazon Go, 105, 196, 229 ANA Avatar XPRIZE, 26 anandamide, 247 Andreesen, Marc, 32 Andrews, T.

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

"Friedman doctrine" OR "shareholder theory", 4chan, Affordable Care Act / Obamacare, Airbnb, AlphaGo, Alvin Roth, Amazon Mechanical Turk, Amazon Robotics, Amazon Web Services, AOL-Time Warner, artificial general intelligence, augmented reality, autonomous vehicles, barriers to entry, basic income, behavioural economics, benefit corporation, Bernie Madoff, Bernie Sanders, Bill Joy: nanobots, bitcoin, Blitzscaling, blockchain, book value, Bretton Woods, Brewster Kahle, British Empire, business process, call centre, Capital in the Twenty-First Century by Thomas Piketty, Captain Sullenberger Hudson, carbon tax, Carl Icahn, Chuck Templeton: OpenTable:, Clayton Christensen, clean water, cloud computing, cognitive dissonance, collateralized debt obligation, commoditize, computer vision, congestion pricing, corporate governance, corporate raider, creative destruction, CRISPR, crowdsourcing, Danny Hillis, data acquisition, data science, deep learning, DeepMind, Demis Hassabis, Dennis Ritchie, deskilling, DevOps, Didi Chuxing, digital capitalism, disinformation, do well by doing good, Donald Davies, Donald Trump, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, fake news, Filter Bubble, Firefox, Flash crash, Free Software Foundation, fulfillment center, full employment, future of work, George Akerlof, gig economy, glass ceiling, Glass-Steagall Act, Goodhart's law, Google Glasses, Gordon Gekko, gravity well, greed is good, Greyball, Guido van Rossum, High speed trading, hiring and firing, Home mortgage interest deduction, Hyperloop, income inequality, independent contractor, index fund, informal economy, information asymmetry, Internet Archive, Internet of things, invention of movable type, invisible hand, iterative process, Jaron Lanier, Jeff Bezos, jitney, job automation, job satisfaction, John Bogle, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John Zimmer (Lyft cofounder), Kaizen: continuous improvement, Ken Thompson, Kevin Kelly, Khan Academy, Kickstarter, Kim Stanley Robinson, knowledge worker, Kodak vs Instagram, Lao Tzu, Larry Ellison, Larry Wall, Lean Startup, Leonard Kleinrock, Lyft, machine readable, machine translation, Marc Andreessen, Mark Zuckerberg, market fundamentalism, Marshall McLuhan, McMansion, microbiome, microservices, minimum viable product, mortgage tax deduction, move fast and break things, Network effects, new economy, Nicholas Carr, Nick Bostrom, obamacare, Oculus Rift, OpenAI, OSI model, Overton Window, packet switching, PageRank, pattern recognition, Paul Buchheit, peer-to-peer, peer-to-peer model, Ponzi scheme, post-truth, race to the bottom, Ralph Nader, randomized controlled trial, RFC: Request For Comment, Richard Feynman, Richard Stallman, ride hailing / ride sharing, Robert Gordon, Robert Metcalfe, Ronald Coase, Rutger Bregman, Salesforce, Sam Altman, school choice, Second Machine Age, secular stagnation, self-driving car, SETI@home, shareholder value, Silicon Valley, Silicon Valley startup, skunkworks, Skype, smart contracts, Snapchat, Social Responsibility of Business Is to Increase Its Profits, social web, software as a service, software patent, spectrum auction, speech recognition, Stephen Hawking, Steve Ballmer, Steve Jobs, Steven Levy, Stewart Brand, stock buybacks, strong AI, synthetic biology, TaskRabbit, telepresence, the built environment, the Cathedral and the Bazaar, The future is already here, The Future of Employment, the map is not the territory, The Nature of the Firm, The Rise and Fall of American Growth, The Wealth of Nations by Adam Smith, Thomas Davenport, Tony Fadell, Tragedy of the Commons, transaction costs, transcontinental railway, transportation-network company, Travis Kalanick, trickle-down economics, two-pizza team, Uber and Lyft, Uber for X, uber lyft, ubercab, universal basic income, US Airways Flight 1549, VA Linux, warehouse automation, warehouse robotics, Watson beat the top human players on Jeopardy!, We are the 99%, web application, Whole Earth Catalog, winner-take-all economy, women in the workforce, Y Combinator, yellow journalism, zero-sum game, Zipcar

Retrieved March 28, 2016, https://web-beta.archive.org/web/20160328210752/https://deepmind.com/. 167 “the hallmark of true artificial general intelligence”: Demis Hassabis, “What We Learned in Seoul with AlphaGo,” Google Blog, March 16, 2016, https://blog.google/topics/machine-learning /what-we-learned-in-seoul-with-alphago/. 167 “getting to true AI”: Ben Rossi, “Google DeepMind’s AlphaGo Victory Not ‘True AI,’ Says Facebook’s AI Chief,” Information Age, March 14, 2016, http://www.information-age.com/google-deepminds-alphago-victory-not-true-ai-says-face books-ai-chief-123461099/. 169 “thinking about how to make people click ads”: Ashlee Vance, “This Tech Bubble Is Different,” Bloomberg Businessweek, April 14, 2011, https://www.bloomberg.com/news/articles/2011-04-14/this-tech-bubble-is-different.

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Everything is amazing, everything is horrible, and it’s all moving too fast. We are heading pell-mell toward a world shaped by technology in ways that we don’t understand and have many reasons to fear. WTF? Google AlphaGo, an artificial intelligence program, beat the world’s best human Go player, an event that was widely predicted to be at least twenty years in the future—until it happened in 2016. If AlphaGo can happen twenty years early, what else might hit us even sooner than we expect? For starters: An AI running on a $35 Raspberry Pi computer beat a top US Air Force fighter pilot trainer in combat simulation.

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Google purchased DeepMind in 2014 for $500 million, after it demonstrated an AI that had learned to play various older Atari computer games simply by watching them being played. The highly publicized victory of AlphaGo over Lee Sedol, one of the top-ranked human Go players, represented a milestone for AI, because of the difficulty of the game and the impossibility of using brute-force analysis of every possible move. But DeepMind cofounder Demis Hassabis wrote, “We’re still a long way from a machine that can learn to flexibly perform the full range of intellectual tasks a human can—the hallmark of true artificial general intelligence.” Yann LeCun also blasted those who oversold the significance of AlphaGo’s victory, writing, “most of human and animal learning is unsupervised learning.

The Autonomous Revolution: Reclaiming the Future We’ve Sold to Machines by William Davidow, Michael Malone

2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, agricultural Revolution, Airbnb, AlphaGo, American Society of Civil Engineers: Report Card, Automated Insights, autonomous vehicles, basic income, benefit corporation, bitcoin, blockchain, blue-collar work, Bob Noyce, business process, call centre, Cambridge Analytica, cashless society, citizen journalism, Clayton Christensen, collaborative consumption, collaborative economy, collective bargaining, creative destruction, crowdsourcing, cryptocurrency, deep learning, DeepMind, disintermediation, disruptive innovation, distributed ledger, en.wikipedia.org, Erik Brynjolfsson, fake news, Filter Bubble, Ford Model T, Francis Fukuyama: the end of history, general purpose technology, Geoffrey West, Santa Fe Institute, gig economy, Gini coefficient, high-speed rail, holacracy, Hyperloop, income inequality, industrial robot, Internet of things, invention of agriculture, invention of movable type, invention of the printing press, invisible hand, Jane Jacobs, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Joseph Schumpeter, license plate recognition, low interest rates, Lyft, Mark Zuckerberg, mass immigration, Network effects, new economy, peer-to-peer lending, QWERTY keyboard, ransomware, Richard Florida, Robert Gordon, robo advisor, Ronald Reagan, Second Machine Age, self-driving car, sharing economy, Shoshana Zuboff, Silicon Valley, Simon Kuznets, Skinner box, Snapchat, speech recognition, streetcar suburb, Stuxnet, surveillance capitalism, synthetic biology, TaskRabbit, The Death and Life of Great American Cities, The Rise and Fall of American Growth, the scientific method, trade route, Turing test, two and twenty, Uber and Lyft, uber lyft, universal basic income, uranium enrichment, urban planning, vertical integration, warehouse automation, zero day, zero-sum game, Zipcar

In 1997, Deep Blue, a chess-playing computer developed by IBM, beat the Russian grandmaster Garry Kasparov in a six-game match.10 Kasparov said that he had sensed a thinking presence inside his computer opponent. Then, in 2016, Google DeepMind’s artificial-intelligence program, AlphaGo, defeated Lee Sedol, a Go champion, 4–1. Go is a more difficult game for a computer to play than chess, and AlphaGo’s victory is perhaps the best harbinger of what is to come. While Deep Blue relied on hard-coded functions written by human experts for its decision-making processes, AlphaGo used neural networks and reinforcement learning. In other words, its system studied numerous games and played games against itself so it could write its own rules.11 The lesson here is that it is now possible to use inexpensive computer power to develop intelligent processes.

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Tanya Lewis, “A Brief History of Artificial Intelligence,” Live Science, December 4, 2014, http://www.livescience.com/49007-history-of-artificial-intelligence.html (accessed June 26, 2019). 10. “Deep Blue,” Wikipedia, https://en.wikipedia.org/wiki/Deep_Blue_(chess_computer) (accessed June 26, 2019). 11. “AlphaGo vs Deep Blue,” Reddit, https://www.reddit.com/r/MachineLearning/comments/4a7lc4/alphago_vs_deep_blue/ (accessed June 26, 2019). 12. “Why AI Researchers Like Video Games,” The Economist, May 13, 2017, https://www.economist.com/news/science-and-technology/21721890-games-help-them-understand-reality-why-ai-researchers-video-games (accessed June 26, 2019). 13.

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Index Aboujaoude, Elias, 144, 147 abundance, 7, 14, 67, 94, 188, 194–195 accidents, 156 Adams, Henry, x, 37 addiction: gambling, 137, 138–139, 140, 143, 150 gaming, 89, 132, 138–144 income inequality and, 166 neuroscience behind, 136–138, 140 protections against, in virtual space, 148–151 smartphone, 133, 138, 145 social media/networking, 144–145, 148–151 virtual space/Internet, 132–133, 136–146, 148–151 advertising industry, 62–64, 89, 90, 120–123, 134–135 Agricultural Revolution: Autonomous Revolution contrasted with, 25–26 cities’ origin in, 24, 25–26, 35, 151–152, 183–184 constitutional rights crafted during, 159 cultural norms’ creation in, 151–152 governance rules and systems shift in, 25 population growth during, 35, 36 Second, 24–25 social phase change of, 6, 11, 13, 14–15, 17, 21, 23–25, 26, 36–37, 134, 183–184 structural transformations early in, 23–24, 134 substitutional equivalence in, 14–15 timeline/rates of change, 13, 17, 21, 25, 36–37, 193 Airbnb model, 44, 70, 86 airline industry, 72, 97–98, 103–104 algorithms and algorithmic prisons, 13, 114, 123–128 Alibaba, 70, 76 AlphaGo, 46–47 Amazon, 64–65, 87–88, 90–91, 119, 150 “Amazon Effect,” 105 Anglo-Saxon culture, 162–163, 166 antitrust violations, 93, 160 anxiety, 148, 150 Apple, 10, 88–90 Arthur, W. Brian, 29, 97–98, 103, 194 artificial intelligence: in behavior prediction and modification, 117 history and evolution of, 45–47 job loss with, 43, 110 in law enforcement, 115 nonmonetizable productivity of, 60 substitutional equivalences with, 15–16, 45 threats from, xi, xii, 4, 16, 43, 48–49, 110, 117 Assante, Michael, 174 authoritarianism, 115, 158–159, 161–162, 180, 193 automatons/automation: in airline industry, 72, 97–98, 103–104 benefits possible from, xii, 4–5, 7, 14, 19 economy impacted by, 12–13 in financial industry, 10, 43, 77–78, 81–83, 102 government service and, 105 health care impacts with, 14, 48 human knowledge pursuit impacted by, xi, 16 job displacement predictions with, 98, 105–106 job market impacts from, 7, 11, 12, 31, 34, 43, 47–49, 73, 77–78, 95–106, 108, 187–189 vehicle, 84, 99–102 ZEVs created with, 12, 48–49 automobile industry: autonomous vehicle evolution and impact on, 84, 99–102 car-sharing impact on, 70, 84–86, 100, 101–102 Ford Motor Company production in, xii, 22, 33, 53, 103 horse-related industry impacted by, 54 Industrial Revolution role of, 53–54, 152 industrial robots and early use in, xii infrastructures as result of, 107–108 innovations leading up to, 52–53 social phase change with, 32, 53–54 Autonomous Economy, 60–61, 96–98 Autonomous Revolution: abundance available with, 7, 14, 67, 94, 188, 194–195 action-oriented approach to, 7, 14, 17, 19, 20, 94, 107, 180 Agricultural Revolution contrasted with, 25–26 cultural norms in adaptation to, 151, 153–157, 159 defining and key factors of, 6, 11, 34, 58, 95 early impacts of, 7, 11, 12 optimism and, 193–195 rate of change in, 13, 17–18, 34, 37, 192–193 recommendations for offsetting negative impacts of, 107–112 substitutional equivalence forms and examples of, 15–17, 42–50.

Future Politics: Living Together in a World Transformed by Tech by Jamie Susskind

3D printing, additive manufacturing, affirmative action, agricultural Revolution, Airbnb, airport security, algorithmic bias, AlphaGo, Amazon Robotics, Andrew Keen, Apollo Guidance Computer, artificial general intelligence, augmented reality, automated trading system, autonomous vehicles, basic income, Bertrand Russell: In Praise of Idleness, Big Tech, bitcoin, Bletchley Park, blockchain, Boeing 747, brain emulation, Brexit referendum, British Empire, business process, Cambridge Analytica, Capital in the Twenty-First Century by Thomas Piketty, cashless society, Cass Sunstein, cellular automata, Citizen Lab, cloud computing, commons-based peer production, computer age, computer vision, continuation of politics by other means, correlation does not imply causation, CRISPR, crowdsourcing, cryptocurrency, data science, deep learning, DeepMind, digital divide, digital map, disinformation, distributed ledger, Donald Trump, driverless car, easy for humans, difficult for computers, Edward Snowden, Elon Musk, en.wikipedia.org, end-to-end encryption, Erik Brynjolfsson, Ethereum, ethereum blockchain, Evgeny Morozov, fake news, Filter Bubble, future of work, Future Shock, Gabriella Coleman, Google bus, Google X / Alphabet X, Googley, industrial robot, informal economy, intangible asset, Internet of things, invention of the printing press, invention of writing, Isaac Newton, Jaron Lanier, John Markoff, Joseph Schumpeter, Kevin Kelly, knowledge economy, Large Hadron Collider, Lewis Mumford, lifelogging, machine translation, Metcalfe’s law, mittelstand, more computing power than Apollo, move fast and break things, natural language processing, Neil Armstrong, Network effects, new economy, Nick Bostrom, night-watchman state, Oculus Rift, Panopticon Jeremy Bentham, pattern recognition, payday loans, Philippa Foot, post-truth, power law, price discrimination, price mechanism, RAND corporation, ransomware, Ray Kurzweil, Richard Stallman, ride hailing / ride sharing, road to serfdom, Robert Mercer, Satoshi Nakamoto, Second Machine Age, selection bias, self-driving car, sexual politics, sharing economy, Silicon Valley, Silicon Valley startup, Skype, smart cities, Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia, smart contracts, Snapchat, speech recognition, Steve Bannon, Steve Jobs, Steve Wozniak, Steven Levy, tech bro, technological determinism, technological singularity, technological solutionism, the built environment, the Cathedral and the Bazaar, The Structural Transformation of the Public Sphere, The Wisdom of Crowds, Thomas L Friedman, Tragedy of the Commons, trolley problem, universal basic income, urban planning, Watson beat the top human players on Jeopardy!, work culture , working-age population, Yochai Benkler

In short, they now beat the finest human players in almost every single one, including backgammon (1979), checkers (1994), and chess, in which IBM’s Deep Blue famously defeated world champion Garry Kasparov (1997). In 2016, to general astonishment, Google DeepMind’s AI system AlphaGo defeated Korean Grandmaster Lee Sedol 4–1 at the ancient game of Go, deploying dazzling and innovative tactics in a game exponentially more complex than chess. ‘I . . . was able to get one single win,’ said Lee Sedol rather poignantly; ‘I wouldn’t exchange it for anything in the world.’16 OUP CORRECTED PROOF – FINAL, 26/05/18, SPi РЕЛИЗ ПОДГОТОВИЛА ГРУППА "What's News" VK.COM/WSNWS 32 FUTURE POLITICS A year later, a version of AlphaGo called AlphaGo Master thrashed Ke Jie, the world’s finest human player, in a 3–0 clean sweep.17 A radically more powerful version now exists, called AlphaGo Zero.

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‘I . . . was able to get one single win,’ said Lee Sedol rather poignantly; ‘I wouldn’t exchange it for anything in the world.’16 OUP CORRECTED PROOF – FINAL, 26/05/18, SPi РЕЛИЗ ПОДГОТОВИЛА ГРУППА "What's News" VK.COM/WSNWS 32 FUTURE POLITICS A year later, a version of AlphaGo called AlphaGo Master thrashed Ke Jie, the world’s finest human player, in a 3–0 clean sweep.17 A radically more powerful version now exists, called AlphaGo Zero. AlphaGo Zero beat AlphaGo Master 100 times in a row.18 As long ago as 2011, IBM’s Watson vanquished the two all-time greatest human champions at Jeopardy!—a TV game show in which the moderator presents general knowledge ‘answers’ relating to sports, science, pop culture, history, art, literature, and other fields and the contestants are required to provide the ‘questions’. Jeopardy! demands deep and wide-ranging knowledge, the ability to process natural language (including wordplay), retrieve relevant information, and answer using an acceptable form of speech—all before the other contestants do the same.19 The human champions were no match for Watson, whose victory marked a milestone in the development of artificial intelligence.

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Cade Metz, ‘Google’s AI Wins Fifth and Final Game Against Go’, Wired, 15 March 2016 <https://www.wired.com/2016/03/googlesai-wins-fifth-final-game-go-genius-lee-sedol/> (accessed 28 November 2017); Nick Bostrom, Superintelligence: Paths, Dangers, Strategies (Oxford: Oxford University Press, 2014), 12–13. Sam Byford, ‘AlphaGo beats Ke Jie Again to Wrap Up Three-part March’, The Verge, 25 May 2017 <https://www.theverge.com/2017/ 5/25/15689462/alphago-ke-jie-game-2-result-google-deepmindchina> (accessed 28 November 2017). David Silver et al., ‘Mastering the Game of Go Without Human Knowledge’, Nature 550 (19 October 2017): 354–9. OUP CORRECTED PROOF – FINAL, 30/05/18, SPi РЕЛИЗ ПОДГОТОВИЛА ГРУППА "What's News" VK.COM/WSNWS Notes 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. 36. 373 Susskind and Susskind, Future of the Professions, 165.

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

3D printing, AlphaGo, Alvin Toffler, autonomous vehicles, basic income, Bernie Sanders, Branko Milanovic, British Empire, business cycle, business process, call centre, Cambridge Analytica, Capital in the Twenty-First Century by Thomas Piketty, Charles Babbage, Clayton Christensen, collective bargaining, computer age, computer vision, Corn Laws, Cornelius Vanderbilt, creative destruction, data science, David Graeber, David Ricardo: comparative advantage, deep learning, DeepMind, deindustrialization, demographic transition, desegregation, deskilling, Donald Trump, driverless car, easy for humans, difficult for computers, Edward Glaeser, Elon Musk, Erik Brynjolfsson, everywhere but in the productivity statistics, factory automation, Fairchild Semiconductor, falling living standards, first square of the chessboard / second half of the chessboard, Ford Model T, Ford paid five dollars a day, Frank Levy and Richard Murnane: The New Division of Labor, full employment, future of work, game design, general purpose technology, Gini coefficient, Great Leap Forward, Hans Moravec, high-speed rail, Hyperloop, income inequality, income per capita, independent contractor, industrial cluster, industrial robot, intangible asset, interchangeable parts, Internet of things, invention of agriculture, invention of movable type, invention of the steam engine, invention of the wheel, Isaac Newton, James Hargreaves, James Watt: steam engine, Jeremy Corbyn, job automation, job satisfaction, job-hopping, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Joseph Schumpeter, Kickstarter, Kiva Systems, knowledge economy, knowledge worker, labor-force participation, labour mobility, Lewis Mumford, Loebner Prize, low skilled workers, machine translation, Malcom McLean invented shipping containers, manufacturing employment, mass immigration, means of production, Menlo Park, minimum wage unemployment, natural language processing, new economy, New Urbanism, Nick Bostrom, Norbert Wiener, nowcasting, oil shock, On the Economy of Machinery and Manufactures, OpenAI, opioid epidemic / opioid crisis, Pareto efficiency, pattern recognition, pink-collar, Productivity paradox, profit maximization, Renaissance Technologies, rent-seeking, rising living standards, Robert Gordon, Robert Solow, robot derives from the Czech word robota Czech, meaning slave, safety bicycle, Second Machine Age, secular stagnation, self-driving car, seminal paper, Silicon Valley, Simon Kuznets, social intelligence, sparse data, speech recognition, spinning jenny, Stephen Hawking, tacit knowledge, The Future of Employment, The Rise and Fall of American Growth, The Wealth of Nations by Adam Smith, Thomas Malthus, total factor productivity, trade route, Triangle Shirtwaist Factory, Turing test, union organizing, universal basic income, warehouse automation, washing machines reduced drudgery, wealth creators, women in the workforce, working poor, zero-sum game

Instead professionals play by recognizing patterns that emerge “when clutches of stones surround empty spaces.”3 As discussed above, humans still held the comparative advantage in pattern recognition when Frank Levy and Richard Murnane published their brilliant book The New Division of Labor in 2004.4 At the time, computers were nowhere near capable of challenging the human brain in identifying patterns. But now they are. Much more important than the fact that AlphaGo won is how it did so. While Deep Blue was a product of the rule-based age of computing, whose success rested upon the ability of a programmer to write explicit if-then-do rules for various board positions, AlphaGo’s evaluation engine was not explicitly programmed. Instead of following prespecified rules of the programmer, the machine was able to mimic tacit human knowledge, circumventing Polanyi’s paradox. Deep Blue was built on top-down programming. AlphaGo, in contrast, was the product of bottom-up machine learning. The computer inferred its own rules from a series of trials using a large data set.

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The computer inferred its own rules from a series of trials using a large data set. To learn, AlphaGo first watched previously played professional Go games, and then it played millions of games against itself, steadily improving its performance. Its training data set, consisting of thirty million board positions reached by 160,000 professional players, was far greater than the experience any professional player could accumulate in a lifetime. The event marks what Erik Brynjolfsson and Andrew McAfee have called the “second half of the chessboard.”5 As Scientific American marveled, “An era is over and a new one is beginning. The methods underlying AlphaGo, and its recent victory, have huge implications for the future of machine intelligence.”6 Deep Blue may have beaten Kasparov at chess.

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The only thing Deep Blue could do was evaluate two hundred million board positions per second. It was designed for one specific purpose. AlphaGo, on the other hand, relies on neural networks, which can be used to perform a seemingly endless number of tasks. Using neural networks, DeepMind has already achieved superhuman performance at some fifty Atari video games, including Video Pinball, Space Invaders, and Ms. Pac-Man.7 Of course, a programmer provided the instruction to maximize the game score, but an algorithm learned the best game strategies by itself over thousands of trials. Unsurprisingly, AlphaGo (or AlphaZero, as the generalized version is called), also outperforms preprogrammed computers at chess.

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

Ada Lovelace, Airbnb, AlphaGo, Amazon Mechanical Turk, Andrew Keen, autonomous vehicles, barriers to entry, basic income, Bernie Sanders, Big Tech, bitcoin, Black Lives Matter, blockchain, Boris Johnson, Californian Ideology, Cambridge Analytica, central bank independence, Chelsea Manning, cloud computing, computer vision, creative destruction, cryptocurrency, Daniel Kahneman / Amos Tversky, data science, deep learning, DeepMind, disinformation, Dominic Cummings, Donald Trump, driverless car, Edward Snowden, Elon Musk, Evgeny Morozov, fake news, Filter Bubble, future of work, general purpose technology, gig economy, global village, Google bus, Hans Moravec, hive mind, Howard Rheingold, information retrieval, initial coin offering, Internet of things, Jeff Bezos, Jeremy Corbyn, job automation, John Gilmore, John Maynard Keynes: technological unemployment, John Perry Barlow, Julian Assange, manufacturing employment, Mark Zuckerberg, Marshall McLuhan, Menlo Park, meta-analysis, mittelstand, move fast and break things, Network effects, Nicholas Carr, Nick Bostrom, off grid, Panopticon Jeremy Bentham, payday loans, Peter Thiel, post-truth, prediction markets, QR code, ransomware, Ray Kurzweil, recommendation engine, Renaissance Technologies, ride hailing / ride sharing, Robert Mercer, Ross Ulbricht, Sam Altman, Satoshi Nakamoto, Second Machine Age, sharing economy, Silicon Valley, Silicon Valley billionaire, Silicon Valley ideology, Silicon Valley startup, smart cities, smart contracts, smart meter, Snapchat, Stanford prison experiment, Steve Bannon, Steve Jobs, Steven Levy, strong AI, surveillance capitalism, TaskRabbit, tech worker, technological singularity, technoutopianism, Ted Kaczynski, TED Talk, the long tail, the medium is the message, the scientific method, The Spirit Level, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, theory of mind, too big to fail, ultimatum game, universal basic income, WikiLeaks, World Values Survey, Y Combinator, you are the product

A few years ago, DeepMind, a Google-owned AI firm, built software to play the game, called AlphaGo. It was trained the ‘classic’ ML way, using thousands of human games; for example, being taught that in position x humans played move y; and in position a, humans played move b, and so on. From that starting point AlphaGo played itself billions of times to improve its knowledge of the game. In 2016, to the surprise of many experts, AlphaGo decisively beat the world’s best Go player, Lee Sedol. This stunning result was quickly surpassed when, in late 2017, Deep Mind released AlphaGo Zero, a software that was given no human examples at all and was taught the rules of how to win by using a deep learning technique with no prior examples.

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This stunning result was quickly surpassed when, in late 2017, Deep Mind released AlphaGo Zero, a software that was given no human examples at all and was taught the rules of how to win by using a deep learning technique with no prior examples. It started off dreadfully bad but improved slightly with each game, and within 40 days of constant self-play it had become so strong that it thrashed the original AlphaGo 100–0. Go is now firmly in the category of ‘games that humans will never win against machines again’. Most people in Silicon Valley agree that machine learning is the next big thing, although some are more optimistic than others. Tesla and SpaceX boss Elon Musk recently said that AI is like ‘summoning the demon’, while others have compared its significance to the ‘scientific method, on steroids’, the invention of penicillin and even electricity.

The Future We Choose: Surviving the Climate Crisis by Christiana Figueres, Tom Rivett-Carnac

3D printing, Airbnb, AlphaGo, Anthropocene, autonomous vehicles, Berlin Wall, biodiversity loss, carbon footprint, circular economy, clean water, David Attenborough, decarbonisation, DeepMind, dematerialisation, Demis Hassabis, disinformation, Donald Trump, driverless car, en.wikipedia.org, Extinction Rebellion, F. W. de Klerk, Fall of the Berlin Wall, Gail Bradbrook, General Motors Futurama, green new deal, Greta Thunberg, high-speed rail, income inequality, Intergovernmental Panel on Climate Change (IPCC), Internet of things, Jeff Bezos, job automation, Lyft, Mahatma Gandhi, Marc Benioff, Martin Wolf, mass immigration, Mustafa Suleyman, Nelson Mandela, new economy, ocean acidification, plant based meat, post-truth, rewilding, ride hailing / ride sharing, self-driving car, smart grid, sovereign wealth fund, the scientific method, trade route, uber lyft, urban planning, urban sprawl, Yogi Berra

Many people alive today will at some point likely encounter a machine that is smarter than they are in almost every way. The world famously got a taste of what that might be like in 2017. The AI program AlphaGo Zero figured out how to win at the ancient and notoriously difficult Chinese strategy game of Go, learning entirely by itself, essentially accumulating thousands of years of human knowledge, and improving on it, in just forty days.75 Deep Mind, the company that developed AlphaGo Zero, says the technology is not limited to machines that can outcompete human beings in strategy games but is intended to be used to inform new technology that will positively impact society.76 But we can’t rely on the promises of corporations to ensure that a technology is aligned with our goals for regenerating nature and pursuing the conditions that will help humanity thrive.

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World Bank, “Accounting Reveals That Costa Rica’s Forest Wealth Is Greater Than Expected,” May 31, 2016, https://www.worldbank.org/​en/​news/​feature/​2016/​05/​31/​accounting-reveals-that-costa-ricas-forest-wealth-is-greater-than-expected. 73. See http://happyplanetindex.org/​countries/​costa-rica. 74. For a helpful introduction to AI, see Snips, “A 6-Minute Intro to AI,” https://snips.ai/​content/​intro-to-ai/​#ai-metrics. 75. David Silver and Demis Hassabis, “AlphaGo Zero: Starting from Scratch,” DeepMind, October 18, 2017, https://deepmind.com/​blog/​alphago-zero-learning-scratch/. 76. DeepMind, https://deepmind.com/. 77. Rupert Neate, “Richest 1% Own Half the World’s Wealth, Study Finds,” Guardian (U.S. edition), November 14, 2017, https://www.theguardian.com/​inequality/​2017/​nov/​14/​worlds-richest-wealth-credit-suisse. 78.

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It will be impossible for so many people to live here if we have the same impact per capita on our atmosphere as we do today. Technology, specifically machine learning and AI, has the potential to transform our presence here. Issues and problems, including how we can effectively use natural resources in a circular rather than linear way, that have long eluded us may finally be unlocked. When AlphaGo Zero was learning to play and win at Go, the developers noticed that as it taught itself techniques perfected by professional players over generations, it occasionally made decisions to discard those techniques in favor of new, better ones that human beings had not yet had time to learn. In a race against time, the speed of learning that AI offers has extraordinary—exponential—potential to accelerate climate solutions, if it is deployed and governed well.

The Economic Singularity: Artificial Intelligence and the Death of Capitalism by Calum Chace

"World Economic Forum" Davos, 3D printing, additive manufacturing, agricultural Revolution, AI winter, Airbnb, AlphaGo, Alvin Toffler, Amazon Robotics, Andy Rubin, artificial general intelligence, augmented reality, autonomous vehicles, banking crisis, basic income, Baxter: Rethink Robotics, Berlin Wall, Bernie Sanders, bitcoin, blockchain, Boston Dynamics, bread and circuses, call centre, Chris Urmson, congestion charging, credit crunch, David Ricardo: comparative advantage, deep learning, DeepMind, Demis Hassabis, digital divide, Douglas Engelbart, Dr. Strangelove, driverless car, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Fairchild Semiconductor, Flynn Effect, full employment, future of work, Future Shock, gender pay gap, Geoffrey Hinton, gig economy, Google Glasses, Google X / Alphabet X, Hans Moravec, Herman Kahn, hype cycle, ImageNet competition, income inequality, industrial robot, Internet of things, invention of the telephone, invisible hand, James Watt: steam engine, Jaron Lanier, Jeff Bezos, job automation, John Markoff, John Maynard Keynes: technological unemployment, John von Neumann, Kevin Kelly, Kiva Systems, knowledge worker, lifelogging, lump of labour, Lyft, machine translation, Marc Andreessen, Mark Zuckerberg, Martin Wolf, McJob, means of production, Milgram experiment, Narrative Science, natural language processing, Neil Armstrong, new economy, Nick Bostrom, Occupy movement, Oculus Rift, OpenAI, PageRank, pattern recognition, post scarcity, post-industrial society, post-work, precariat, prediction markets, QWERTY keyboard, railway mania, RAND corporation, Ray Kurzweil, RFID, Rodney Brooks, Sam Altman, Satoshi Nakamoto, Second Machine Age, self-driving car, sharing economy, Silicon Valley, Skype, SoftBank, software is eating the world, speech recognition, Stephen Hawking, Steve Jobs, TaskRabbit, technological singularity, TED Talk, The future is already here, The Future of Employment, Thomas Malthus, transaction costs, Two Sigma, Tyler Cowen, Tyler Cowen: Great Stagnation, Uber for X, uber lyft, universal basic income, Vernor Vinge, warehouse automation, warehouse robotics, working-age population, Y Combinator, young professional

The system has to figure out how to behave according to those signals.[lxx]) A match against the world champion Lee Se-Dol followed in March 2016. Se-Dol was confident, believing it would take a few more years before a computer could beat him. He was genuinely shocked to lose the series four games to one, and observers were impressed by AlphaGo’s sometimes unorthodox style of play. AlphaGo’s achievement was another landmark in computer science, and perhaps equally a landmark in human understanding that something important is happening, especially in the Far East, where the game of Go is far more popular than it is in the West. DeepMind did not rest on its laurels.

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It is nowhere near an artificial general intelligence which is human-level or beyond in all respects. It is not conscious. It does not even know that it won the Jeopardy match. But it may prove to be an early step in the direction of artificial general intelligence. In January 2016, an AI system called AlphaGo developed by Google's DeepMind beat Fan Hui, the European champion of Go, a board game. This was hailed as a major step forward: the game of chess has more possible moves (3580) than there are atoms in the visible universe, but Go has even more – 250150.[lxix] The system uses a hybrid of AI techniques: it was partly programmed by its creators, but it also taught itself using a machine learning approach called deep reinforcement learning.

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utm_content=bufferb9e5d&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer [ccxxxix] http://forbesindia.com/article/hidden-gems/thyrocare-technologies-testing-new-waters-in-medical-diagnostics/41051/1 [ccxl] http://www.ucsf.edu/news/2011/03/9510/new-ucsf-robotic-pharmacy-aims-improve-patient-safety [ccxli] http://www.qmed.com/news/ibms-watson-could-diagnose-cancer-better-doctors [ccxlii] http://www.ft.com/cms/s/2/dced8150-b300-11e5-8358-9a82b43f6b2f.html#axzz3xL3RoRdy [ccxliii] http://www.ft.com/cms/s/2/dced8150-b300-11e5-8358-9a82b43f6b2f.html#axzz3xL3RoRdy [ccxliv] http://www.theverge.com/2016/3/10/11192774/demis-hassabis-interview-alphago-google-deepmind-ai [ccxlv] http://qz.com/567658/searching-for-eureka-ibms-path-back-to-greatness-and-how-it-could-change-the-world/ [ccxlvi] http://www.forbes.com/sites/peterhigh/2016/01/18/ibm-watson-head-mike-rhodin-on-the-future-of-artificial-intelligence/#24204aab3e2922228b9c30cc [ccxlvii] http://www.dotmed.com/news/story/29020 [ccxlviii] http://www.wsj.com/articles/SB10001424052702303983904579093252573814132 [ccxlix] http://www.outpatientsurgery.net/outpatient-surgery-news-and-trends/general-surgical-news-and-reports/ethicon-pulling-sedasys-anesthesia-system--03-10-16 [ccl] http://www.wired.co.uk/news/archive/2016-05/05/autonomous-robot-surgeon [ccli] https://www.edsurge.com/news/2016-04-18-gradescope-raises-2-6m-to-apply-artificial-intelligence-to-grading-exams [cclii] http://www.wsj.com/articles/if-your-teacher-sounds-like-a-robot-you-might-be-on-to-something-1462546621 [ccliii] https://www.sigfig.com/site/#/home [ccliv] http://www.nytimes.com/2016/01/23/your-money/robo-advisers-for-investors-are-not-one-size-fits-all.html?

Prediction Machines: The Simple Economics of Artificial Intelligence by Ajay Agrawal, Joshua Gans, Avi Goldfarb

Abraham Wald, Ada Lovelace, AI winter, Air France Flight 447, Airbus A320, algorithmic bias, AlphaGo, Amazon Picking Challenge, artificial general intelligence, autonomous vehicles, backpropagation, basic income, Bayesian statistics, Black Swan, blockchain, call centre, Capital in the Twenty-First Century by Thomas Piketty, Captain Sullenberger Hudson, carbon tax, Charles Babbage, classic study, collateralized debt obligation, computer age, creative destruction, Daniel Kahneman / Amos Tversky, data acquisition, data is the new oil, data science, deep learning, DeepMind, deskilling, disruptive innovation, driverless car, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, everywhere but in the productivity statistics, financial engineering, fulfillment center, general purpose technology, Geoffrey Hinton, Google Glasses, high net worth, ImageNet competition, income inequality, information retrieval, inventory management, invisible hand, Jeff Hawkins, job automation, John Markoff, Joseph Schumpeter, Kevin Kelly, Lyft, Minecraft, Mitch Kapor, Moneyball by Michael Lewis explains big data, Nate Silver, new economy, Nick Bostrom, On the Economy of Machinery and Manufactures, OpenAI, paperclip maximiser, pattern recognition, performance metric, profit maximization, QWERTY keyboard, race to the bottom, randomized controlled trial, Ray Kurzweil, ride hailing / ride sharing, Robert Solow, Salesforce, Second Machine Age, self-driving car, shareholder value, Silicon Valley, statistical model, Stephen Hawking, Steve Jobs, Steve Jurvetson, Steven Levy, strong AI, The Future of Employment, the long tail, The Signal and the Noise by Nate Silver, Tim Cook: Apple, trolley problem, Turing test, Uber and Lyft, uber lyft, US Airways Flight 1549, Vernor Vinge, vertical integration, warehouse automation, warehouse robotics, Watson beat the top human players on Jeopardy!, William Langewiesche, Y Combinator, zero-sum game

What we have called AI in this book is not general artificial intelligence but decidedly narrower prediction machines. Developments such as AlphaGo Zero by Google’s DeepMind have raised the specter that superintelligence might not be so far away. It outperformed the world champion–beating AlphaGo at the board game Go without human training (learning by playing games against itself), but it isn’t ready to be called superintelligence. If the game board changed from nineteen by nineteen to twenty-nine by twenty-nine or even eighteen by eighteen, the AI would struggle, whereas a human would adjust. And don’t even think of asking AlphaGo Zero to make you a grilled cheese sandwich; it’s not that smart.

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Some regular citizens experienced their AI moment later that year when renowned physicist Stephen Hawking emphatically explained, “[E]verything that civilisation has to offer is a product of human intelligence … [S]uccess in creating AI would be the biggest event in human history.”1 Others experienced their AI moment the first time they took their hands off the wheel of a speeding Tesla, navigating traffic using Autopilot AI. The Chinese government experienced its AI moment when it witnessed DeepMind’s AI, AlphaGo, beating Lee Se-dol, a South Korean master of the board game Go, and then later that year beating the world’s top-ranked player, Ke Jie of China. The New York Times described this game as China’s “Sputnik moment.”2 Just as massive American investment in science followed the Soviet Union’s launch of Sputnik, China responded to this event with a national strategy to dominate the AI world by 2030 and a financial commitment to make that claim plausible.

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Learning by Simulation One intermediate step to soften this trade-off is to use simulated environments. When human pilots are training, before they get their hands on a real plane in flight, they spend hundreds of hours in what are very sophisticated and realistic simulators. A similar approach is available for AI. Google trained DeepMind’s AlphaGo AI to defeat the best Go players in the world not just by looking at thousands of games played between humans but also by playing against another version of itself. One form of this approach is called adversarial machine learning, which pits the main AI and its objective against another AI that tries to foil that objective.

The Book of Why: The New Science of Cause and Effect by Judea Pearl, Dana Mackenzie

affirmative action, Albert Einstein, AlphaGo, Asilomar, Bayesian statistics, computer age, computer vision, Computing Machinery and Intelligence, confounding variable, correlation coefficient, correlation does not imply causation, Daniel Kahneman / Amos Tversky, data science, deep learning, DeepMind, driverless car, Edmond Halley, Elon Musk, en.wikipedia.org, experimental subject, Great Leap Forward, Gregor Mendel, Isaac Newton, iterative process, John Snow's cholera map, Loebner Prize, loose coupling, Louis Pasteur, Menlo Park, Monty Hall problem, pattern recognition, Paul Erdős, personalized medicine, Pierre-Simon Laplace, placebo effect, Plato's cave, prisoner's dilemma, probability theory / Blaise Pascal / Pierre de Fermat, randomized controlled trial, Recombinant DNA, selection bias, self-driving car, seminal paper, Silicon Valley, speech recognition, statistical model, Stephen Hawking, Steve Jobs, strong AI, The Design of Experiments, the scientific method, Thomas Bayes, Turing test

Perhaps the prototypical example is AlphaGo, a convolutional neural-network-based program that plays the ancient Asian game of Go, developed by DeepMind, a subsidiary of Google. Among human games of perfect information, Go had always been considered the toughest nut for AI. Though computers conquered humans in chess in 1997, they were not considered a match even for the lowest-level professional Go players as recently as 2015. The Go community thought that computers were still a decade or more away from giving humans a real battle. That changed almost overnight with the advent of AlphaGo. Most Go players first heard about the program in late 2015, when it trounced a human professional 5–0.

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Most Go players first heard about the program in late 2015, when it trounced a human professional 5–0. In March 2016, AlphaGo defeated Lee Sedol, for years considered the strongest human player, 4–1. A few months later it played sixty online games against top human players without losing a single one, and in 2017 it was officially retired after beating the current world champion, Ke Jie. The one game it lost to Sedol is the only one it will ever lose to a human. All of this is exciting, and the results leave no doubt: deep learning works for certain tasks. But it is the antithesis of transparency. Even AlphaGo’s programmers cannot tell you why the program plays so well.

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Even AlphaGo’s programmers cannot tell you why the program plays so well. They knew from experience that deep networks have been successful at tasks in computer vision and speech recognition. Nevertheless, our understanding of deep learning is completely empirical and comes with no guarantees. The AlphaGo team could not have predicted at the outset that the program would beat the best human in a year, or two, or five. They simply experimented, and it did. Some people will argue that transparency is not really needed. We do not understand in detail how the human brain works, and yet it runs well, and we forgive our meager understanding. So, they argue, why not unleash deep-learning systems and create a new kind of intelligence without understanding how it works?

The Myth of Artificial Intelligence: Why Computers Can't Think the Way We Do by Erik J. Larson

AI winter, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Albert Einstein, Alignment Problem, AlphaGo, Amazon Mechanical Turk, artificial general intelligence, autonomous vehicles, Big Tech, Black Swan, Bletchley Park, Boeing 737 MAX, business intelligence, Charles Babbage, Claude Shannon: information theory, Computing Machinery and Intelligence, conceptual framework, correlation does not imply causation, data science, deep learning, DeepMind, driverless car, Elon Musk, Ernest Rutherford, Filter Bubble, Geoffrey Hinton, Georg Cantor, Higgs boson, hive mind, ImageNet competition, information retrieval, invention of the printing press, invention of the wheel, Isaac Newton, Jaron Lanier, Jeff Hawkins, John von Neumann, Kevin Kelly, Large Hadron Collider, Law of Accelerating Returns, Lewis Mumford, Loebner Prize, machine readable, machine translation, Nate Silver, natural language processing, Nick Bostrom, Norbert Wiener, PageRank, PalmPilot, paperclip maximiser, pattern recognition, Peter Thiel, public intellectual, Ray Kurzweil, retrograde motion, self-driving car, semantic web, Silicon Valley, social intelligence, speech recognition, statistical model, Stephen Hawking, superintelligent machines, tacit knowledge, technological singularity, TED Talk, The Coming Technological Singularity, the long tail, the scientific method, The Signal and the Noise by Nate Silver, The Wisdom of Crowds, theory of mind, Turing machine, Turing test, Vernor Vinge, Watson beat the top human players on Jeopardy!, Yochai Benkler

In Alice’s context, ­we’ll call ­these System X, for competence on well-­defined tasks like game play (as in chess or Go) and System Y, for general intelligence. The latter system includes Bob’s competence at reading and conversation, but also the murkier area of novel ideas and insights. Bob is terrible at chess, and in fact his X system is pathetic compared not only to a system like AlphaGo but also to many other ­humans. His short-­term memory is worse than most ­people’s; he scores poorly on IQ tests; and he strug­gles with crossword puzzles. As for his Y system, his general intelligence shows a con­spic­u­ous lack of interest in or ability at novel or insightful thinking. Bob is not the kind of neighbor that gets many invitations to dinner parties.

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But, we already have “human-­level” intelligence—­we’re ­human. Can we do this? What are the intelligence explosion promoters ­really talking about? This is another way of saying that the powers of the h­ uman mind outstrip our ability to mechanize it in the sense necessary for “scaling up,” from AlphaGo to a Bob-­Machine to a Turing-­Machine, and beyond. The intelligence explosion idea itself is not a particularly good System Y candidate for pro­g ress on AI ­toward general intelligence. T H E E VOLU T ION A RY T ECH NOLOGISTS Many AI enthusiasts who hold to an inevitability thesis (superintelligent machines are coming, no ­matter what we do) hold to this ­because it plays on evolutionary themes, and thus con­ve­niently absolves individual 42 T he S implified W orld scientists from the responsibility of needing to make scientific breakthroughs or develop revolutionary ideas.

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P rob­lems with D eduction and I nduction 125 I N DUCT ION WOR K S ON G A M E S, NOT LI FE The real world is a dynamic environment, which means it’s constantly changing in both predictable and unpredictable ways, and we ­can’t enclose it in a system of rules. Board games, though, are enclosed in a system of rules, which helps explain why inductive approaches that learn from experience of gameplay work so well. AlphaGo (or its successor AlphaZero) uses a kind of machine learning known as deep learning to play the difficult game of Go. It plays against itself, using something called deep reinforcement learning, and induces hypotheses about the best moves to make on the board given its position and the opponent’s.

Ten Lessons for a Post-Pandemic World by Fareed Zakaria

"there is no alternative" (TINA), 15-minute city, AlphaGo, An Inconvenient Truth, anti-fragile, Asian financial crisis, basic income, Bernie Sanders, Boris Johnson, butterfly effect, Capital in the Twenty-First Century by Thomas Piketty, car-free, carbon tax, central bank independence, clean water, cloud computing, colonial rule, contact tracing, coronavirus, COVID-19, Credit Default Swap, David Graeber, Day of the Dead, deep learning, DeepMind, deglobalization, Demis Hassabis, Deng Xiaoping, digital divide, Dominic Cummings, Donald Trump, Edward Glaeser, Edward Jenner, Elon Musk, Erik Brynjolfsson, failed state, financial engineering, Francis Fukuyama: the end of history, future of work, gentrification, George Floyd, gig economy, Gini coefficient, global pandemic, global reserve currency, global supply chain, green new deal, hiring and firing, housing crisis, imperial preference, income inequality, Indoor air pollution, invention of the wheel, Jane Jacobs, Jeff Bezos, Jeremy Corbyn, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Snow's cholera map, junk bonds, lockdown, Long Term Capital Management, low interest rates, manufacturing employment, Marc Andreessen, Mark Zuckerberg, Martin Wolf, means of production, megacity, Mexican peso crisis / tequila crisis, middle-income trap, Monroe Doctrine, Nate Silver, Nick Bostrom, oil shock, open borders, out of africa, Parag Khanna, Paris climate accords, Peter Thiel, plutocrats, popular capitalism, Productivity paradox, purchasing power parity, remote working, reserve currency, reshoring, restrictive zoning, ride hailing / ride sharing, Ronald Reagan, secular stagnation, Silicon Valley, social distancing, software is eating the world, South China Sea, Steve Bannon, Steve Jobs, Steven Pinker, Suez crisis 1956, TED Talk, the built environment, The Death and Life of Great American Cities, The inhabitant of London could order by telephone, sipping his morning tea in bed, the various products of the whole earth, The Spirit Level, The Wealth of Nations by Adam Smith, Thomas L Friedman, Tim Cook: Apple, trade route, UNCLOS, universal basic income, urban planning, Washington Consensus, white flight, Works Progress Administration, zoonotic diseases

That leap in cognitive capacity was a watershed moment for AI. The board game Go is considered to be the most complex in the world, with vastly more potential moves than there are atoms in the observable universe. Google’s AlphaGo learned the game, and in March 2016, consistently beat the eighteen-time world champion, Lee Sedol. (In 2017, its successor program, AlphaZero, taught itself Go in just three days and defeated AlphaGo, one hundred games to zero.) AlphaGo was seen by computer scientists as a mark that machines could teach themselves and also think in nonlinear, creative ways. In March 2020, its makers revealed that another one of their programs merely watched the screen as a series of Atari video games were played—and then mastered all fifty-seven games, outperforming humans in every single one.

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Norton, 1963), 358–73. 113 Jetson of the 1960s cartoon: “works three hours a day, three days a week,” per Sarah Ellison, “Reckitt Turns to Jetsons to Launch Detergent Gels,” Wall Street Journal, January 13, 2003; pushing a button, per Hanna-Barbera Wiki, “The Jetsons,” https://hanna-barbera.fandom.com/wiki/The_Jetsons. 113 four-day workweek: Zoe Didali, “As PM Finland’s Marin Could Renew Call for Shorter Work Week,” New Europe, January 2, 2020, https://www.neweurope.eu/article/finnish-pm-marin-calls-for-4-day-week-and-6-hours-working-day-in-the-country/. 114 “bullshit jobs”: David Graeber, Bullshit Jobs: A Theory (New York: Simon & Schuster, 2018). 115 “slaves of time without purpose”: McEwan, Machines Like Me. 116 atoms in the observable universe: David Silver and Demis Hassabis, “AlphaGo: Mastering the Ancient Game of Go with Machine Learning,” Google DeepMind, January 27, 2016, https://ai.googleblog.com/2016/01/alphago-mastering-ancient-game-of-go.html. 116 all fifty-seven games: Kyle Wiggers, “DeepMind’s Agent57 Beats Humans at 57 Classic Atari Games,” Venture Beat, March 31, 2020; Rebecca Jacobson, “Artificial Intelligence Program Teaches Itself to Play Atari Games—And It Can Beat Your High Score,” PBS NewsHour, February 20, 2015. 117 Stuart Russell: Stuart Russell, “3 Principles for Creating Safer AI,” TED2017, https://www.ted.com/talks/stuart_russell_3_principles_for_creating_safer_ai/transcript?

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But if AI produces better answers than we can without revealing its logic, then we will be going back to our species’ childhood and relying on faith. We will worship artificial intelligence that, as was said of God, works in a mysterious way, his wonders to perform. Perhaps the period from Gutenberg to AlphaGo will prove to be the exception, a relatively short era in history when humans believed they were in control. Before that, for millennia, they saw themselves as small cogs in a vast system they did not fully comprehend, subject to laws of God and nature. The AI age could return us to a similarly humble role.

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

4chan, Airbnb, airport security, algorithmic trading, Alignment Problem, AlphaGo, Automated Insights, banking crisis, Big Tech, bitcoin, blockchain, Boeing 737 MAX, Brian Krebs, Capital in the Twenty-First Century by Thomas Piketty, cloud computing, computerized trading, coronavirus, corporate personhood, COVID-19, cryptocurrency, dark pattern, deepfake, defense in depth, disinformation, Donald Trump, Double Irish / Dutch Sandwich, driverless car, Edward Thorp, Elon Musk, fake news, financial innovation, Financial Instability Hypothesis, first-past-the-post, Flash crash, full employment, gig economy, global pandemic, Goodhart's law, GPT-3, Greensill Capital, high net worth, Hyman Minsky, income inequality, independent contractor, index fund, information security, intangible asset, Internet of things, Isaac Newton, Jeff Bezos, job automation, late capitalism, lockdown, Lyft, Mark Zuckerberg, money market fund, moral hazard, move fast and break things, Nate Silver, offshore financial centre, OpenAI, payday loans, Peter Thiel, precautionary principle, Ralph Nader, recommendation engine, ride hailing / ride sharing, self-driving car, sentiment analysis, Skype, smart cities, SoftBank, supply chain finance, supply-chain attack, surveillance capitalism, systems thinking, TaskRabbit, technological determinism, TED Talk, The Wealth of Nations by Adam Smith, theory of mind, TikTok, too big to fail, Turing test, Uber and Lyft, uber lyft, ubercab, UNCLOS, union organizing, web application, WeWork, When a measure becomes a target, WikiLeaks, zero day

It can be impossible to understand how the system reached its conclusion, even if you are the system’s designer and can examine the code. Researchers don’t know precisely how an AI image-classification system differentiates turtles from rifles, let alone why one of them mistook one for the other. In 2016, the AI program AlphaGo won a five-game match against one of the world’s best Go players, Lee Sedol—something that shocked both the AI and the Go-playing worlds. AlphaGo’s most famous move was in game two: move thirty-seven. It’s hard to explain without diving deep into Go strategy, but it was a move that no human would ever have chosen to make. It was an instance of an AI thinking differently.

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Martin Ford (2018), Architects of Intelligence: The Truth About AI from the People Building It, Packt Publishing. 208Def: Robot /bät/ (noun): Kate Darling (2021), The New Breed: What Our History with Animals Reveals about Our Future with Robots, Henry Holt. 52. THE EXPLAINABILITY PROBLEM 212Deep Thought informs them: Douglas Adams (1978), The Hitchhiker’s Guide to the Galaxy, BBC Radio 4. 212AlphaGo won a five-game match: Cade Metz (16 Mar 2016), “In two moves, AlphaGo and Lee Sedol redefined the future,” Wired, https://www.wired.com/2016/03/two-moves-alphago-lee-sedol-redefined-future. 213“the magical number seven”: George A. Miller (1956), “The magical number seven, plus or minus two: Some limits on our capacity for processing information,” Psychological Review 63, no. 2, http://psychclassics.yorku.ca/Miller. 214explainability is especially important: J.

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A/B testing, 225 abortion, 133–34 Abrams, Stacey, 167 addiction, 185–87 Adelson, Sheldon, 169 administrative burdens, 132–34, 163, 164, 165 administrative state, 154 adversarial machine-learning, 209–10 advertising attention and, 183, 184–85 fear and, 197 persuasion and, 188–89 trust and, 194 AI hacking ability to find vulnerabilities and, 229–30 cognitive hacks and, 181–82, 201–2, 216, 218–19 competitions for, 228–29 computer acceleration of, 224–26, 242–43 defenses against, 236–39 experimentation and, 225 fear and, 197 financial systems and, 241–43, 275n future of, 4–5, 205–6, 240–44, 272n, 275n goals and, 231–35, 240 governance systems for, 245–48 humanization and, 216–19 persuasion and, 188, 218–19, 220–23 politics and, 220–22, 225–26 scale and, 225–26, 242–43, 274n scope and, 226, 243 sophistication and, 226, 243 speed and, 224–25, 242 trust and, 193, 194, 218 AI systems ability to find vulnerabilities, 229–30, 238–39 ambiguity and, 240–41 defined, 206 explainability problem, 212–15, 234 hacking vulnerabilities of, 4, 209–11, 226–27 qualities of, 207–8 specialized vs. general, 206–7, 272n value alignment and, 237 AIBO, 222–23 Air Bud, 259n Airbnb, 124 airline frequent-flier hacks, 38–40, 46 Alexa, 217 Alita: Battle Angel, 218 AlphaGo, 212 Alternative Minimum Tax (AMT), 61 Amazon, 124–25 ambiguity, 240–41 American Jobs Creation Act (2004), 157 Anonymous, 103 ant farms, 1–2 antitrust laws, 185 architecture, 109 artificial intelligence. See AI hacking; AI systems ATM hacks, 31–34, 46, 47, 63 attention, 183–87 authoritarian governments, 174–75 AutoRun, 58, 68 Bank Holding Company Act (1956), 75 banking hacks, 74–78, 119, 260n Barrett, Amy Coney, 121 beneficial ownership, 86, 88 Berkoff, David, 42 Biden, Joseph, 129, 130 Big Lie technique, 189 biological systems, 19–20 Bipartisan Campaign Reform Act (2002), 169 Black Codes, 162–63 Boeing 737 MAX, 116–17 Bongo, Ali, 193 border closures, 126 Borodin, Andrey, 87 bots, 188, 210, 220, 221–22, 225–26, 274n Boxie, 218 brands, 194 Breaking Bad, 32 Breakout, 236–37 Briffault, Richard, 151 bug bounties, 56–57 bugs, 14–15 bureaucracy hacks, 115–18 Burr, Aaron, 155 business email compromise, 53–54, 192 buyers’ agency, 99 capitalism.

What We Owe the Future: A Million-Year View by William MacAskill

Ada Lovelace, agricultural Revolution, Albert Einstein, Alignment Problem, AlphaGo, artificial general intelligence, Bartolomé de las Casas, Bletchley Park, British Empire, Brownian motion, carbon footprint, carbon tax, charter city, clean tech, coronavirus, COVID-19, cuban missile crisis, decarbonisation, deep learning, DeepMind, Deng Xiaoping, different worldview, effective altruism, endogenous growth, European colonialism, experimental subject, feminist movement, framing effect, friendly AI, global pandemic, GPT-3, hedonic treadmill, Higgs boson, income inequality, income per capita, Indoor air pollution, Intergovernmental Panel on Climate Change (IPCC), Isaac Newton, Islamic Golden Age, iterative process, Jeff Bezos, job satisfaction, lab leak, Lao Tzu, Large Hadron Collider, life extension, lockdown, long peace, low skilled workers, machine translation, Mars Rover, negative emissions, Nick Bostrom, nuclear winter, OpenAI, Peter Singer: altruism, Peter Thiel, QWERTY keyboard, Robert Gordon, Rutger Bregman, Sam Altman, seminal paper, Shenzhen special economic zone , Shenzhen was a fishing village, Silicon Valley, special economic zone, speech recognition, Stanislav Petrov, stem cell, Steven Pinker, strong AI, synthetic biology, total factor productivity, transatlantic slave trade, Tyler Cowen, William MacAskill, women in the workforce, working-age population, World Values Survey, Y Combinator

DeepMind claims that AlphaGo “was a decade ahead of its time” (DeepMind 2020). This might refer to a 2014 prediction by Rémi Coulom, the developer of one of the best Go programmes prior to AlphaGo (Levinovitz 2014). However, this may be exaggerated. Go programmes had been reliably improving for years, and a simple trend extrapolation would have predicted that programmes would beat the best human players within a few years of 2016—see, e.g., Katja Grace (2013, Section 5.2). After correcting for the unprecedented amount of hardware DeepMind was willing to employ, it is not clear whether AlphaGo deviates from the trend of algorithmic improvements at all (Brundage 2016). 37.

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Machine learning is a method of creating useful algorithms that does not require explicitly programming them; instead, it relies on learning from data, such as images, the results of computer games, or patterns of mouse clicks. One well-publicised breakthrough was DeepMind’s AlphaGo in 2016, which beat eighteen-time international champion Go player Lee Sedol.36 But AlphaGo is just a tiny sliver of all the impressive achievements that have come out of recent developments in machine learning. There have also been breakthroughs in generating and recognising speech, images, art, and music; in real-time strategy games like StarCraft; and in a wide variety of tasks associated with understanding and generating humanlike text.37 You probably use artificial intelligence every day, for example in a Google search.38 AI has also driven significant improvements in voice recognition, email text completion, and machine translation.39 The ultimate achievement of AI research would be to create artificial general intelligence, or AGI: a single system, or collection of systems working together, that is capable of learning as wide an array of tasks as human beings can and performing them to at least the same level as human beings.40 Once we develop AGI, we will have created artificial agents—beings (not necessarily conscious) that are capable of forming plans and executing on them in just the way that human beings can.

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An AGI could learn not only to play board games but also to drive, to have conversations, to do mathematics, and countless other tasks. So far, artificial intelligence has been narrow. AlphaGo is extraordinarily good at playing Go but is incapable of doing anything else.41 But some of the leading AI labs, such as DeepMind and OpenAI, have the explicit goal of building AGI.42 And there have been indications of progress, such as the performance of GPT-3, an AI language model which can perform a variety of tasks it was never explicitly trained to perform, such as translation or arithmetic.43 AlphaZero, a successor to AlphaGo, taught itself how to play not only Go but also chess and shogi, ultimately achieving world-class performance.44 About two years later, MuZero achieved the same feat despite initially not even knowing the rules of the game.45 The development of AGI would be of monumental longterm importance for two reasons.

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

3D printing, Airbnb, algorithmic trading, AlphaGo, Alvin Toffler, Amazon Web Services, anti-work, antiwork, artificial general intelligence, asset light, autonomous vehicles, basic income, behavioural economics, business cycle, cloud computing, collective bargaining, Computing Machinery and Intelligence, correlation does not imply causation, creative destruction, data is the new oil, data science, David Graeber, David Ricardo: comparative advantage, deep learning, DeepMind, deindustrialization, Demis Hassabis, deskilling, disintermediation, do what you love, Donald Trump, driverless car, Erik Brynjolfsson, fake news, feminist movement, Ford Model T, Frederick Winslow Taylor, future of work, Future Shock, general purpose technology, gig economy, global supply chain, income inequality, independent contractor, informal economy, Internet of things, Jarndyce and Jarndyce, Jarndyce and Jarndyce, job automation, job polarisation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, Joseph Schumpeter, knowledge economy, Loebner Prize, low skilled workers, Lyft, Mark Zuckerberg, means of production, moral panic, Network effects, new economy, Nick Bostrom, off grid, pattern recognition, post-work, Ronald Coase, scientific management, Second Machine Age, self-driving car, sharing economy, SoftBank, Steve Jobs, strong AI, tacit knowledge, technological determinism, technoutopianism, TED Talk, The Chicago School, The Future of Employment, the market place, The Nature of the Firm, The Wealth of Nations by Adam Smith, Thorstein Veblen, Turing test, Uber for X, uber lyft, universal basic income, wealth creators, working poor

Human chess champions, for instance highlight the humanity in the heroic battles of two people over a chessboard, and are not perturbed much by the fact that there is software that can beat them. Moreover, AI spectacles such as AlphaGo beating the world Go champion serve to make the game more popular, rather than less. For instance, the world apparently ran out of Go boards to sell, shortly after the AlphaGo event (Shead 2016). A more sensible view of AI abilities, which is held by the majority of practitioners in the field, should extend to projections of software counterparts taking over from human lawyers, doctors, scientists, journalists, and so on, speculation of which is rife across the media and in some academic circles.

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In essence, the main difference to rule-­ based age of computing is that top down programming is no longer required for automation to happen. Instead of having a programmer specifying what a computer technology must do at any given contingency, computers can now infer the rules themselves through “examples” or “experience” provided in what is known as “big data”. As is well-­ known, to beat the world champion at Go, AlphaGo drew upon a training dataset of 30 million board positions from 160,000 professional players. Thus, its experience was far greater than that of any professional Go player. This way, computers are already learning how to perform a variety of non-rule-based tasks, like diagnosing disease, writing shorter news stories, and driving trucks, which were non-automatable only a decade ago.

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We (AI researchers) love chess and Go, precisely because playing them is a relatively easy activity for software: given the closed world, simple rules and transparent nature of the competition, they are ideally suited to AI-style search techniques, and indeed games continue to be a huge driving force for our field. Hence we should take a more realistic look at recent breakthroughs in AI, for instance the super-human Go playing abilities exhibited by the AlphaGo Zero system from Google DeepMind (Silver et al. 2018). While it’s a huge achievement, especially as the software learns to be a grandmaster from scratch by repeatedly playing against itself, we should not extrapolate too far from this milestone being reached. Importantly, of course, this level of super-human intelligence is not likely to negatively impact the world of work.

Possible Minds: Twenty-Five Ways of Looking at AI by John Brockman

AI winter, airport security, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Alignment Problem, AlphaGo, artificial general intelligence, Asilomar, autonomous vehicles, basic income, Benoit Mandelbrot, Bill Joy: nanobots, Bletchley Park, Buckminster Fuller, cellular automata, Claude Shannon: information theory, Computing Machinery and Intelligence, CRISPR, Daniel Kahneman / Amos Tversky, Danny Hillis, data science, David Graeber, deep learning, DeepMind, Demis Hassabis, easy for humans, difficult for computers, Elon Musk, Eratosthenes, Ernest Rutherford, fake news, finite state, friendly AI, future of work, Geoffrey Hinton, Geoffrey West, Santa Fe Institute, gig economy, Hans Moravec, heat death of the universe, hype cycle, income inequality, industrial robot, information retrieval, invention of writing, it is difficult to get a man to understand something, when his salary depends on his not understanding it, James Watt: steam engine, Jeff Hawkins, Johannes Kepler, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, Kevin Kelly, Kickstarter, Laplace demon, Large Hadron Collider, Loebner Prize, machine translation, market fundamentalism, Marshall McLuhan, Menlo Park, military-industrial complex, mirror neurons, Nick Bostrom, Norbert Wiener, OpenAI, optical character recognition, paperclip maximiser, pattern recognition, personalized medicine, Picturephone, profit maximization, profit motive, public intellectual, quantum cryptography, RAND corporation, random walk, Ray Kurzweil, Recombinant DNA, Richard Feynman, Rodney Brooks, self-driving car, sexual politics, Silicon Valley, Skype, social graph, speech recognition, statistical model, Stephen Hawking, Steven Pinker, Stewart Brand, strong AI, superintelligent machines, supervolcano, synthetic biology, systems thinking, technological determinism, technological singularity, technoutopianism, TED Talk, telemarketer, telerobotics, The future is already here, the long tail, the scientific method, theory of mind, trolley problem, Turing machine, Turing test, universal basic income, Upton Sinclair, Von Neumann architecture, Whole Earth Catalog, Y2K, you are the product, zero-sum game

Such a deep-learning program was used to teach a computer to play Go, a game that only a few years ago was thought to be beyond the reach of AI because it was so hard to calculate how well you were doing. It seemed that top Go players relied a great deal on intuition and a feel for position, so proficiency was thought to require a particularly human kind of intelligence. But the AlphaGo program produced by DeepMind, after being trained on thousands of high-level Go games played by humans and then millions of games with itself, was able to beat the top human players in short order. Even more amazingly, the related AlphaGo Zero program, which learned from scratch by playing itself, was stronger than the version trained initially on human games! It was as though the humans had been preventing the computer from reaching its true potential.

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You may need to scroll forward from that location to find the corresponding reference on your e-reader. Adams, Douglas, 97 Adams, Scott, 249 aesthetics, of computer-generated images, 211–13 Afterwords (Brockman), xxi AI visualization programs, 211–13 al-Khwarizmi, 233 AlphaGo, 16, 184–85 AlphaGo Zero, 184–85, 225–26 altruistic objectives of intelligent machines argument against AI risk, 28, 81 amplification, 179 analog and digital computation, distinguished, 35–39 Anderson, Chris, 143–50 AI, and gradient descent, 148–50 background and overview of work of, 143–44 gradient descent, 145–50 human brain, and gradient descent, 147–48 local minima/local maxima problem, 147, 149–50 mosquito example of gradient descent, 145–46 universe, and gradient descent, 146–47 Anderson, Philip, 68 Aristotle, 222 Arnold, Matthew, 157 Artificial Intelligence: A Modern Approach (Russell and Norvig), 141 artificial stupidity, 210–11 artistic decision making, 213 Ascent of Man, The (Bronowski), 118 Ashby, W.

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But this argument has its limitations. The reason we can forgive our meager understanding of how human brains work is because our brains work the same way, and that enables us to communicate with other humans, learn from them, instruct them, and motivate them in our own native language. If our robots will all be as opaque as AlphaGo, we won’t be able to hold a meaningful conversation with them, and that would be unfortunate. We will need to retrain them whenever we make a slight change in the task or in the operating environment. So rather than experimenting with opaque learning machines, I am trying to understand their theoretical limitations and examine how these limitations can be overcome.

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

23andMe, Airbnb, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Albert Einstein, AlphaGo, AltaVista, Amazon Web Services, AOL-Time Warner, Asilomar, augmented reality, Ben Horowitz, bitcoin, Bitcoin Ponzi scheme, Bletchley Park, blockchain, Bob Noyce, British Empire, Brownian motion, Burning Man, business process, butterfly effect, carbon footprint, cellular automata, Claude Shannon: information theory, Clayton Christensen, cloud computing, computer age, computer vision, crony capitalism, cross-subsidies, cryptocurrency, Danny Hillis, decentralized internet, deep learning, DeepMind, Demis Hassabis, disintermediation, distributed ledger, don't be evil, Donald Knuth, Donald Trump, double entry bookkeeping, driverless car, Elon Musk, Erik Brynjolfsson, Ethereum, ethereum blockchain, fake news, fault tolerance, fiat currency, Firefox, first square of the chessboard, first square of the chessboard / second half of the chessboard, floating exchange rates, Fractional reserve banking, game design, Geoffrey Hinton, George Gilder, Google Earth, Google Glasses, Google Hangouts, index fund, inflation targeting, informal economy, initial coin offering, Internet of things, Isaac Newton, iterative process, Jaron Lanier, Jeff Bezos, Jim Simons, Joan Didion, John Markoff, John von Neumann, Julian Assange, Kevin Kelly, Law of Accelerating Returns, machine translation, Marc Andreessen, Mark Zuckerberg, Mary Meeker, means of production, Menlo Park, Metcalfe’s law, Money creation, money: store of value / unit of account / medium of exchange, move fast and break things, Neal Stephenson, Network effects, new economy, Nick Bostrom, Norbert Wiener, Oculus Rift, OSI model, PageRank, pattern recognition, Paul Graham, peer-to-peer, Peter Thiel, Ponzi scheme, prediction markets, quantitative easing, random walk, ransomware, Ray Kurzweil, reality distortion field, Recombinant DNA, Renaissance Technologies, Robert Mercer, Robert Metcalfe, Ronald Coase, Ross Ulbricht, Ruby on Rails, Sand Hill Road, Satoshi Nakamoto, Search for Extraterrestrial Intelligence, self-driving car, sharing economy, Silicon Valley, Silicon Valley ideology, Silicon Valley startup, Singularitarianism, Skype, smart contracts, Snapchat, Snow Crash, software is eating the world, sorting algorithm, South Sea Bubble, speech recognition, Stephen Hawking, Steve Jobs, Steven Levy, Stewart Brand, stochastic process, Susan Wojcicki, TED Talk, telepresence, Tesla Model S, The Soul of a New Machine, theory of mind, Tim Cook: Apple, transaction costs, tulip mania, Turing complete, Turing machine, Vernor Vinge, Vitalik Buterin, Von Neumann architecture, Watson beat the top human players on Jeopardy!, WikiLeaks, Y Combinator, zero-sum game

Also at Google in late October 2017, the DeepMind program launched yet another iteration of the AlphaGo program, which, you may recall, repeatedly defeated Lee Sedol, the five-time world champion Go player. The tree search in AlphaGo evaluated positions and selected moves using deep neural networks trained by immersion in records of human expert moves and by reinforcement from self-play. The blog Kurzweil.ai now reports a new iteration of AlphaGo based solely on reinforcement learning, without direct human input beyond the rules of the game and the reward structure of the program. In a form of “generic adversarial program,” AlphaGo plays against itself and becomes its own teacher.

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In a form of “generic adversarial program,” AlphaGo plays against itself and becomes its own teacher. “Starting tabula rasa,” the Google paper concludes, “our new program AlphaGo Zero achieved superhuman performance, winning 100–0 against the previously published, champion-defeating AlphaGo.”10 The claim of “superhuman performance” seemed rather overwrought to me. Outperforming unaided human beings is what machines—from a 3D printer to a plow—are supposed to do. Otherwise we wouldn’t build them. A deterministic problem with few constraints—a galactic field to plow—Go is perfectly suited to a super-fast computer. Functioning at millions of iterations per second, the machine soon reduces all human games of Go ever played to an infinitesimal subset of its own experience.

Shape: The Hidden Geometry of Information, Biology, Strategy, Democracy, and Everything Else by Jordan Ellenberg

Albert Einstein, AlphaGo, Andrew Wiles, autonomous vehicles, British Empire, Brownian motion, Charles Babbage, Claude Shannon: information theory, computer age, coronavirus, COVID-19, deep learning, DeepMind, Donald Knuth, Donald Trump, double entry bookkeeping, East Village, Edmond Halley, Edward Jenner, Elliott wave, Erdős number, facts on the ground, Fellow of the Royal Society, Geoffrey Hinton, germ theory of disease, global pandemic, government statistician, GPT-3, greed is good, Henri Poincaré, index card, index fund, Isaac Newton, Johannes Kepler, John Conway, John Nash: game theory, John Snow's cholera map, Louis Bachelier, machine translation, Mercator projection, Mercator projection distort size, especially Greenland and Africa, Milgram experiment, multi-armed bandit, Nate Silver, OpenAI, Paul Erdős, pets.com, pez dispenser, probability theory / Blaise Pascal / Pierre de Fermat, Ralph Nelson Elliott, random walk, Rubik’s Cube, self-driving car, side hustle, Snapchat, social distancing, social graph, transcontinental railway, urban renewal

That’s the goal Kevin Hassett and his cubic model failed at. We’re a lot like AlphaGo. The program learns an approximate law that assigns a score to each position of the board. The score does not tell us, on the nose, whether a position is a W, an L, or a D; that’s beyond the capacity of any machine to compute, whether it’s implemented on a cluster or inside our skull. But the job of the program isn’t to get that answer exactly right; it’s to give us good advice about which of the many paths before us is most likely to have victory at the end. Modeling a pandemic is harder than AlphaGo in at least one way; in Go, the rules stay the same the whole game.

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The chess tree is a redwood to checkers’s shrub, and we don’t know whether the root should be marked W, L, or D. But what if we did? Would people still give their lives to chess if they knew a perfect game always ended in a tie, that there was no winning by magnificence, only losing by screwing up? Or would it feel empty? Lee Se-dol, one of the best Go players alive, quit the game after losing a match to AlphaGo, a machine player developed by the AI firm DeepMind. “Even if I become the number one,” he said, “there is an entity that cannot be defeated.” And Go isn’t even solved! Compared to the redwood that’s chess, Go is—well, if there were a tree somewhat bigger than a googol redwoods it would be that tree.

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The first computer program that played Go didn’t come until the late 1960s, when Albert Zobrist wrote one as part of his University of Wisconsin PhD thesis in computer science. In 1994, while Chinook was matching Marion Tinsley blow for blow, Go machines were helpless against professional human players. Things have changed fast, as Lee Se-dol found out. What does a top-tier Go machine like AlphaGo, without a small human crouched inside it to move the pieces, actually do? It doesn’t label each node of the Go tree with a W or an L (we don’t need D, since there aren’t draws in standard Go). The tree of Go is deep and bushy; no one can solve the damn thing. But as with Fermat’s test, we can be content with an approximation, a function that assigns each position of the board a score in some readily computable way.

Succeeding With AI: How to Make AI Work for Your Business by Veljko Krunic

AI winter, Albert Einstein, algorithmic trading, AlphaGo, Amazon Web Services, anti-fragile, anti-pattern, artificial general intelligence, autonomous vehicles, Bayesian statistics, bioinformatics, Black Swan, Boeing 737 MAX, business process, cloud computing, commoditize, computer vision, correlation coefficient, data is the new oil, data science, deep learning, DeepMind, en.wikipedia.org, fail fast, Gini coefficient, high net worth, information retrieval, Internet of things, iterative process, job automation, Lean Startup, license plate recognition, minimum viable product, natural language processing, recommendation engine, self-driving car, sentiment analysis, Silicon Valley, six sigma, smart cities, speech recognition, statistical model, strong AI, tail risk, The Design of Experiments, the scientific method, web application, zero-sum game

WIRED. 2008 Jun 23 [cited 2018 Jul 2]. Available from: https:// www.wired.com/2008/06/pb-theory/ Wikimedia Foundation. AlphaGo versus Lee Sedol. Wikipedia. [Cited 2018 Jun 21.] Available from: https://en.wikipedia.org/w/index.php?title=AlphaGo_versus _Lee_Sedol&oldid=846917953 DeepMind. AlphaGo. DeepMind. [Cited 2018 Jul 2.] Available from: https:// deepmind.com/research/alphago/ Wikimedia Foundation. AlphaGo. Wikipedia. [Cited 2019 Jul 10.] Available from: https://en.wikipedia.org/w/index.php?title=AlphaGo The AlphaStar Team. AlphaStar: Mastering the real-time strategy game StarCraft II. DeepMind. [Cited 2019 Sep 9.]

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

"Margaret Hamilton" Apollo, 3D printing, additive manufacturing, AI winter, Albert Einstein, algorithmic trading, AlphaGo, Anthropocene, artificial general intelligence, augmented reality, autonomous vehicles, backpropagation, barriers to entry, basic income, behavioural economics, Benoit Mandelbrot, Berlin Wall, Big bang: deregulation of the City of London, Big Tech, Bletchley Park, blockchain, Boeing 747, brain emulation, Brexit referendum, call centre, carbon tax, charter city, citizen journalism, Claude Shannon: information theory, Clayton Christensen, clean tech, clean water, cognitive load, Columbian Exchange, coronavirus, cosmic microwave background, COVID-19, creative destruction, CRISPR, crony capitalism, cyber-physical system, dark matter, David Graeber, deep learning, DeepMind, deindustrialization, dematerialisation, Demis Hassabis, demographic dividend, Deng Xiaoping, deplatforming, discovery of penicillin, disruptive innovation, Donald Trump, double entry bookkeeping, Easter island, Edward Jenner, Edward Lorenz: Chaos theory, Elon Musk, en.wikipedia.org, endogenous growth, energy security, energy transition, epigenetics, Eratosthenes, Ernest Rutherford, Eroom's law, fail fast, false flag, Fellow of the Royal Society, flying shuttle, Ford Model T, Francis Fukuyama: the end of history, general purpose technology, germ theory of disease, glass ceiling, global pandemic, Goodhart's law, Google Glasses, Google X / Alphabet X, GPT-3, Haber-Bosch Process, hedonic treadmill, Herman Kahn, Higgs boson, hive mind, hype cycle, Hyperloop, Ignaz Semmelweis: hand washing, Innovator's Dilemma, intangible asset, interchangeable parts, Internet of things, invention of agriculture, invention of the printing press, invention of the steam engine, invention of the telegraph, invisible hand, Isaac Newton, ITER tokamak, James Watt: steam engine, James Webb Space Telescope, Jeff Bezos, jimmy wales, job automation, Johannes Kepler, John von Neumann, Joseph Schumpeter, Kenneth Arrow, Kevin Kelly, Kickstarter, knowledge economy, knowledge worker, Large Hadron Collider, liberation theology, lockdown, lone genius, loss aversion, Louis Pasteur, Mark Zuckerberg, Martin Wolf, megacity, megastructure, Menlo Park, Minecraft, minimum viable product, mittelstand, Modern Monetary Theory, Mont Pelerin Society, Murray Gell-Mann, Mustafa Suleyman, natural language processing, Neal Stephenson, nuclear winter, nudge unit, oil shale / tar sands, open economy, OpenAI, opioid epidemic / opioid crisis, PageRank, patent troll, Peter Thiel, plutocrats, post scarcity, post-truth, precautionary principle, public intellectual, publish or perish, purchasing power parity, quantum entanglement, Ray Kurzweil, remote working, rent-seeking, Republic of Letters, Richard Feynman, Robert Gordon, Robert Solow, secular stagnation, shareholder value, Silicon Valley, Silicon Valley ideology, Simon Kuznets, skunkworks, Slavoj Žižek, sovereign wealth fund, spinning jenny, statistical model, stem cell, Steve Jobs, Stuart Kauffman, synthetic biology, techlash, TED Talk, The Rise and Fall of American Growth, the scientific method, The Wealth of Nations by Adam Smith, Thomas Bayes, Thomas Kuhn: the structure of scientific revolutions, Thomas Malthus, TikTok, total factor productivity, transcontinental railway, Two Sigma, Tyler Cowen, Tyler Cowen: Great Stagnation, universal basic income, uranium enrichment, We wanted flying cars, instead we got 140 characters, When a measure becomes a target, X Prize, Y Combinator

The pivotal moment in the contest was the now-legendary thirty-seventh move of the second game. AlphaGo played a move completely outside the game's conventional thinking. It just didn't make sense. But later in the match it proved decisive. With that move Go was changed forever. So was our worldview; machines could forge new paths, paths hidden from us. They could be radically creative and deploy revolutionary insight. In the history of Go, move thirty-seven was a big idea that, in thousands of years, humans hadn't thought of. A machine did. Thanks to the program, previously unthinkable moves are now part of the tactical lexicon. AlphaGo, like AlphaFold, jolted the game out of a local maximum.

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Founded in London in 2010, its stated goal was to ‘solve intelligence’ by pioneering the fusion and furtherance of modern ML techniques and neuroscience: to build not just artificial intelligence (AI), but artificial general intelligence (AGI), a multi-purpose learning engine analogous to the human mind. DeepMind made headlines when it created the first software to beat a human champion at Go. In 2016 its AlphaGo program played 9th dan Go professional Lee Sedol over five matches in Seoul and, in a shock result beyond even that of CASP13, won four of them. This was years, even decades ahead of what anyone had expected. There are 1082 atoms in the observable universe but 10172 possible positions in Go; this makes it exceptionally difficult for classic machine-driven approaches, exponentially tougher than chess.7 Only a new approach to AI could have triumphed.

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Yet what AI will do to human knowledge, to our ability to comprehend and see and discover and create, has received coverage incommensurate with its potential impact. That should change. AI is a cognitive technology, a meta-idea, and so goes to the heart of questions about how ideas are produced. New forms of knowledge and perception, quite unlike those of humans, beyond our unaided capabilities, are starting to accelerate the production of ideas. AlphaGo and AlphaFold are signposts to an era where those closest to a particular toolset are best positioned to push back the frontiers of knowledge. Proximity to these tools helps accelerate discovery, producing watershed moments like those at Seoul and Cancun, not to mention other moves from DeepMind alone into areas like medical diagnosis and the modelling of physical processes.

Red Flags: Why Xi's China Is in Jeopardy by George Magnus

"World Economic Forum" Davos, 3D printing, 9 dash line, Admiral Zheng, AlphaGo, Asian financial crisis, autonomous vehicles, balance sheet recession, banking crisis, Bear Stearns, Bretton Woods, Brexit referendum, BRICs, British Empire, business process, capital controls, carbon footprint, Carmen Reinhart, cloud computing, colonial exploitation, corporate governance, crony capitalism, currency manipulation / currency intervention, currency peg, demographic dividend, demographic transition, Deng Xiaoping, Doha Development Round, Donald Trump, financial deregulation, financial innovation, financial repression, fixed income, floating exchange rates, full employment, general purpose technology, Gini coefficient, global reserve currency, Great Leap Forward, high net worth, high-speed rail, hiring and firing, Hyman Minsky, income inequality, industrial robot, information security, Internet of things, invention of movable type, Joseph Schumpeter, Kenneth Rogoff, Kickstarter, labour market flexibility, labour mobility, land reform, Malacca Straits, means of production, megacity, megaproject, middle-income trap, Minsky moment, money market fund, moral hazard, non-tariff barriers, Northern Rock, offshore financial centre, old age dependency ratio, open economy, peer-to-peer lending, pension reform, price mechanism, purchasing power parity, regulatory arbitrage, rent-seeking, reserve currency, rising living standards, risk tolerance, Shenzhen special economic zone , smart cities, South China Sea, sovereign wealth fund, special drawing rights, special economic zone, speech recognition, The Wealth of Nations by Adam Smith, total factor productivity, trade route, urban planning, vertical integration, Washington Consensus, women in the workforce, working-age population, zero-sum game

We can see the relevance of this and other governance issues by taking a detailed look at one of the exciting areas that could help China to generate new productivity gains in future and circumvent the middle-income trap: technology. Going all out for technology leadership In March 2016, Lee Sedol, a South Korean master of the ancient and complex board game Go, was defeated by AlphaGo, a Google computer program. Two months later, AlphaGo was deployed in China to take on the world’s leading Go player, Ke Jie, and won. The event is alleged to have had profound consequences on the thinking of leading Chinese scientists and politicians, who were taken aback by the cutting edge in artificial intelligence (AI) seemingly shown by the US.

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Their focus is on key sectors, including advanced rail, ship, aviation and aerospace equipment, agricultural machinery and technology, low and new-energy vehicles, new materials, robotics, biopharmaceuticals and high-end medical equipment, integrated circuits, and 5G mobile telecommunications. Taken aback by AlphaGo’s victory, as noted earlier, China stepped up a few gears to formalise and launch nationally an ambitious AI strategy, already underway at the local government level. A year after the match, the State Council set out the Next Generation AI Development Plan with the goal of boosting China’s AI status, from being in line with competitors by 2202, to world-leading by 2025, and the world’s primary source by 2030.

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Xu Huang and Michael Harris Bond, Edward Elgar Publishing, 2012 Yasheng Huang, Capitalism with Chinese Characteristics: Entrepreneurship and the State, MIT Press, 2008 INDEX Unattributed entries, for example geography, refer to the book’s metatopic, China. 1st Five-Year Plan (i) 1st Party Congress (Chinese Communist Party) (i) 5G networks (i) 9/11 (i) 11th Central Committee, third plenum (i) 11th Party Congress (i) 13th Five-Year Plan advanced information and digital systems (i) aims of (i) BRI incorporated into (i) manufacturing and technology (i) pension schemes (i) transport (i) 14th Party Congress (i) 15th Party Congress (i) 18th Party Congress (i), (ii) third plenum (i), (ii), (iii), (iv) 19th Party Congress ‘central contradiction’ restated (i) supply-side reforms (i) Xi addresses (i), (ii), (iii) 21st-Century Maritime Silk Road see Belt and Road Initiative 2000 Olympic Games (i) 2008 Olympic Games (i), (ii) Abe, Shinzō (i) Acemoglu, Daron (i) Action Plan (AI) (i) Addis Ababa (i), (ii) Africa Admiral Zheng (i) BRI concept and (i) Chinese interest in (i) colonialist criticism (i) Japan and (i) loans to (i) metal ore from (i) Silk Road (i) Sub-Saharan Africa (i) ageing trap (i) see also population statistics birth rate (i) consequences of ageing (i) demographic dividends (i), (ii) family structures (i) healthcare (i) ‘iron rice bowl’ (i) mortality rates (i) non-communicable disease (i) old-age dependency ratios (i), (ii), (iii) pensions (i) retirement age (i) Agricultural Bank of China (i) Agricultural Development Bank of China (i) agriculture (i), (ii), (iii) Agriculture and Rural Affairs, Ministry of (i) AI (i), (ii), (iii) AI Innovation and Development Megaproject (i) AI Potential Index (i) Air China (i) Airbus (i), (ii) Aixtron SE (i) Alibaba (i), (ii), (iii), (iv) Alphabet (i) AlphaGo (i), (ii) Alsace-Lorraine (i) Amoy (i) Anbang Insurance (i), (ii), (iii) Angola (i) Angus Maddison project (i) Ant Financial (i), (ii) anti-corruption campaigns 2014 (i) in financial sector (i) Ming dynasty (i) Xi launches (i), (ii), (iii) Apple (i), (ii), (iii) Arab Spring (i) Arabian Sea (i) Arctic (i) Argentina (i), (ii), (iii) Armenia (i) Article IV report (IMF) (i) see also IMF ASEAN (Association of South East Asian Nations) (i), (ii) Asia China the dominant power (i), (ii) Global Innovation Index (i) Obama tours (i) Paul Krugman’s book (i) ‘Pivot to Asia’ (i) state enterprises and intervention (i) Asia-Pacific Economic Cooperation (i) Asian Development Bank (i), (ii) Asian Financial Crisis (1997–98) (i), (ii), (iii), (iv) Asian Infrastructure Investment Bank (i), (ii), (iii), (iv) Asian Tiger economies (i), (ii), (iii), (iv) Atatürk, Mustafa Kemal (i) Australia Chinese investment in (i) Chinese seapower and (i) free trade agreement with (i) immigration rates and WAP (i) innovation statistics (i) pushing back against China (i), (ii) Renminbi reserves (i) Austria (i), (ii) Austria-Hungary (i) automobiles (i), (ii) Babylonia (i) bad debt see debt bad loans (i), (ii) Baidu (i), (ii) Balkans (i) Baltic (i) Baluchistan (i) Bandung (i), (ii) Bangladesh heavy involvement with (i) Indian sphere of influence (i) low value manufacturing moves to (i), (ii) Padma Bridge project (i) Bank of China (i), (ii) Bank for International Settlements (i) banks (i) see also debt and finance; WMPs (wealth management products) assets growth, effects of (i) bad loans problem (i) bank failures (i) central bank created (i) major banks see individual entries non-performing loans (i), (ii), (iii), (iv), (v), (vi) regulators step in (i) repo market (i), (ii) shadow banks (i), (ii), (iii), (iv), (v), (vi), (vii), (viii), (ix) n18 smaller banks at risk (i) Baoneng Group (i) Baosteel (i) BBC (i) Bear Stearns (i) Beijing see also Peking 1993 (i) central and local government (i), (ii), (iii) Mao arrives (i) Olympics (i) pollution (i) price rises (i) US delegation (i) water supply (i) Beijing-Hangzhou Grand Canal (i) Belarus (i) Belgrade (i) Bell (i) Belt and Road Initiative (BRI) (i) debt problems in recipient nations (i) description, size and nature (i) economic drivers (i) financing and funding (i), (ii) first Forum (i) geopolitical drivers and disputes (i) Marshall Plan and (i), (ii) project investment (i), (ii) reordering of Indo-Pacific (i) Silk Road and (i), (ii), (iii) ways of looking at (i), (ii) benevolent dictators (i) Bering Strait (i) big data (i) birth rate (i) see also population statistics Bloomberg (i) Bo Xilai (i) Boeing (i), (ii) bond markets (i) Bosphorus Strait (i) Boxers (i), (ii) Brazil BRICS (i), (ii), (iii) middle income, example of (i), (ii), (iii) US steel imports (i) Bretton Woods (i) Brexit (i), (ii) BRICS (i) ‘Building Better Global BRICs’ (Goldman Sachs) (i) BRICS Bank (i), (ii) Britain (i) Boxer Rebellion (i) Brexit (i), (ii) Hong Kong (i) new claims (i) Renminbi reserves (i) Broadcom (i) Brunei Darussalam (i), (ii), (iii) Brzezinski, Zbigniew (i) Budapest (i) budget constraints (i), (ii) Bulgaria (i) Bund, the (Shanghai) (i) Bundesbank (i) bureaucracy (i), (ii), (iii), (iv) Bush, George W.

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

"World Economic Forum" Davos, Ada Lovelace, agricultural Revolution, AI winter, Albert Einstein, Alexey Pajitnov wrote Tetris, algorithmic management, algorithmic trading, AlphaGo, Amazon Mechanical Turk, Apple II, artificial general intelligence, Automated Insights, autonomous vehicles, backpropagation, Bletchley Park, book scanning, borderless world, call centre, cellular automata, Charles Babbage, Claude Shannon: information theory, cloud computing, computer vision, Computing Machinery and Intelligence, correlation does not imply causation, crowdsourcing, deep learning, DeepMind, driverless car, drone strike, Elon Musk, Flash crash, Ford Model T, friendly AI, game design, Geoffrey Hinton, global village, Google X / Alphabet X, Hans Moravec, hive mind, industrial robot, information retrieval, Internet of things, iterative process, Jaron Lanier, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, Kickstarter, Kodak vs Instagram, Law of Accelerating Returns, life extension, Loebner Prize, machine translation, Marc Andreessen, Mark Zuckerberg, Menlo Park, Mustafa Suleyman, natural language processing, Nick Bostrom, Norbert Wiener, out of africa, PageRank, paperclip maximiser, pattern recognition, radical life extension, Ray Kurzweil, recommendation engine, remote working, RFID, scientific management, self-driving car, Silicon Valley, Skype, smart cities, Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia, social intelligence, speech recognition, Stephen Hawking, Steve Jobs, Steve Wozniak, Steven Pinker, strong AI, superintelligent machines, tech billionaire, technological singularity, The Coming Technological Singularity, The Future of Employment, Tim Cook: Apple, Tony Fadell, too big to fail, traumatic brain injury, Turing machine, Turing test, Vernor Vinge, warehouse robotics, Watson beat the top human players on Jeopardy!

Just ten hours later, Google announced that DeepMind had built an AI able to not only beat every Go program ever built, but also (for the first time) a professional-level human player. Things moved quickly from there. By March 2016, the world’s greatest Go player, Lee Sedol, was taking on Google’s AlphaGo AI in a South Korean hotel room, watched by more than 60 million people around the globe. At the end of a series, AlphaGo had beaten Sedol four games to one. Not everything about the myriad changes prompted by AI is rosy, of course. Artificial Intelligence will also be responsible for the disruption of many professions and livelihoods over the years to come, although this will also create new, previously unimagined opportunities for human workers.

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_r=1 2 Rogers, Adam, ‘We Asked a Robot to Write an Obit for AI Pioneer Marvin Minsky’, Wired, 26 January 2016: wired.com/2016/01/we-asked-a-robot-to-write-an-obit-for-ai-pioneer-marvin-minsky/ 3 Minsky, Marvin, Society of Mind (New York: Simon and Schuster, 1986). 4 HAL 90210, ‘No Go: Facebook Fails to Spoil Google’s Big AI Day’, Guardian, 28 January 2016: theguardian.com/technology/2016/jan/28/go-playing-facebook-spoil-googles-ai-deepmind 5 Moyer, Christopher, ‘How Google’s AlphaGo Beat a Go World Champion,’ Atlantic, 28 March 2016: http://www.theatlantic.com/technology/archive/2016/03/the-invisible-opponent/475611 6 ‘US Military Shelves Google Robot Plan Over “Noise Concerns”’, BBC News, 30 December 2015: bbc.co.uk/news/technology-35201183 7 Collins, Ben, ‘Meet the Robot Writing “Friends” Sequels’, Daily Beast, 20 January 2016: thedailybeast.com/articles/2016/01/20/meet-the-robot-writing-friends-sequels.html Index The page references in this index correspond to the printed edition from which this ebook was created.

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To find a specific word or phrase from the index, please use the search feature of your ebook reader. 2001: A Space Odyssey (1968) 2, 228, 242–4 2045 Initiative 217 accountability issues 240–4, 246–8 Active Citizen 120–2 Adams, Douglas 249 Advanced Research Projects Agency (ARPA) 19–20, 33 Affectiva 131 Age of Industry 6 Age of Information 6 agriculture 150–1, 183 AI Winters 27, 33 airlines, driverless 144 algebra 20 algorithms 16–17, 59, 67, 85, 87, 88, 145, 158–9, 168, 173, 175–6, 183–4, 186, 215, 226, 232, 236 evolutionary 182–3, 186–8 facial recognition 10–11, 61–3 genetic 184, 232, 237, 257 see also back-propagation AliveCor 87 AlphaGo (AI Go player) 255 Amazon 153, 154, 198, 236 Amy (AI assistant) 116 ANALOGY program 20 Analytical Engine 185 Android 59, 114, 125 animation 168–9 Antabi, Bandar 77–9 antennae 182, 183–5 Apple 6, 35, 56, 65, 90–1, 108, 110–11, 113–14, 118–19, 126–8, 131–2, 148–9, 158, 181, 236, 238–9, 242 Apple iPhone 108, 113, 181 Apple Music 158–9 Apple Watch 66, 199 architecture 186 Artificial Artificial Intelligence (AAI) 153, 157 Artificial General Intelligence (AGI) 226, 230–4, 239–40, 254 Artificial Intelligence (AI) 2 authentic 31 development problems 23–9, 32–3 Good Old-Fashioned (Symbolic) 22, 27, 29, 34, 36, 37, 39, 45, 49–52, 54, 60, 225 history of 5–34 Logical Artificial Intelligence 246–7 naming of 19 Narrow/Weak 225–6, 231 new 35–63 strong 232 artificial stupidity 234–7 ‘artisan economy’ 159–61 Asimov, Isaac 227, 245, 248 Athlone Industries 242 Atteberry, Kevan J. 112 Automated Land Vehicle in a Neural Network (ALVINN) 54–5 automation 141, 144–5, 150, 159 avatars 117, 193–4, 196–7, 201–2 Babbage, Charles 185 back-propagation 50–3, 57, 63 Bainbridge, William Sims 200–1, 202, 207 banking 88 BeClose smart sensor system 86 Bell Communications 201 big business 31, 94–6 biometrics 77–82, 199 black boxes 237–40 Bletchley Park 14–15, 227 BMW 128 body, machine analogy 15 Bostrom, Nick 235, 237–8 BP 94–95 brain 22, 38, 207–16, 219 Brain Preservation Foundation 219 Brain Research Through Advanced Innovative Neurotechnologies 215–16 brain-like algorithms 226 brain-machine interfaces 211–12 Breakout (video game) 35, 36 Brin, Sergey 6–7, 34, 220, 231 Bringsjord, Selmer 246–7 Caenorhabditis elegans 209–10, 233 calculus 20 call centres 127 Campbell, Joseph 25–6 ‘capitalisation effect’ 151 cars, self-driving 53–56, 90, 143, 149–50, 247–8 catering 62, 189–92 chatterbots 102–8, 129 Chef Watson 189–92 chemistry 30 chess 1, 26, 28, 35, 137, 138–9, 152–3, 177, 225 Cheyer, Adam 109–10 ‘Chinese Room, the’ 24–6 cities 89–91, 96 ‘clever programming’ 31 Clippy (AI assistant) 111–12 clocks, self-regulating 71–2 cognicity 68–9 Cognitive Assistant that Learns and Organises (CALO) 112 cognitive psychology 12–13 Componium 174, 176 computer logic 8, 10–11 Computer Science and Artificial Intelligence Laboratory (CSAIL) 96–7 Computer-Generated Imagery (CGI) 168, 175, 177 computers, history of 12–17 connectionists 53–6 connectomes 209–10 consciousness 220–1, 232–3, 249–51 contact lenses, smart 92 Cook, Diane 84–6 Cook, Tim 91, 179–80 Cortana (AI assistant) 114, 118–19 creativity 163–92, 228 crime 96–7 curiosity 186 Cyber-Human Systems 200 cybernetics 71–4 Dartmouth conference 1956 17–18, 19, 253 data 56–7, 199 ownership 156–7 unlabelled 57 death 193–8, 200–1, 206 Deep Blue 137, 138–9, 177 Deep Knowledge Ventures 145 Deep Learning 11–12, 56–63, 96–7, 164, 225 Deep QA 138 DeepMind 35–7, 223, 224, 245–6, 255 Defense Advanced Research Projects Agency (DARPA) 33, 112 Defense Department 19, 27–8 DENDRAL (expert system) 29–31 Descartes, René 249–50 Dextro 61 DiGiorgio, Rocco 234–5 Digital Equipment Corporation (DEC) 31 Digital Reasoning 208–9 ‘Digital Sweatshops’ 154 Dipmeter Advisor (expert system) 31 ‘do engines’ 110, 116 Dungeons and Dragons Online (video game) 197 e-discovery firms 145 eDemocracy 120–1 education 160–2 elderly people 84–6, 88, 130–1, 160 electricity 68–9 Electronic Numeric Integrator and Calculator (ENIAC) 12, 13, 92 ELIZA programme 129–30 Elmer and Elsie (robots) 74–5 email filters 88 employment 139–50, 150–62, 163, 225, 238–9, 255 eNeighbor 86 engineering 182, 183–5 Enigma machine 14–15 Eterni.me 193–7 ethical issues 244–8 Etsy 161 Eurequa 186 Eve (robot scientist) 187–8 event-driven programming 79–81 executives 145 expert systems 29–33, 47–8, 197–8, 238 Facebook 7, 61–2, 63, 107, 153, 156, 238, 254–5 facial recognition 10–11, 61–3, 131 Federov, Nikolai Fedorovich 204–5 feedback systems 71–4 financial markets 53, 224, 236–7 Fitbit 94–95 Flickr 57 Floridi, Luciano 104–5 food industry 141 Ford 6, 230 Foxbots 149 Foxconn 148–9 fraud detection 88 functional magnetic resonance imaging (fMRI) 211 Furbies 123–5 games theory 100 Gates, Bill 32, 231 generalisation 226 genetic algorithms 184, 232, 237, 257 geometry 20 glial cells 213 Go (game) 255 Good, Irving John 227–8 Google 6–7, 34, 58–60, 67, 90–2, 118, 126, 131, 155–7, 182, 213, 238–9 ‘Big Dog’ 255–6 and DeepMind 35, 245–6, 255 PageRank algorithm 220 Platonic objects 164, 165 Project Wing initiative 144 and self-driving cars 56, 90, 143 Google Books 180–1 Google Brain 61, 63 Google Deep Dream 163–6, 167–8, 184, 186, 257 Google Now 114–16, 125, 132 Google Photos 164 Google Translate 11 Google X (lab) 61 Government Code and Cypher School 14 Grain Marketing Adviser (expert system) 31 Grímsson, Gunnar 120–2 Grothaus, Michael 69, 93 guilds 146 Halo (video game) 114 handwriting recognition 7–8 Hank (AI assistant) 111 Hawking, Stephen 224 Hayworth, Ken 217–21 health-tracking technology 87–8, 92–5 Healthsense 86 Her (film, 2013) 122 Herd, Andy 256–7 Herron, Ron 89–90 High, Rob 190–1 Hinton, Geoff 48–9, 53, 56, 57–61, 63, 233–4 hive minds 207 holograms 217 HomeChat app 132 homes, smart 81–8, 132 Hopfield, John 46–7, 201 Hopfield Nets 46–8 Human Brain Project 215–16 Human Intelligence Tasks (HITs) 153, 154 hypotheses 187–8 IBM 7–11, 136–8, 162, 177, 189–92 ‘IF THEN’ rules 29–31 ‘If-This-Then-That’ rules 79–81 image generation 163–6, 167–8 image recognition 164 imagination 178 immortality 204–7, 217, 220–1 virtual 193–8, 201–4 inferences 97 Infinium Robotics 141 information processing 208 ‘information theory’ 16 Instagram 238 insurance 94–5 Intellicorp 33 intelligence 208 ambient 74 ‘intelligence explosion’ 228 top-down view 22, 25, 246 see also Artificial Intelligence internal combustion engine 140–1, 150–1 Internet 10, 56 disappearance 91 ‘Internet of Things’ 69, 70, 83, 249, 254 invention 174, 178, 179, 182–5, 187–9 Jawbone 78–9, 92–3, 254 Jennings, Ken 133–6, 138–9, 162, 189 Jeopardy!

The Ages of Globalization by Jeffrey D. Sachs

Admiral Zheng, AlphaGo, Big Tech, biodiversity loss, British Empire, Cape to Cairo, circular economy, classic study, colonial rule, Columbian Exchange, Commentariolus, coronavirus, cotton gin, COVID-19, cuban missile crisis, decarbonisation, DeepMind, demographic transition, Deng Xiaoping, domestication of the camel, Donald Trump, en.wikipedia.org, endogenous growth, European colonialism, general purpose technology, global supply chain, Great Leap Forward, greed is good, income per capita, invention of agriculture, invention of gunpowder, invention of movable type, invention of the steam engine, invisible hand, Isaac Newton, James Watt: steam engine, job automation, John von Neumann, joint-stock company, lockdown, Louis Pasteur, low skilled workers, mass immigration, Nikolai Kondratiev, ocean acidification, out of africa, packet switching, Pax Mongolica, precision agriculture, profit maximization, profit motive, purchasing power parity, rewilding, South China Sea, spinning jenny, Suez canal 1869, systems thinking, The inhabitant of London could order by telephone, sipping his morning tea in bed, the various products of the whole earth, The Wealth of Nations by Adam Smith, trade route, transatlantic slave trade, Turing machine, Turing test, urban planning, warehouse robotics, Watson beat the top human players on Jeopardy!, wikimedia commons, zoonotic diseases

More recently, we have seen stunning breakthroughs in deep neural networks, that is neural networks with hundreds of layers of artificial neurons. In 2016, an AI system, AlphaGo from the company Deep Mind, took on the world’s eighteen-time world Go champion, Lee Sedol. Go is a board game of such sophistication and subtlety that it was widely believed that machines would be unable to compete with human experts for years or decades to come. Sedol, like Kasparov before him, believed that he would triumph easily over AlphaGo. In the event, he was decisively defeated by the system. Then, to make matters even more dramatic, AlphaGo was decisively defeated by a next-generation AI system that learned Go from scratch in self-play over a few hours.

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Abbasid Caliphate, 87 Achaemenid Empire, 74–75, 77 Achaemenid Persia, 66 Africa, 23, 33; diseases of, 152; European empires dividing, 153; Europe’s onslaught of, 151–52; farm animals of, 55; indigenous people and slaves from, 116–20; migration from, 34–35, 41; slave trade from, 118, 118–19; tsetse-infested, 56; wild ass of, 58 Agenda 21, 197 agriculture, 3; in ecological zones, 45–46; emergence of, 41–42, 42; horses used in, 65–66; in Neolithic Age, 5, 8; population and, 135; sedentism leading to, 41; in Song Dynasty, 90; sustainable, 13 air pollution, 187–88, 190, 190 Akkadian Empire, 66 Alexander the Great, 28, 65–66, 75–76, 76 Alexander VI (Spanish pope), 108–9 Alexandria, 70 algal blooms, 190, 191 algorithms, 174–75 Allison, Graham, 193 alluvial civilizations, 46–48 alpacas, 56, 61 AlphaGo (AI system), 176 Anatolia, migration from, 64 ancient urban centers, 67 Anglo-American hegemony, 130, 153–56 animal domestication, 54–56 animal husbandry, 50 anopheles gambiae (mosquito), 152 Anthony, Marc, 77 anti-fascist alliance, 207 anti-trade policy, of China, 97 Aquinas, Thomas, 78 Arab caliphates, 88 Aristotle, 69, 70, 75, 77, 212 artificial intelligence, 174, 175–76, 185 artificial neural networks, 174 Asia: climate and population in, 113; East, 165; Europe’s divergence with, 144–45; fossil fuels polluting air of, 190; migration from, 227n11; steppes of, 53; trade control sought by, 107–8 Asian tigers, 180 Assyrian kingdom, 66 Athens, 74 Aurelius, Marcus, 84 automobiles, 141 Avars, 86 Axial Age, 70–72 Babylonian kingdom, 66 Bacon, Francis, 106 Bacon, Roger, 136 Battle of Plassey (1757), 148 Battle of Tours (732 CE), 87 Bayt-al-Hikmah (House of Wisdom), 78 Beckert, Sven, 120–21 Bell Labs, 173 Belt and Road Initiative (BRI), 205, 206 Beringian land bridge, 19 biodegradable waste products, 199–200 biodiversity, 17–18, 184, 188–89, 199 biology, 75 biomass burning, 190 Black Death, 92 blank-slate learning (tabula rasa), 176 Bolshevik Revolution, 113 book writings, 71 botany, 106 Boulton, Richard, 137 Boxer Rebellion, 147 BRI.

The Job: The Future of Work in the Modern Era by Ellen Ruppel Shell

"Friedman doctrine" OR "shareholder theory", 3D printing, Abraham Maslow, affirmative action, Affordable Care Act / Obamacare, Airbnb, airport security, Albert Einstein, AlphaGo, Amazon Mechanical Turk, basic income, Baxter: Rethink Robotics, big-box store, blue-collar work, Buckminster Fuller, call centre, Capital in the Twenty-First Century by Thomas Piketty, Clayton Christensen, cloud computing, collective bargaining, company town, computer vision, corporate governance, corporate social responsibility, creative destruction, crowdsourcing, data science, deskilling, digital divide, disruptive innovation, do what you love, Donald Trump, Downton Abbey, Elon Musk, emotional labour, Erik Brynjolfsson, factory automation, follow your passion, Frederick Winslow Taylor, future of work, game design, gamification, gentrification, glass ceiling, Glass-Steagall Act, hiring and firing, human-factors engineering, immigration reform, income inequality, independent contractor, industrial research laboratory, industrial robot, invisible hand, It's morning again in America, Jeff Bezos, Jessica Bruder, job automation, job satisfaction, John Elkington, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, Joseph Schumpeter, Kickstarter, knowledge economy, knowledge worker, Kodak vs Instagram, labor-force participation, low skilled workers, Lyft, manufacturing employment, Marc Andreessen, Mark Zuckerberg, means of production, move fast and break things, new economy, Norbert Wiener, obamacare, offshore financial centre, Paul Samuelson, precariat, Quicken Loans, Ralph Waldo Emerson, risk tolerance, Robert Gordon, Robert Shiller, Rodney Brooks, Ronald Reagan, scientific management, Second Machine Age, self-driving car, shareholder value, sharing economy, Silicon Valley, Snapchat, Steve Jobs, stock buybacks, TED Talk, The Chicago School, The Theory of the Leisure Class by Thorstein Veblen, Thomas L Friedman, Thorstein Veblen, Tim Cook: Apple, Uber and Lyft, uber lyft, universal basic income, urban renewal, Wayback Machine, WeWork, white picket fence, working poor, workplace surveillance , Y Combinator, young professional, zero-sum game

One striking hallmark of that change came in March 2016, when the Google artificial intelligence program AlphaGo beat South Korean world master Lee Sedol in Go, an ancient board game known for its baffling complexity. Each game of Go has 10,360 possible moves, an unimaginably large number that makes exhaustive evaluation of individual moves utterly unrealistic. This complexity makes the game far more unpredictable than chess; rather than see possibilities, players perceive possibilities either consciously or unconsciously by gazing at the pieces on the board. To do something similar, AlphaGo relies on what scientists call neural networks, essentially mathematical versions of the networks of nerve cells operating in biological systems.

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To do something similar, AlphaGo relies on what scientists call neural networks, essentially mathematical versions of the networks of nerve cells operating in biological systems. Much like the human brain, AlphaGo has an unquenchable ability to learn, and not only from “observing” games played by human experts. The program is designed to play millions of games against itself, continuously improving its performance without any human intervention. In other words, AlphaGo seems to have what in the previous chapter was deemed critical for good work in the digital age—analytic skill. That is, these machines have the ability to visualize, articulate, conceptualize, or solve problems by making decisions that are sensible given the available information.

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That is, these machines have the ability to visualize, articulate, conceptualize, or solve problems by making decisions that are sensible given the available information. And they are getting better and better at it. AlphaGo is just a sample of what scientists hope to accomplish with this line of research, and any number of corporations—large and small—are hot on the trail. For example, tech giant IBM boasts that “over the next five years, machine learning applications will lead to new breakthroughs that will amplify human abilities, assist us in making good choices, look out for us and help us navigate our world in powerful new ways.” The company targets five arenas ripe for disruption: medicine, education, retail, online security, and what it calls “sentient cities”—apparently, cities that through technology know what residents want and need before the residents themselves do.

Human + Machine: Reimagining Work in the Age of AI by Paul R. Daugherty, H. James Wilson

3D printing, AI winter, algorithmic management, algorithmic trading, AlphaGo, Amazon Mechanical Turk, Amazon Robotics, augmented reality, autonomous vehicles, blockchain, business process, call centre, carbon footprint, circular economy, cloud computing, computer vision, correlation does not imply causation, crowdsourcing, data science, deep learning, DeepMind, digital twin, disintermediation, Douglas Hofstadter, driverless car, en.wikipedia.org, Erik Brynjolfsson, fail fast, friendly AI, fulfillment center, future of work, Geoffrey Hinton, Hans Moravec, industrial robot, Internet of things, inventory management, iterative process, Jeff Bezos, job automation, job satisfaction, knowledge worker, Lyft, machine translation, Marc Benioff, natural language processing, Neal Stephenson, personalized medicine, precision agriculture, Ray Kurzweil, recommendation engine, RFID, ride hailing / ride sharing, risk tolerance, robotic process automation, Rodney Brooks, Salesforce, Second Machine Age, self-driving car, sensor fusion, sentiment analysis, Shoshana Zuboff, Silicon Valley, Snow Crash, software as a service, speech recognition, tacit knowledge, telepresence, telepresence robot, text mining, the scientific method, uber lyft, warehouse automation, warehouse robotics

Banks use them for fraud detection; dating websites use them to suggest potential matches; marketers use them to try to predict who will respond favorably to an ad; and photo-sharing sites use them for automatic face recognition. We’ve come a long way since checkers. In 2016, Google’s AlphaGo demonstrated a significant machine-learning advance. For the first time, a computer beat a human champion of Go, a game far more complex than checkers or chess. In a sign of the times, AlphaGo exhibited moves that were so unexpected that some observers deemed them to actually be creative and even “beautiful.”c The growth of AI and machine learning has been intermittent over the decades, but the way that they’ve crept into products and business operations in recent years shows that they’re more than ready for prime time.

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See General Electric (GE) Geekbot, 196 General Data Protection Regulation (GDPR), 108, 124 General Electric (GE), 10, 75, 183–184, 194–195, 209 Predix system, 27, 29–30, 75 General Motors (GM), 67, 128 Gershgorn, Dave, 23 gesture recognition, 65 Gigster, 52–54, 59 GlaxoSmithKline, 99 GNS Healthcare, 10, 72–74, 80 Goldman Sachs, 49 Google, 209 AlphaGo, 42–43 autocomplete feature, 197, 200 Home, 146 marketing, 99 PAIR initiative, 179 trainers at, 179 Gorbis, Marina, 187 government regulations, 213 GPS navigation, 6–7 Gridspace Sift, 196 guardrails, 168–169 Harrison, Brent, 130 Haverford College, 71 health care, 82 cost reduction in, 167–168 embodied AI in, 150–151 hospital bed allocation in, 173 personalized, 79–80 precision medicine in, 72–74 radiology augmentation in, 139, 141–143 referrals for, 96–97 rehumanizing time in, 187–188 risk management in, 81 Sophie in, 119 Heller, Laura, 162 Heppenstall, Tal, 188 Hido, Shohei, 21–22 Hill, Colin, 73, 80 Hill, Kashmir, 79 HireVue, 51–52 hiring and recruitment, 51–52, 133, 198–199 Hitachi, 23 H&M, 91 holistic melding, 12, 197, 200–201 hospital bed allocation, 173 Hoyle, Rhiannon, 28 Huffington Post, 49 humanness attribute training, 116 human resources (HR), 53–54, 133 humans AI vs., 7, 19, 106, 209 augmentation of, 5 collaboration of with AI, 1–3, 25 judgment integration, 191–193 replacement of, 4–5, 19 roles of in developing and deploying AI, 113–133 skills of machines vs., 20–21, 105–106 Hwange, Tim, 170 IBM’s Watson.

Hands-On Machine Learning With Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems by Aurélien Géron

AlphaGo, Amazon Mechanical Turk, Anton Chekhov, backpropagation, combinatorial explosion, computer vision, constrained optimization, correlation coefficient, crowdsourcing, data science, deep learning, DeepMind, don't repeat yourself, duck typing, Elon Musk, en.wikipedia.org, friendly AI, Geoffrey Hinton, ImageNet competition, information retrieval, iterative process, John von Neumann, Kickstarter, machine translation, natural language processing, Netflix Prize, NP-complete, OpenAI, optical character recognition, P = NP, p-value, pattern recognition, pull request, recommendation engine, self-driving car, sentiment analysis, SpamAssassin, speech recognition, stochastic process

Reinforcement Learning For example, many robots implement Reinforcement Learning algorithms to learn how to walk. DeepMind’s AlphaGo program is also a good example of Reinforcement Learning: it made the headlines in March 2016 when it beat the world champion Lee Sedol at the game of Go. It learned its winning policy by analyzing millions of games, and then playing many games against itself. Note that learning was turned off during the games against the champion; AlphaGo was just applying the policy it had learned. Batch and Online Learning Another criterion used to classify Machine Learning systems is whether or not the system can learn incrementally from a stream of incoming data.

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., Google Images), powering speech recognition services (e.g., Apple’s Siri), recommending the best videos to watch to hundreds of millions of users every day (e.g., YouTube), or learning to beat the world champion at the game of Go by examining millions of past games and then playing against itself (DeepMind’s AlphaGo). In this chapter, we will introduce artificial neural networks, starting with a quick tour of the very first ANN architectures. Then we will present Multi-Layer Perceptrons (MLPs) and implement one using TensorFlow to tackle the MNIST digit classification problem (introduced in Chapter 3). From Biological to Artificial Neurons Surprisingly, ANNs have been around for quite a while: they were first introduced back in 1943 by the neurophysiologist Warren McCulloch and the mathematician Walter Pitts.

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But a revolution took place in 2013 when researchers from an English startup called DeepMind demonstrated a system that could learn to play just about any Atari game from scratch,2 eventually outperforming humans3 in most of them, using only raw pixels as inputs and without any prior knowledge of the rules of the games.4 This was the first of a series of amazing feats, culminating in March 2016 with the victory of their system AlphaGo against Lee Sedol, the world champion of the game of Go. No program had ever come close to beating a master of this game, let alone the world champion. Today the whole field of RL is boiling with new ideas, with a wide range of applications. DeepMind was bought by Google for over 500 million dollars in 2014.

AI 2041 by Kai-Fu Lee, Chen Qiufan

3D printing, Abraham Maslow, active measures, airport security, Albert Einstein, AlphaGo, Any sufficiently advanced technology is indistinguishable from magic, artificial general intelligence, augmented reality, autonomous vehicles, basic income, bitcoin, blockchain, blue-collar work, Cambridge Analytica, carbon footprint, Charles Babbage, computer vision, contact tracing, coronavirus, corporate governance, corporate social responsibility, COVID-19, CRISPR, cryptocurrency, DALL-E, data science, deep learning, deepfake, DeepMind, delayed gratification, dematerialisation, digital map, digital rights, digital twin, Elon Musk, fake news, fault tolerance, future of work, Future Shock, game design, general purpose technology, global pandemic, Google Glasses, Google X / Alphabet X, GPT-3, happiness index / gross national happiness, hedonic treadmill, hiring and firing, Hyperloop, information security, Internet of things, iterative process, job automation, language acquisition, low earth orbit, Lyft, Maslow's hierarchy, mass immigration, mirror neurons, money: store of value / unit of account / medium of exchange, mutually assured destruction, natural language processing, Neil Armstrong, Nelson Mandela, OpenAI, optical character recognition, pattern recognition, plutocrats, post scarcity, profit motive, QR code, quantitative easing, Richard Feynman, ride hailing / ride sharing, robotic process automation, Satoshi Nakamoto, self-driving car, seminal paper, Silicon Valley, smart cities, smart contracts, smart transportation, Snapchat, social distancing, speech recognition, Stephen Hawking, synthetic biology, telemarketer, Tesla Model S, The future is already here, trolley problem, Turing test, uber lyft, universal basic income, warehouse automation, warehouse robotics, zero-sum game

In the past five years, however, AI has become the world’s hottest technology. A stunning turning point came in 2016 when AlphaGo, a machine built by DeepMind engineers, defeated Lee Sedol in a five-round Go contest known as the Google DeepMind Challenge Match. Go is a board game more complex than chess by one million trillion trillion trillion trillion times. Also, in contrast to chess, the game of Go is believed by its millions of enthusiastic fans to require true intelligence, wisdom, and Zen-like intellectual refinement. People were shocked that the AI competitor vanquished the human champion. AlphaGo, like most of the commercial breakthroughs in AI, was built on deep learning, a technology that draws on large data sets to teach itself things.

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The deceivingly simple name of the exhibition was nowhere near a sufficient representation of the diversity and complexity it contained. Each room of the exhibit revealed new wonders, all with a connection to the curators’ expansive definition of what AI encompasses. There was Golem, a mythical creature in Jewish folklore; Doraemon, the well-loved Japanese anime hero; Charles Babbage’s preliminary computer science experiments; AlphaGo, the program designed to challenge humans’ fundamental intellect; Joy Buolamwini’s analysis on the gender bias of facial recognition software; and teamLab’s large-scale interactive digital art infused with Shinto philosophy and aesthetics. It was a magnificent and mind-expanding reminder of the power of interdisciplinary thinking.

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Among the many subfields of AI, machine learning is the field that has produced the most successful applications, and within machine learning, the biggest advance is “deep learning”—so much so that the terms “AI,” “machine learning,” and “deep learning” are sometimes used interchangeably (if imprecisely). Deep learning supercharged excitement in AI in 2016 when it powered AlphaGo’s stunning victory over a human competitor in Go, Asia’s most popular intellectual board game. After that headline-grabbing turn, deep learning became a prominent part of most commercial AI applications, and it is featured in most of the stories in AI 2041. “The Golden Elephant” explores deep learning’s stunning potential—as well as its potential pitfalls, like perpetuating bias.

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

3D printing, AI winter, AlphaGo, Apollo 11, artificial general intelligence, Asperger Syndrome, augmented reality, autism spectrum disorder, backpropagation, Berlin Wall, Bletchley Park, brain emulation, Buckminster Fuller, call centre, cognitive bias, cognitive dissonance, computer age, computer vision, Computing Machinery and Intelligence, crowdsourcing, deep learning, DeepMind, Dunning–Kruger effect, Elon Musk, en.wikipedia.org, epigenetics, Fairchild Semiconductor, friendly AI, Geoffrey Hinton, ghettoisation, industrial robot, Internet of things, invention of writing, Jacques de Vaucanson, job automation, John von Neumann, Kevin Kelly, Law of Accelerating Returns, Loebner Prize, Menlo Park, meta-analysis, Metcalfe’s law, mirror neurons, Neil Armstrong, neurotypical, Nick Bostrom, Oculus Rift, old age dependency ratio, pattern recognition, planned obsolescence, pneumatic tube, RAND corporation, Ray Kurzweil, Rodney Brooks, self-driving car, Skype, social intelligence, SoftBank, software as a service, SQL injection, Stephen Hawking, Steven Pinker, superintelligent machines, technological singularity, TED Talk, telepresence, telepresence robot, The future is already here, The Future of Employment, the scientific method, theory of mind, Turing test, twin studies, Two Sigma, undersea cable, Vernor Vinge, Watson beat the top human players on Jeopardy!, Whole Earth Review, working-age population, zero day

In 2012, a University of Toronto artificial intelligence team made up of Hinton and two of his students won the annual ImageNet Large Scale Visual Recognition Competition with a deep learning neural network that blew the competition away.5 More recently, Google DeepMind used deep learning to develop the Go-playing AI, AlphaGo, training it by using a database of thirty million recorded moves from expert-level games. In March 2016, AlphaGo beat the world Go grandmaster, Lee Sedol, in four out of five games. Playing Go is considered a much bigger AI challenge than playing chess. Performance at this level wasn’t expected within AI circles for another decade. As important as the underlying algorithms are, the method of training is at least as important.

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Today, in the twenty-first century, we find ourselves facing a future in which our machines are consistently and repeatedly besting us in all manner of intellectual pursuits. IBM’s Deep Blue beat world chess champion Garry Kasparov in a six-game match in 1997. In 2011, IBM’s Watson (DeepQA) defeated the two all-time Jeopardy champions Brad Rutter and Ken Jennings in a two-day contest of general knowledge. Google’s AlphaGo soundly trounced the longtime world Go grandmaster, Lee Sedol, in four games out of five in March 2016. Given all this, it seems one of the few remaining aspects of machine intelligence left to explore in fiction is how they interact with the world emotionally. In A.I. Artificial Intelligence, Steven Spielberg tells the story of David, a mecha or highly advanced robot in the form of an eleven-year-old child who wants to become a “real boy” so that his mother will love him.

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See Access-consciousness AARP (2010 study), 153 Abigail, 3–4, 161–162 Access-consciousness, 242–249, 270 ACLU, 145 adaptive learning technology, 117–118 addictive behaviors and digitized emotion, 220 adrenaline, 186, 221 Affdex, 66, 69 affect, 47 Affect in Speech, 57 Affectiva, 66, 68–72, 118, 275 Affective Computing Company (tACC), 72 Affective Computing (Picard), 47–48, 51 Affective Computing Research Group, Media Lab, 52–54, 57, 60 AI and social experiments, 195–198 AI Watson, 197 “AI winter,” 37–38 AIBO, 200 “AI-human symbiote,” 264 Air Force Research Lab, Wright-Patterson AFB, OH, 128–129 Aldebaran, 82, 112–113, 152 alexithymia, 34 Alone Together (Turkle), 199 AlphaGo, 68, 233 Alzheimer’s disease, 205 AM (deranged supercomputer), 232 American Psychiatric Association’s Diagnostic and Statistical Manual of Mental Disorders (DSM-5), 187 Amin, Wael, 59 amygdala, 19, 34, 221 anterior cingulate cortex (ACC), 19–20, 34, 247 anthropomorphism, 80–81 Apollo Program, 272 Apple, 75 application programming interfaces (APIs), 65, 72 Ardipethicus ramidus, 14 artificial intelligence, 52–53 development of, 35–36 foundations of, 36 term coined, 37 artificial neural networks (ANNs), 66, 251 artificially generated emotions, 102.

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

"World Economic Forum" Davos, 2021 United States Capitol attack, 3D printing, 9 dash line, AI winter, AlphaGo, artificial general intelligence, asset allocation, assortative mating, autonomous vehicles, bank run, banking crisis, basic income, Bear Stearns, Big Tech, bitcoin, Bletchley Park, blockchain, Boston Dynamics, Bretton Woods, British Empire, business cycle, business process, call centre, carbon tax, Carmen Reinhart, cashless society, central bank independence, collateralized debt obligation, Computing Machinery and Intelligence, coronavirus, COVID-19, creative destruction, credit crunch, crony capitalism, cryptocurrency, currency manipulation / currency intervention, currency peg, data is the new oil, David Ricardo: comparative advantage, debt deflation, decarbonisation, deep learning, DeepMind, deglobalization, Demis Hassabis, democratizing finance, Deng Xiaoping, disintermediation, Dogecoin, Donald Trump, Elon Musk, en.wikipedia.org, energy security, energy transition, Erik Brynjolfsson, Ethereum, ethereum blockchain, eurozone crisis, failed state, fake news, family office, fiat currency, financial deregulation, financial innovation, financial repression, fixed income, floating exchange rates, forward guidance, Fractional reserve banking, Francis Fukuyama: the end of history, full employment, future of work, game design, geopolitical risk, George Santayana, Gini coefficient, global pandemic, global reserve currency, global supply chain, GPS: selective availability, green transition, Greensill Capital, Greenspan put, Herbert Marcuse, high-speed rail, Hyman Minsky, income inequality, inflation targeting, initial coin offering, Intergovernmental Panel on Climate Change (IPCC), Internet of things, invention of movable type, Isaac Newton, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, junk bonds, Kenneth Rogoff, knowledge worker, Long Term Capital Management, low interest rates, low skilled workers, low-wage service sector, M-Pesa, margin call, market bubble, Martin Wolf, mass immigration, means of production, meme stock, Michael Milken, middle-income trap, Mikhail Gorbachev, Minsky moment, Modern Monetary Theory, money market fund, money: store of value / unit of account / medium of exchange, moral hazard, mortgage debt, Mustafa Suleyman, Nash equilibrium, natural language processing, negative equity, Nick Bostrom, non-fungible token, non-tariff barriers, ocean acidification, oil shale / tar sands, oil shock, paradox of thrift, pets.com, Phillips curve, planetary scale, Ponzi scheme, precariat, price mechanism, price stability, public intellectual, purchasing power parity, quantitative easing, race to the bottom, Ralph Waldo Emerson, ransomware, Ray Kurzweil, regulatory arbitrage, reserve currency, reshoring, Robert Shiller, Ronald Reagan, Salesforce, Satoshi Nakamoto, Savings and loan crisis, Second Machine Age, short selling, Silicon Valley, smart contracts, South China Sea, sovereign wealth fund, Stephen Hawking, TED Talk, The Great Moderation, the payments system, Thomas L Friedman, TikTok, too big to fail, Turing test, universal basic income, War on Poverty, warehouse robotics, Washington Consensus, Watson beat the top human players on Jeopardy!, working-age population, Yogi Berra, Yom Kippur War, zero-sum game, zoonotic diseases