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

3D printing, Ada Lovelace, Albert Einstein, algorithmic bias, AlphaGo, Alvin Roth, Andrew Wiles, Automated Insights, Benoit Mandelbrot, Bletchley Park, Cambridge Analytica, Charles Babbage, Claude Shannon: information theory, computer vision, Computing Machinery and Intelligence, correlation does not imply causation, crowdsourcing, data is the new oil, data science, deep learning, DeepMind, Demis Hassabis, Donald Trump, double helix, Douglas Hofstadter, driverless car, Elon Musk, Erik Brynjolfsson, Fellow of the Royal Society, Flash crash, Gödel, Escher, Bach, Henri Poincaré, Jacquard loom, John Conway, Kickstarter, Loebner Prize, machine translation, mandelbrot fractal, Minecraft, move 37, music of the spheres, Mustafa Suleyman, Narrative Science, natural language processing, Netflix Prize, PageRank, pattern recognition, Paul Erdős, Peter Thiel, random walk, Ray Kurzweil, recommendation engine, Rubik’s Cube, Second Machine Age, Silicon Valley, speech recognition, stable marriage problem, Turing test, Watson beat the top human players on Jeopardy!, wikimedia commons

The first game was one that experts could follow and appreciate why AlphaGo was playing the moves it was. They were moves a human champion would play. But as I watched game 2 on my laptop at home, something rather strange happened. Sedol played move 36 and then retired to the roof of the hotel for a cigarette break. While he was away, AlphaGo on move 37 instructed Huang, its human representative, to place a black stone on the line five steps in from the edge of the board. Everyone was shocked. The conventional wisdom is that during the early part of the game you play stones on the outer four lines. The third line builds up short-term territory strength on the edge of the board while playing on the fourth line contributes to your strength later in the game as you move into the centre of the board.

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Beautiful. Beautiful.’ Beautiful and deadly it turned out to be. Not a mistake but an extraordinarily insightful move. Some fifty moves later, as the black and white stones fought over territory from the lower left-hand corner of the board, they found themselves creeping towards the black stone of move 37. It was joining up with this stone that gave AlphaGo the edge, allowing it to clock up its second win. AlphaGo 2 Humans 0. Sedol’s mood in the press conference that followed was notably different. ‘Yesterday I was surprised. But today I am speechless … I am in shock. I can admit that … the third game is not going to be easy for me.’

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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

Now, standing outside the commentary room on the seventh floor of the Four Seasons, in the few minutes following Move 37, he came to see the effect of this strange move. “It’s not a human move. I’ve never seen a human play this move,” he said. “So beautiful.” He kept repeating the word. Beautiful. Beautiful. Beautiful. The next morning, David Silver slipped into the control room, just so he could revisit the decisions AlphaGo made in choosing Move 37. In the midst of each game, drawing on its training with tens of millions of human moves, AlphaGo calculated the probability that a human would make a particular play. With Move 37, the probability was one in ten thousand.

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Lee returned after several minutes and spent several more just staring at the board. In all, he took nearly fifteen minutes to respond, a huge chunk of the two hours he was allotted for play in the first phase of the game—and he never quite regained his footing. More than four hours later, he resigned. He was down two games to nil. Move 37 had also surprised Fan Hui, the man who had been so comprehensively beaten by the machine a few months earlier and who had since joined the DeepMind team to serve as AlphaGo’s sparring partner in the run-up to the match with Lee Sedol. He had never managed to beat DeepMind’s AI, but his encounters with AlphaGo had opened his eyes to new ways of playing.

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The match revealed both the power of the technology and the fears that it would one day eclipse humanity, before reaching a moment of optimism, underscoring the often surprising ways the technology could push humanity to new heights. Even as Elon Musk warned of the dangers, it was a period of extreme promise for AI. After reading about the match, Jordi Ensign, a forty-five-year-old computer programmer from Florida, went out and got two tattoos. AlphaGo’s Move 37 was tattooed on the inside of her right arm—and Lee Sedol’s Move 78 was on the left. 11 EXPANSION “GEORGE WIPED OUT THE WHOLE FIELD WITHOUT EVEN KNOWING ITS NAME.” The Aravind Eye Hospital sits at the southern tip of India, in the middle of a sprawling, crowded, ancient city called Madurai.

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

At first Fan Hui, who was watching the game with Cade Metz, a writer for Wired magazine, was as befuddled as anyone else. “It’s not a human move,” he told Metz. “I’ve never seen a human play this move.” As Metz would later note,10 nothing in the 2,500 years of collected Go knowledge and understanding prepared anyone for move 37 of the second game in the series. Except Hui. Since losing to AlphaGo the previous fall Hui had spent hours helping the Google DeepMind team train the software for the match with Sedol, an experience that allowed him to understand how the move connected the black stones at the bottom of the board with the strategy AlphaGo was about to pursue.

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This was not mere “tesuji”—a clever play that can put an opponent off guard. This was a work of aesthetic as well as strategic brilliance, possibly even a myoshu. Sedol continued to play nearly flawless Go, but it wasn’t enough to counter the striking creativity the DeepMind software displayed, even after move 37. By the end of the day the big news wasn’t that AlphaGo had won a second game, but that it had displayed such deeply human qualities—improvisation, creativity, even a kind of grace—in doing so. The machine, we learned, had a soul. A few weeks after the conclusion of the Humans vs. Machines Showdown, Demis Hassabis—one of the artificial intelligence researchers behind Google’s DeepMind—gave a talk at MIT to discuss the match, and how his team had developed AlphaGo.

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Then, later, after playing an expert human, it made both the human and itself stronger as it continued to learn. In his talk Hassabis revealed breakthrough after breakthrough—some things researchers in the room had previously said they thought might be impossible. The excitement was palpable. He also showed images and videos from the rest of the match between AlphaGo and Lee Sedol. As it turned out, move 37 wasn’t the last dramatic moment of the match. After the second game Sedol had done his homework, developing a strategy based on known weaknesses of the Monte Carlo Tree Search algorithm. Sedol opened the third game by forcing a “ko fight,” in which one side removes its opponent’s stone, forcing him or her to retaliate or relinquish the initiative.

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

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.

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AlphaGo used the 30 million human games of go as a starting point, but by playing against itself could reach levels of game play beyond even the best human players. This superhuman game play was demonstrated in the 4–1 victory AlphaGo delivered over the world’s top-ranked human go player, Lee Sedol, in March 2016. AlphaGo won the first game solidly, but in game 2 demonstrated its virtuosity. Partway through game 2, on move 37, AlphaGo made a move so surprising, so un-human, that it stunned professional players watching the match. Seemingly ignoring a contest between white and black stones that was under way in one corner of the board, AlphaGo played a black stone far away in a nearly empty part of the board. It was a surprising move not seen in professional games, so much so that one commentator remarked, “I thought it was a mistake.”

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 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. Chief among these was confirmation that if AI is creative, its creativity differs from what we usually mean by it. 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.

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It may make startling moves that appear bolder, or more risk tolerant than a human would make. Would human strategists working with the machine go along with that? The stakes in war would be rather higher than those facing the human part of the centaur chess team. If the machine says to do something whacky, like AlphaGo did in move 37, would you go along with its judgment? You might think twice, especially now you know about its tactical brilliance and strategic naivety. Conflicting human tendencies may ensue. On one hand, there is the tendency in some circumstances overly to trust the product of machine intelligence: psychologists refer to ‘automation bias’.

Deep Thinking: Where Machine Intelligence Ends and Human Creativity Begins by Garry Kasparov

3D printing, Ada Lovelace, AI winter, Albert Einstein, AlphaGo, AltaVista, Apple Newton, barriers to entry, Berlin Wall, Bletchley Park, business process, call centre, Charles Babbage, Charles Lindbergh, clean water, computer age, cotton gin, Daniel Kahneman / Amos Tversky, David Brooks, DeepMind, Donald Trump, Douglas Hofstadter, driverless car, Drosophila, Elon Musk, Erik Brynjolfsson, factory automation, Freestyle chess, gamification, Gödel, Escher, Bach, Hans Moravec, job automation, Ken Thompson, Leonard Kleinrock, low earth orbit, machine translation, Max Levchin, Mikhail Gorbachev, move 37, Nate Silver, Nick Bostrom, Norbert Wiener, packet switching, pattern recognition, Ray Kurzweil, Richard Feynman, rising living standards, rolodex, Second Machine Age, self-driving car, Silicon Valley, Silicon Valley startup, Skype, speech recognition, stem cell, Stephen Hawking, Steven Pinker, technological singularity, The Coming Technological Singularity, The Signal and the Noise by Nate Silver, Turing test, Vernor Vinge, Watson beat the top human players on Jeopardy!, zero-sum game

The moves in game two that left me with a lost position and crushed morale were unique only for the time. Within five years, commercial engines running on standard Intel servers could reproduce all of Deep Blue’s best moves, even improving on some of the “humanlike” moves that so impressed me and everyone else at the time. The engine on my laptop today slightly favors the “shockingly humanlike” move 37.Be4 from game two in less than ten seconds, although it rates it nearly equal to the queen sortie I had expected because 37.Be4 turns out not to have been as superior as we all believed at the time. Had I played better defense instead of collapsing and resigning, game two would have been considered a very impressive game for a machine but nothing more, no matter the eventual result.

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IBM took this as a warning that I might not appear either if Thompson didn’t, and they warned the media that there might not be a game that day. Thirty minutes before the game started, we got a message from Newborn saying that the printouts had been given to the Appeals Board, but when we arrived at the thirty-fifth floor, Thompson said that they were only for one move, 37.Qb6. Without the other moves to show context, this was useless. This secretive and antagonistic behavior manifested in other ways as well, as reported in the New York Times after game five: “One reporter, Jeff Kisseloff, who had been hired by IBM to report on the Kasparov team for the match Web site, lost his reporting privileges after he included damning comments about Deep Blue from the champion’s supporters in his report.

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

That number, however, is vastly exceeded by the number of possible Go positions—there are about 2.081681994 × 10170, which is two hundred quinquinquagintillion—and vastly more than the number of atoms in the universe, which helps explain why it was a much more interesting challenge than a game like checkers or chess.29 Go has been played for at least 3,000 years, and the game’s experts had predicted in 2015 that it would be at least another decade before AI could win. It took combining DNN (supervised and reinforcement learning) with GOFAI, in the latter case a Monte Carlo tree search.30 The key winning move (move 37), as it turned out, was viewed as highly creative—despite the fact that a machine made it—and perhaps more importantly, it was made in defiance of human wisdom.31 That was certainly a monumental AI achievement for such a remarkably complex and ancient game. But it didn’t take long before even that achievement was superseded.

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Further, the majority of medical studies published to date are retrospective, performed in silico, yet to be prospectively validated in a real-world, clinical setting. BLACK BOXES If there’s one thing that the human brain and AI certainly share, it’s opacity. Much of a neural network’s learning ability is poorly understood, and we don’t have a way to interrogate an AI system to figure out how it reached its output. Move 37 in the historic AlphaGo match against Lee Sodol is a case in point: the creators of the algorithm can’t explain how it happened. The same phenomenon comes up in medical AI. One example is the capacity for deep learning to match the diagnostic capacities of a team of twenty-one board-certified dermatologists in classifying skin lesions as cancerous or benign.

Human Nature: The Categorial Framework by P. M. S. Hacker

conceptual framework, discovery of DNA, move 37, profit maximization

But it is altogether obscure what is meant by causing one’s arm to move without doing anything to make it move. In general, for a volitional agent to cause something to move is to spin 36 L. Wittgenstein, Philosophical Investigations, §§618, 620. HNC05 25/04/2007 02:09PM Page 155 Agency 155 or rotate it, to push, pull or throw it, as a consequence of which it moves.37 It is indeed to make it move (wobble, rotate, rise or fall, roll or slide, etc.) by acting on it. To make another thing move is to bring it about that it moves. But when we move our eyes, grimace (smile or scowl), move our limbs, rotate our body, nod or shake our head, wiggle our toes or fingers, when we turn over or turn round, when we walk or run, when we talk or laugh, we do not cause our limbs, features, or ourselves to move.

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When I nod my head in assent, I don’t make my head nod or bring it about that my head nods – that I do when I seize it in my hands and move it up and down.38 When I move, or move my limbs, voluntarily, I don’t act on myself or on my limbs – I don’t do something to myself. My body, the body I have (see ch. 9), is not the reflexive patient on which I act. I am a body, a living spatio-temporal continuant of a certain kind; but when I move, I don’t move myself, I just move. 37 Note that to pick something up and put it down elsewhere, to carry something from one place to another, is not to make it move, but merely to move it – to bring about a change in its location. To hit a billiard ball with a cue, on the other hand, will make the ball move (cause it to move). 38 If I raise my head, does my head rise?

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 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. The computer abandoned one group of stones, shifting its play to an entirely different part of the board. “That’s a very surprising move,” as one Go expert observed. “I thought it was a mistake,” said another. But after pondering the strategy, it hit them: Oh my God, that’s a gorgeous move! “So beautiful,” cooed Fan Hui, who was watching, fresh from his own AlphaGo defeat. “It’s not a human move. I’ve never seen a human play this move.” When AlphaGo made move 37, Sedol himself appeared stunned.

Smart Mobs: The Next Social Revolution by Howard Rheingold

"hyperreality Baudrillard"~20 OR "Baudrillard hyperreality", A Pattern Language, Alvin Toffler, AOL-Time Warner, augmented reality, barriers to entry, battle of ideas, Brewster Kahle, Burning Man, business climate, citizen journalism, computer vision, conceptual framework, creative destruction, Dennis Ritchie, digital divide, disinformation, Douglas Engelbart, Douglas Engelbart, experimental economics, experimental subject, Extropian, Free Software Foundation, Garrett Hardin, Hacker Ethic, Hedy Lamarr / George Antheil, Herman Kahn, history of Unix, hockey-stick growth, Howard Rheingold, invention of the telephone, inventory management, Ivan Sutherland, John Markoff, John von Neumann, Joi Ito, Joseph Schumpeter, Ken Thompson, Kevin Kelly, Lewis Mumford, Metcalfe's law, Metcalfe’s law, more computing power than Apollo, move 37, Multics, New Urbanism, Norbert Wiener, packet switching, PalmPilot, Panopticon Jeremy Bentham, pattern recognition, peer-to-peer, peer-to-peer model, pez dispenser, planetary scale, pre–internet, prisoner's dilemma, radical decentralization, RAND corporation, recommendation engine, Renaissance Technologies, RFID, Richard Stallman, Robert Metcalfe, Robert X Cringely, Ronald Coase, Search for Extraterrestrial Intelligence, seminal paper, SETI@home, sharing economy, Silicon Valley, skunkworks, slashdot, social intelligence, spectrum auction, Steven Levy, Stewart Brand, the Cathedral and the Bazaar, the scientific method, Tragedy of the Commons, transaction costs, ultimatum game, urban planning, web of trust, Whole Earth Review, Yochai Benkler, zero-sum game

He ran fourteen entries against each other and against a random rule, over and over. “To my considerable surprise,” Axelrod reported, “the winner was the simplest of all the programs submitted, TIT FOR TAT. TIT FOR TAT is merely the strategy of starting with cooperation and thereafter doing what the other player did on the previous move.”37 If the opponent cooperates on the first move, then TIT FOR TAT cooperates on the next move; if the opponent defects on the first move, then TIT FOR TAT defects on the next move. If the opponent switches from defection to cooperation, TIT FOR TAT switches from defection to cooperation on the following move, punishing the opponent but forgiving it.

The Health Gap: The Challenge of an Unequal World by Michael Marmot

active measures, active transport: walking or cycling, Affordable Care Act / Obamacare, Atul Gawande, Bonfire of the Vanities, Broken windows theory, cakes and ale, Capital in the Twenty-First Century by Thomas Piketty, Carmen Reinhart, Celtic Tiger, centre right, clean water, cognitive load, congestion charging, correlation does not imply causation, Doha Development Round, epigenetics, financial independence, future of work, Gini coefficient, Growth in a Time of Debt, illegal immigration, income inequality, Indoor air pollution, Kenneth Rogoff, Kibera, labour market flexibility, longitudinal study, lump of labour, Mahatma Gandhi, Mahbub ul Haq, meta-analysis, microcredit, move 37, New Urbanism, obamacare, paradox of thrift, race to the bottom, Rana Plaza, RAND corporation, road to serfdom, Simon Kuznets, Socratic dialogue, structural adjustment programs, the built environment, The Spirit Level, trickle-down economics, twin studies, urban planning, Washington Consensus, Winter of Discontent, working poor

One study, among many, examined the population of England and showed that people living near green space had better mental health and greater well-being than those with poorer access.36 The effect was not enormous, about a tenth of the positive effect from being employed compared with unemployed, but it was worth having. In later research these same investigators showed that moving to a greener area enhanced mental health, whereas the very thought of going the other way seemed to suffice to lessen mental health – it got worse before those destined to go to more built-up areas made the move.37 If the thought occurred that ‘I’m just dying for a bit of green space’, it may well be true. Lack of access to urban green space may make a contribution to the social gradient in deaths from heart disease.38 Richard Mitchell in Glasgow and Frank Popham at St Andrews in Scotland have conducted research that addresses whether access to green space might be protective against the negative impact on health of low income.

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

In this paradigm, there is no need for a human to laboriously define the manner in which a task should take place. Instead, we just specify a high-level goal and rely on a machine to figure out the optimal way of getting there. Keeping humans “in the loop,” as the saying goes, is desirable, but optional. Nobody told AlphaGo that move 37 was a good idea. It discovered this insight largely on its own. It was precisely this feature that struck me so forcibly watching DQN play Breakout. Given some clearly specified objective, systems now exist that can find their own strategies to be effective. AlphaGo and DQN were not in themselves autonomous.

Class Acts: Service and Inequality in Luxury Hotels by Rachel Sherman

Abraham Maslow, deskilling, emotional labour, income inequality, indoor plumbing, invisible hand, knowledge worker, means of production, move 37, new economy, pink-collar, Savings and loan crisis, The Theory of the Leisure Class by Thorstein Veblen, Thorstein Veblen, union organizing, upwardly mobile, work culture , yield management

She joked, “I’m going to call the door when it’s busy in here and tell him I need some help at the desk.” In order to avoid managerial reprimands, workers in one area sometimes took actions that interfered with other workers’ games. For example, Zeke complained that front desk workers asked bellmen to bring guests’ bags to their rooms when the guests were not there (what was known as a “dead move”).37 He said, “If a bellman brings up bags to an empty room, he makes no money.” Later, I asked Jackie at the front desk if she usually sent up the bags to the empty room, and she said, “Yes, just to get them out of the way.” I mentioned that the bellmen did not receive a tip for the “dead move,” and she said, “Well, I can’t always look out for the bellmen.”

The Ice at the End of the World: An Epic Journey Into Greenland's Buried Past and Our Perilous Future by Jon Gertner

American ideology, Anthropocene, Charles Lindbergh, fear of failure, Intergovernmental Panel on Climate Change (IPCC), mass immigration, move 37, RAND corporation, risk tolerance, Silicon Valley, three-masted sailing ship, trade route

In the United States, meanwhile, glaciology was an arcane practice as well as an arcane word—a little-known extension of geology that one journalist noted was “a rare branch of learning which attracts only the most hardy intellectuals.”36 Mainly, the early glaciologists in the United States were either focused on measuring the glaciers of Alaska and the Pacific Northwest or were intent on trying to work out a theoretical framework for how and why these large masses of ice move.37 But in sum, there were very few of them. “Glaciology was something that if you wanted to do it, nobody understood why you wanted to do it,” recalls one scientist of the era.38 By an accident of history, Bader found himself to be in the right scientific field, in the right country, at the right time.

The Patterning Instinct: A Cultural History of Humanity's Search for Meaning by Jeremy Lent

Admiral Zheng, agricultural Revolution, Albert Einstein, Alfred Russel Wallace, Anthropocene, Atahualpa, Benoit Mandelbrot, Bretton Woods, British Empire, Buckminster Fuller, Capital in the Twenty-First Century by Thomas Piketty, cognitive dissonance, commoditize, complexity theory, conceptual framework, dematerialisation, demographic transition, different worldview, Doomsday Book, Easter island, en.wikipedia.org, European colonialism, failed state, Firefox, Ford Model T, Francisco Pizarro, Garrett Hardin, Georg Cantor, Great Leap Forward, Hans Moravec, happiness index / gross national happiness, hedonic treadmill, income inequality, Intergovernmental Panel on Climate Change (IPCC), Internet of things, invention of gunpowder, invention of writing, Isaac Newton, Jevons paradox, Johann Wolfgang von Goethe, Johannes Kepler, language acquisition, Lao Tzu, Law of Accelerating Returns, mandelbrot fractal, mass immigration, megacity, Metcalfe's law, Mikhail Gorbachev, move 37, Neil Armstrong, Nicholas Carr, Nick Bostrom, Norbert Wiener, oil shale / tar sands, out of africa, peak oil, Pierre-Simon Laplace, Plato's cave, QWERTY keyboard, Ray Kurzweil, Robert Solow, Sapir-Whorf hypothesis, scientific management, Scientific racism, scientific worldview, seminal paper, shareholder value, sharing economy, Silicon Valley, Simon Kuznets, social intelligence, South China Sea, Stephen Hawking, Steven Pinker, Stuart Kauffman, synthetic biology, systems thinking, technological singularity, the scientific method, The Theory of the Leisure Class by Thorstein Veblen, theory of mind, Thomas Kuhn: the structure of scientific revolutions, Thomas Malthus, Thorstein Veblen, Tragedy of the Commons, Turing test, ultimatum game, urban sprawl, Vernor Vinge, wikimedia commons

The issue was rather one of due process and, above all, maintenance of the church's authority in interpreting scriptures. If the church had to change its official position about something as fundamental as the position of the earth in the heavens, the authorities needed to make sure it was well founded before taking such a drastic move.37 Galileo was well aware of church policy. He had been told by a cardinal that the Bible should be interpreted literally except for passages where there was a “compelling necessity” to view its content as merely metaphorical. Was there, then, a “compelling necessity” for the Catholic Church to change its view of the solar system in the early seventeenth century?