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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

Ewen Callaway, “‘It will change everything’: DeepMind’s AI makes gigantic leap in solving protein structures,” Nature, November 30, 2020, www.nature.com/articles/d41586-020-03348-4. 2. Andrew Senior, Demis Hassabis, John Jumper and Pushmeet Kohli, “AlphaFold: Using AI for scientific discovery,” DeepMind Research Blog, January 15, 2020, deepmind.com/blog/article/AlphaFold-Using-AI-for-scientific-discovery. 3. Ian Sample, “Google’s DeepMind predicts 3D shapes of proteins,” The Guardian, December 2, 2018, www.theguardian.com/science/2018/dec/02/google-deepminds-ai-program-alphafold-predicts-3d-shapes-of-proteins. 4.

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The leader in reinforcement learning is the London-based company DeepMind, which is now owned by Google’s parent, Alphabet. DeepMind has made massive investments in research based on the technique, merging it with powerful convolutional neural networks to develop what the company calls “deep reinforcement learning.” DeepMind began working on applying reinforcement learning to build AI systems that could play video games shortly after its founding in 2010. In January 2013, the company announced that it had created a system called DQN that was capable of playing classic Atari games, including Space Invaders, Pong and Breakout. DeepMind’s system was able to teach itself to play the games by using only raw pixels and the game score as the learning inputs.

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Pierr Johnson, “With the public clouds of Amazon, Microsoft and Google, big data is the proverbial big deal,” Forbes, June 15, 2017, www.forbes.com/sites/johnsonpierr/2017/06/15/with-the-public-clouds-of-amazon-microsoft-and-google-big-data-is-the-proverbial-big-deal/. 6. Richard Evans and Jim Gao, “DeepMind AI reduces Google data centre cooling bill by 40%,” DeepMind Research Blog, July 20, 2016, deepmind.com/blog/article/deepmind-ai-reduces-google-data-centre-cooling-bill-40. 7. Urs Hölzle, “Data centers are more energy efficient than ever,” Google Blog, February 27, 2020, www.blog.google/outreach-initiatives/sustainability/data-centers-energy-efficient/. 8.

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

Larry Page and Sergey Brin spun off several Google projects: Conor Dougherty, “Google to Reorganize as Alphabet to Keep Its Lead as an Innovator,” New York Times, August 10, 2015, https://www.nytimes.com/2015/08/11/technology/google-alphabet-restructuring.html. they found little common ground: David Rowan, “DeepMind: Inside Google’s Super-Brain,” Wired UK, June 22, 2015, https://www.wired.co.uk/article/deepmind. Hassabis would propose complex: Ibid. “We have to engage with the real world today”: Ibid. Suleyman unveiled what he called DeepMind Health: Jordan Novet, “Google’s DeepMind AI Group Unveils Health Care Ambitions,” Venturebeat, February 24, 2016, https://venturebeat.com/2016/02/24/googles-deepmind-ai-group-unveils-heath-care-ambitions/. revealed the agreement between DeepMind: Hal Hodson, “Revealed: Google AI has access to huge haul of NHS patient data,” New Scientist, April 29, 2016, https://www.newscientist.com/article/2086454-revealed-google-ai-has-access-to-huge-haul-of-nhs-patient-data/.

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But Not in a Bad Way,” Wired, May 1, 2019, https://www.wired.com/story/company-wants-billions-make-ai-safe-humanity/. DeepMind announced that Google was taking over the practice: Rory Cellan-Jones, “Google Swallows DeepMind Health,” BBC, September 18, 2019, https://www.bbc.com/news/technology-49740095. Google had invested $1.2 billion: Nate Lanxon, “Alphabet’s DeepMind Takes on Billion-Dollar Debt and Loses $572 Million,” Bloomberg News, August 7, 2019, https://www.bloomberg.com/news/articles/2019-08-07/alphabet-s-deepmind-takes-on-billion-dollar-debt-as-loss-spirals. Larry Page and Sergey Brin, DeepMind’s biggest supporters, announced they were retiring: Jack Nicas and Daisuke Wakabayashi, “Era Ends for Google as Founders Step Aside from a Pillar of Tech,” New York Times, December 3, 2019, https://www.nytimes.com/2019/12/03/technology/google-alphabet-ceo-larry-page-sundar-pichai.html.

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Shortly after Mnih and his team built this system, DeepMind sent a video to the company’s investors at the Founders Fund, including a man named Luke Nosek. Alongside Peter Thiel and Elon Musk, Nosek had originally risen to prominence as part of the team that created PayPal—the so-called “PayPal Mafia.” Soon after receiving the video of DeepMind’s Atari-playing AI, as Nosek later told a colleague, he was on a private plane with Musk, and as they watched the video and discussed DeepMind, they were overheard by another Silicon Valley billionaire who happened to be on the flight: Larry Page. This was how Page learned about DeepMind, sparking a courtship that would culminate in the Gulfstream flight to London.

Artificial Intelligence: A Guide for Thinking Humans by Melanie Mitchell

Ada Lovelace, AI winter, Alignment Problem, AlphaGo, Amazon Mechanical Turk, Apple's 1984 Super Bowl advert, artificial general intelligence, autonomous vehicles, backpropagation, Bernie Sanders, Big Tech, Boston Dynamics, Cambridge Analytica, Charles Babbage, Claude Shannon: information theory, cognitive dissonance, computer age, computer vision, Computing Machinery and Intelligence, dark matter, deep learning, DeepMind, Demis Hassabis, Douglas Hofstadter, driverless car, Elon Musk, en.wikipedia.org, folksonomy, Geoffrey Hinton, Gödel, Escher, Bach, I think there is a world market for maybe five computers, ImageNet competition, Jaron Lanier, job automation, John Markoff, John von Neumann, Kevin Kelly, Kickstarter, license plate recognition, machine translation, Mark Zuckerberg, natural language processing, Nick Bostrom, Norbert Wiener, ought to be enough for anybody, paperclip maximiser, pattern recognition, performance metric, RAND corporation, Ray Kurzweil, recommendation engine, ride hailing / ride sharing, Rodney Brooks, self-driving car, sentiment analysis, Silicon Valley, Singularitarianism, Skype, speech recognition, Stephen Hawking, Steve Jobs, Steve Wozniak, Steven Pinker, strong AI, superintelligent machines, tacit knowledge, tail risk, TED Talk, the long tail, theory of mind, There's no reason for any individual to have a computer in his home - Ken Olsen, trolley problem, Turing test, Vernor Vinge, Watson beat the top human players on Jeopardy!, world market for maybe five computers

DeepMind first presented this work in 2013 at an international machine-learning conference.7 The audience was dazzled. Less than a year later, Google announced that it was acquiring DeepMind for £440 million (about $650 million at the time), presumably because of these results. Yes, reinforcement learning occasionally leads to big rewards. With a lot of money in their pockets and the resources of Google behind them, DeepMind—now called Google DeepMind—took on a bigger challenge, one that had in fact long been considered one of AI’s “grand challenges”: creating a program that learns to play the game Go better than any human. DeepMind’s program AlphaGo builds on a long history of AI in board games. Let’s start with a brief survey of that history, which will help in explaining how AlphaGo works and why it is so significant.

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In 2013, a group of Canadian AI researchers released a software platform called the Arcade Learning Environment that made it easy to test machine-learning systems on forty-nine of these games.3 This was the platform used by the DeepMind group in their work on reinforcement learning. Deep Q-Learning The DeepMind group combined reinforcement learning—in particular Q-learning—with deep neural networks to create a system that could learn to play Atari video games. The group called their approach deep Q-learning. To explain how deep Q-learning works, I’ll use Breakout as a running example, but DeepMind used the same method on all the Atari games they tackled. Things will get a bit technical here, so fasten your seat belt (or skip to the next section).

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In an episode of Q-learning, at each iteration the learning agent (Rosie) does the following: it figures out its current state, looks up that state in the Q-table, uses the values in the table to choose an action, performs that action, possibly receives a reward, and—the learning step—updates the values in its Q-table. DeepMind’s deep Q-learning is exactly the same, except that a convolutional neural network takes the place of the Q-table. Following DeepMind, I’ll call this network the Deep Q-Network (DQN). Figure 28 illustrates a DQN that is similar to (though simpler than) the one used by DeepMind for learning to play Breakout. The input to the DQN is the state of the system at a given time, which here is defined to be the current “frame”—the pixels of the current screen—plus three prior frames (screen pixels from three previous time steps).

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

GO TO NOTE REFERENCE IN TEXT In the last six years “Research & Development,” in Artificial Intelligence Index Report 2021, Stanford University Human-Centered Artificial Intelligence, March 2021, aiindex.stanford.edu/​wp-content/​uploads/​2021/​03/​2021-AI-Index-Report-_Chapter-1.pdf. GO TO NOTE REFERENCE IN TEXT Everywhere you look, software To paraphrase Marc Andreessen. GO TO NOTE REFERENCE IN TEXT At DeepMind we developed systems “DeepMind AI Reduces Google Data Centre Cooling Bill by 40%,” DeepMind, July 20, 2016, www.deepmind.com/​blog/​deepmind-ai-reduces-google-data-centre-cooling-bill-by-40. GO TO NOTE REFERENCE IN TEXT With 1.5 billion parameters “Better Language Models and Their Implications,” OpenAI, Feb. 14, 2019, openai.com/​blog/​better-language-models.

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GO TO NOTE REFERENCE IN TEXT The result has been an explosion Ewen Callaway, “What’s Next for AlphaFold and the AI Protein-Folding Revolution,” Nature, April 13, 2022, www.nature.com/​articles/​d41586-022-00997-5. GO TO NOTE REFERENCE IN TEXT DeepMind uploaded some 200 million Madhumita Murgia, “DeepMind Research Cracks Structure of Almost Every Known Protein,” Financial Times, July 28, 2022, www.ft.com/​content/​6a088953-66d7-48db-b61c-79005a0a351a; DeepMind, “AlphaFold Reveals the Structure of the Protein Universe,” DeepMind Research, July 28, 2022, www.deepmind.com/​blog/​alphafold-reveals-the-structure-of-the-protein-universe. GO TO NOTE REFERENCE IN TEXT In 2019, electrodes surgically implanted Kelly Servick, “In a First, Brain Implant Lets Man with Complete Paralysis Spell Out ‘I Love My Cool Son,’ ” Science, March 22, 2022, www.science.org/​content/​article/​first-brain-implant-lets-man-complete-paralysis-spell-out-thoughts-i-love-my-cool-son.

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GO TO NOTE REFERENCE IN TEXT McKinsey estimates that up McKinsey Global Institute, “The Bio Revolution: Innovations Transforming Economies, Societies, and Our Lives,” McKinsey & Company, May 13, 2020, www.mckinsey.com/​industries/​life-sciences/​our-insights/​the-bio-revolution-innovations-transforming-economies-societies-and-our-lives. GO TO NOTE REFERENCE IN TEXT If you used traditional brute-force computation DeepMind, “AlphaFold: A Solution to a 50-Year-Old Grand Challenge in Biology,” DeepMind Research, Nov. 20, 2020, www.deepmind.com/​blog/​alphafold-a-solution-to-a-50-year-old-grand-challenge-in-biology. GO TO NOTE REFERENCE IN TEXT Mohammed AlQuraishi, a well-known researcher Mohammed AlQuraishi, “AlphaFold @ CASP13: ‘What Just Happened?,’ ” Some Thoughts on a Mysterious Universe, Dec. 9, 2018, moalquraishi.wordpress.com/​2018/​12/​09/​alphafold-casp13-what-just-happened.

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

Lessin, “Deep Confusion: Tensions Lingered Within Google Over DeepMind,” Information, April 19, 2018, https://www.theinformation.com/articles/deep-confusion-tensions-lingered-within-google-over-deepmind. 18. James Vincent, “Google’s DeepMind and UK Hospitals Made Illegal Deal for Health Data, Says Watchdog,” Verge, July 3, 2017, https://www.theverge.com/2017/7/3/15900670/google-deepmind-royal-free-2015-data-deal-ico-ruling-illegal. 19. Mustafa Suleyman and Dominic King, “The Information Commissioner, the Royal Free, and What We’ve Learned,” DeepMind (blog), July 3, 2017, https://deepmind.com/blog/ico-royal-free/. 20. “Microsoft Launches Fifth Generation of Popular AI Xiaoice,” Microsoft News Center, https://www.microsoft.com/en-us/ard/news/newsinfo.aspx?

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It was a big investment at the time: Google paid nearly $600 million for DeepMind, with $400 million guaranteed up front and the remaining $200 million to be paid over a five-year period. In the months after the acquisition, it was abundantly clear that the DeepMind team was advancing AI research—but it wasn’t entirely clear how it would earn back the investment. Inside of Google, DeepMind was supposed to be working on artificial general intelligence, and it would be a very long-term process. Soon, the enthusiasm for what DeepMind might someday accomplish got pushed aside for more immediate financial returns on their research projects. As the five-year anniversary of DeepMind’s acquisition neared, Google was on the hook to make earn-out payments to the company’s shareholders and its original 75 employees.

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As the five-year anniversary of DeepMind’s acquisition neared, Google was on the hook to make earn-out payments to the company’s shareholders and its original 75 employees. It seemed as if health care was one industry in which DeepMind’s technology could be put to commercial use.17 So in 2017, in order to appease its parent company, part of the DeepMind team inked a deal with the Royal Free NHS Foundation Trust, which runs several hospitals in the United Kingdom, to develop an all-in-one app to manage health care. Its initial product was to use DeepMind’s AI to alert doctors whether patients were at risk for acute kidney injury. DeepMind was granted access to the personal data and health records of 1.6 million UK hospital patients—who, it turned out, weren’t asked for consent or told exactly how their data was going to be used.

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

AlphaGo has since retired from competitive play. The Go team at DeepMind has been disbanded. Hassabis proved his Cambridge lecturer wrong. DeepMind has now set its sights on other goals: health care, climate change, energy efficiency, speech recognition and generation, computer vision. It’s all getting very serious. Given that Go was always my shield against computers doing mathematics, was my own subject next in DeepMind’s cross hairs? To truly judge the potential of this new AI we are going to need to look more closely at how it works and dig around inside. The crazy thing is that the tools DeepMind is using to create the programs that might put me out of a job are precisely the ones that mathematicians have created over the centuries.

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First blood Previous computer programs built to play Go had not come close to playing competitively against even a pretty good amateur, so most pundits were highly sceptical of DeepMind’s dream to create code that could get anywhere near an international champion of the game. Most people still agreed with the view expressed in The New York Times by the astrophysicist Piet Hut after DeepBlue’s success at chess in 1997: ‘It may be a hundred years before a computer beats humans at Go – maybe even longer. If a reasonably intelligent person learned to play Go, in a few months he could beat all existing computer programs. You don’t have to be a Kasparov.’ Just two decades into that hundred years, the DeepMind team believed they might have cracked the code.

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But as the match approached, you could hear doubts beginning to creep into his view of whether AI will ultimately be too powerful for humans to defeat it even in the game of Go. In February he stated: ‘I have heard that DeepMind’s AI is surprisingly strong and getting stronger, but I am confident that I can win … at least this time.’ Most people still felt that despite great inroads into programming, an AI Go champion was still a distant goal. Rémi Coulom, the creator of Crazy Stone, the only program to get close to playing Go at any high standard, was still predicting another decade before computers would beat the best humans at the game. As the date for the match approached, the team at DeepMind felt they needed someone to really stretch AlphaGo and to test it for any weaknesses.

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

There are limited techniques available that can do various aspects of these things relatively poorly, and I think that there just needs to be a great deal of improvement in those areas in order for us to get all the way to full human general intelligence. MARTIN FORD: DeepMind seems to be one of the very few companies that’s focused specifically on AGI. Are there other players that you would point to that are doing important work, that you think may be competitive with what DeepMind is doing? NICK BOSTROM: DeepMind is certainly among the leaders, but there are many places where there is exciting work being done on machine learning or work that might eventually contribute to achieving artificial general intelligence.

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We’re not doing all this work just to solve games; we want to build these general algorithms that we can apply to real-world problems. CO-FOUNDER & CEO OF DEEPMIND AI RESEARCHER AND NEUROSCIENTIST Demis Hassabis is a former child chess prodigy, who started coding and designing video games professionally at age 16. After graduating from Cambridge University, Demis spent a decade leading and founding successful startups focused on video games and simulation. He returned to academia to complete a PhD in cognitive neuroscience at University College London, followed by postdoctoral research at MIT and Harvard. He co-founded DeepMind in 2010. DeepMind was acquired by Google in 2014 and is now part of Alphabet’s portfolio of companies.

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MARTIN FORD: One thing that’s obvious from listening to you is that you combine a deep interest in both neuroscience and computer science. Is that combined approach true for DeepMind as a whole? How does the company integrate knowledge and talent from those two areas? DEMIS HASSABIS: I’m definitely right in the middle for both those fields, as I’m equally trained in both. I would say DeepMind is clearly more skewed towards machine learning; however, our biggest single group here at DeepMind is made up of neuroscientists led by Matt Botvinick, an amazing neuroscientist and professor from Princeton. We take it very seriously. The problem with neuroscience is that it’s a massive field in itself, way bigger than machine learning.

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

Before we can use deep learning in sensitive applications, we need to understand these problems in much more detail. DeepMind The story of DeepMind, which I referred to earlier in this chapter, perfectly epitomizes the rise of deep learning. The company was founded in 2010 by Demis Hassabis, an AI researcher and computer games enthusiast, together with his school friend and entrepreneur Mustafa Suleyman, and they were joined by Shane Legg, a computational neuroscientist that Hassabis met while working at University College London. As we heard, Google acquired DeepMind early in 2014; I can recall seeing stories in the press about the acquisition, and starting in surprise when I saw that DeepMind were an AI company.

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Like the story of AI itself, the story of neural networks is a troubled one: there have been two ‘neural net winters’, and as recently as the turn of the century, many in AI regarded neural networks as a dead or dying field. But neural nets ultimately triumphed, and the new idea driving their resurgence is a technique called deep learning. Deep learning is the core technology of DeepMind. I will tell you the DeepMind story, and how the systems that DeepMind built attracted global adulation. But while deep learning is a powerful and important technique, it isn’t the end of the story for AI, so, just as we did with other AI technologies, we’ll discuss its limitations in detail too. Machine Learning, Briefly The goal of machine learning is to have programs that can compute a desired output from a given input, without being given an explicit recipe for how to do this.

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Let me conclude by rashly making some concrete proposals for what progress towards conscious machines might look like, and how we might create it. (I look forward to re-reading this section in my dotage, to see how my predictions turn out.) Let’s go back to DeepMind’s celebrated Atari-playing system from Chapter 5. Recall that DeepMind built an agent that learned to play a large number of Atari video games. These games were in many ways relatively simple, and of course DeepMind has subsequently progressed beyond these to much more complex games such as StarCraft.11 At present, the main concerns in experiments like this are: to be able to handle games with very large branching factors; games in which there is imperfect information about the state of the game and the actions of other players; games where the rewards available in the game are distant from the actions that lead to those rewards; and games where the actions an agent must perform are not simple binary decisions, such as in Breakout, but ones which involve long and complex sequences, possibly coordinated with – or in competition with – the actions of others.

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

The use of electronic games as a test-bed for reinforcement learning has been a particular research focus. The attraction to military minds is obvious—games are adversarial, and the goal is to win. The differences, however, are also profound, as we’ll see. 2015 saw the public arrival of DeepMind, a relative British newcomer to AI research, newly acquired by Google. DeepMind’s founder Demis Hassabis had trained in neuroscience, and he was explicit: DeepMind intended to create ‘general’ AI, with the attributes of human intelligence. Its first landmark breakthrough was an eighties throwback: classic Atari arcade games. The scoreboard in Space Invaders is an ideal motivator for reinforcement learning.

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Like dopamine in the brain of a teenage arcade goer, the network responded to the reward of a higher score—pruning its connections accordingly.10 Combine that with a ConvNet that would capture what was happening on the screen, and the AI was all set to play a mean pinball, or rather Space Invaders. In fact, DeepMind’s breakthrough arcade AI playing Atari performed creditably on six games, surpassing expert human level on three. Six years later, its latest version, Agent57, now performs better than humans on all 57 Atari 2600 games. DeepMind again illustrated the new landscape of AI research—a hitherto obscure company, rapidly acquired by Google, which proceeded thereafter to draw in research talent, creating a snowball effect that continues today.

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DeepMind again illustrated the new landscape of AI research—a hitherto obscure company, rapidly acquired by Google, which proceeded thereafter to draw in research talent, creating a snowball effect that continues today. This was civilian research, and abstract rather than applied. Space Invaders battled in a very simple virtual world, but the real-world possibilities eluded no one with a military mindset. DeepMind amply demonstrated, like Ng’s helicopter, the potential for superhuman skill in physical control, and also the particular strengths of ANN in optimising scores. In the Atari games, DeepMind’s algorithm knew nothing about its world except the score—but that was still enough to produce novel and highly effective tactics—in one game, Breakout, sending its bullet through a narrow channel to ricochet destructively behind the banks of approaching coloured bricks.

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

., 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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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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The fact that Heron Systems’ AI dogfighting agent was able to execute forward-quarter gunshots that are banned in training by human pilots could arguably be seen as an unfair advantage. In testing AI agents playing a capture-the-flag computer game, DeepMind slowed down its agents’ reaction times and tagging accuracy to match that of humans. AlphaStar’s superhuman click rate was a source of controversy even after DeepMind slowed it down. (DeepMind later slowed it down even further.) AI agents’ superior strategic abilities, however, are often celebrated, such as their prowess at chess or go. In war, militaries may view these benefits differently.

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 “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”. 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.

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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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See Darcie Thompson-Fields, “AI Companion Aims to Improve Life for the Elderly”, Access AI, 12 January 2017, http://​www.​access-ai.​com/​news/​511/​ai-companion-aims-to-improve-life-for-the-elderly/​, accessed 1 June 2018. 93Daniela Hernandez, “Artificial Intelligence Is Now Telling Doctors How to Treat You”, Wired Business/Kaiser Health News, 2 June 2014, https://​www.​wired.​com/​2014/​06/​ai-healthcare/​. Alphabet’s DeepMind has been partnering with healthcare providers, including the NHS, on a variety of initiatives, including an app called Streams, which has the capability to analyse medical history and test results to alert doctors and nurses of potential dangers which might not have otherwise been spotted, see “DeepMind—Health”, https://​deepmind.​com/​applied/​deepmind-health/​, accessed 1 June 2018. 94Rena S. Miller and Gary Shoerter, “High Frequency Trading: Overview of Recent Developments”, US Congressional Research Service, 4 April 2016, 1, https://​fas.​org/​sgp/​crs/​misc/​R44443.​pdf, accessed 1 June 2018. 95Laura Noonan, “ING Launches Artificial Intelligence Bond Trading Tool Katana”, Financial Times, 12 December 2017, https://​www.​ft.​com/​content/​1c63c498-de79-11e7-a8a4-0a1e63a52f9c, accessed 1 June 2018. 96Alex Marshall, “From Jingles to Pop Hits, A.I.

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

., Re-identification of Genomic Data Using Long Range Familial Searches. bioRxiv, 2018. 48. Shead, S., “Google DeepMind Has Doubled the Size of Its Healthcare Team,” Business Insider. 2016; Shead, S., “DeepMind’s First Deal with the NHS Has Been Torn Apart in a New Academic Study,” Business Insider. 2017. 49. Shead, “Google DeepMind Has Doubled the Size of Its Healthcare Team”; Shead, “DeepMind’s First Deal with the NHS Has Been Torn Apart in a New Academic Study.” 50. Kahn, J., “Alphabet’s DeepMind Is Trying to Transform Health Care—but Should an AI Company Have Your Health Records?,” Bloomberg. 2017. 51. Kahn, J., “Alphabet’s DeepMind Is Trying to Transform Health Care.” 52.

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The algorithm integrated a convolutional neural network with reinforcement learning, maneuvering a paddle to hit a brick on a wall.26 This qualified as a “holy shit” moment for Max Tegmark, as he recounted in his book Life 3.0: “The AI was simply told to maximize the score by outputting, at regular intervals, numbers which we (but not the AI) would recognize as codes for which keys to press.” According to DeepMind’s leader, Demis Hassabis, the strategy DeepMind learned to play was unknown to any human “until they learned it from the AI they’d built.” You could therefore interpret this as AI not only surpassing the video game performance of human professionals, but also of its creators. Many other video games have been taken on since, including forty-nine different Atari games.27 A year later, in 2016, DNN AI began taking on humans directly, when a program called AlphaGo triumphed over Lee Sodol, a world champion at the Chinese game of Go.

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There’s also the Orwellian specter of machine vision AI, with the proliferation of surveillance cameras everywhere, markedly facilitating identification and compromising any sense of privacy. The story of DeepMind, an AI company, and the Royal Free London National Health Foundation Trust from 2017 illustrates the tension in medical circles.48 In November 2015, the National Health Service (NHS) entrusted DeepMind Technologies (a subsidiary of Google/Alphabet) to transfer a database of electronic patient records, with identifiable data but without explicit consent, from NHS systems to the company’s own. The data encompassed records for 1.6 million UK citizens going back more than five years.

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

Indeed, when AI catapulted into the mainstream in the early 2010s, it was often framed, as in earlier iterations, around building machines that surpass human cognition—but mostly kept apart from discussions of surveillance and the national security state. A New York Times report on Google’s acquisition of the startup DeepMind—published in January 2014, less than a year after Snowden went public—emphasizes how DeepMind’s “artificial intelligence technology” could further boost Google’s “world domination of search.” The article does not link search (or AI) to surveillance nor the Pentagon. It ends with a quote from DeepMind’s cofounder about humanity’s downfall in the face of AI: “If a super-intelligent machine (or any kind of super-intelligent agent) decided to get rid of us, I think it would do so pretty efficiently.”9 This false separation of AI from mass surveillance could not last long, however, and the consequences of Snowden’s disclosures for the rebranded AI were not lost on those cheering for the national security state.

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One is DeepMind’s system for playing Atari computer games, which reportedly outperforms human players. Another celebrated system is AlphaGo, also developed by Google’s DeepMind, which has beaten human champions at the game of Go.26 These systems exemplify the aspiration to a radical empiricism. The Atari-playing system, for instance, receives as input images of the game and learns to play based on reinforcement signals (i.e., how many points it scored in the game). Both the Atari and Go playing systems are presented as free of any human knowledge. The Go-playing system, according to DeepMind, has apparently “learned completely from scratch” and is “completely tabula rasa,” which allows the system to “untie from the specifics [of games].”

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Whittaker, was employed by Google. Another core team member, S. M. West, was formerly a Google policy fellow, while the cofounder and head of Applied AI at DeepMind, M. Suleyman, serves on AI Now’s advisory board. AI Now also receives funding from Google, DeepMind, and Microsoft, though the amounts are undisclosed. The symposium marking the launch of the institute, organized in collaboration with the White House, featured speakers from the corporate world (including Facebook, Intel, and Google’s DeepMind), academic social scientists, as well as representatives of the White House and the National Economic Council.   42.   As with AI Now, the Berkman Klein Center’s “Ethics and Governance of AI” initiative has numerous ties to major platform companies.

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!

Another few hundred games and DeepMind’s AI is the equivalent of Luke Skywalker at the end of Star Wars: A New Hope or Neo from The Matrix – effortlessly batting the square ball back and forth with a lazy ease. All signs of extraneous movement are gone, and a clear strategy has emerged. The second reason DeepMind’s AI is so significant is because it does not require masses of human-led training. The central tenet of Good Old-Fashioned AI is that rules had to be pre-loaded into the system, like a teacher preparing a child for an exam by having them learn every answer in order. DeepMind, instead, learns on its own.

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Today video games feature plenty of non-player-controlled characters which are programmed with simple rules that combine to give rise to complex behaviours. So what is so special about DeepMind’s game playing? There are two answers to this question. The first is that it gets better as it plays. Like seeing your child grow up, the change is barely noticeable if you watch the computer constantly. Drop in every fifty or so games, however, and the effect is startling. At first, DeepMind’s AI is crushingly awful at Breakout. It misses easy shots and seems baffled about what’s going on: like handing a PS4 controller to your ninety-year-old great aunt and expecting her to immediately understand what she’s meant to do.

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With nothing more than the instruction to maximise its score, it picks up the ‘rules’ by which the game is played and then hones the strategies needed to perfect them. Nor is Breakout the only game it can play. DeepMind’s AI started out playing Space Invaders, and has also learned forty-eight additional titles with the sparsest of information. These include boxing simulators, martial-arts titles and even 3-D racing games. There is still a distance to go until it moves beyond the ‘micro-world’ of a retro video game, but it remains an astonishing achievement that hints at the next step in AI’s life cycle. That step? According to DeepMind’s own mission statement, it is no less than to ‘solve intelligence’. The Importance of Learning Learning is a profoundly important part of what makes us human.

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

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. 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.

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This is an idea that, after getting a modest amount of traction in the machine-learning community, has just recently reared its (multiple) head(s) in one of the flagship neural networks of the 2010s, AlphaGo Zero. When DeepMind iterated on their champion-dethroning AlphaGo architecture, they realized that the system they’d built could be enormously simplified by merging its two primary networks into one double-headed network. The original AlphaGo used a “policy network” to estimate what move to play in a given position, and a “value network” to estimate the degree of advantage or disadvantage for each player in that position. Presumably, DeepMind realized, the relevant intermediate-level “features”—who controlled which territory, how stable or fragile certain structures were—would be extremely similar for both networks.

…

See Leike and Hutter, “Bad Universal Priors and Notions of Optimality.” 34. The paper is Christiano et al., “Deep Reinforcement Learning from Human Preferences.” For OpenAI’s blog post about the paper, see “Learning from Human Preferences,” https://openai.com/blog/deep-reinforcement-learning-from-human-preferences/, and for DeepMind’s blog post, see “Learning Through Human Feedback,” https://deepmind.com/blog/learning-through-human-feedback/. For earlier work exploring the idea of learning from human preferences and human feedback, see, e.g., Wilson, Fern, and Tadepalli, “A Bayesian Approach for Policy Learning from Trajectory Preference Queries”; Knox, Stone, and Breazeal, “Training a Robot via Human Feedback”; Akrour, Schoenauer, and Sebag, “APRIL”; and Akrour et al., “Programming by Feedback.”

Outnumbered: From Facebook and Google to Fake News and Filter-Bubbles – the Algorithms That Control Our Lives by David Sumpter

affirmative action, algorithmic bias, AlphaGo, Bernie Sanders, Brexit referendum, Cambridge Analytica, classic study, cognitive load, Computing Machinery and Intelligence, correlation does not imply causation, crowdsourcing, data science, DeepMind, Demis Hassabis, disinformation, don't be evil, Donald Trump, Elon Musk, fake news, Filter Bubble, Geoffrey Hinton, Google Glasses, illegal immigration, James Webb Space Telescope, Jeff Bezos, job automation, Kenneth Arrow, Loebner Prize, Mark Zuckerberg, meta-analysis, Minecraft, Nate Silver, natural language processing, Nelson Mandela, Nick Bostrom, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, p-value, post-truth, power law, prediction markets, random walk, Ray Kurzweil, Robert Mercer, selection bias, self-driving car, Silicon Valley, Skype, Snapchat, social contagion, speech recognition, statistical model, Stephen Hawking, Steve Bannon, Steven Pinker, TED Talk, The Signal and the Noise by Nate Silver, traveling salesman, Turing test

One is commercial. It doesn’t hurt DeepMind to have a bit of buzz around artificial intelligence. Demis Hassabis has toned down the emphasis on ‘solving intelligence’ his company had when Google first acquired DeepMind, and in recent interviews focuses more on solving mathematical optimisation problems. The work on Go demonstrates that DeepMind has a leading edge on problems like drug discovery and energy optimisation in power networks that require heavy computation to find the best solution out of many available alternatives. Without a bit of hype early on, DeepMind might not have acquired the resources to solve some of these important problems.

…

The research division now consisted of small units, each of which worked on its own project and shared ideas and data internally within the groups.1 After some more quizzing, one of the Googlers finally mentioned a project. ‘I heard we are using DeepMind to look at medical diagnostics around kidney failure,’ he said. The plan was to use machine learning to find patterns in kidney disease that doctors had missed. The DeepMind in question is the branch of Google that has programmed a computer to become the best go player in the world and trained an algorithm to master playing Space Invaders and other old arcade games. Now it would search through the UK’s National Health Service (NHS) patient records to try and find patterns in the occurrence of diseases. DeepMind would become an intelligent computing assistant to doctors.

…

Mark didn’t need his assistant to help him save the world, but he did want to see just how intelligent a home help he could create using Facebook’s library of algorithms. Google has ambitions that stretch beyond personal butlers. The DeepMind team, which had won at Go and Space Invaders, has helped Google improve energy efficiency of its servers and developed more realistic speech for the company’s personal assistant. Another application area is DeepMind Health, a project one of the London Googlers had told me about when I visited them. The aim is to look at how the National Health Service in the UK collects and manages patient data in order to see how the process can be improved. DeepMind’s CEO Demis Hassabis talks about his team one day producing a ‘high-quality scientific paper where the first author is an AI’.

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

Teh, “A Fast Learning Algorithm for Deep Belief Nets,” Neural Computation 18, no. 7 (2006): 1527–54. 77 The software engineer Jeff Dean: Jeff Dean, “Large-Scale Deep Learning for Intelligent Computer Systems,” accessed January 26, 2017, http://www.wsdm-conference.org/2016/slides/WSDM2016-Jeff-Dean.pdf. 78 When control of an actual data center: Richard Evans and Jim Gao, “DeepMind AI Reduces Google Data Centre Cooling Bill by 40%,” DeepMind, July 20, 2016, https://deepmind.com/blog/deepmind-ai-reduces-google-data-centre-cooling-bill-40. 79 Tech giants including Microsoft: Tom Simonite, “Google and Microsoft Want Every Company to Scrutinize You with AI,” MIT Technology Review, August 1, 2016, https://www.technologyreview.com/s/602037/google-and-microsoft-want-every-company-to-scrutinize-you-with-ai. 79 nonrice farms in Japan average only 1.5 hectares: “Field Work: Farming in Japan,” Economist, April 13, 2013, http://www.economist.com/news/asia/21576154-fewer-bigger-plots-and-fewer-part-time-farmers-agriculture-could-compete-field-work. 80 about one and a half baseball fields: Metric Views, “How Big Is a Hectare?”

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The total amount of energy used for cooling fell by as much as 40%, and the facility’s overhead—the energy not used directly for IT equipment, which includes ancillary loads and electrical losses—improved by about 15%. DeepMind cofounder Mustafa Suleyman told us these were among the largest improvements the Google data center team had ever seen. Suleyman also stressed to us that DeepMind’s approach is highly generalizable. The neural networks used by the team do not need to be completely reconfigured for each new data center. They simply need to be trained with as much detailed historical data as possible.

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Louis, 163 fees Stripe, 172–73 in two-sided networks, 215 fiat currencies, 280, 286, 305 FICO scores, 46–47 file sharing platforms, 144–45 film photography, 131 financial crisis (2008), 285, 308 financial services automated investing, 266–70 crowdlending platforms, 263 as least-trusted industry, 296 and regulation, 202 TØ.com, 290 virtualization of, 91 find-fix-verify, 260 firms economics of, 309–12 theory of, See TCE (transaction cost economics) FirstBuild, 11–14 Fitbit, 163 5G wireless technology, 96 fixed costs, 137 flat hierarchy, 325 Fleiss, Jennifer, 187 Flexe, 188 focus groups, 189–90 “food computers,” 272 food preparation recipes invented by Watson, 118 robotics in, 93–94 Forbes magazine, 303 forks, operating system, 244 Forsyth, Mark, 70 “foxes,” 60–61 fraud detection, 173 “free, perfect, instant” information goods complements, 160–63 economics of, 135–37 free goods, complements and, 159 freelance workers, 189 free market, See market “freemium” businesses, 162 Friedman, Thomas, 135 Friendster, 170 Fukoku Mutual Life, 83 Gallus, Jana, 249n garments, 186–88 Garvin, David, 62 Gazzaniga, Michael, 45n GE Appliances, 15 Gebbia, Joe, 209–10 geeky leadership, 244–45, 248–49 gene editing, 257–58 General Electric (GE), 10–15, 261 General Growth Properties, 134 General Theory of Employment, Interest, and Money, The (Keynes), 278–79 generative design, 112–13 genome sequencing, 252–55, 260–61 Georgia, Republic of, 291 Gershenfeld, Neil, 308 GFDL, 248 Gill, Vince, 12n Giuliano, Laura, 40 global financial crisis (2008), 285, 308 GNU General Public License (GPL), 243 Go (game), 1–6 Goethe, Johann Wolfgang von, 178 Go-Jek, 191 golden ratio, 118 Goldman Sachs, 134 gold standard, 280n Goodwin, Tom, 6–10, 14 Google, 331; See also Android acquiring innovation by acquiring companies, 265 Android purchased by, 166–67 Android’s share of Google revenue/profits, 204 autonomous car project, 17 DeepMind, 77–78 hiring decisions, 56–58 iPhone-specific search engine, 162 and Linux, 241 origins of, 233–34 and self-driving vehicles, 82 as stack, 295 Google AdSense, 139 Google DeepMind, 4, 77–78 Google News, 139–40 Google search data bias in, 51–52 incorporating into predictive models, 39 Graboyes, Robert, 274–75 Granade, Matthew, 270 Grant, Amy, 12n graphics processing units (GPUs), 75 Great Recession (2008), 285, 308 Greats (shoe designer), 290 Grid, The (website design startup), 118 Grokster, 144 Grossman, Sandy, 314 group drive, 20, 24 group exercise, See ClassPass Grove, William, 41 Grubhub, 186 Guagua Xiche, 191–92 gut instincts, 56 gyro sensor, 98 Haidt, Jonathan, 45 Hammer, Michael, 32, 34–35, 37, 59 hands, artificial, 272–75 Hannover Messe Industrial Trade Fair, 93–94 Hanson, Robin, 239 Hanyecz, Laszlo, 285–86 Hao Chushi, 192 “hard fork,” 304–5, 318 Harper, Caleb, 272 Hart, Oliver, 313–15 Hayek, Friedrich von, 151, 235–39, 279, 332 health care, 123–24 health coaches, 124, 334 health insurance claims, 83 Hearn, Mike, 305–6 heat exchangers, 111–13 “hedgehogs,” 60–61 Hefner, Cooper, 133 Hefner, Hugh, 133 hierarchies flat, 325 production costs vs. coordination costs in, 313–14 Hinton, Geoff, 73, 75–76 HiPPOs (highest-paid person’s opinions), 45, 63, 85 hiring decisions, 56–58 Hispanic students, 40 HIStory (Michael Jackson), 131 hive mind, 97 HMV (record store chain), 131, 134 Holberton School of Software Engineering, 289 “hold-up problem,” 316 Holmström, Bengt, 313, 315 Honor (home health care platform), 186 hotels limits to Airbnb’s effects on, 221–23 Priceline and, 223–24 revenue management’s origins, 182 “hot wallet,” 289n housing sales, 39 Howell, Emily (music composition software), 117 Howells, James, 287 Hughes, Chris, 133 human condition, 121, 122 human genome, 257–58 human judgment, See judgment, human Hyman, Jennifer, 187 hypertext, 33 IBM; See also Watson (IBM supercomputer) and Linux, 241 System/360 computer, 48 ice nugget machine, 11–14 idAb algorithm, 253, 254 incentives, ownership’s effect on, 316 incomplete contracting, 314–17 incremental revenue, 180–81 incumbents advantages in financial services, 202 inability to foresee effects of technological change, 21 limits to disruption by platforms, 221–24 platforms’ effect on, 137–48, 200–204 threats from platform prices, 220–21 Indiegogo, 13–14, 263, 272 industrial trusts, 22–23 information business processes and, 88–89 in economies, 235–37 O2O platforms’ handling of, 192–93 information asymmetries, 206–10 information goods bundling, 146–47 as “free, perfect, instant,” 135–37 and solutionism, 297–98 information transfer protocols, 138 infrared sensors, 99 InnoCentive, 259 innovation crowd and, 264–66 ownership’s effect on, 316 Instagram, 133, 264–66 institutional investors, 263 Intel, 241, 244 Internet as basis for new platforms, 129–49 economics of “free, perfect, instant” information goods, 135–37 evolution into World Wide Web, 33–34 in late 1990s, 129–31 as platform of platforms, 137–38 pricing plans, 136–37 intuition, See System 1/System 2 reasoning inventory, perishing, See perishing/perishable inventory investing, automated, 266–70 investment advising, 91 Iora Health, 124, 334 Iorio, Luana, 105 iOS, 164–67, 203 iPhone apps for, 151–53, 161–63 Blackberry vs., 168 curation of apps for, 165 demand curve for, 156 introduction of, 151–52 and multisided markets, 218 opening of platform to outside app builders, 163–64 user interface, 170 widespread adoption of, 18 iron mining, 100 Irving, Washington, 252 Isaac, Earl, 46 Isaacson, Walter, 152, 165 iteration, 173, 323; See also experimentation iTunes, 217–18 iTunes Store, 145, 165 Jackson, Michael, 131 Java, 204n Jelinek, Frederick, 84 Jeopardy!

Surviving AI: The Promise and Peril of Artificial Intelligence by Calum Chace

3D printing, Ada Lovelace, AI winter, Airbnb, Alvin Toffler, artificial general intelligence, augmented reality, barriers to entry, basic income, bitcoin, Bletchley Park, blockchain, brain emulation, Buckminster Fuller, Charles Babbage, cloud computing, computer age, computer vision, correlation does not imply causation, credit crunch, cryptocurrency, cuban missile crisis, deep learning, DeepMind, dematerialisation, Demis Hassabis, discovery of the americas, disintermediation, don't be evil, driverless car, Elon Musk, en.wikipedia.org, epigenetics, Erik Brynjolfsson, everywhere but in the productivity statistics, Flash crash, friendly AI, Geoffrey Hinton, Google Glasses, hedonic treadmill, hype cycle, industrial robot, Internet of things, invention of agriculture, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, Kevin Kelly, life extension, low skilled workers, machine translation, Mahatma Gandhi, means of production, mutually assured destruction, Neil Armstrong, Nicholas Carr, Nick Bostrom, paperclip maximiser, pattern recognition, peer-to-peer, peer-to-peer model, Peter Thiel, radical life extension, Ray Kurzweil, Robert Solow, Rodney Brooks, Second Machine Age, self-driving car, Silicon Valley, Silicon Valley ideology, Skype, South Sea Bubble, speech recognition, Stanislav Petrov, Stephen Hawking, Steve Jobs, strong AI, technological singularity, TED Talk, The future is already here, The Future of Employment, theory of mind, Turing machine, Turing test, universal basic income, Vernor Vinge, wage slave, Wall-E, zero-sum game

The system is now available in 45 languages on a number of Microsoft platforms, including Skype. One of the most impressive recent demonstrations of AI functionality was DeepMind’s presentation at Lake Tahoe in December 2013 of an AI system teaching itself to play old-style Atari video games like Breakout and Pong. These are games which previous AI systems have found hard to play because they involve hand-to-eye co-ordination. The most striking thing about DeepMind’s system is that it solves problems and masters skills without being specifically programmed to do so. It shows true general learning ability. The system was not given instructions for how to play the game well, or even told the rules and purpose of the game: it was simply rewarded when it played well and not rewarded when it played less well.

…

The system’s first attempt at each game was disastrous but by playing continuously for 24 hours or so it worked out – through trial and error – the subtleties in the gameplay and scoring system, and played the game better than the best human player. It took longer to master Space Invaders, where the winning strategies are less obvious. DeepMind’s founder, Demis Hassabis, remarked that it is easier to experiment with AI using video games than robots because it avoids the messy business of hydraulics, power and gravity that dealing with the real world entails. But the hand-eye co-ordination could well prove useful with real-world robots as well as video games. Perhaps part of the attraction of DeepMind to Google was the potential to accelerate the development of all those robot companies it had bought. The science of what we can’t yet do A famous cartoon shows a man in a small room writing notes to stick on the wall behind him.

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Intelligence, like most words used to describe what the brain does, is hard to pin down: there are many rival definitions. Most of them contain the notion of the ability to acquire information, and use it to achieve a goal. One of the most popular recent definitions is from German academic Marcus Hutter and Shane Legg, a co-founder of a company called DeepMind that we will hear about later. It states that “intelligence measures an agent’s general ability to achieve goals in a wide range of environments.” (2) As well as being hard to define, intelligence is also hard to measure. There are many types of information that an intelligent being might want to acquire, and many types of goals it might want to achieve.

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

5 There was a sense of ‘melancholy’, ‘existential angst’, at how it was possible for outsiders to make a jump that, in AlQuraishi's words, worked at twice the pace of regular advance, and possibly more. It was ‘an anomalous leap’ in one of the core scientific problems of our time. What did just happen? The artificial intelligence company DeepMind, part of the Alphabet group, had been quietly working on software called AlphaFold. DeepMind uses deep learning neural networks, a newly potent technique of machine learning (ML), to predict how proteins fold. These networks aim to mimic the functioning of the human brain, using layers of mathematical functions that can, by changing their weightings, appear to learn.

…

In the words of Paul Bates of the Francis Crick Institute, ‘They did blow the field apart.’ 6 The truth is, as the Idea Paradox suggests, that we are left with the truly hard problems; and protein folding is a problem of savage complexity. It demands constant ascent through the technological and methodological gears. Had a new gear been found? DeepMind was already known for its ambitious use of ML. 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.

…

Reinhardt, Ben (2021), Shifting the impossible to the inevitable: A Private ARPA User Manual, benreinhardt.com, accessed 12 April 2021, available at https://benjaminreinhardt.com/parpa Reller, Tom (2016), ‘Elsevier publishing – a look at the numbers, and more’, Elsevier.com, accessed June 8, 2019, available at https://www.elsevier.com/connect/elsevier-publishing-a-look-at-the-numbers-and-more Renwick, Chris (2017), Bread For All: The Origins of the Welfare State, London: Allen Lane Reynolds, Matt (2020), ‘DeepMind's AI is getting closer to its first big real-world application’, Wired, accessed 5 February 2020, available at https://www.wired.co.uk/article/deepmind-protein-folding-alphafold Ricón, José Luis (2015), ‘Is there R&D spending myopia?’, Nintil, accessed 6 January 2021, available at https://nintil.com/is-there-rd-spending-myopia/ Ricón, José Luis (2019), ‘On Bloom's two sigma problem: A systematic review of the effectiveness of mastery learning, tutoring, and direct instruction’, Nintil, accessed 20 July 2020, available at https://nintil.com/bloom-sigma/ Ricón, José Luis (2020a), ‘Fund people, not projects I: The HHMI and the NIH Director's Pioneer Award’, Nintil, accessed 24 January 2021, available at https://nintil.com/hhmi-and-nih/ Ricón, José Luis (2020b), ‘Was Planck right?

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

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. Amy Sterling, “Millions of Jobs Have Been Lost to Automation.

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Nicolas Miailhe, “AI & Global Governance: Why We Need an Intergovernmental Panel for Artificial Intelligence,” United Nations University Centre for Policy Research, December 10, 2018, https://cpr.unu.edu/​ai-global-governance-why-we-need-an-intergovernmental-panel-for-artificial-intelligence.html. 84. Tom Simonite, “Canada, France Plan Global Panel to Study the Effects of AI,” Wired, December 6, 2018, https://www.wired.com/​story/​canada-france-plan-global-panel-study-ai/. 85. Richard Evans and Jim Gao, “DeepMind AI Reduces Google Data Centre Cooling Bill by 40%,” DeepMind, July 20, 2016, https://deepmind.com/​blog/​deepmind-ai-reduces-google-data-centre-cooling-bill-40/. 86. United Nations Division for the Advancement of Women (UNDAW), “Equal Participation of Women and Men in Decision-Making Processes, with Particular Emphasis on Political Participation and Leadership,” report of the Expert Group Meeting, October 24–25, 2005; Kathy Caprino, “How Decision-Making Is Different Between Men and Women and Why It Matters in Business,” Forbes, May 12, 2016, https://www.forbes.com/​sites/​kathycaprino/​2016/​05/​12/​how-decision-making-is-different-between-men-and-women-and-why-it-matters-in-business/; Virginia Tech, “Study Finds Less Corruption in Countries Where More Women Are in Government,” ScienceDaily, June 15, 2018, https://www.sciencedaily.com/​releases/​2018/​06/​180615094850.htm. 87.

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A humbling story of how this might unfold took place at Google’s data centers in 2016. For more than ten years Google engineers had been at the cutting edge of optimizing their data systems. Their servers were among the most efficient in the world, and it seemed that any improvements from then on would be marginal. Then they unleashed DeepMind algorithms on the system. Energy demand for cooling was consistently reduced by 40 percent.85 This illustration is just a tiny example of the power of AI to make possible what seems impossible to the human mind. At present, investment in applying AI to the climate crisis is lower than it should be.

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

3D printing, Ada Lovelace, AI winter, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Alfred Russel Wallace, algorithmic bias, AlphaGo, Andrew Wiles, artificial general intelligence, Asilomar, Asilomar Conference on Recombinant DNA, augmented reality, autonomous vehicles, basic income, behavioural economics, Bletchley Park, blockchain, Boston Dynamics, brain emulation, Cass Sunstein, Charles Babbage, Claude Shannon: information theory, complexity theory, computer vision, Computing Machinery and Intelligence, connected car, CRISPR, crowdsourcing, Daniel Kahneman / Amos Tversky, data science, deep learning, deepfake, DeepMind, delayed gratification, Demis Hassabis, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Ernest Rutherford, fake news, Flash crash, full employment, future of work, Garrett Hardin, Geoffrey Hinton, Gerolamo Cardano, Goodhart's law, Hans Moravec, ImageNet competition, Intergovernmental Panel on Climate Change (IPCC), Internet of things, invention of the wheel, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John Nash: game theory, John von Neumann, Kenneth Arrow, Kevin Kelly, Law of Accelerating Returns, luminiferous ether, machine readable, machine translation, Mark Zuckerberg, multi-armed bandit, Nash equilibrium, Nick Bostrom, Norbert Wiener, NP-complete, OpenAI, openstreetmap, P = NP, paperclip maximiser, Pareto efficiency, Paul Samuelson, Pierre-Simon Laplace, positional goods, probability theory / Blaise Pascal / Pierre de Fermat, profit maximization, RAND corporation, random walk, Ray Kurzweil, Recombinant DNA, recommendation engine, RFID, Richard Thaler, ride hailing / ride sharing, Robert Shiller, robotic process automation, Rodney Brooks, Second Machine Age, self-driving car, Shoshana Zuboff, Silicon Valley, smart cities, smart contracts, social intelligence, speech recognition, Stephen Hawking, Steven Pinker, superintelligent machines, surveillance capitalism, Thales of Miletus, The Future of Employment, The Theory of the Leisure Class by Thorstein Veblen, Thomas Bayes, Thorstein Veblen, Tragedy of the Commons, transport as a service, trolley problem, Turing machine, Turing test, universal basic income, uranium enrichment, vertical integration, Von Neumann architecture, Wall-E, warehouse robotics, Watson beat the top human players on Jeopardy!, web application, zero-sum game

The program learned essentially from scratch, by playing against itself and observing the rewards of winning and losing.60 In 1992, Gerry Tesauro applied the same idea to the game of backgammon, achieving world-champion-level play after 1,500,000 games.61 Beginning in 2016, DeepMind’s AlphaGo and its descendants used reinforcement learning and self-play to defeat the best human players at Go, chess, and shogi. Reinforcement learning algorithms can also learn how to select actions based on raw perceptual input. For example, DeepMind’s DQN system learned to play forty-nine different Atari video games entirely from scratch—including Pong, Freeway, and Space Invaders.62 It used only the screen pixels as input and the game score as a reward signal.

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See also assistance games Gates, Bill, 56, 153 GDPR (General Data Protection Regulation), 127–29 Geminoid DK (robot), 125 General Data Protection Regulation (GDPR), 127–29 general-purpose artificial intelligence, 46–48, 100, 136 geometric objects, 33 Glamour, 129 Global Learning XPRIZE competition, 70 Go, 6, 46–47, 49–50, 51, 55, 56 combinatorial complexity and, 259–61 propositional logic and, 269 supervised learning algorithm and, 286–87 thinking, learning from, 293–95 goals, 41–42, 48–53, 136–42, 165–69 God and Golem (Wiener), 137–38 Gödel, Kurt, 51, 52 Goethe, Johann Wolfgang von, 137 Good, I. J., 142–43, 153, 208–9 Goodhart’s law, 77 Goodman, Nelson, 85 Good Old-Fashioned AI (GOFAI), 271 Google, 108, 112–13 DeepMind (See DeepMind) Home, 64–65 misclassifying people as gorillas in Google Photo, 60 tensor processing units (TPUs), 35 gorilla problem, 132–36 governance of AI, 249–53 governmental reward and punishment systems, 106–7 Great Decoupling, 117 greed (as an instrumental goal), 140–42 Grice, H.

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Beginning around 2011, deep learning techniques began to produce dramatic advances in speech recognition, visual object recognition, and machine translation—three of the most important open problems in the field. By some measures, machines now match or exceed human capabilities in these areas. In 2016 and 2017, DeepMind’s AlphaGo defeated Lee Sedol, former world Go champion, and Ke Jie, the current champion—events that some experts predicted wouldn’t happen until 2097, if ever.6 Now AI generates front-page media coverage almost every day. Thousands of start-up companies have been created, fueled by a flood of venture funding.

Hello World: Being Human in the Age of Algorithms by Hannah Fry

23andMe, 3D printing, Air France Flight 447, Airbnb, airport security, algorithmic bias, algorithmic management, augmented reality, autonomous vehicles, backpropagation, Brixton riot, Cambridge Analytica, chief data officer, computer vision, crowdsourcing, DARPA: Urban Challenge, data science, deep learning, DeepMind, Douglas Hofstadter, driverless car, Elon Musk, fake news, Firefox, Geoffrey Hinton, Google Chrome, Gödel, Escher, Bach, Ignaz Semmelweis: hand washing, John Markoff, Mark Zuckerberg, meta-analysis, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, pattern recognition, Peter Thiel, RAND corporation, ransomware, recommendation engine, ride hailing / ride sharing, selection bias, self-driving car, Shai Danziger, Silicon Valley, Silicon Valley startup, Snapchat, sparse data, speech recognition, Stanislav Petrov, statistical model, Stephen Hawking, Steven Levy, systematic bias, TED Talk, Tesla Model S, The Wisdom of Crowds, Thomas Bayes, trolley problem, Watson beat the top human players on Jeopardy!, web of trust, William Langewiesche, you are the product

In 2016, DeepMind, the artificial intelligence arm of Google, signed a contract with the Royal Free NHS Trust in London. DeepMind was granted access to the medical data from three of the city’s hospitals in return for an app that could help doctors identify acute kidney injuries. The initial intention was to use clever learning algorithms to help with healthcare; but the researchers found that they had to rein in their ambitions and opt for something much simpler, because the data just wasn’t good enough for them to reach their original goals. Beyond these purely practical challenges, DeepMind’s collaboration with the NHS raised a more controversial issue.

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The researchers only ever promised to alert doctors to kidney injuries, but the Royal Free didn’t have a kidney dataset to give them. So instead DeepMind was granted access to everything on record: medical histories for some 1.6 million patients going back over a full five years. In theory, having this incredible wealth of information could help to save innumerable lives. Acute kidney injuries kill one thousand people a month, and having data that reached so far back could potentially help DeepMind to identify important historical trends. Plus, since kidney injuries are more common among people with other diseases, a broad dataset would make it much easier to hunt for clues and connections to people’s future health.

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Plus, since kidney injuries are more common among people with other diseases, a broad dataset would make it much easier to hunt for clues and connections to people’s future health. Instead of excitement, though, news of the project was met with outrage. And not without justification. Giving DeepMind access to everything on record meant exactly that. The company was told who was admitted to hospital and when. Who came to visit patients during their stay. The results of pathology reports, of radiology ­exams. Who’d had abortions, who’d had depression, even who had been diagnosed with HIV. And worst of all? The patients themselves were never asked for their consent, never given an opt-out, never even told they were to be part of the study.47 It’s worth adding that Google was forbidden to use the information in any other part of its business.

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

We could induce that all swans have beaks by the same inductive strategy, but the induction would be less power­ful, ­because all birds have beaks, and swans are a small subset of birds. Prior knowledge is used to form hypotheses. Intuition provides mathematicians with in­ter­est­ing prob­lems. When the developers of DeepMind claimed, in a much-­read article in the prestigious journal Nature, that it had mastered Go “without ­human knowledge,” they misunderstood the nature of inference, mechanical or other­w ise. The article clearly “overstated the case,” as Marcus and Davis put it.5 In fact, DeepMind’s scientists engineered into AlphaGo a rich model of the game of Go, and went to the trou­ble 162 T he P rob­lem of I nference of finding the best algorithms to solve vari­ous aspects of the game—­a ll before the system ever played in a real competition.

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As Marcus and Davis explain, “the system relied heavi­ly on ­things that ­human researchers had discovered over the last few de­c ades about how to get machines to play games like Go, most notably Monte Carlo Tree Search . . . ​random sampling from a tree of dif­fer­ent game possibilities, which has nothing intrinsic to do with deep learning. DeepMind also (unlike [the Atari system]) built in rules and some other detailed knowledge about the game. The claim that ­human knowledge w ­ asn’t involved simply w ­ asn’t factually accurate.” 6 A more succinct way of putting this is that the DeepMind team used h­ uman inferences—­ namely, abductive ones—to design the system to successfully accomplish its task. ­These inferences w ­ ere supplied from outside the inductive framework.

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., 248 Byron, Lord, 238 Capek, Karel, 82–83 causation: correlation and, 259; Hume on, 120; ladder of, 130–131, 174; relevance prob­lems in, 112 chess: Deep Blue for, 219; played by computers, 284n1; Turing’s interest in, 19–20 Chollet, François, 27 Chomsky, Noam, 52, 95 classification, in supervised learning, 134 cognition, Legos theory of, 266 color, 79, 289n16 common sense, 2, 131–132, 177; scripts approach to, 181–182; Winograd schemas test of, 196–203 computational knowledge, 178–182 computers: chess played by, 19–20, 284n1; earliest, 232–233; in history of technology, 44; machine learning by, 133; translation by, 52–55; as Turing machines, 16, 17; Turing’s paper on, 10–11 Comte, August, 63–66 Condorcet (Marie Jean Antoine Nicolas Caritat, the Marquis de Condorcet), 288n4 conjectural inference, 163 consciousness, 77–80, 277 conversations, Grice’s Maxims for, 215–216 Copernicus, Nicolaus, 104 counterfactuals, 174 creative abduction, 187–189 Cukier, Kenneth, 143, 144, 257 Czecho­slo­va­k ia, 60–61 Dartmouth Conference (Dartmouth Summer Research Proj­ect on Artificial Intelligence; 1956), 50–51 data: big data, 142–146; observations turned into, 291n12 I ndex Data Brain proj­ects, 251–254, 261, 266, 268, 269 data science, 144 Davis, Ernest, 131, 183; on brittleness prob­lem, 126; on correlation and causation, 259; on DeepMind, 127, 161–162; on Google Duplex, 227; on limitations of AI, 75–76; on machine reading comprehension, 195; on Talk to Books, 228 deduction, 106–110, 171–172; extensions to, 167, 175; knowledge missing from, 110–112; relevance in, 112–115 deductive inference, 189 Deep Blue (chess computer), 219 deep learning, 125, 127, 134, 135; as dead end, 275; fooling systems for, 165–166; not used by Watson, 231 DeepMind (computer program), 127, 141, 161–162 DeepQA (Jeopardy! computer), 222–224 deep reinforcement learning, 125, 127 Dostoevsky, Fyodor, 64 Dreyfus, Hubert, 48, 74 earthquake prediction, 260–261 Eco, Umberto, 186 Edison, Thomas, 45 Einstein, Albert, 239, 276 ELIZA (computer program), 58–59, 192–193, 229 email, filtering spam in, 134–135 empirical constraint, 146–149, 173 Enigma (code making machine), 21, 23–24 entity recognition, 137 305 Etzioni, Oren, 129, 143–144 Eugene Goostman (computer program), 191–195, 214–216 evolutionary technology, 41–42 Ex Machina (film, Garland), 61, 78–80, 82, 84, 277 Facebook, 147, 229, 243 facts, data turned into, 291n12 Farecast (firm), 143–144 feature extraction, 146–147 Ferrucci, Dave, 222, 226 filter ­bubbles, 151 financial markets, 124 Fisch, Max H., 96–97 Fodor, Jerry, 53 formal systems, 284n6 Frankenstein (fictional character), 238 Frankenstein: Or, a Modern Prometheus (novel, Shelly), 238, 280 frequency assumptions, 150–154, 173 Fully Automated High-­Quality Machine Translation, 48 functions, 139 Galileo, 160 gambler’s fallacy, 122 games, 125–126 Gardner, Dan, 69–70 Garland, Alex, 79, 80, 289n16 Gates, Bill, 75 general intelligence, 2, 31, 36; abduction in, 4; in machines, 38; nonexistance of, 27; pos­si­ble theory of, 271 General Prob­lem Solver (AI program), 51 306 I ndex Germany: Enigma machine of, 23–24; during World War II, 20–21 Go (game), 125, 131, 161–162 Gödel, Kurt, 11, 22, 239; incompleteness theorems of, 12–15; Turing on, 16–18 Golden, Rebecca, 250 Good, I.

What Algorithms Want: Imagination in the Age of Computing by Ed Finn

Airbnb, Albert Einstein, algorithmic bias, algorithmic management, algorithmic trading, AlphaGo, Amazon Mechanical Turk, Amazon Web Services, bitcoin, blockchain, business logic, Charles Babbage, Chuck Templeton: OpenTable:, Claude Shannon: information theory, commoditize, Computing Machinery and Intelligence, Credit Default Swap, crowdsourcing, cryptocurrency, data science, DeepMind, disruptive innovation, Donald Knuth, Donald Shoup, Douglas Engelbart, Douglas Engelbart, Elon Musk, Evgeny Morozov, factory automation, fiat currency, Filter Bubble, Flash crash, game design, gamification, Google Glasses, Google X / Alphabet X, Hacker Conference 1984, High speed trading, hiring and firing, Ian Bogost, industrial research laboratory, invisible hand, Isaac Newton, iterative process, Jaron Lanier, Jeff Bezos, job automation, John Conway, John Markoff, Just-in-time delivery, Kickstarter, Kiva Systems, late fees, lifelogging, Loebner Prize, lolcat, Lyft, machine readable, Mother of all demos, Nate Silver, natural language processing, Neal Stephenson, Netflix Prize, new economy, Nicholas Carr, Nick Bostrom, Norbert Wiener, PageRank, peer-to-peer, Peter Thiel, power law, Ray Kurzweil, recommendation engine, Republic of Letters, ride hailing / ride sharing, Satoshi Nakamoto, self-driving car, sharing economy, Silicon Valley, Silicon Valley billionaire, Silicon Valley ideology, Silicon Valley startup, SimCity, Skinner box, Snow Crash, social graph, software studies, speech recognition, statistical model, Steve Jobs, Steven Levy, Stewart Brand, supply-chain management, tacit knowledge, TaskRabbit, technological singularity, technological solutionism, technoutopianism, the Cathedral and the Bazaar, The Coming Technological Singularity, the scientific method, The Signal and the Noise by Nate Silver, The Structural Transformation of the Public Sphere, The Wealth of Nations by Adam Smith, transaction costs, traveling salesman, Turing machine, Turing test, Uber and Lyft, Uber for X, uber lyft, urban planning, Vannevar Bush, Vernor Vinge, wage slave

At least, we think we see that glimpse in the strange renderings the program produces—perhaps it is just the human observers dreaming up these electric sheep on behalf of the machine, obeying our persistent impulse to anthropomorphize and project intentionality into every complex system we encounter. DeepMind is remarkable for the range of its achievements. A few weeks before Google purchased it, the company made international news with a machine learning algorithm that had learned to play twenty-nine Atari games better than the average human with no direct supervision.1 Now the same algorithm has replaced “sixty handcrafted rule-based systems” at Google, from image recognition to speech transcription.2 Most spectacularly, in March 2016 DeepMind’s AlphaGo defeated go grandmaster Lee Sedol 4–1, demonstrating its conquest of one of humanity’s subtlest and most artistic games.3 After a long doldrums, Google and a range of other research outfits seem to be making progress on systems that can gracefully adapt themselves to a wide range of conceptual challenges.

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In Human-Computer Interaction. Applications and Services, edited by Masaaki Kurosu, 276–283. Lecture Notes in Computer Science 8005. Berlin: Springer, 2013. http://link.springer.com.ezproxy1.lib.asu.edu/chapter/10.1007/978-3-642-39262-7_31. Reese, Hope. “Google DeepMind: The Smart Person’s Guide.” TechRepublic, August 3, 2016, http://www.techrepublic.com/article/google-deepmind-the-smart-persons-guide. Rendell, Paul. Turing Machine Universality of the Game of Life. Cham, Switzerland: Springer, 2016. Emergence, Complexity, and Computation 18. Rice, Stephen P. Minding the Machine: Languages of Class in Early Industrial America.

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As Hayles argues in How We Became Posthuman, theoretical models of biophysical reality like the early McCulloch–Pitts Neuron (which the logician Walter Pitts proved to be computationally equivalent to a Turing machine) allowed cybernetics to establish correlations between computational and biological processes at paradigmatic and operational levels and lay claim to being what informatics scholar Geoffrey Bowker calls a “universal discipline.”33 Via cybernetics, information was the banner under which “effective computability” expanded to vast new territories, first presenting the tantalizing prospect that Wolfram and others would later reach for as universal computation.34 As early as The Human Use of Human Beings, Wiener popularized these links between the Turing machine, neural networks, and learning in biological organisms, work that is now coming to startling life in the stream of machine learning breakthroughs announced by the Google subsidiary DeepMind over the past few years. This is Wiener ascending the ladder of abstraction, positioning cybernetics as a new Liebnitzian mathesis universalis capable of uniting a variety of fields. Central to this upper ascent is the notion of homeostasis, or the way that a system responds to feedback to preserve its core patterns and identity.

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

Go takes a lifetime to master. Prior to DeepMind, attempts to build go-playing AI software had fallen woefully short of human professional players. To craft its AI, called AlphaGo, DeepMind took a different approach. They built an AI composed of deep neural networks and fed it data from 30 million games of go. As explained in a DeepMind blog post, “These neural networks take a description of the Go board as an input and process it through 12 different network layers containing millions of neuron-like connections.” Once the neural network was trained on human games of go, DeepMind then took the network to the next level by having it play itself.

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More than just another realm of competition in which AIs now top humans, the way DeepMind trained AlphaGo is what really matters. As explained in the DeepMind blog post, “AlphaGo isn’t just an ‘expert’ system built with hand-crafted rules; instead it uses general machine learning techniques to figure out for itself how to win at Go.” DeepMind didn’t program rules for how to win at go. They simply fed a neural network massive amounts of data and let it learn all on its own, and some of the things it learned were surprising. In 2017, DeepMind surpassed their earlier success with a new version of AlphaGo. With an updated algorithm, AlphaGo Zero learned to play go without any human data to start.

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With only access to the board and the rules of the game, AlphaGo Zero taught itself to play. Within a mere three days of self-play, AlphaGo Zero had eclipsed the previous version that had beaten Lee Sedol, defeating it 100 games to 0. These deep learning techniques can solve a variety of other problems. In 2015, even before DeepMind debuted AlphaGo, DeepMind trained a neural network to play Atari games. Given only the pixels on the screen and the game score as input and told to maximize the score, the neural network was able to learn to play Atari games at the level of a professional human video game tester. Most importantly, the same neural network architecture could be applied across a vast array of Atari games—forty-nine games in all.

The Internet Trap: How the Digital Economy Builds Monopolies and Undermines Democracy by Matthew Hindman

A Declaration of the Independence of Cyberspace, accounting loophole / creative accounting, activist fund / activist shareholder / activist investor, AltaVista, Amazon Web Services, barriers to entry, Benjamin Mako Hill, bounce rate, business logic, Cambridge Analytica, cloud computing, computer vision, creative destruction, crowdsourcing, David Ricardo: comparative advantage, death of newspapers, deep learning, DeepMind, digital divide, discovery of DNA, disinformation, Donald Trump, fake news, fault tolerance, Filter Bubble, Firefox, future of journalism, Ida Tarbell, incognito mode, informal economy, information retrieval, invention of the telescope, Jeff Bezos, John Perry Barlow, John von Neumann, Joseph Schumpeter, lake wobegon effect, large denomination, longitudinal study, loose coupling, machine translation, Marc Andreessen, Mark Zuckerberg, Metcalfe’s law, natural language processing, Netflix Prize, Network effects, New Economic Geography, New Journalism, pattern recognition, peer-to-peer, Pepsi Challenge, performance metric, power law, price discrimination, recommendation engine, Robert Metcalfe, search costs, selection bias, Silicon Valley, Skype, sparse data, speech recognition, Stewart Brand, surveillance capitalism, technoutopianism, Ted Nelson, The Chicago School, the long tail, The Soul of a New Machine, Thomas Malthus, web application, Whole Earth Catalog, Yochai Benkler

Shute et al., 2012; Corbett et al., 2012. 23. Verma et al., 2015, p. 1. 24. On Google’s acquisition of UK-based machine learning startup DeepMind, see “What DeepMind brings to Alphabet,” 2016. Access to Google’s computing power was reportedly a key factor in why DeepMind agreed to be acquired by Google. On TensorFlow, see Abadi et al., 2016. 25. Jouppi et al., 2017. 26. S. Levy, 2012; McMillan, 2012. 27. TeleGeography, 2012. 28. Labovitz et al., 2009. 29. Google, 2013. 30. DeepMind, 2016. 31. McKusick and Quinlan, 2009. 32. Mayer, 2007. 33. Hölzle, 2012. 34. Schurman and Brutlag, 2009. 35. Artz, 2009. 36. Hölzle, 2012. 37.

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Retrieved from https://www.bcgperspectives.com/content/articles/media_entertainment_strategic _planning_4_2_trillion_opportunity_internet_economy_g20/. Dean, J., and Ghemawat, S. (2008). MapReduce: simplified data processing on large clusters. Communications of the ACM, 51(1), 107–13. DeepMind. (2016). DeepMind AI reduces Google data centre cooling bill by 40%. Press release. Retrieved from https://deepmind.com/blog/deepmind-ai-reduces-google -data-centre-cooling-bill-40/. DeNardis, L. (2014). The global war for Internet governance. New Haven, CT: Yale University Press. Department of Justice & Federal Trade Commission. (2010, August). Horizontal merger guidelines.

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Brand loyalty and user skills. Journal of Economic Behavior & Organization, 6 (4), 381–85. ———. (1991). Brand loyalty and market equilibrium. Marketing Science, 10(3), 229–45. 224 • Bibliography “What DeepMind brings to Alphabet.” (2016, December). The Economist. Retrieved from https://www.economist.com/news/business/21711946-ai-firms-main-value-alphabet -new-kind-algorithm-factory-what-deepmind-brings. Wheeler, T. (2013). Net effects: the past, present, and future impact of our networks. Washington, D.C.: Federal Communications Commission. Retrieved from http://www.amazon.com/NET-EFFECTS-Present-Future-Networks-ebook /dp/B00H1ZS4TQ.

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

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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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. A month after its European Go victory it presented a system able to navigate a maze in a video game without access to any maps, or to the code of the game. Using a technique called asynchronous reinforcement learning, the system looked at the screen and ran scenarios through multiple versions of itself.

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[xciii] In December 2015, Baidu announced that its speech recognition system Deep Speech 2 performed better than humans with short phrases out of context.[xciv] It uses deep learning techniques to recognise Mandarin. Learning and innovating It can no longer be said that machines do not learn, or that they cannot invent. In December 2013, DeepMind demonstrated an AI system which used a deep learning technique called unsupervised learning to teach itself to play old-style Atari video games like Breakout and Pong.[xcv] These are games which previous AI systems found hard to play because they involve hand-to-eye co-ordination. The system was not given instructions for how to play the games well, or even told the rules and purpose of the games: it was simply rewarded when it played well and not rewarded when it played less well.

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

Companies like Facebook and Google had become the go-to internet platforms for socializing and searching. In the process, they had steamrolled local startups in countries from France to Indonesia. 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.”

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That’s why companies like Google and Facebook have scrambled to snap up the small core of deep-learning experts, paying them millions of dollars to pursue ambitious research projects. In 2013, Google acquired the startup founded by Geoffrey Hinton, and the following year scooped up British AI startup DeepMind—the company that went on to build AlphaGo—for over $500 million. The results of these projects have continued to awe observers and grab headlines. They’ve shifted the cultural zeitgeist and given us a sense that we stand at the precipice of a new era, one in which machines will radically empower and/or violently displace human beings.

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In 2015, a team from Microsoft Research Asia blew the competition out of the water at the global image-recognition competition, ImageNet. The team’s breakthrough algorithm was called ResNet, and it identified and classified objects from 100,000 photographs into 1,000 different categories with an error rate of just 3.5 percent. Two years later, when Google’s DeepMind built AlphaGo Zero—the self-taught successor to AlphaGo—they used ResNet as one of its core technological building blocks. The Chinese researchers behind ResNet didn’t stay at Microsoft for long. Of the four authors of the ResNet paper, one joined Yann LeCun’s research team at Facebook, but the other three have founded and joined AI startups in China.

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

They learn to translate by reading millions of pages of (for example) multilingual European Union documents (they never get bored!). They learn to identify dogs, cats, and human faces by ‘crunching’ through millions of images viewed from different perspectives. Exciting advances have been spearheaded by DeepMind, a London company now owned by Google. DeepMind’s cofounder and CEO, Demis Hassabis, has had a precocious career. At thirteen he was ranked the number two chess champion in the world for his category. He qualified for admission to Cambridge at fifteen but delayed admission for two years, during which time he worked on computer games, including conceiving the highly successful Theme Park.

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In regard to all these post-2050 speculations, we don’t know where the boundary lies between what may happen and what will remain science fiction—just as we don’t know whether to take seriously Freeman Dyson’s vision of biohacking by children. There are widely divergent views. Some experts, for instance Stuart Russell at Berkeley, and Demis Hassabis of DeepMind, think that the AI field, like synthetic biotech, already needs guidelines for ‘responsible innovation’. Moreover, the fact that AlphaGo achieved a goal that its creators thought would have taken several more years to reach has rendered DeepMind’s staff even more bullish about the speed of advancement. But others, like the roboticist Rodney Brooks (creator of the Baxter robot and the Roomba vacuum cleaner) think these concerns are too far from realisation to be worth worrying about—they remain less anxious about artificial intelligence than about real stupidity.

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After studying computer science at Cambridge, he started a computer games company. He then returned to academia and earned a PhD at University College London, followed by postdoctoral work on cognitive neuroscience. He studied the nature of episodic memory and how to simulate groups of human brain cells in neural net machines. In 2016, DeepMind achieved a remarkable feat—its computer beat the world champion of the game of Go. 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.

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

AI winter, Albert Einstein, algorithmic bias, algorithmic trading, AlphaGo, Amazon Web Services, Any sufficiently advanced technology is indistinguishable from magic, augmented reality, autonomous vehicles, backpropagation, Baxter: Rethink Robotics, behavioural economics, bioinformatics, cellular automata, Claude Shannon: information theory, cloud computing, complexity theory, computer vision, conceptual framework, constrained optimization, Conway's Game of Life, correlation does not imply causation, crowdsourcing, Danny Hillis, data science, deep learning, DeepMind, delayed gratification, Demis Hassabis, Dennis Ritchie, discovery of DNA, Donald Trump, Douglas Engelbart, driverless car, Drosophila, Elon Musk, en.wikipedia.org, epigenetics, Flynn Effect, Frank Gehry, future of work, Geoffrey Hinton, Google Glasses, Google X / Alphabet X, Guggenheim Bilbao, Gödel, Escher, Bach, haute couture, Henri Poincaré, I think there is a world market for maybe five computers, industrial robot, informal economy, Internet of things, Isaac Newton, Jim Simons, John Conway, John Markoff, John von Neumann, language acquisition, Large Hadron Collider, machine readable, Mark Zuckerberg, Minecraft, natural language processing, Neil Armstrong, Netflix Prize, Norbert Wiener, OpenAI, orbital mechanics / astrodynamics, PageRank, pattern recognition, pneumatic tube, prediction markets, randomized controlled trial, Recombinant DNA, recommendation engine, Renaissance Technologies, Rodney Brooks, self-driving car, Silicon Valley, Silicon Valley startup, Socratic dialogue, speech recognition, statistical model, Stephen Hawking, Stuart Kauffman, theory of mind, Thomas Bayes, Thomas Kuhn: the structure of scientific revolutions, traveling salesman, Turing machine, Von Neumann architecture, Watson beat the top human players on Jeopardy!, world market for maybe five computers, X Prize, Yogi Berra

In one game, AlphaZero made a bold bishop sacrifice, sometimes used to gain positional advantage, followed by a queen sacrifice, which seemed like a colossal blunder until it led to a checkmate many moves later that neither Stockfish nor humans saw coming. The aliens have landed and the earth will never be the same again. AlphaGo’s developer, DeepMind, was cofounded in 2010 by neuroscientist Demis Hassabis (figure 1.10, left), who had been a postdoctoral fellow at University College London’s Gatsby Computational Neuroscience Unit (directed by Peter Dayan, a former postdoctoral fellow in my lab and winner of the prestigious Brain Prize in 2017 along with Raymond Dolan and Wolfram Schultz for their research on reward learning). DeepMind was acquired by Google for $600 million in 2014. The company employs more than 400 engineers and neuroscientists in a culture that is a blend between academia and start-ups.

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Games are a much simpler environment than the real world. A steppingstone toward more complex and uncertain environments comes from the world of video games. DeepMind had shown in 2015 that temporal difference learning could learn to play Atari arcade games such as Pong at superhuman levels, taking the pixels of the screen as input.15 The next stepping-stone is video games in a three-dimensional environment. StarCraft is among the best competitive video games of all time. DeepMind is using it to develop autonomous deep learning networks that can thrive in that world. Microsoft Research recently bought the rights to Minecraft, another popular video game, and has made it open source so others could customize its three-dimensional environment and speed up the progress of its artificial intelligence.

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To the shock of poker experts, it beat the best of the poker players by a sizable margin, one standard deviation, but it beat the thirty-three players overall by four standard deviations—an immense margin.27 If this achievement is replicated in other areas where human judgment based on imperfect information is paramount, such as politics and international relations, the consequences could be far reaching.28 16 Chapter 1 Figure 1.7 Heads-up no-limit Texas hold ’em. Aces in the hole. Bluffing in high stakes poker has been mastered by DeepStack, which has beaten professional poker players at their own game by a wide margin. Learning How to Play Go In March 2016, Lee Sedol, the Korean Go 18-time world champion, played and lost a five-game match against DeepMind’s AlphaGo (figure 1.8), a Go-playing program that used deep learning networks to evaluate board positions and possible moves.29 Go is to Chess in difficulty as chess is to checkers. If chess is a battle, Go is a war. A 19×19 Go board is much larger than an 8×8 chessboard, which makes it possible to have several battles raging in different parts of the board.

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

For more detail on how artificial intelligence might enable value lock-in or otherwise allow contingent features of civilisation to persist for a very long time, see Finnveden, Riedel, and Shulman (2022). 36. Silver et al. 2016, 2017. 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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For instance, perhaps value lock-in could come about through the cumulative effects of deploying multiple different AI systems rather than one AGI, or perhaps AI might enable value lock-in when still lacking some key capabilities, such as the ability to directly manipulate the physical world (if robotics lags behind other areas of AI). 41. DeepMind 2020. 42. “Our teams research and build safe AI systems. We’re committed to solving intelligence, to advance science and benefit humanity” (DeepMind, n.d.). “Our mission is to ensure that artificial general intelligence benefits all of humanity” (OpenAI 2021a). 43. See whatweowethefuture.com/notes. 44. Silver et al. 2018. 45. Schrittwieser et al. 2020a, 2020b. 46.

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Because of the success of machine learning as a paradigm, we’ve made enormous progress in AI over the last ten years. 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.

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

HOPE As of this writing, I’m cautiously optimistic that the AI-risk message can save humanity from extinction, just as the Soviet-occupation message ended up liberating hundreds of millions of people. As of 2015, it had reached and converted 40 percent of AI researchers. It wouldn’t surprise me if a new survey now would show that the majority of AI researchers believe AI safety to be an important issue. I’m delighted to see the first technical AI-safety papers coming out of DeepMind, OpenAI, and Google Brain and the collaborative problem-solving spirit flourishing among the AI-safety research teams in these otherwise very competitive organizations. The world’s political and business elite are also slowly waking up: AI safety has been covered in reports and presentations by the Institute of Electrical and Electronics Engineers (IEEE), the World Economic Forum, and the Organization for Economic Cooperation and Development (OECD).

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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!

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BOTTOM-UP DEEP LEARNING In the 1980s, computer scientists devised an ingenious way to get computers to detect patterns in data: connectionist, or neural-network, architecture (the “neural” part was, and still is, metaphorical). The approach fell into the doldrums in the 1990s but has recently been revived with powerful “deep-learning” methods like Google’s DeepMind. For example, you can give a deep-learning program a bunch of Internet images labeled “cat,” others labeled “house,” and so on. The program can detect the patterns differentiating the two sets of images and use that information to label new images correctly. Some kinds of machine learning, called unsupervised learning, can detect patterns in data with no labels at all; they simply look for clusters of features—what scientists call a factor analysis.

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

Some computer scientists experienced their AI moment in 2012 when a student team from the University of Toronto delivered such an impressive win in the visual object recognition competition ImageNet that the following year all top finalists used the then-novel “deep learning” approach to compete. Object recognition is more than just a game; it enables machines to “see.” Some technology CEOs experienced their AI moment when they read the headline in January 2014 that Google had just paid more than $600 million to acquire UK-based DeepMind, even though the startup had generated negligible revenue relative to the purchase price but had demonstrated that its AI had learned—on its own, without being programmed—to play certain Atari video games with superhuman performance. 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.

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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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The psychologist Pavlov rang a bell when giving dogs a treat and then found that ringing the bell triggered a saliva response in those dogs. The dogs learned to associate the bell with receiving food and came to know that a bell predicted nearby food and prepared accordingly. In AI, much progress in reinforcement learning has come in teaching machines to play games. DeepMind gave its AI a set of controls to video games such as Breakout and “rewarded” the AI for getting a higher score without any other instructions. The AI learned to play a host of Atari games better than the best human players. This is learning-by-using. The AIs played the game thousands of times and learned to play better, just as a human would, except the AI could play more games, more quickly, than any human ever could.7 Learning occurs by having the machine make certain moves and then using the move data along with past experience (of moves and resulting scores) to predict which moves will lead to the biggest increases in score.

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

Aron Smith, “Public Attitudes Toward Computer Algorithms,” Pew Research Center, November 2018. 94.  Daisuke Wakabayashi and Cade Metz, “Google Promises Its A.I. Will Not Be Used for Weapons,” New York Times, 7 June 2018; Hal Hodson, “Revealed: Google AI Has Access to Huge Haul of NHS Patient Data,” New Scientist, 29 April 2016, and the response from DeepMind, https://deepmind.com/blog/ico-royal-free/ (accessed August 2018). 95.  Eric Topol, “Medicine Needs Frugal Innovation,” MIT Technology Review, 12 December 2011. 96.  Frey, “Technology at Work v2.0.” 97.  Steve Johnson, “Chinese Wages Now Higher Than in Brazil, Argentina and Mexico,” Financial Times, 26 February 2017. 98.  

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To many of them, the pursuit of an understanding of human intelligence for its own sake must look like an increasingly esoteric activity for daydreaming scholars. In order to stay relevant, many researchers—even those inclined to the purist side—have had to align themselves more closely with these companies and their commercial ambitions. Take DeepMind, for example, the British AI company that developed AlphaGo. It was bought by Google in 2014 for $600 million, and is now staffed by the leading minds in the field, poached from top academic departments by pay packages that would make their former colleagues blush—an average of $345,000 per employee.40 The company’s mission statement says that it is trying “to solve intelligence,” which at first glance suggests they might be interested in figuring out the puzzle of the human brain.

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Some say AGIs are a few decades away, others say more like centuries; a recent survey converged, with improbable precision, on 2047.26 Today, we do see some small steps in the direction of “general” capabilities, although these are just very early and primitive examples of it at work. As part of its portfolio of innovations, for instance, DeepMind has developed a machine that is able to compete with human experts at forty-nine different Atari video games. The only data this machine receives is the pattern of pixels on the computer screen and the number of points it has won in the game; yet even so, it has been able to learn how to play each distinct game, often to a level that rivals the finest human players.27 This is the sort of general capability that AGI enthusiasts are chasing after.

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

Popular excitement was inflamed by DeepMind’s creators’ claim that their algorithms “are capable of learning for themselves directly from raw experience or data.” 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.”

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Awakening,” New York Times Magazine, December 14, 2016, https://www.nytimes.com/2016/12/14/magazine/the-great-ai-awakening.html. 167 algorithmic detection of fake news: Jennifer Slegg, “Google Tackles Fake News, Inaccurate Content & Hate Sites in Rater Guidelines Update,” SEM Post, March 14, 2017, http://www.thesempost.com/google-tackles-fake-news-inaccurate-content-hate-sites-rater-guidelines-update/. 167 “directly from raw experience or data”: This claim has been removed from the deepmind.com website, but it can still be found via the Internet Archive. 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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Machine learning takes advantage of the ability of computers to do the same thing, or slight variations of the same thing, over and over again very fast. Yann once waggishly remarked, “The main problem with the real world is that you can’t run it faster than real time.” But computers do this all the time. AlphaGo, the AI-based Go player created by UK company DeepMind that defeated one of the world’s best human Go players in 2016, was first trained on a database of 30 million Go positions from historical games played by human experts. It then played millions of games against itself in order to refine its model of the game even further. Machine learning has become a bigger part of Google Search.

The Industries of the Future by Alec Ross

"World Economic Forum" Davos, 23andMe, 3D printing, Airbnb, Alan Greenspan, algorithmic bias, algorithmic trading, AltaVista, Anne Wojcicki, autonomous vehicles, banking crisis, barriers to entry, Bernie Madoff, bioinformatics, bitcoin, Black Lives Matter, blockchain, Boston Dynamics, Brian Krebs, British Empire, business intelligence, call centre, carbon footprint, clean tech, cloud computing, collaborative consumption, connected car, corporate governance, Credit Default Swap, cryptocurrency, data science, David Brooks, DeepMind, Demis Hassabis, disintermediation, Dissolution of the Soviet Union, distributed ledger, driverless car, Edward Glaeser, Edward Snowden, en.wikipedia.org, Erik Brynjolfsson, Evgeny Morozov, fiat currency, future of work, General Motors Futurama, global supply chain, Google X / Alphabet X, Gregor Mendel, industrial robot, information security, Internet of things, invention of the printing press, Jaron Lanier, Jeff Bezos, job automation, John Markoff, Joi Ito, Kevin Roose, Kickstarter, knowledge economy, knowledge worker, lifelogging, litecoin, low interest rates, M-Pesa, machine translation, Marc Andreessen, Mark Zuckerberg, Max Levchin, Mikhail Gorbachev, military-industrial complex, mobile money, money: store of value / unit of account / medium of exchange, Nelson Mandela, new economy, off-the-grid, offshore financial centre, open economy, Parag Khanna, paypal mafia, peer-to-peer, peer-to-peer lending, personalized medicine, Peter Thiel, precision agriculture, pre–internet, RAND corporation, Ray Kurzweil, recommendation engine, ride hailing / ride sharing, Rubik’s Cube, Satoshi Nakamoto, selective serotonin reuptake inhibitor (SSRI), self-driving car, sharing economy, Silicon Valley, Silicon Valley startup, Skype, smart cities, social graph, software as a service, special economic zone, supply-chain management, supply-chain management software, technoutopianism, TED Talk, The Future of Employment, Travis Kalanick, underbanked, unit 8200, Vernor Vinge, Watson beat the top human players on Jeopardy!, women in the workforce, work culture , Y Combinator, young professional

As a kid, Hassabis was: Samuel Gibbs, “Demis Hassabis: 15 Facts about the DeepMind Technologies Founder,” Guardian, January 28, 2014, http://www.theguardian.com/technology/shortcuts/2014/jan/28/demis-hassabis-15-facts-deepmind-technologies-founder-google; “Breakthrough of the Year: The Runners-Up,” Science 318, no. 5858 (2007): 1844–49, doi:10.1126/science.318.5858.1844a. At DeepMind, Demis and his colleagues: “The Last AI Breakthrough DeepMind Made before Google Bought It for $400m,” Physics arXiv (Blog), https://medium.com/the-physics-arxiv-blog/the-last-ai-breakthrough-deepmind-made-before-google-bought-it-for-400m-7952031ee5e1.

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Google purchased Boston Dynamics, a leading robotics design company with Pentagon contracts, for an untold sum in December 2013. It also bought DeepMind, a London-based artificial intelligence company founded by wunderkind Demis Hassabis. As a kid, Hassabis was the second-highest-ranked chess player in the world under the age of 14, and while he was getting his PhD in cognitive neuroscience, he was acknowledged by Science magazine for making one of the ten most important science breakthroughs of the year after developing a new biological theory for how imagination and memory work in the brain. At DeepMind, Demis and his colleagues effectively created the computer equivalent of hand-eye coordination, something that had never been accomplished before in robotics.

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In a demo, Demis showed me how he had taught his computers how to play old Atari 2600 video games in the same way that humans play them, based on looking at a screen and adjusting actions through neural processes responding to an opponent’s actions. He’d taught computers how to think in much the way that humans do. Then Google bought DeepMind for half a billion dollars and is applying its expertise in machine learning and systems neuroscience to power the algorithms it is developing as it expands beyond Internet search and further into robotics. Most corporate research and development in robotics comes from within big companies (like Google, Toyota, and Honda), but venture capital funding in robotics is growing at a steep rate.

The Simulation Hypothesis by Rizwan Virk

3D printing, Albert Einstein, AlphaGo, Apple II, artificial general intelligence, augmented reality, Benoit Mandelbrot, bioinformatics, butterfly effect, Colossal Cave Adventure, Computing Machinery and Intelligence, DeepMind, discovery of DNA, Dmitri Mendeleev, Elon Musk, en.wikipedia.org, Ernest Rutherford, game design, Google Glasses, Isaac Newton, John von Neumann, Kickstarter, mandelbrot fractal, Marc Andreessen, Minecraft, natural language processing, Nick Bostrom, OpenAI, Pierre-Simon Laplace, Plato's cave, quantum cryptography, quantum entanglement, Ralph Waldo Emerson, Ray Kurzweil, Richard Feynman, Schrödinger's Cat, Search for Extraterrestrial Intelligence, Silicon Valley, Stephen Hawking, Steve Jobs, Steve Wozniak, technological singularity, TED Talk, time dilation, Turing test, Vernor Vinge, Zeno's paradox

The milestones included, writing poetry, orchestrating music, translating from one language to another, and generally accomplishing other tasks that only humans would be capable of at the time. Deep Mind, Alpha Go and Video Games Not only is the history of AI and games intertwined, it continues to be in the near future. Google’s DeepMind group created AlphaGo, the first computer program to beat a professional Go player in 2015. It also beat the South Korean Go champion Lee Sedol in 2016. An interesting twist on the “AI learns to play games” mechanic was when the DeepMind team trained the AI to play video games. This was done not through rules-based AI for a specific game, like the Tic Tac Toe algorithm I had written as a kid, but by watching the screen and controls.

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The human mind, as we understand it, is an incredible learning machine, and every single character in the game, assuming characters are going through the normal cycle of birth and growth, would need to exhibit the ability to learn over time. If babies could suddenly speak complete sentences or speak languages they had never been taught, this might be an interesting clue that we are in some kind of simulation. Spatial Awareness. As Google’s DeepMind and Musk’s OpenAI showed, AI can learn to play video games. This means that they can become aware of a 2D space and examine pixels to see what’s going on. With competitive eSports games like DOTA2, this is even more significant because these games are like MMORPGs – they are a 3D world. For a bot to be able to fight and defeat an opponent within a world, the bot would need to be aware of the 3D space.

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This is a much scarier question and brings up nightmare scenarios, including AGI that decides it doesn’t really need humans anymore, and this, in turn, brings up the question of how “intelligent” we should let AI get. In 2018, more than 2,000 AI researchers signed a letter stating that we should be very careful about developing “killer AI” or autonomous lethal weapons that could kill humans while completely under the control of computer programs. The signers included one of the co-founders of Google DeepMind and Elon Musk. While today these researchers grapple with the ethical challenges presented by AI, noted sci-fi writer Isaac Asimov anticipated these challenges back in 1942 in his collection of stories, I, Robot. In one of its stories Asimov introduced a set of rules—the three laws of robotics—for how robots or AI would interact with each other and with humans, and that are what we would call “hardcoded into the operating system”: A robot may not injure a human being or, through inaction, allow a human being to come to harm.

Framers: Human Advantage in an Age of Technology and Turmoil by Kenneth Cukier, Viktor Mayer-Schönberger, Francis de Véricourt

Albert Einstein, Andrew Wiles, Apollo 11, autonomous vehicles, Ben Bernanke: helicopter money, Berlin Wall, bitcoin, Black Lives Matter, blockchain, Blue Ocean Strategy, circular economy, Claude Shannon: information theory, cognitive dissonance, cognitive load, contact tracing, coronavirus, correlation does not imply causation, COVID-19, credit crunch, CRISPR, crowdsourcing, cuban missile crisis, Daniel Kahneman / Amos Tversky, deep learning, DeepMind, defund the police, Demis Hassabis, discovery of DNA, Donald Trump, double helix, Douglas Hofstadter, Elon Musk, en.wikipedia.org, fake news, fiat currency, framing effect, Francis Fukuyama: the end of history, Frank Gehry, game design, George Floyd, George Gilder, global pandemic, global village, Gödel, Escher, Bach, Higgs boson, Ignaz Semmelweis: hand washing, informal economy, Isaac Newton, Jaron Lanier, Jeff Bezos, job-hopping, knowledge economy, Large Hadron Collider, lockdown, Louis Pasteur, Mark Zuckerberg, Mercator projection, meta-analysis, microaggression, Mustafa Suleyman, Neil Armstrong, nudge unit, OpenAI, packet switching, pattern recognition, Peter Thiel, public intellectual, quantitative easing, Ray Kurzweil, Richard Florida, Schrödinger's Cat, scientific management, self-driving car, Silicon Valley, Steve Jobs, Steven Pinker, TED Talk, The Structural Transformation of the Public Sphere, Thomas Kuhn: the structure of scientific revolutions, TikTok, Tim Cook: Apple, too big to fail, transaction costs, Tyler Cowen

On AlphaZero: This section benefited greatly from interviews in March 2019 by Kenneth Cukier with Demis Hassabis of DeepMind, as well as the chess grand master Matthew Sadler and master Natasha Regan, for which the authors extend their thanks. AlphaZero’s specifics on model training: David Silver et al., “A General Reinforcement Learning Algorithm That Masters Chess, Shogi and Go,” DeepMind, December 6, 2018, https://deepmind.com/blog/article/alphazero-shedding-new-light-grand-games-chess-shogi-and-go; David Silver et al., “Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm,” DeepMind, December 5, 2017, https://arxiv.org/pdf/1712.01815.pdf.

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Computers work only in a world that exists; humans live in ones they imagine through framing. Consider the computer’s shortcomings in the very arena where it is usually feted for its excellence: board games. Even people who are familiar with this story extract the wrong lesson. In 2018 Google DeepMind unveiled a system called AlphaZero that learned to win at chess, Go, and shogi purely by playing against itself, with zero human input other than the rules. After just nine hours, during which it played itself in forty-four million games of chess, it was beating the world’s best chess program, Stockfish.

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Likewise, I thank the team at the British think tank Chatham House, Wilton Park, and the Ditchley Foundation led by James Arroyo, for producing reports and events that improve people’s framing. Many interviews for The Economist’s Babbage podcast and Open Future were helpful in writing this book, including with Mustafa Suleyman of DeepMind, Patrick Collison of Stripe, Aaron Levie of Box, the entrepreneurs Elad Gil and Daniel Gross, Matt Ridley, Eric Topol, David Eagleman, Adam Grant, Howard Gardner, Daniel Levitin, Bill Janeway, Andrew McAfee, Roy Bahat, Zavain Dar, Nan Li, Benedict Evans, Azeem Azhar, David McCourt, James Field, Dan Levin, Steven Johnson, Bina Venkataraman, Sean McFate, and Shane Parrish.

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

Humans lost the top position in backgammon in 1992, in checkers in 1994, and in 1999, IBM’s Deep Blue beat Garry Kasparov, the reigning chess world champion. Then, in 2016, we totally lost gaming to a subsidiary of the giant Google. For years, Google’s DeepMind Technologies had used gaming as a method of developing artificial intelligence. In 2016, DeepMind developed AlphaGo – a computer AI capable of playing an ancient Chinese board game, Go. Go is known to be the most complex game on our planet because of the infinite different strategies available to the player at any point in time. To give you an idea of the scale we’re talking about here, there are more possible moves on the Go board than there are atoms in the entire universe.

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To win in Go, a computer needs intuition, it needs to think intelligently like a human, but be smarter. That’s what DeepMind achieved. 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.

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That first time, unfortunately, I was shallow enough to ignore the universe sending me this message loud and clear. Instead, I focused, as most geeks would, on the coolness of what we were building. A couple of years or so before the yellow ball, Google had acquired DeepMind. Back then, the brilliant Demis Hassabis (CEO and founder of DeepMind) stood before the senior leadership group of Google to present to us the technology they had developed. This was the time when they taught AI to play Atari games. It’s not a huge stretch to spot the connection between the way machines learn and the way children do when the demo that is shown to you is of a machine playing a game.

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

A Declaration of the Independence of Cyberspace, AI winter, airport security, Andy Rubin, Apollo 11, Apple II, artificial general intelligence, Asilomar, augmented reality, autonomous vehicles, backpropagation, basic income, Baxter: Rethink Robotics, Bill Atkinson, Bill Duvall, bioinformatics, Boston Dynamics, Brewster Kahle, Burning Man, call centre, cellular automata, Charles Babbage, Chris Urmson, Claude Shannon: information theory, Clayton Christensen, clean water, cloud computing, cognitive load, collective bargaining, computer age, Computer Lib, computer vision, crowdsourcing, Danny Hillis, DARPA: Urban Challenge, data acquisition, Dean Kamen, deep learning, DeepMind, deskilling, Do you want to sell sugared water for the rest of your life?, don't be evil, Douglas Engelbart, Douglas Engelbart, Douglas Hofstadter, Dr. Strangelove, driverless car, dual-use technology, Dynabook, Edward Snowden, Elon Musk, Erik Brynjolfsson, Evgeny Morozov, factory automation, Fairchild Semiconductor, Fillmore Auditorium, San Francisco, From Mathematics to the Technologies of Life and Death, future of work, Galaxy Zoo, General Magic , Geoffrey Hinton, Google Glasses, Google X / Alphabet X, Grace Hopper, Gunnar Myrdal, Gödel, Escher, Bach, Hacker Ethic, Hans Moravec, haute couture, Herbert Marcuse, hive mind, hype cycle, hypertext link, indoor plumbing, industrial robot, information retrieval, Internet Archive, Internet of things, invention of the wheel, Ivan Sutherland, Jacques de Vaucanson, Jaron Lanier, Jeff Bezos, Jeff Hawkins, job automation, John Conway, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John Perry Barlow, John von Neumann, Kaizen: continuous improvement, Kevin Kelly, Kiva Systems, knowledge worker, Kodak vs Instagram, labor-force participation, loose coupling, Marc Andreessen, Mark Zuckerberg, Marshall McLuhan, medical residency, Menlo Park, military-industrial complex, Mitch Kapor, Mother of all demos, natural language processing, Neil Armstrong, new economy, Norbert Wiener, PageRank, PalmPilot, pattern recognition, Philippa Foot, pre–internet, RAND corporation, Ray Kurzweil, reality distortion field, Recombinant DNA, Richard Stallman, Robert Gordon, Robert Solow, Rodney Brooks, Sand Hill Road, Second Machine Age, self-driving car, semantic web, Seymour Hersh, shareholder value, side project, Silicon Valley, Silicon Valley startup, Singularitarianism, skunkworks, Skype, social software, speech recognition, stealth mode startup, Stephen Hawking, Steve Ballmer, Steve Jobs, Steve Wozniak, Steven Levy, Stewart Brand, Strategic Defense Initiative, strong AI, superintelligent machines, tech worker, technological singularity, Ted Nelson, TED Talk, telemarketer, telepresence, telepresence robot, Tenerife airport disaster, The Coming Technological Singularity, the medium is the message, Thorstein Veblen, Tony Fadell, trolley problem, Turing test, Vannevar Bush, Vernor Vinge, warehouse automation, warehouse robotics, Watson beat the top human players on Jeopardy!, We are as Gods, Whole Earth Catalog, William Shockley: the traitorous eight, zero-sum game

Five years later, however, the question of machine autonomy emerged again. In 2013, when Google acquired DeepMind, a British artificial intelligence firm that specialized in machine learning, popular belief held that roboticists were very close to building completely autonomous robots. The tiny start-up had produced a demonstration that showed its software playing video games, in some cases better than human players. Reports of the acquisition were also accompanied by the claim that Google would set up an “ethics panel” because of concerns about potential uses and abuses of the technology. Shane Legg, one of the cofounders of DeepMind, acknowledged that the technology would ultimately have dark consequences for the human race.

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Economic Growth.” 49.Craig Trudell, Yukiko Hagiwara, and Jie Ma, “Humans Replacing Robots Herald Toyota’s Vision of Future,” BloombergBusiness, April 7, 2014, http://www.bloomberg.com/news/2014-04-06/humans-replacing-robots-herald-toyota-s-vision-of-future.html. 50.Stewart Brand, “We Are As Gods,” Whole Earth Catalog, Fall 1968, http://www.wholeearth.com/issue/1010/article/195/we.are.as.gods. 51.Amir Efrati, “Google Beat Facebook for DeepMind, Creates Ethics Board,” Information, January 27, 2014, https://www.theinformation.com/google-beat-facebook-for-deepmind-creates-ethics-board. 52.“Foxconn Chairman Likens His Workforce to Animals,” WantChina Times, January 19, 2012, http://www.wantchinatimes.com/news-subclass-cnt.aspx?id=20120119000111&cid=1102. 53.“World Population Ageing 2013,” Department of Economic and Social Affairs Population Division, (New York: United Nations, 2013) http://www.un.org/en/development/desa/population/publications/pdf/ageing/WorldPopulationAgeing2013.pdf. 54.

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Singularitarians, however, argue that such human-machine partnerships are simply an interim stage during which human knowledge is transferred and at some point creativity will be transferred to or will even arise on its own in some future generation of brilliant machines. They point to small developments in the field of machine learning that suggest that computers will exhibit humanlike learning skills at some point in the not-too-distant future. In 2014, for example, Google paid $650 million to acquire DeepMind Technologies, a small start-up with no commercial products that had shown machine-learning algorithms with the ability to play video games, in some cases better than humans. When the acquisition was first reported it was rumored that because of the power and implications of the technology Google would set up an “ethics board” to evaluate any unspecified “advances.”51 It has remained unclear whether such oversight will be substantial or whether it was just a publicity stunt to hype the acquisition and justify its price.

How to Fix the Future: Staying Human in the Digital Age by Andrew Keen

"World Economic Forum" Davos, 23andMe, Ada Lovelace, Affordable Care Act / Obamacare, Airbnb, Albert Einstein, AlphaGo, Andrew Keen, Apple's 1984 Super Bowl advert, augmented reality, autonomous vehicles, basic income, Bernie Sanders, Big Tech, bitcoin, Black Swan, blockchain, Brewster Kahle, British Empire, carbon tax, Charles Babbage, computer age, Cornelius Vanderbilt, creative destruction, crowdsourcing, data is the new oil, death from overwork, DeepMind, Demis Hassabis, Didi Chuxing, digital capitalism, digital map, digital rights, disinformation, don't be evil, Donald Trump, driverless car, Edward Snowden, Elon Musk, Erik Brynjolfsson, European colonialism, fake news, Filter Bubble, Firefox, fulfillment center, full employment, future of work, gig economy, global village, income inequality, independent contractor, informal economy, Internet Archive, Internet of things, invisible hand, Isaac Newton, James Watt: steam engine, Jane Jacobs, Jaron Lanier, Jeff Bezos, jimmy wales, job automation, Joi Ito, Kevin Kelly, knowledge economy, Lyft, Marc Andreessen, Marc Benioff, Mark Zuckerberg, Marshall McLuhan, Menlo Park, Mitch Kapor, move fast and break things, Network effects, new economy, Nicholas Carr, Norbert Wiener, OpenAI, Parag Khanna, peer-to-peer, Peter Thiel, plutocrats, post-truth, postindustrial economy, precariat, Ralph Nader, Ray Kurzweil, Recombinant DNA, rent-seeking, ride hailing / ride sharing, Rutger Bregman, Salesforce, Sam Altman, Sand Hill Road, Second Machine Age, self-driving car, sharing economy, Silicon Valley, Silicon Valley billionaire, Silicon Valley ideology, Silicon Valley startup, Skype, smart cities, Snapchat, social graph, software is eating the world, Stephen Hawking, Steve Jobs, Steve Wozniak, subscription business, surveillance capitalism, Susan Wojcicki, tech baron, tech billionaire, tech worker, technological determinism, technoutopianism, The Future of Employment, the High Line, the new new thing, Thomas L Friedman, Tim Cook: Apple, Travis Kalanick, Triangle Shirtwaist Factory, Uber and Lyft, Uber for X, uber lyft, universal basic income, Unsafe at Any Speed, Upton Sinclair, urban planning, WikiLeaks, winner-take-all economy, Y Combinator, Yogi Berra, Zipcar

Yet for all his concerns about the demonic potential of artificial intelligence, Price isn’t entirely pessimistic about the future. He is encouraged, for example, by what he describes as the ethical maturity of the three cofounders of DeepMind, particularly Demis Hassabis, its young Cambridge-educated CEO. This is the London-based tech company whose investors include Jaan Tallinn and Elon Musk, a start-up founded in 2011 and then acquired by Google for $500 million in 2014. DeepMind made the headlines in March 2016 when AlphaGo, its specially designed algorithm, defeated a South Korean world champion Go player in this 5,500-year-old Chinese board game, the oldest and one of the most complex games ever invented by humans.

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DeepMind made the headlines in March 2016 when AlphaGo, its specially designed algorithm, defeated a South Korean world champion Go player in this 5,500-year-old Chinese board game, the oldest and one of the most complex games ever invented by humans. But in addition to the commercial development of artificial intelligence, Price explains, the DeepMind founders—with other Big Tech companies like Microsoft, Facebook, IBM, and Amazon—are helping engineer an industrywide moral code about smart technology. This self-policing initiative, known, rather awkwardly, as the Partnership on Artificial Intelligence to Benefit People and Society, was formally launched in September 2016. Its goal is to make the world a better place. Trust us, the companies in this alliance say, promising a laundry list of feel-good issues, including “ethics, fairness, and inclusivity; transparency, privacy, and interoperability; collaboration between people and AI systems; and the trustworthiness, reliability, and robustness of the technology.”4 Trust us with your future, they are saying.

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Trust us, the companies in this alliance say, promising a laundry list of feel-good issues, including “ethics, fairness, and inclusivity; transparency, privacy, and interoperability; collaboration between people and AI systems; and the trustworthiness, reliability, and robustness of the technology.”4 Trust us with your future, they are saying. Trust us is, indeed, becoming a familiar promise from the tech community. The self-policing strategy of the DeepMind coalition sounds similar to the goals of another idealistic Elon Musk start-up—OpenAI, a Silicon Valley–based nonprofit research company focused on the promotion of an open-source platform for artificial intelligence technology. Musk cofounded OpenAI with Sam Altman, the thirty-one-year-old CEO of Y Combinator, Silicon Valley’s most successful seed investment fund.

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

For years it was recommended to use linear combinations of hand-crafted features extracted from the state (e.g., distance of the closest ghosts, their directions, and so on) to estimate Q-Values, but DeepMind showed that using deep neural networks can work much better, especially for complex problems, and it does not require any feature engineering. A DNN used to estimate Q-Values is called a deep Q-network (DQN), and using a DQN for Approximate Q-Learning is called Deep Q-Learning. In the rest of this chapter, we will use Deep Q-Learning to train an agent to play Ms. Pac-Man, much like DeepMind did in 2013. The code can easily be tweaked to learn to play the majority of Atari games quite well.

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It must then learn by itself what is the best strategy, called a policy, to get the most reward over time. A policy defines what action the agent should choose when it is in a given situation. Figure 1-12. 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.

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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).

More From Less: The Surprising Story of How We Learned to Prosper Using Fewer Resources – and What Happens Next by Andrew McAfee

back-to-the-land, Bartolomé de las Casas, Berlin Wall, bitcoin, Blitzscaling, Branko Milanovic, British Empire, Buckminster Fuller, call centre, carbon credits, carbon footprint, carbon tax, Charles Babbage, clean tech, clean water, cloud computing, congestion pricing, Corn Laws, creative destruction, crony capitalism, data science, David Ricardo: comparative advantage, decarbonisation, DeepMind, degrowth, dematerialisation, Demis Hassabis, Deng Xiaoping, do well by doing good, Donald Trump, Edward Glaeser, en.wikipedia.org, energy transition, Erik Brynjolfsson, failed state, fake news, Fall of the Berlin Wall, Garrett Hardin, Great Leap Forward, Haber-Bosch Process, Hans Rosling, humanitarian revolution, hydraulic fracturing, income inequality, indoor plumbing, intangible asset, James Watt: steam engine, Jeff Bezos, job automation, John Snow's cholera map, joint-stock company, Joseph Schumpeter, Khan Academy, Landlord’s Game, Louis Pasteur, Lyft, Marc Andreessen, Marc Benioff, market fundamentalism, means of production, Michael Shellenberger, Mikhail Gorbachev, ocean acidification, oil shale / tar sands, opioid epidemic / opioid crisis, Paul Samuelson, peak oil, precision agriculture, price elasticity of demand, profit maximization, profit motive, risk tolerance, road to serfdom, Ronald Coase, Ronald Reagan, Salesforce, Scramble for Africa, Second Machine Age, Silicon Valley, Steve Jobs, Steven Pinker, Stewart Brand, Ted Nordhaus, TED Talk, telepresence, The Wealth of Nations by Adam Smith, Thomas Davenport, Thomas Malthus, Thorstein Veblen, total factor productivity, Tragedy of the Commons, Uber and Lyft, uber lyft, Veblen good, War on Poverty, We are as Gods, Whole Earth Catalog, World Values Survey

“We never had a technology before that could educate”: Sara Castellanos, “Google Chief Economist Hal Varian Argues Automation Is Essential,” Wall Street Journal, February 8, 2018, https://blogs.wsj.com/cio/2018/02/08/google-chief-economist-hal-varian-argues-automation-is-essential/. “We [haven’t] solved the protein-folding problem, this is just a first step”: Ian Sample, “Google’s DeepMind Predicts 3D Shapes of Proteins,” Guardian, December 2, 2018, https://www.theguardian.com/science/2018/dec/02/google-deepminds-ai-program-alphafold-predicts-3d-shapes-of-proteins. increase the energy efficiency of data centers by as much as 30 percent: “Safety-First AI for Autonomous Data Centre Cooling and Industrial Control,” DeepMind, accessed March 25, 2019, https://deepmind.com/blog/safety-first-ai-autonomous-data-centre-cooling-and-industrial-control/. accounting for about 1 percent of global electricity demand: Nicola Jones, “How to Stop Data Centres from Gobbling Up the World’s Electricity,” News Feature, Nature, September 12, 2018, https://www.nature.com/articles/d41586-018-06610-y.

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The cells in our bodies are assembly lines for proteins, but we currently understand little about how these assembly lines work—how they fold a two-dimensional string of amino acids into a complicated 3-D protein. But thanks to digital tools, we’re learning quickly. In 2018, as part of a contest, the AlphaFold software developed by Google DeepMind correctly guessed the structure of twenty-five out of forty-three proteins it was shown; the second-place finisher guessed correctly three times. DeepMind cofounder Demis Hassabis says, “We [haven’t] solved the protein-folding problem, this is just a first step… but we have a good system and we have a ton of ideas we haven’t implemented yet.” As these good ideas accumulate, they might well let us make spider-strength materials.

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Møller-Maersk, 257 Apollo 8 mission, 53–54 apparent consumption, 78–79 Apple, 102, 111, 169, 235, 257 Applebaum, Anne, 218 Arab oil embargo, 161 Ardekani, Siamak, 75 artificial intelligence, 205 Asch, Solomon, 226 Atacama Desert, 17 “Atoms for Peace,” 58–59 Audubon, John James, 43, 258 Austin, Benjamin, 254 Ausubel, Jesse, 4–5, 75, 76, 78, 183 authoritarianism, 174, 217–18, 220 automobiles, 161–63 back to the land movement, 67–68, 91–93 bananas, 24 Baron, Jonathan, 127 BASF, 31 Beach, Brian, 41 beefalo, 182 Bergius, Friedrich, 31 Berzin, Alfred, 164 Bezos, Jeff, 206 Bhattacharjee, Amit, 127 Bishop, Bill, 227 Bismarck, Otto von, 225 bison, 44–46, 96, 152–53, 183 Blake, William, 40–41 Blomqvist, Linus, 270 Bloom, Paul, 210 blue whales, 47 Borlaug, Norman, 31–32, 262 Bosch, Carl, 31 Boulding, Kenneth, 63–65 Boulton, Matthew, 15–16, 20, 121, 206 Bowling Alone (Putnam), 213 Brand, Stewart, 67–68, 182, 183 Brandeis, Louis, 259 Brazil, 173, 174 Brynjolfsson, Erik, 112, 205 Bump, Philip, 224 cap-and-trade programs, 143–44, 145, 187, 188 capitalism, 2–3, 4, 5, 36, 99–123, 113, 125–39, 141, 151, 158–59, 161, 167–68 critiques of, 126–31 defining of, 115–18 negatives of, 142–43 spread of, 170–73 carbon capture systems, 187 carbon dioxide, 185, 188–89 carbon offsets, 259–60 carbon taxes, 187, 249–50, 252, 257, 259 Caro, Robert, 29n Case, Steve, 256 CBS Evening News with Walter Cronkite, 53 central planning, 116, 122, 170 cerium, 107 Chávez, Hugo, 134–35 Chicago and North Western Railway, 105–06 child labor, 35, 38–39, 167 child mortality, 196–97 China, 85, 93–94, 106, 110, 133, 145, 154, 172, 174, 185 chlorofluorocarbons, 149–50, 185, 228, 249 cholera, 22–23, 26 Christensen, Clay, 265 Christmas Carol, A (Dickens), 24 chromium, 72 Church, George, 182 Cichon, Steve, 101–02 circle of sympathy, 176 Civil War, US, 38 Clapham, Phillip, 163 Clark, Gregory, 10–11, 20 Clean Air Act, 66, 95, 122, 143, 147, 161 Clean Water Act (1972), 66, 190, 252–53 climate change, 60, 158, 185, 228, 243, 248, 257, 269, 274 Clinton, Hillary, 201, 224 Closing Circle, The (Commoner), 64 coal, 16, 18, 19, 40, 41, 56 as finite resource, 48–49 Coal Question, The (Jevons), 48, 49 Coase, Ronald, 143 collusion, 129 colonialism, 35, 39–40, 167 Commoner, Barry, 64 commons, 183–84 communism, 133, 172 Communist Manifesto, The (Marx and Engels), 21 comparative advantage, 19n competition, 109–10, 116, 129, 203 computer-aided design, 113 computers, 141 concentration, 199–210, 218, 224 economic, 202–03 industrial, 204 of wealth, 205–07 Condition of the Working Class in England, The (Engels), 21 Congo Free State, 39 conservationists, 95–96 conspicuous consumption, 152 Constitution, US, 38 consumption, 63–64, 88–90 contract enforcement, 116 Cooke, Earl, 60 Coors, 101 copper, 79, 80, 90, 107, 120 coprolite, 18 Cordier, Daniel, 106–07 Corn Laws, 18, 172 Corporate Average Fuel Economy (CAFE) standards, 162 corporatism, 129 corruption, 175 cotton industry, 38 cotton textiles, 19 Cramer, Kathy, 221 CRIB, 62–68, 87–97 cronyism, 129 Crookes, William, 30 crude oil, 58 “Crude Oil” (GAO), 103 Crutzen, Paul, 150 Cuba, 133 Cutler, David, 28 Cutter, Bo, 105 Cuyahoga River, 54 Daimler, Gottlieb, 26–27 Dana, Jason, 127 Davenport, Thomas, 27 de-extinction movement, 182 death penalty, 176 deaths of despair, 214, 216, 219–20, 247 Deaton, Angus, 210, 213–14, 220 DeepMind, 239–40 deforestation, 43, 184–85 degrowth, 63–64, 88 demand, 50–51 dematerialization, 4–5, 71, 72–73, 75–85, 87, 125, 141, 144, 151–52, 160, 167, 168, 235, 247–48, 259 causes of, 99–123 paths to, 110–11 Demick, Barbara, 94 democracy, 174 Democracy in America (Tocqueville), 89–90 democratic socialism, 133–34 Deng Xiaoping, 170 Denmark, 117–18 developing countries, 56 Devezas, Tessaleno, 73 Dickens, Charles, 24 digital tools, 234–35 Dijkstra, Lewis, 199 Dimon, Jamie, 256 Ding Xuedong, 253 disconnection, 211–29, 247, 253–54, 255, 270–71 diversity, 216–17 Dodge, Irving, 45 Donora, Pa., 41, 55, 66, 145 Dragusanu, Raluca, 268 data centers, 240 Duolingo, 236 DuPont, 149 Durkheim, Emile, 215–16, 219 Earth Day, 3, 53, 60–61 Earthrise, 53–54 Ecology as Politics (Gorz), 63–64 Edison, Thomas, 27 education, 177, 195, 256 Ehrlich, Paul, 55, 59, 62, 65, 71–72, 75, 151, 244–45 Eisenhower, Dwight, 58 electrical power, 26–28, 29, 30, 36 Elephant Graph, 221–23 elephants, 153–54 Elop, Stephen, 102 Emancipation Proclamation, 38 emancipative values, 176 energy consumption, 58–60, 59 Energy Information Administration, US, 103 Engels, Friedrich, 21 Engels Pause, 20, 23 England, 18–20, 22, 38 abolitionist movement in, 37 air pollution in, 41 population of, 10–11 population versus wages in, 20 Enlightenment, 122–23 Enlightenment Now (Pinker), 37, 176, 179 environmental movement, 53, 65, 68, 122 Environmental Protection Agency, 66, 95 ephemeralization, 70–71 epidemiology, 22 Essay on the Principle of Population, An, (Malthus), 8–9, 10, 13 Evans, Benedict, 173 externalities, 142 extinctions, 35, 36, 42–43, 61, 96, 151–52, 167, 181–82 Factfulness (Rosling), 179 Factory Act (1833), 38 factory ships, 47 Fair Trade certification, 268 false imprisonment, 175 famine, 12, 13, 61, 62, 69 Famine 1975!

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

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. “Beautiful,” he said, then repeated the word several more times. 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.

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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. Held in one of the university’s largest lecture halls, the DeepMind event drew a standing-room-only crowd—students were all but hanging off the walls to hear Hassabis describe how their approach to machine learning had allowed their team to prove the experts who had predicted it would take ten years for a computer to beat a virtuoso like Sedol wrong.

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In fact, at the time the common consensus within both the Go and machine-learning communities was that it would be many years before artificial intelligence reached a point at which it could compete against the best human players without the benefit of a handicap. A machine simply couldn’t replicate the improvisational, creative kind of genius that animated the highest level of play. That was before the scientific journal Nature published a bombshell article in January 2016 reporting that Google’s artificial intelligence project, DeepMind, had entered the race.6 Its program, called AlphaGo, first learned from a huge history of past Go matches, but then, through an innovative form of reinforcement learning, played itself over and over again until it got better and better. The previous November, the article revealed, Google had orchestrated a five-game match between European Go champion Fan Hui and AlphaGo.

The Smartphone Society by Nicole Aschoff

"Susan Fowler" uber, 4chan, A Declaration of the Independence of Cyberspace, Airbnb, algorithmic bias, algorithmic management, Amazon Web Services, artificial general intelligence, autonomous vehicles, barriers to entry, Bay Area Rapid Transit, Bernie Sanders, Big Tech, Black Lives Matter, blockchain, carbon footprint, Carl Icahn, Cass Sunstein, citizen journalism, cloud computing, correlation does not imply causation, crony capitalism, crowdsourcing, cryptocurrency, data science, deep learning, DeepMind, degrowth, Demis Hassabis, deplatforming, deskilling, digital capitalism, digital divide, do what you love, don't be evil, Donald Trump, Downton Abbey, Edward Snowden, Elon Musk, Evgeny Morozov, fake news, feminist movement, Ferguson, Missouri, Filter Bubble, financial independence, future of work, gamification, gig economy, global value chain, Google Chrome, Google Earth, Googley, green new deal, housing crisis, income inequality, independent contractor, Jaron Lanier, Jeff Bezos, Jessica Bruder, job automation, John Perry Barlow, knowledge economy, late capitalism, low interest rates, Lyft, M-Pesa, Mark Zuckerberg, minimum wage unemployment, mobile money, moral panic, move fast and break things, Naomi Klein, Network effects, new economy, Nicholas Carr, Nomadland, occupational segregation, Occupy movement, off-the-grid, offshore financial centre, opioid epidemic / opioid crisis, PageRank, Patri Friedman, peer-to-peer, Peter Thiel, pets.com, planned obsolescence, quantitative easing, Ralph Waldo Emerson, RAND corporation, Ray Kurzweil, RFID, Richard Stallman, ride hailing / ride sharing, Rodney Brooks, Ronald Reagan, Salesforce, Second Machine Age, self-driving car, shareholder value, sharing economy, Sheryl Sandberg, Shoshana Zuboff, Sidewalk Labs, Silicon Valley, single-payer health, Skype, Snapchat, SoftBank, statistical model, Steve Bannon, Steve Jobs, surveillance capitalism, TaskRabbit, tech worker, technological determinism, TED Talk, the scientific method, The Structural Transformation of the Public Sphere, TikTok, transcontinental railway, transportation-network company, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, upwardly mobile, Vision Fund, W. E. B. Du Bois, wages for housework, warehouse robotics, WikiLeaks, women in the workforce, yottabyte

Google founders Sergey Brin and Larry Page created Alphabet in 2015 to organize their growing pile of tech companies—a conglomerate that, in addition to Google, includes companies focused on biotech (Calico), cybersecurity (Chronicle), wind power (Makani), and the life sciences (Verily). Add to that Waymo and Wing, which develop self-driving car and drone delivery technology, and DeepMind, Alphabet’s artificial intelligence subsidiary. Throw in venture capital and private equity firms (GV, Capital G), a tech incubator (Jigsaw), broadband and balloon internet providers (Google Fiber and Loon), an urban innovation organization (Sidewalk Labs), and a “semisecret” research and development facility called X Development, and one gets a sense of the growing reach of the behemoth that started with a search engine.

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The insurer will react by either sending a message warning the person to walk more carefully or else automatically increase the premium and coverage while the policyholder is walking down that road.67 Granted, not all uses of big data are designed to sell things. There is much new knowledge that can be gleaned. Efficiencies can be gained. Google sister-company DeepMind, for example, uses electricity usage data to reduce waste. Big data can also be used to predict weather patterns, improve crop yields, and develop new drugs. But the vast majority of current big data use revolves around selling digital selves for profit, and the well of data seems infinite. Leading up to Facebook’s IPO market researchers estimated that between 2009 and 2011 alone the company had collected more than two trillion pieces of “monetizable content.”

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Self-driving cars and the ability of the Alpha Go Zero program to teach itself how to play the ancient and extremely difficult Chinese game of Go using only the game rules and reinforcement learning are just the beginning of a seismic shift rooted in the power of data. The motto of Alphabet subsidiary DeepMind encapsulates this vision of the future: “Solve intelligence and use that to solve everything else.” Run by Demis Hassabis, a neuroscientist, video game developer, and former child chess prodigy, and a team of about two hundred computer scientists and neuroscientists, the Alphabet subsidiary’s researchers have operationalized the idea that intelligence, thought, and perhaps even consciousness are nothing more than a collection of discrete, local processes that can be “solved” with enough computing power and data.

Applied Artificial Intelligence: A Handbook for Business Leaders by Mariya Yao, Adelyn Zhou, Marlene Jia

Airbnb, algorithmic bias, AlphaGo, Amazon Web Services, artificial general intelligence, autonomous vehicles, backpropagation, business intelligence, business process, call centre, chief data officer, cognitive load, computer vision, conceptual framework, data science, deep learning, DeepMind, en.wikipedia.org, fake news, future of work, Geoffrey Hinton, industrial robot, information security, Internet of things, iterative process, Jeff Bezos, job automation, machine translation, Marc Andreessen, natural language processing, new economy, OpenAI, pattern recognition, performance metric, price discrimination, randomized controlled trial, recommendation engine, robotic process automation, Salesforce, self-driving car, sentiment analysis, Silicon Valley, single source of truth, skunkworks, software is eating the world, source of truth, sparse data, speech recognition, statistical model, strong AI, subscription business, technological singularity, The future is already here

Neural networks were invented in the 1950s, but recent advances in computational power and algorithm design—as well as the growth of big data—have enabled deep learning algorithms to approach human-level performance in tasks such as speech recognition and image classification. Deep learning, in combination with reinforcement learning, enabled Google DeepMind’s AlphaGo to defeat human world champions of Go in 2016, a feat that many experts had considered to be computationally impossible. Much media attention has been focused on deep learning, and an increasing number of sophisticated technology companies have successfully implemented deep learning for enterprise-scale products.

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Designing safe and ethical AI is a monumental challenge and a critical one to tackle now. To be effective, we must develop more sophisticated and nuanced policies that go far deeper and wider than simplistic, science fiction solutions like Asimov’s Three Laws of Robotics.(36) In a joint study, Google DeepMind and the Future of Humanity Institute explored fail-safe mechanisms for shutting down rogue AI.(37) In practical terms, these “big red buttons” will be signals that trick the machine to make an internal decision to stop, without registering the input as a shutdown signal by an external human operator.

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Right now, only a handful of leading technology companies—i.e. Google, Facebook, Microsoft, and Amazon—possess the culture, talent, and infrastructure to innovate at the cutting edge of artificial intelligence. Not only have they hired the world’s most brilliant AI talent to staff research groups like Google Brain, DeepMind, and Facebook AI Research (FAIR), they’ve also developed powerful internal machine learning platforms like Facebook’s FBLearner, Uber’s Michelangelo, Google’s TFX, and Twitter’s Cortex to enable their engineers and other employees to rapidly develop models and capabilities into product teams, business units, and end-user experiences.

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

Chapter 5 1.Melissa Cefkin, “Nissan Anthropologist: We Need a Universal Language for Autonomous Cars,” 2025AD, January 27, 2017, https://www.2025ad.com/latest/nissan-melissa-cefkin-driverless-cars/. 2.Kim Tingley, “Learning to Love Our Robot Co-Workers,” New York Times, February 23, 2017, https://www.nytimes.com/2017/02/23/magazine/learning-to-love-our-robot-co-workers.html. 3.Rossano Schifanella, Paloma de Juan, Liangliang Cao and Joel Tetreault, “Detecting Sacarsm in Multimodal Social Platforms,” August 8, 2016, https://arxiv.org/pdf/1608.02289. 4.Elizabeth Dwoskin, “The Next Hot Job in Silicon Valley Is for Poets,” Washington Post, April 7, 2016, https://www.washingtonpost.com/news/the-switch/wp/2016/04/07/why-poets-are-flocking-to-silicon-valley. 5.“Init.ai Case Study,” Mighty AI, https://mty.ai/customers/init-ai/, accessed October 25, 2017. 6.Matt Burgess, “DeepMind’s AI Has Learnt to Become ‘Highly Aggressive” When It Feels Like It’s Going to Lose,” Wired, February 9, 2017, www.wired.co.uk/article/artificial-intelligence-social-impact-deepmind. 7.Paul X. McCarthy, “Your Garbage Data Is a Gold Mine,” Fast Company, August 24, 2016, https://www.fastcompany.com/3063110/the-rise-of-weird-data. 8.John Lippert, “ZestFinance Issues Small, High-Rate Loans, Uses Big Data to Weed Out Deadbeats,” Washington Post, October 11, 2014, https://www.washingtonpost.com/business/zestfinance-issues-small-high-rate-loans-uses-big-data-to-weed-out-deadbeats/2014/10/10/e34986b6-4d71-11e4-aa5e-7153e466a02d_story.html. 9.Jenna Burrell, “How the Machine ‘Thinks’: Understanding Opacity in Machine Learning Algorithms,” Big Data & Society (January–June 2016): 1–12, http://journals.sagepub.com/doi/abs/10.1177/2053951715622512. 10.Ibid. 11.Kim Tingley, “Learning to Love Our Robot Co-Workers,” New York Times, February 23, 2017, https://www.nytimes.com/2017/02/23/magazine/learning-to-love-our-robot-co-workers.html. 12.Isaac Asimov, “Runaround,” Astounding Science Fiction (March 1942). 13.Accenture Research Survey, January 2016. 14.Vyacheslav Polonski, “Would You Let an Algorithm Choose the Next US President?”

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Init.ai could then utilize the resulting data to build its own conversation models, from which the company could then train its machine-learning platform.5 Clearly, AI systems will only be as good as the data they are trained on. These applications search for patterns in data, and any biases in that information will then be reflected in subsequent analyses. It’s like garbage in, garbage out, but the more accurate saying would be biases in, biases out. In an intriguing experiment, computer scientists at DeepMind, a Google-owned firm, trained an AI system to play two different games: one that involved hunting and another that focused on fruit gathering. The results were striking. When trained on the hunting game, the AI system later exhibited behavior that could be “highly aggressive.” When trained on the fruit-gathering game, it instead later displayed a much greater tendency toward cooperation.6 That’s why the role of data hygienist is crucial.

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

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

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.

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Deep Blue was a special-purpose engine exquisitely optimized for—and therefore completely useless at anything other than—the rules of chess. By contrast, AlphaGo is a general learning machine, here being applied to the rules of go simply because that is the richest challenge its designers could conceive of, the highest bar they could set for it. In March 2016, in a hotel ballroom in Seoul, DeepMind set its AlphaGo against Lee Sedol, a player of 9-dan—the highest rank. Lee has been playing go professionally since the age of twelve, and is regarded among cognoscenti as one of the game’s all-time greatest players. His mastery is of a particularly counterintuitive sort: he is fond of gambits that would surely entrain disaster in the hands of any other player, including one called the “broken ladder” that is literally taught to beginners as the very definition of a situation to avoid.

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His mastery is of a particularly counterintuitive sort: he is fond of gambits that would surely entrain disaster in the hands of any other player, including one called the “broken ladder” that is literally taught to beginners as the very definition of a situation to avoid. And from these vulnerable positions Lee all but invariably prevails. 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.

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

3D printing, Abraham Maslow, AI winter, air gap, anthropic principle, artificial general intelligence, Asilomar, augmented reality, Automated Insights, autonomous vehicles, availability heuristic, backpropagation, blue-collar work, Boston Dynamics, brain emulation, call centre, cognitive bias, combinatorial explosion, computer vision, Computing Machinery and Intelligence, create, read, update, delete, cuban missile crisis, David Attenborough, DeepMind, disinformation, driverless car, Elon Musk, en.wikipedia.org, epigenetics, Ernest Rutherford, factory automation, feminist movement, finite state, Flynn Effect, friendly AI, general-purpose programming language, Google Glasses, Google X / Alphabet X, Gödel, Escher, Bach, Hans Moravec, industrial robot, Isaac Newton, job automation, John von Neumann, Law of Accelerating Returns, license plate recognition, Mahatma Gandhi, mandelbrot fractal, natural language processing, Nick Bostrom, Parkinson's law, patent troll, patient HM, pattern recognition, phenotype, ransomware, Ray Kurzweil, Recombinant DNA, self-driving car, semantic web, Silicon Valley, Singularitarianism, Skype, sorting algorithm, speech recognition, statistical model, stem cell, Stephen Hawking, Stuxnet, superintelligent machines, technological singularity, Thomas Malthus, Turing machine, Turing test, uranium enrichment, Von Neumann architecture, Watson beat the top human players on Jeopardy!, wikimedia commons, zero day

Google has invested heavily in numerous AI technologies and companies, and would not benefit from fear of or regulation of its artificial intelligence activities. One of the most ambitions of Google’s recent acquisitions is the secretive DeepMind company whose unabashed goal is to “solve intelligence”. One of its original founders, Shane Legg, warned that artificial intelligence is the “number one risk for this century”, and believes it could contribute to human extinction. “Eventually, I think human extinction will probably occur, and technology will likely play a part in this”. DeepMind’s sale to Google came with a condition that it include an ethics board. In January 2015 the Future of life institute published an open letter highlighting the dangers of AI and calling for more research to ensure that AI systems are robust and beneficial saying “our AI systems must do what we want them to do”.

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Norvig estimated that Google employed well over 5% of the world’s experts in machine learning some time ago. In late 2013, Google purchased Boston Dynamics, a leading producer of intelligent robots and supplier of robots for the DARPA robotic challenge. Google’s Schaft robot won the 2013 DARPA robotic challenge. Perhaps more interestingly, Google also purchased DeepMind in 2013 for some $400 million. DeepMind’s stated ambition is to produce artificial general intelligence, although what that really means is unclear. Google has made other AI purchases including Bot & Dolly, Meka Robotics, Holomni, Redwood Robotics, and, DNNresearch. Corporate, Fair use In 2013 IBM also pledged to spend a massive billion dollars on further developing its Watson project.

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In October 2014 technology billionaire Elon Musk warned that research into artificial intelligence was “summoning the devil”, that artificial intelligence is our biggest existential threat, and that we were already at the stage where there should be some regulatory oversight. Musk is CEO of Tesla, Solar City and SpaceX and co-founder PayPal. He has recently invested in the DeepMind AI company to “keep an eye on what’s going on”. In December 2014 world famous physicist Stephen Hawking, expressed his concerns that humans who are limited by slow biological evolution would not be able to compete with computers that were continuously redesigning themselves. He said that “The primitive forms of artificial intelligence we already have, have proved very useful.

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

Machines have been beating humans at chess for years, but Go is more difficult for machines because of the sheer number of possible moves: in the course of a match, there are more possible combinations than there are atoms in the universe. 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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AI is what’s known as a ‘general purpose’ technology, meaning it can be applied in a wide variety of contexts. Although the specific application is very different, driverless vehicles like Stefan’s Starsky trucks use similar techniques of data extraction and analysis as AI-powered crime-prediction technology or CV analysis. Google’s DeepMind, for example, doesn’t just win at Go – it is currently pioneering exciting new medical research and has already dramatically cut the energy bills at Google’s huge data centres by using deep learning to optimise the air conditioning systems.6 There are countervailing tendencies, of course – some experts have got together to develop ‘open source’ AI which is more transparent and, hopefully, carefully designed, but the direction of progress is clear – just follow the money.

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Google’s DeepMind, for example, doesn’t just win at Go – it is currently pioneering exciting new medical research and has already dramatically cut the energy bills at Google’s huge data centres by using deep learning to optimise the air conditioning systems.6 There are countervailing tendencies, of course – some experts have got together to develop ‘open source’ AI which is more transparent and, hopefully, carefully designed, but the direction of progress is clear – just follow the money. Over the past few years, big tech firms have bought promising AI start-ups by the truckload. Google’s DeepMind is one of only a dozen they have recently acquired. Apple splashed out $200 million for Turi, a machine learning start-up, in 2016, and Intel has invested over $1 billion in AI companies over the past couple of years.7 Market leaders in AI like Google, with the data, the geniuses, the experience and the computing power, won’t be limited to just search and information retrieval.

Elon Musk by Walter Isaacson

4chan, activist fund / activist shareholder / activist investor, Airbnb, Albert Einstein, AltaVista, Apollo 11, Apple II, Apple's 1984 Super Bowl advert, artificial general intelligence, autism spectrum disorder, autonomous vehicles, basic income, Big Tech, blockchain, Boston Dynamics, Burning Man, carbon footprint, ChatGPT, Chuck Templeton: OpenTable:, Clayton Christensen, clean tech, Colonization of Mars, computer vision, Computing Machinery and Intelligence, coronavirus, COVID-19, crowdsourcing, cryptocurrency, deep learning, DeepMind, Demis Hassabis, disinformation, Dogecoin, Donald Trump, Douglas Engelbart, drone strike, effective altruism, Elon Musk, estate planning, fail fast, fake news, game design, gigafactory, GPT-4, high-speed rail, hiring and firing, hive mind, Hyperloop, impulse control, industrial robot, information security, Jeff Bezos, Jeffrey Epstein, John Markoff, John von Neumann, Jony Ive, Kwajalein Atoll, lab leak, large language model, Larry Ellison, lockdown, low earth orbit, Marc Andreessen, Marc Benioff, Mars Society, Max Levchin, Michael Shellenberger, multiplanetary species, Neil Armstrong, Network effects, OpenAI, packet switching, Parler "social media", paypal mafia, peer-to-peer, Peter Thiel, QAnon, Ray Kurzweil, reality distortion field, remote working, rent control, risk tolerance, Rubik’s Cube, Salesforce, Sam Altman, Sam Bankman-Fried, San Francisco homelessness, Sand Hill Road, Saturday Night Live, self-driving car, seminal paper, short selling, Silicon Valley, Skype, SpaceX Starlink, Stephen Hawking, Steve Jobs, Steve Jurvetson, Steve Wozniak, Steven Levy, Streisand effect, supply-chain management, tech bro, TED Talk, Tesla Model S, the payments system, Tim Cook: Apple, universal basic income, Vernor Vinge, vertical integration, Virgin Galactic, wikimedia commons, William MacAskill, work culture , Y Combinator

Musk paused silently for almost a minute as he processed this possibility. During such trancelike periods, he says, he runs visual simulations about the ways that multiple factors may play out over the years. He decided that Hassabis might be right about the danger of AI, and he invested $5 million in DeepMind as a way to monitor what it was doing. A few weeks after his conversations with Hassabis, Musk described DeepMind to Google’s Larry Page. They had known each other for more than a decade, and Musk often stayed at Page’s Palo Alto house. The potential dangers of artificial intelligence became a topic that Musk would raise, almost obsessively, during their late-night conversations.

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“Well, yes, I am pro-human,” Musk responded. “I fucking like humanity, dude.” Musk was therefore dismayed when he heard at the end of 2013 that Page and Google were planning to buy DeepMind. Musk and his friend Luke Nosek tried to put together financing to stop the deal. At a party in Los Angeles, they went to an upstairs closet for an hour-long Skype call with Hassabis. “The future of AI should not be controlled by Larry,” Musk told him. The effort failed, and Google’s acquisition of DeepMind was announced in January 2014. Page initially agreed to create a “safety council,” with Musk as a member. The first and only meeting was held at SpaceX.

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In his modern London office is an original edition of Alan Turing’s seminal 1950 paper, “Computing Machinery and Intelligence,” which proposed an “imitation game” that would pit a human against a ChatGPT–like machine. If the responses of the two were indistinguishable, he wrote, then it would be reasonable to say that machines could “think.” Influenced by Turing’s argument, Hassabis cofounded a company called DeepMind that sought to design computer-based neural networks that could achieve artificial general intelligence. In other words, it sought to make machines that could learn how to think like humans. “Elon and I hit it off right away, and I went to visit him at his rocket factory,” Hassabis says. While sitting in the canteen overlooking the assembly lines, Musk explained that his reason for building rockets that could go to Mars was that it might be a way to preserve human consciousness in the event of a world war, asteroid strike, or civilization collapse.

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

Tim Bradshaw, “Google Buys UK Artificial Intelligence Start-up,” Financial Times, January 27, 2014, https://www.ft.com/content/f92123b2-8702-11e3-aa31-00144feab7de. 34. Amy Webb, The Big Nine: How the Tech Titans and Their Thinking Machines Could Warp Humanity (New York: PublicAffairs, 2019), 47. 35. DeepMind, “Match 1—Google DeepMind Challenge Match: Lee Sedol vs. AlphaGo,” posted by YouTube user DeepMind on March 8, 2016, https://www.youtube.com/watch?v=vFr3K2DORc8&t=1670. This is the link to the first of five matches. From this link, readers can load and watch the other four matches too. 36. Kai-fu Lee, AI Superpowers, 1–2. Chapter 15. The Big Brain 1.

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For years, AI engineers had believed Go was so hopelessly complex—it has 361 pieces—that it would be impossible to write a software program that could win over a human. The number of possible positions on the board exceeded the number of atoms in the known universe, requiring incredible computing power and pattern recognition.31 Google, which had been investing in AI since its founding in 1998,32 bought the start-up DeepMind in early 2014. 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.

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See Noor Mohammad, “The Doctrine of Jihad: An Introduction,” Journal of Law and Religion 3, no. 2 (1985): 381–97, https://www.jstor.org/stable/1051182?seq=1. 3. Megvii executive, interview by the author, May 31, 2020. 4. Sam Byford, “AlphaGo Beats Ke Jie Again to Wrap Up Three-Part Match,” Verge, May 25, 2017, https://www.theverge.com/2017/5/25/15689462/alphago-ke-jie-game-2-result-google-deepmind-china. 5. Kai-fu Lee, AI Superpowers, 1–2. 6. Ministry of National Defense of the People’s Republic of China, “The National Intelligence Law (中华人民共和国国家情报法),” June 27, 2017. http://www.mod.gov.cn/regulatory/2017-06/28/content_4783851.htm. The author used the English translation available at https://www.chinalawtranslate.com/en/national-intelligence-law-of-the-p-r-c-2017/.

The Internet Is Not the Answer by Andrew Keen

"World Economic Forum" Davos, 3D printing, A Declaration of the Independence of Cyberspace, Airbnb, AltaVista, Andrew Keen, AOL-Time Warner, augmented reality, Bay Area Rapid Transit, Berlin Wall, Big Tech, bitcoin, Black Swan, Bob Geldof, Boston Dynamics, Burning Man, Cass Sunstein, Charles Babbage, citizen journalism, Clayton Christensen, clean water, cloud computing, collective bargaining, Colonization of Mars, computer age, connected car, creative destruction, cuban missile crisis, data science, David Brooks, decentralized internet, DeepMind, digital capitalism, disintermediation, disruptive innovation, Donald Davies, Downton Abbey, Dr. Strangelove, driverless car, Edward Snowden, Elon Musk, Erik Brynjolfsson, fail fast, Fall of the Berlin Wall, Filter Bubble, Francis Fukuyama: the end of history, Frank Gehry, Frederick Winslow Taylor, frictionless, fulfillment center, full employment, future of work, gentrification, gig economy, global village, Google bus, Google Glasses, Hacker Ethic, happiness index / gross national happiness, holacracy, income inequality, index card, informal economy, information trail, Innovator's Dilemma, Internet of things, Isaac Newton, Jaron Lanier, Jeff Bezos, job automation, John Perry Barlow, Joi Ito, Joseph Schumpeter, Julian Assange, Kevin Kelly, Kevin Roose, Kickstarter, Kiva Systems, Kodak vs Instagram, Lean Startup, libertarian paternalism, lifelogging, Lyft, Marc Andreessen, Mark Zuckerberg, Marshall McLuhan, Martin Wolf, Mary Meeker, Metcalfe’s law, military-industrial complex, move fast and break things, Nate Silver, Neil Armstrong, Nelson Mandela, Network effects, new economy, Nicholas Carr, nonsequential writing, Norbert Wiener, Norman Mailer, Occupy movement, packet switching, PageRank, Panopticon Jeremy Bentham, Patri Friedman, Paul Graham, peer-to-peer, peer-to-peer rental, Peter Thiel, plutocrats, Potemkin village, power law, precariat, pre–internet, printed gun, Project Xanadu, RAND corporation, Ray Kurzweil, reality distortion field, ride hailing / ride sharing, Robert Metcalfe, Robert Solow, San Francisco homelessness, scientific management, Second Machine Age, self-driving car, sharing economy, Sheryl Sandberg, Silicon Valley, Silicon Valley billionaire, Silicon Valley ideology, Skype, smart cities, Snapchat, social web, South of Market, San Francisco, Steve Jobs, Steve Wozniak, Steven Levy, Stewart Brand, subscription business, TaskRabbit, tech bro, tech worker, TechCrunch disrupt, Ted Nelson, telemarketer, The future is already here, The Future of Employment, the long tail, the medium is the message, the new new thing, Thomas L Friedman, Travis Kalanick, Twitter Arab Spring, Tyler Cowen, Tyler Cowen: Great Stagnation, Uber for X, uber lyft, urban planning, Vannevar Bush, warehouse robotics, Whole Earth Catalog, WikiLeaks, winner-take-all economy, work culture , working poor, Y Combinator

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

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It underpins the automation of classrooms, libraries, hospitals, shops, churches, and homes.”24 With its massive investment in the development of intelligent labor-saving technologies like self-driving cars and killer robots, Google—which has imported Ray Kurzweil, the controversial evangelist of “singularity,” to direct its artificial intelligence engineering strategy25—is already invested in the building and management of the glass cage. Not content with the acquisition of Boston Dynamics and seven other robotics companies in the second half of 2013,26 Google also made two important purchases at the beginning of 2014 to consolidate its lead in this market. It acquired the secretive British company DeepMind, “the last large independent company with a strong focus on artificial intelligence,” according to one inside source, for $500 million; and it bought Nest Labs, a leader in smart home devices such as intelligent thermostats, for $3.23 billion. According to the Wall Street Journal, Google is even working with Foxconn, the huge Taiwanese contract manufacturer that already makes most of Apple’s products, “to carry out the US company’s vision for robotics.”27 With all these acquisitions and partnerships, Google clearly is, as the technology journalist Dan Rowinski put it, playing a game of Moneyball28 in the age of artificial intelligence—setting itself up to be the dominant player in the age of intelligent computing.

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As Google’s then CEO Eric Schmidt confessed to the Financial Times back in 2007, Google wants to know us better than we know ourselves so that it can tell us not only what jobs we should take but also how we want to spend our day.58 “We know where you are. We know where you’ve been,” Schmidt told the Atlantic’s editor James Bennet in September 2010. “We can more or less know what you’re thinking about.”59 This is the real reason why Google spent $500 million in 2014 on the artificial intelligence startup DeepMind—a technology that, according to The Information’s Amir Efrati, wants to “make computers think like humans.”60 By thinking like us, by being able to join the dots in our mind, Google will own us. And by owning us—our desires, our intentions, our career goals, above all our buying habits—Google will own the networked future.

Cogs and Monsters: What Economics Is, and What It Should Be by Diane Coyle

3D printing, additive manufacturing, Airbnb, Al Roth, Alan Greenspan, algorithmic management, Amazon Web Services, autonomous vehicles, banking crisis, barriers to entry, behavioural economics, Big bang: deregulation of the City of London, biodiversity loss, bitcoin, Black Lives Matter, Boston Dynamics, Bretton Woods, Brexit referendum, business cycle, call centre, Carmen Reinhart, central bank independence, choice architecture, Chuck Templeton: OpenTable:, cloud computing, complexity theory, computer age, conceptual framework, congestion charging, constrained optimization, coronavirus, COVID-19, creative destruction, credit crunch, data science, DeepMind, deglobalization, deindustrialization, Diane Coyle, discounted cash flows, disintermediation, Donald Trump, Edward Glaeser, en.wikipedia.org, endogenous growth, endowment effect, Erik Brynjolfsson, eurozone crisis, everywhere but in the productivity statistics, Evgeny Morozov, experimental subject, financial deregulation, financial innovation, financial intermediation, Flash crash, framing effect, general purpose technology, George Akerlof, global supply chain, Goodhart's law, Google bus, haute cuisine, High speed trading, hockey-stick growth, Ida Tarbell, information asymmetry, intangible asset, Internet of things, invisible hand, Jaron Lanier, Jean Tirole, job automation, Joseph Schumpeter, Kenneth Arrow, Kenneth Rogoff, knowledge economy, knowledge worker, Les Trente Glorieuses, libertarian paternalism, linear programming, lockdown, Long Term Capital Management, loss aversion, low earth orbit, lump of labour, machine readable, market bubble, market design, Menlo Park, millennium bug, Modern Monetary Theory, Mont Pelerin Society, multi-sided market, Myron Scholes, Nash equilibrium, Nate Silver, Network effects, Occupy movement, Pareto efficiency, payday loans, payment for order flow, Phillips curve, post-industrial society, price mechanism, Productivity paradox, quantitative easing, randomized controlled trial, rent control, rent-seeking, ride hailing / ride sharing, road to serfdom, Robert Gordon, Robert Shiller, Robert Solow, Robinhood: mobile stock trading app, Ronald Coase, Ronald Reagan, San Francisco homelessness, savings glut, school vouchers, sharing economy, Silicon Valley, software is eating the world, spectrum auction, statistical model, Steven Pinker, tacit knowledge, The Chicago School, The Future of Employment, The Great Moderation, the map is not the territory, The Rise and Fall of American Growth, the scientific method, The Signal and the Noise by Nate Silver, the strength of weak ties, The Wealth of Nations by Adam Smith, total factor productivity, transaction costs, Uber for X, urban planning, winner-take-all economy, Winter of Discontent, women in the workforce, Y2K

It expands on some of the themes of Chapters One and Two, particularly about rational choice and homo economicus, and was also informed by the two years or so I spent as a Fellow (unpaid) of AI company DeepMind’s Ethics and Society group. So the main question posed in this chapter is how should we assess whether our expert policy advice is making things better, or not, and particularly when digital transformation is changing the character of the economy so much. 3 Homo Economicus, AIs, Rats and Humans Rationality in the Wild Take three kinds of experiment. The artificial intelligence (AI) company DeepMind set its AI agents—decision-making rules on a computer—competing for scarce resources in an apple-picking game (Leibo et al. 2017a,b).

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Act of Union, 148 ad hoc models, 89–92, 94, 150 advertising, 60; aggregate data and, 141; digital technology and, 141, 175, 177, 203–6; fixed preferences and, 5, 92–93, 123; impulse purchases and, 92; Packard on, 109; targeting women, 93 agglomeration, 127, 132, 202, 207 Airbnb, 142, 173–75 Akerlof, George, 93, 159 Alemanno, Gianni, 68–69 algebra, 16, 26, 89–90, 179 algorithms: artificial intelligence (AI) and, 12, 25–27, 33, 116, 118, 139, 157, 160–61, 184–85, 188, 195, 200; Harris on, 27; policy-design, 157; prisons and, 33; progress and, 139, 157, 160–61; public responsibilities and, 25–27, 33; rationality and, 116, 118; twenty-first-century policy and, 184–85, 188, 195, 200; ultra-high frequency trading (HFT) and, 25–27 Alibaba, 173 Allende, Salvador, 184 Alphabet, 133 altruism, 49, 92, 117 Amazon, 133, 142, 170, 173, 175, 197 American Economic Association, 9, 34 Anderson, Elizabeth, 34 animal spirits, 22 Annual Abstract of Statistics for the United Kingdom, 150 Apple, 116–17, 123, 133, 173, 195 Arrow, Kenneth, 123 artificial intelligence (AI): adoption rates of, 172–73; algorithms and, 12, 25–27, 33, 116, 118, 139, 157, 160–61, 184–85, 188, 195, 200; automated decision making and, 116, 186–87; bias and, 13, 161, 165, 187; central planning and, 184, 186–87; changing technology and, 171–72; cloud computing and, 150, 170–72, 184, 197; DeepMind and, 115–16; facial recognition and, 165; homo economicus and, 161; machine learning and, 12–13, 137, 141, 160–61, 187; progress and, 137–39, 154, 159–62; public responsibilities and, 28, 40; rationality and, 116–18; reinforcement learning and, 116; socialist calculation debate and, 184, 186–87; twenty-first-century policy and, 184, 186–87, 195 Atkinson, A., 128–29 Aumann, Robert, 48 austerity, 19, 73, 101, 158, 164, 192 Austin, John, 23 automation, 139, 154, 165–66, 195 Azure, 170 Baidu, 173 Baldwin, Richard, 196 Banerjee, Abhijit, 109 Bank for International Settlements (BIS), 24n4 Bank of America, 28 Bank of England, 53, 84–85 bankruptcy, 23 Basu, Kaushik, 159–60 Bateson, Gregory, 104 Baumol, W.

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

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

Adam Curtis, agricultural Revolution, AI winter, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, algorithmic trading, Anthropocene, artificial general intelligence, augmented reality, autism spectrum disorder, autonomous vehicles, backpropagation, basic income, behavioural economics, bitcoin, blockchain, bread and circuses, Charles Babbage, clean water, cognitive dissonance, Colonization of Mars, complexity theory, computer age, computer vision, constrained optimization, corporate personhood, cosmological principle, cryptocurrency, cuban missile crisis, Danny Hillis, dark matter, data science, deep learning, DeepMind, Demis Hassabis, digital capitalism, digital divide, digital rights, discrete time, Douglas Engelbart, driverless car, Elon Musk, Emanuel Derman, endowment effect, epigenetics, Ernest Rutherford, experimental economics, financial engineering, Flash crash, friendly AI, functional fixedness, global pandemic, Google Glasses, Great Leap Forward, Hans Moravec, hive mind, Ian Bogost, income inequality, information trail, Internet of things, invention of writing, iterative process, James Webb Space Telescope, Jaron Lanier, job automation, Johannes Kepler, John Markoff, John von Neumann, Kevin Kelly, knowledge worker, Large Hadron Collider, lolcat, loose coupling, machine translation, microbiome, mirror neurons, Moneyball by Michael Lewis explains big data, Mustafa Suleyman, natural language processing, Network effects, Nick Bostrom, Norbert Wiener, paperclip maximiser, pattern recognition, Peter Singer: altruism, phenotype, planetary scale, Ray Kurzweil, Recombinant DNA, recommendation engine, Republic of Letters, RFID, Richard Thaler, Rory Sutherland, Satyajit Das, Search for Extraterrestrial Intelligence, self-driving car, sharing economy, Silicon Valley, Skype, smart contracts, social intelligence, speech recognition, statistical model, stem cell, Stephen Hawking, Steve Jobs, Steven Pinker, Stewart Brand, strong AI, Stuxnet, superintelligent machines, supervolcano, synthetic biology, systems thinking, tacit knowledge, TED Talk, the scientific method, The Wisdom of Crowds, theory of mind, Thorstein Veblen, too big to fail, Turing machine, Turing test, Von Neumann architecture, Watson beat the top human players on Jeopardy!, We are as Gods, Y2K

But until we replicate the embodied emotional being—a feat I don’t believe we can achieve—our machines will continue to serve as occasional analogies for thought and to evolve according to our needs. ENVOI: A SHORT DISTANCE AHEAD—AND PLENTY TO BE DONE DEMIS HASSABIS Vice President of Engineering, Google DeepMind; cofounder, DeepMind Technologies SHANE LEGG AI researcher; cofounder, DeepMind Technologies MUSTAFA SULEYMAN Head of applied AI, Google DeepMind; cofounder, DeepMind Technologies For years we’ve been making the case that artificial intelligence, and in particular the field of machine learning, is making rapid progress and is set to make a whole lot more progress. Along with this, we’ve been standing up for the idea that the safety and ethics of artificial intelligence is an important topic that all of us should be thinking about very seriously.

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In 1992, Gerald Tesauro at IBM, using reinforcement learning, trained a neural network to play backgammon at a world-champion level. The network played itself, and the only feedback it received was which side won the game. Brains use reinforcement learning to make sequences of decisions toward achieving goals, such as finding food under uncertain conditions. Recently, DeepMind, a company acquired by Google in 2014, used deep reinforcement learning to play seven classic Atari games. The only inputs to the learning system were the pixels on the video screen and the score, the same inputs humans use. The program for several of the games could play better than expert humans.

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Along with this, we’ve been standing up for the idea that the safety and ethics of artificial intelligence is an important topic that all of us should be thinking about very seriously. The potential benefits of artificial intelligence will be vast, but like any powerful technology these benefits will depend on the technology being applied with care. While some researchers have cheered us on since the start of DeepMind, others have been skeptical. However, in recent years the climate for ambitious artificial intelligence research has much improved, no doubt due to a string of stunning successes in the field. Not only have a number of longstanding challenges finally been met but there’s a growing sense among the community that the best is yet to come.

The Driver in the Driverless Car: How Our Technology Choices Will Create the Future by Vivek Wadhwa, Alex Salkever

23andMe, 3D printing, Airbnb, AlphaGo, artificial general intelligence, augmented reality, autonomous vehicles, barriers to entry, benefit corporation, Bernie Sanders, bitcoin, blockchain, clean water, correlation does not imply causation, CRISPR, deep learning, DeepMind, distributed ledger, Donald Trump, double helix, driverless car, Elon Musk, en.wikipedia.org, epigenetics, Erik Brynjolfsson, gigafactory, Google bus, Hyperloop, income inequality, information security, Internet of things, job automation, Kevin Kelly, Khan Academy, Kickstarter, Law of Accelerating Returns, license plate recognition, life extension, longitudinal study, Lyft, M-Pesa, Mary Meeker, Menlo Park, microbiome, military-industrial complex, mobile money, new economy, off-the-grid, One Laptop per Child (OLPC), personalized medicine, phenotype, precision agriculture, radical life extension, RAND corporation, Ray Kurzweil, recommendation engine, Ronald Reagan, Second Machine Age, self-driving car, seminal paper, Silicon Valley, Skype, smart grid, stem cell, Stephen Hawking, Steve Wozniak, Stuxnet, supercomputer in your pocket, synthetic biology, Tesla Model S, The future is already here, The Future of Employment, Thomas Davenport, Travis Kalanick, Turing test, Uber and Lyft, Uber for X, uber lyft, uranium enrichment, Watson beat the top human players on Jeopardy!, zero day

The rise of machine learning, too, heralds a generation of robots that can learn through doing and that will become smarter as they spend more time with us. Google is on the verge of completing real-time text-and voice- translation software, built, in part, with human input through Google Translate. Google’s DeepMind system, which beat the world’s leading Go player in 2016, learned to play this millennia-old board game, orders of magnitude more complicated than chess, by watching humans play Go.3 Even more fascinating, DeepMind surprised human Go experts with moves that, at first glance, made no sense but ultimately proved innovative. The humans taught the robot not just to play like a human but how to think for itself in novel ways.

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Such programming would, he believes, be a moral violation. Other critics, such as AJung Moon, cofounder of the Open Roboethics initiative, fear that allowing autonomous lethal force will tip us down the slippery slope toward a world in which the machines could act autonomously beyond the intent programmed into them.7 And, as DeepMind demonstrated on the Go board, robots made smart enough will likely have minds of their own, within at least the rules and environment they have mastered. The military supporters of autonomous lethal force argue that robots in the battlefield might prove to be far more moral than their human counterparts.

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“Planet Money,” National Public Radio 8 May 2015, http://www.npr.org/templates/transcript/transcript.php?storyId=405270046 (accessed 21 October 2016). 2. The Verge, “The 2015 DARPA Robotics Challenge Finals,” https://www.youtube.com/watch?v=8P9geWwi9e0 (accessed 21 October 2016). 3. Richard Lawler, “Google DeepMind AI wins final Go match for 4– 1 series win,” Engadget 14 March 2016, https://www.engadget.com/2016/03/14/the-final-lee-sedol-vs-alphago-match-is-about-to-start (accessed 21 October 2016). 4. Wan He, Daniel Goodkind, and Paul Kowal, U.S. Census Bureau, An Aging World: 2015, International Population Reports P95/16-1, Washington, D.C.: U.S.

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

Google was the recognized intellectual leader of the industry, and its AI ostentation was widely acclaimed. Indeed it signed up most of the world’s AI celebrities, including its spearheads of “deep learning” prowess, from Geoffrey Hinton and Andrew Ng to Jeff Dean, the beleaguered Anthony Levandowski, and Demis Hassabis of DeepMind. If Google had been a university, it would have utterly outshone all others in AI talent. It must have been discouraging, then, to find that Amazon had shrewdly captured much of the market for AI services with its 2014 Alexa and Echo projects. It launched actual hardware to bring AI to everyone’s household in the form of elegantly designed devices that answered questions and ordered products while eschewing ads.

…

A tenured contingent consisted of the technologist Stuart Russell, the philosopher David Chalmers, the catastrophe theorist Nick Bostrom, the nanotech prophet Eric Drexler, the cosmologist Lawrence Krauss, the economist Erik Brynjolfsson, and the “Singularitarian” Vernor Vinge, along with scores of other celebrity scientists.1 They gathered at Asilomar preparing to alert the world to the dire threat posed by . . . well, by themselves—Silicon Valley. Their computer technology, advanced AI, and machine learning—acclaimed in hundreds of press releases as the Valley’s principal activity and hope for the future, with names such as TensorFlow, DeepMind, Machine Learning, Google Brain, and the Singularity—had gained such power and momentum that it was now deemed nothing less than a menace to mankind. In 1965 I. J. Good, whom Turing taught to play Go at Bletchley Park while they worked on cracking the Enigma cipher, penned the first (and still the pithiest) warning: Let an ultra-intelligent machine be defined as a machine that can far surpass all the intellectual activities of any man however clever.

…

As Kurzweil acknowledges, semantic search is an “extension of human intelligence” rather than a replacement for it. A human being reinforced by AI prosthetics is less likely, not more likely, to be ambushed by a usurper digital machine. Semantic search delays the machine-learning eschaton. 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 Inevitable: Understanding the 12 Technological Forces That Will Shape Our Future by Kevin Kelly

A Declaration of the Independence of Cyberspace, Aaron Swartz, AI winter, Airbnb, Albert Einstein, Alvin Toffler, Amazon Web Services, augmented reality, bank run, barriers to entry, Baxter: Rethink Robotics, bitcoin, blockchain, book scanning, Brewster Kahle, Burning Man, cloud computing, commoditize, computer age, Computer Lib, connected car, crowdsourcing, dark matter, data science, deep learning, DeepMind, dematerialisation, Downton Abbey, driverless car, Edward Snowden, Elon Musk, Filter Bubble, Freestyle chess, Gabriella Coleman, game design, Geoffrey Hinton, Google Glasses, hive mind, Howard Rheingold, index card, indoor plumbing, industrial robot, Internet Archive, Internet of things, invention of movable type, invisible hand, Jaron Lanier, Jeff Bezos, job automation, John Markoff, John Perry Barlow, Kevin Kelly, Kickstarter, lifelogging, linked data, Lyft, M-Pesa, machine readable, machine translation, Marc Andreessen, Marshall McLuhan, Mary Meeker, means of production, megacity, Minecraft, Mitch Kapor, multi-sided market, natural language processing, Netflix Prize, Network effects, new economy, Nicholas Carr, off-the-grid, old-boy network, peer-to-peer, peer-to-peer lending, personalized medicine, placebo effect, planetary scale, postindustrial economy, Project Xanadu, recommendation engine, RFID, ride hailing / ride sharing, robo advisor, Rodney Brooks, self-driving car, sharing economy, Silicon Valley, slashdot, Snapchat, social graph, social web, software is eating the world, speech recognition, Stephen Hawking, Steven Levy, Ted Nelson, TED Talk, The future is already here, the long tail, the scientific method, transport as a service, two-sided market, Uber for X, uber lyft, value engineering, Watson beat the top human players on Jeopardy!, WeWork, Whole Earth Review, Yochai Benkler, yottabyte, zero-sum game

, Intel, Dropbox, LinkedIn, Pinterest, and Twitter have all purchased AI companies since 2014. Private investment in the AI sector has been expanding 70 percent a year on average for the past four years, a rate that is expected to continue. One of the early stage AI companies Google purchased is DeepMind, based in London. In 2015 researchers at DeepMind published a paper in Nature describing how they taught an AI to learn to play 1980s-era arcade video games, like Video Pinball. They did not teach it how to play the games, but how to learn to play the games—a profound difference. They simply turned their cloud-based AI loose on an Atari game such as Breakout, a variant of Pong, and it learned on its own how to keep increasing its score.

…

It keeps learning so fast that in the second hour it figures out a loophole in the Breakout game that none of the millions of previous human players had discovered. This hack allowed it to win by tunneling around a wall in a way that even the game’s creators had never imagined. At the end of several hours of first playing a game, with no coaching from the DeepMind creators, the algorithms, called deep reinforcement machine learning, could beat humans in half of the 49 Atari video games they mastered. AIs like this one are getting smarter every month, unlike human players. Amid all this activity, a picture of our AI future is coming into view, and it is not the HAL 9000—a discrete machine animated by a charismatic (yet potentially homicidal) humanlike consciousness—or a Singularitan rapture of superintelligence.

…

I was not the only avid user of its search site who thought it would not last long. But Page’s reply has always stuck with me: “Oh, we’re really making an AI.” I’ve thought a lot about that conversation over the past few years as Google has bought 13 other AI and robotics companies in addition to DeepMind. At first glance, you might think that Google is beefing up its AI portfolio to improve its search capabilities, since search constitutes 80 percent of its revenue. But I think that’s backward. Rather than use AI to make its search better, Google is using search to make its AI better. Every time you type a query, click on a search-generated link, or create a link on the web, you are training the Google AI.

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

"World Economic Forum" Davos, 3D printing, agricultural Revolution, AI winter, Albert Einstein, AlphaGo, Alvin Toffler, anti-work, antiwork, autonomous vehicles, basic income, Ben Bernanke: helicopter money, Bernie Sanders, Bletchley Park, blockchain, call centre, Cambridge Analytica, Capital in the Twenty-First Century by Thomas Piketty, Carl Icahn, Chris Urmson, computer age, Computing Machinery and Intelligence, conceptual framework, corporate governance, correlation does not imply causation, creative destruction, David Ricardo: comparative advantage, deep learning, DeepMind, deindustrialization, Demis Hassabis, deskilling, Dr. Strangelove, driverless car, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, everywhere but in the productivity statistics, facts on the ground, fake news, financial intermediation, full employment, future of work, Future Shock, general purpose technology, Great Leap Forward, Hans Moravec, income inequality, income per capita, industrial robot, Internet of things, invention of the wheel, Isaac Newton, James Watt: steam engine, Jeff Bezos, Jeremy Corbyn, job automation, job satisfaction, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, Joseph Schumpeter, Kevin Kelly, license plate recognition, low interest rates, machine translation, Marc Andreessen, Mark Zuckerberg, market bubble, mega-rich, natural language processing, Network effects, new economy, Nicholas Carr, Ocado, Paul Samuelson, Peter Thiel, Phillips curve, positional goods, quantitative easing, RAND corporation, Ray Kurzweil, Richard Florida, ride hailing / ride sharing, rising living standards, road to serfdom, Robert Gordon, Robert Shiller, Robert Solow, Rutger Bregman, Second Machine Age, secular stagnation, self-driving car, seminal paper, Silicon Valley, Silicon Valley billionaire, Simon Kuznets, Skype, social intelligence, spinning jenny, Stanislav Petrov, Stephen Hawking, Steven Pinker, synthetic biology, technological singularity, The Future of Employment, The Wealth of Nations by Adam Smith, Thomas Malthus, trade route, universal basic income, US Airways Flight 1549, Vernor Vinge, warehouse automation, warehouse robotics, Watson beat the top human players on Jeopardy!, We wanted flying cars, instead we got 140 characters, wealth creators, winner-take-all economy, world market for maybe five computers, Y2K, Yogi Berra

Deep Blue was able to evaluate between 100 and 200 million positions per second. Kasparov said: “I had played a lot of computers but had never experienced anything like this. I could feel – I could smell – a new kind of intelligence across the table.” In 2001 an IBM machine called Watson beat the best human players at the TV quiz game Jeopardy! In 2013 a DeepMind AI system taught itself to play Atari video games like Breakout and Pong, which involve hand–eye coordination. This was much more significant that it might have seemed. The AI system wasn’t taught how to play video games, but rather how to learn to play the games. Kevin Kelly thinks that AI has now made a decided leap forward, but its significance is still not fully appreciated.

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Indeed, once a machine is able to accomplish a particular thing, we often stop referring to it as AI. Tesler’s Theorem defines artificial intelligence as that which a machine cannot yet do.10 And the category of things that a machine cannot do appears to be shrinking all the time. In 2016 an AI system developed by Google’s DeepMind called AlphaGo beat Fan Hui, the European Champion at the board game Go. The system taught itself using a machine learning approach called “deep reinforcement learning.” Two months later AlphaGo defeated the world champion four games to one. This result was regarded as especially impressive in Asia, where Go is much more popular than it is in Europe or America.

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More importantly, we could see the widespread use of shared-use vehicles or electric vehicles, or both, without seeing a large-scale move to driverless vehicles. For, even without the ride sharing and the switch from petrol to electric, the widespread use of driverless cars is not as straightforward as is usually implied. Feasibility is not the issue. Safety is. Demis Hassabis, one of the founders of DeepMind, said in May 2018: “How do you ensure, mathematically, that systems are safe and will only do what we think they are going to do when they are out in the wild.”8 His misgivings are fully justified. Despite the claims of the manufacturers and developers of driverless vehicles that they are ultrasafe, a 2015 study from the University of Michigan discovered that the crash rate is higher for driverless vehicles.9 The study suggested that, when they occur, crashes are almost always not the fault of the driverless cars.

Like, Comment, Subscribe: Inside YouTube's Chaotic Rise to World Domination by Mark Bergen

23andMe, 4chan, An Inconvenient Truth, Andy Rubin, Anne Wojcicki, Big Tech, Black Lives Matter, book scanning, Burning Man, business logic, call centre, Cambridge Analytica, citizen journalism, cloud computing, Columbine, company town, computer vision, coronavirus, COVID-19, crisis actor, crowdsourcing, cryptocurrency, data science, David Graeber, DeepMind, digital map, disinformation, don't be evil, Donald Trump, Edward Snowden, Elon Musk, fake news, false flag, game design, gender pay gap, George Floyd, gig economy, global pandemic, Golden age of television, Google Glasses, Google X / Alphabet X, Googley, growth hacking, Haight Ashbury, immigration reform, James Bridle, John Perry Barlow, Justin.tv, Kevin Roose, Khan Academy, Kinder Surprise, Marc Andreessen, Marc Benioff, Mark Zuckerberg, mass immigration, Max Levchin, Menlo Park, Minecraft, mirror neurons, moral panic, move fast and break things, non-fungible token, PalmPilot, paypal mafia, Peter Thiel, Ponzi scheme, QAnon, race to the bottom, recommendation engine, Rubik’s Cube, Salesforce, Saturday Night Live, self-driving car, Sheryl Sandberg, side hustle, side project, Silicon Valley, slashdot, Snapchat, social distancing, Social Justice Warrior, speech recognition, Stanford marshmallow experiment, Steve Bannon, Steve Jobs, Steven Levy, surveillance capitalism, Susan Wojcicki, systems thinking, tech bro, the long tail, The Wisdom of Crowds, TikTok, Walter Mischel, WikiLeaks, work culture

For a moment onstage, though, he looked lively, a boyish charm familiar to those from Google’s golden years. He was discussing his newest prize: DeepMind, a London company that researched artificial intelligence but did not sell any products or services. Google paid $650 million for it. DeepMind’s brilliance came from its fix for “unsupervised” learning, Page whispered into the mic, and when Rose didn’t immediately cotton, Page asked, “Maybe I can show the video?” A screen behind them lit up with old arcade games. DeepMind had constructed a computer model to master these games on its own, without instructions or supervision, as old chess computers had required.

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A B C D E F G H I J K L M N O P Q R S T U V W X Y Z A ABC television network, 76 “Abortion Man,” 75 abortion-related content, 86 Accenture, 317–18, 319 addictiveness of videos, 239 “The Adpocalypse: What it Means” (vlogbrothers), 289 advertising/advertisers and ad-friendly mandate for creators, 267–68, 274, 282–83 algorithms’ role in placement of, 284, 285, 295 banner ads, 73, 197, 200 and boycotts, 283–90, 295, 300, 308, 329, 356 and child-directed content, 173, 368, 394 and child-exploitation debacle, 311–14 and comments removed from kids’ videos, 371 and copyright concerns, 108 creators’ control over types of, 347 and data shared with marketers, 284–85, 295 dismantling of targeted, 390 Dynamic Ad Loads (Dallas), 191–92, 194 and eligibility thresholds for partner program, 329 on Facebook, 252, 284 and first profit of YouTube, 50 and fraudsters, 284 and Google Preferred, 210 and home page of YouTube, 101 Hurley’s reluctance to employ, 68 increase in videos eligible for, 110 on non-partner program channels, 391–92 and partner program, 69–70, 163–64 and pay-per-view business model, 133 pop-up ads, 68, 73 “pre-rolls,” 68 and product placements, 67 and Project MASA, 296, 313, 381 and questionable/troubling content, 67, 75, 87, 255, 285–87, 296 recent sales revenues, 391, 401 removed from creators’ videos, 267–68, 314, 324 Russia Today (RT), 340–41 and sell-through rate, 108, 109–10 shortage of slots for, 164 skippable, 192 and Spotlight (influencer campaigns), 248 television model for, 110–11 and user experience, 68 and viewability/measurability, 284–85 and Wojcicki, 195, 196, 197–98, 200, 213 AdWords, 51 Agha-Soltan, Neda, 137 Aghdam, Nasim Najafi, 331–35 Akilah Obviously, 261 al-Awlaki, Anwar, 214 Alchemy House, 256 algorithms of YouTube for ad placements, 284, 285, 295 adult content from kids’ search terms, 306 and advertiser-friendly content, 267 and authoritative news sources, 325–26 and changes to reward system, 155, 156–60, 164 and clickbait, 150 and comments vs. likes, 158, 276 and conspiracy theories, 325–29 and creators of color, 339 creators’ understanding of, 297, 385 and daily viewers, 252, 254 disclosure of information about, 297, 398 and Google Preferred content, 210 and government regulation, 401 and home-page of YouTube, 99–102, 135, 298 Jho on improvements in, 394–95 and keyword stuffing, 308–9 and “Leanback” feature, 189–90 limitations of, 255–56 and machine learning, 191–92 and Paul’s video of suicide victim, 323 and PewDiePie, 275 and presidential election of 2016, 272, 326 and presidential election of 2020, 388 and quality content, 175 responsibility metric, 328 screeners’ role in training, 320 and skeptics of YouTube, 223 skin-detection by, 255–56 titles of content chosen for, 172 watch time favored in, 156–60 and YouTube Kids app, 238, 244–45 Allen & Company (investment bank), 49 Alphabet, 257 alt-right, 263, 269–70, 275, 277–78 Amazing Atheist, 223 Amazon, 210, 232–33, 253 Anderson, Erica, 350 Andreessen, Marc, 72 Android, 147, 177 animated videos, 241 Annoying Orange (YouTuber), 128, 140, 160 anonymous creators, 172–73 Anti-Defamation League (ADL), 281 antisemitism, 86, 275, 277, 281 Apple, 35, 56, 149, 176–77, 207–8 Arab Spring, 139, 140, 141–43, 145, 149, 164, 213 Argento, Dario, 383 Armstrong, Tim, 73 Arnspiger, Dianna, 334 artificial intelligence and neural networks content moderation with, 233–35, 292–93, 315, 399–400 and DeepMind acquisition by Google, 230–31 detection of red flags, 396 and DistBelief system, 232 and Google Brain, 231–35, 298 Google’s application of, 233 inability to precisely control or predict, 308 “precision and recall” protocols for, 309 and problematic content targeted at kids, 308–10 in recommendation engine of YouTube, 233–35 Reinforce program, 298 Whittaker’s criticisms of, 355 See also algorithms of YouTube; machine learning Ask a Ninja, 69 ASMR videos, 7, 208 AT&T, 210, 286 atheists/atheism, 221–22, 223, 226 audience of YouTube ages of viewers, 86, 169 and Arab Spring content, 145 and audience is king credo, 254, 297 average time on platform, 126 and billion-hours-of-viewing goal, 228, 270 and channels model, 127 communities built by, 122 complaints from users, 25 and COVID-19 pandemic, 376, 377–78 and cumulative hours of viewed footage, 154 daily viewers, 252, 254 emphasis on growth of, 91 and initiative to recruit female viewers, 369 and length of viewing sessions, 252 (see also watch time of audience) loyalty to YouTube, 394 number of videos watched daily, 49, 140 satisfaction ratings of, 296–97 See also engagement of users Auletta, Ken, 97 authoritative sources, 368, 388 Authors Guild, 48 auto-play function, 167 AwesomenessTV, 132, 210 B “Baby Shark,” 5, 306 bad actors, 308, 316, 329.

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See coolhunters cyberporn, 168 D Daily Mail, 242, 286 The Daily Show with Jon Stewart, 24 The Daily Stormer, 277, 278, 279 Daly, Carson, 40, 49 Damore, James, 301–3 Daniels, Susanne, 280 “Danny Diamond Gay Bar” (Zappin), 106 dating site, early visions of YouTube as, 17–18, 22 Dauman, Philippe, 62, 97 Dawkins, Richard, 223 Dawson, Shane, 119 Day, Mark, 39, 57, 102 DeepMind, 230–31 DeFranco, Philip, 112, 268, 290 de Kerchove, Gilles, 216–17 DeKort, Michael, 87 deletion of videos, 99, 296, 314, 379 “Delicious”/“Nutritious” content, 174–75, 305 Demand Media, 157 “demonetized” creators, 268, 310 denialism, 366, 399 Depardieu, Gérard, 266 detergent pods, consuming, 324 Diamond, Danny.

The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World by Pedro Domingos

Albert Einstein, Amazon Mechanical Turk, Arthur Eddington, backpropagation, basic income, Bayesian statistics, Benoit Mandelbrot, bioinformatics, Black Swan, Brownian motion, cellular automata, Charles Babbage, Claude Shannon: information theory, combinatorial explosion, computer vision, constrained optimization, correlation does not imply causation, creative destruction, crowdsourcing, Danny Hillis, data is not the new oil, data is the new oil, data science, deep learning, DeepMind, double helix, Douglas Hofstadter, driverless car, Erik Brynjolfsson, experimental subject, Filter Bubble, future of work, Geoffrey Hinton, global village, Google Glasses, Gödel, Escher, Bach, Hans Moravec, incognito mode, information retrieval, Jeff Hawkins, job automation, John Markoff, John Snow's cholera map, John von Neumann, Joseph Schumpeter, Kevin Kelly, large language model, lone genius, machine translation, mandelbrot fractal, Mark Zuckerberg, Moneyball by Michael Lewis explains big data, Narrative Science, Nate Silver, natural language processing, Netflix Prize, Network effects, Nick Bostrom, NP-complete, off grid, P = NP, PageRank, pattern recognition, phenotype, planetary scale, power law, pre–internet, random walk, Ray Kurzweil, recommendation engine, Richard Feynman, scientific worldview, Second Machine Age, self-driving car, Silicon Valley, social intelligence, speech recognition, Stanford marshmallow experiment, statistical model, Stephen Hawking, Steven Levy, Steven Pinker, superintelligent machines, the long tail, the scientific method, The Signal and the Noise by Nate Silver, theory of mind, Thomas Bayes, transaction costs, Turing machine, Turing test, Vernor Vinge, Watson beat the top human players on Jeopardy!, white flight, yottabyte, zero-sum game

Nevertheless, reinforcement learning with neural networks has had some notable successes. An early one was a human-level backgammon player. More recently, a reinforcement learner from DeepMind, a London-based startup, beat an expert human player at Pong and other simple arcade games. It used a deep network to predict actions’ values from the console screen’s raw pixels. With its end-to-end vision, learning, and control, the system bore at least a passing resemblance to an artificial brain. This may help explain why Google paid half a billion dollars for DeepMind, a company with no products, no revenues, and few employees. Gaming aside, researchers have used reinforcement learning to balance poles, control stick-figure gymnasts, park cars backward, fly helicopters upside down, manage automated telephone dialogues, assign channels in cell phone networks, dispatch elevators, schedule space-shuttle cargo loading, and much else.

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Arthur Samuel’s pioneering research on learning to play checkers is described in his paper “Some studies in machine learning using the game of checkers”* (IBM Journal of Research and Development, 1959). This paper also marks one of the earliest appearances in print of the term machine learning. Chris Watkins’s formulation of the reinforcement learning problem appeared in his PhD thesis Learning from Delayed Rewards* (Cambridge University, 1989). DeepMind’s reinforcement learner for video games is described in “Human-level control through deep reinforcement learning,”* by Volodymyr Mnih et al. (Nature, 2015). Paul Rosenbloom retells the development of chunking in “A cognitive odyssey: From the power law of practice to a general learning mechanism and beyond” (Tutorials in Quantitative Methods for Psychology, 2006).

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See also Machine learning Data scientist, 9 Data sharing, 270–276 Data unions, 274–275 Dawkins, Richard, 284 Decision making, artificial intelligence and, 282–286 Decision theory, 165 Decision tree induction, 85–89 Decision tree learners, 24, 301 Decision trees, 24, 85–90, 181–182, 188, 237–238 Deduction induction as inverse of, 80–83, 301 Turing machine and, 34 Deductive reasoning, 80–81 Deep learning, 104, 115–118, 172, 195, 241, 302 DeepMind, 222 Democracy, machine learning and, 18–19 Dempster, Arthur, 209 Dendrites, 95 Descartes, René, 58, 64 Descriptive theories, normative theories vs., 141–142, 304 Determinism, Laplace and, 145 Developmental psychology, 203–204, 308 DiCaprio, Leonardo, 177 Diderot, Denis, 63 Diffusion equation, 30 Dimensionality, curse of, 186–190, 307 Dimensionality reduction, 189–190, 211–215, 255 nonlinear, 215–217 Dirty Harry (film), 65 Disney animators, S curves and, 106 “Divide and conquer” algorithm, 77–78, 80, 81, 87 DNA sequencers, 84 Downweighting attributes, 189 Driverless cars, 8, 113, 166, 172, 306 Drones, 21, 281 Drugs, 15, 41–42, 83.

Them and Us: How Immigrants and Locals Can Thrive Together by Philippe Legrain

affirmative action, Albert Einstein, AlphaGo, autonomous vehicles, Berlin Wall, Black Lives Matter, Boris Johnson, Brexit referendum, British Empire, call centre, centre right, Chelsea Manning, clean tech, coronavirus, corporate social responsibility, COVID-19, creative destruction, crowdsourcing, data science, David Attenborough, DeepMind, Demis Hassabis, demographic dividend, digital divide, discovery of DNA, Donald Trump, double helix, Edward Glaeser, en.wikipedia.org, eurozone crisis, failed state, Fall of the Berlin Wall, future of work, illegal immigration, immigration reform, informal economy, Jane Jacobs, job automation, Jony Ive, labour market flexibility, lockdown, low cost airline, low interest rates, low skilled workers, lump of labour, Mahatma Gandhi, Mark Zuckerberg, Martin Wolf, Mary Meeker, mass immigration, moral hazard, Mustafa Suleyman, Network effects, new economy, offshore financial centre, open borders, open immigration, postnationalism / post nation state, purchasing power parity, remote working, Richard Florida, ride hailing / ride sharing, Rishi Sunak, Ronald Reagan, Silicon Valley, Skype, SoftBank, Steve Jobs, tech worker, The Death and Life of Great American Cities, The future is already here, The Future of Employment, Tim Cook: Apple, Tyler Cowen, urban sprawl, WeWork, Winter of Discontent, women in the workforce, working-age population

‘Demis and I had conversations about how to impact the world, and he’d argue that we need to build these grand simulations that one day will model all the complex dynamics of our financial systems and solve our toughest social problems,’ Mustafa explains. ‘I’d say we have to engage with the real world today.’3 Demis went on to become a neuroscientist and met Shane Legg, a Kiwi machine-learning researcher, at University College London. Combining their different talents and perspectives, they co-founded DeepMind, which was bought by Google for $500 million (£385 million) in 2014. In 2017 DeepMind’s AlphaGo bested the world number one at the Japanese game of Go – not by copying successful human strategies, but by devising its own better ones. Less than a third of recent patents and only a fifth of recent scientific papers were written by a single author – and even lone authors are stimulated by others.4 ‘Creativity comes from spontaneous meetings, from random discussions,’ observed the late Apple founder, Steve Jobs.

…

, Migration Policy Debates 19, OECD, May 2019. https://www.oecd.org/migration/mig/migration-policy-debates-19.pdf 77 The top ten countries in terms of their attractiveness to highly educated workers, before factoring in visa rules, are the US, Australia, New Zealand, Canada, Sweden, Ireland, Switzerland, Norway, the Netherlands and the UK. 78 The top ten most attractive OECD countries to highly educated workers are Australia, Sweden, Switzerland, New Zealand, Canada, Ireland, the US, the Netherlands, Slovenia and Norway. 10 Diversity Dividend 1 Chris Bascombe, ‘Jurgen Klopp delights in diverse personalities of Liverpool’s record-hunting defensive bedrock’, Telegraph, 4 April 2019. https://www.telegraph.co.uk/football/2019/04/04/jurgen-klopp-delights-diverse-personalities-liverpools-record/ 2 Leslie Pray, ‘Discovery of DNA structure and function: Watson and Crick’, Nature Education, 1:1, 2008. https://www.nature.com/scitable/topicpage/discovery-of-dna-structure-and-function-watson-397/ 3 David Rowan, ‘DeepMind: inside Google’s super-brain’, Wired, 22 June 2015. https://www.wired.co.uk/article/deepmind 4 Ernest Miguelez, Julio Raffo, Christian Chacua, Massimiliano Coda-Zabetta, Deyun Yin, Francesco Lissoni, Gianluca Tarasconi, ‘Tied in: The Global Network of Local Innovation’, WIPO Economic Research Working Paper 58, November 2019. https://www.wipo.int/publications/en/details.jsp?

…

English physicist Francis Crick and American biologist James Watson concluded that it consisted of a three-dimensional double helix, based on the earlier discovery of DNA by a Swiss scientist, Friedrich Miescher, developed by Phoebus Levene, a Lithuanian-born American biochemist, and Erwin Chargaff, an Austro-Hungarian one.2 Or consider DeepMind, a London-based company doing groundbreaking practical research on artificial intelligence. Mustafa Suleyman, whose father was a Syrian-born taxi driver and mother an English nurse, met Demis Hassabis, whose father was Greek-Cypriot and mother Chinese Singaporean, when they were teenagers in north London.

Ways of Being: Beyond Human Intelligence by James Bridle

Ada Lovelace, Airbnb, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Anthropocene, Any sufficiently advanced technology is indistinguishable from magic, autonomous vehicles, behavioural economics, Benoit Mandelbrot, Berlin Wall, Big Tech, Black Lives Matter, blockchain, Californian Ideology, Cambridge Analytica, carbon tax, Charles Babbage, cloud computing, coastline paradox / Richardson effect, Computing Machinery and Intelligence, corporate personhood, COVID-19, cryptocurrency, DeepMind, Donald Trump, Douglas Hofstadter, Elon Musk, experimental subject, factory automation, fake news, friendly AI, gig economy, global pandemic, Gödel, Escher, Bach, impulse control, James Bridle, James Webb Space Telescope, John von Neumann, Kickstarter, Kim Stanley Robinson, language acquisition, life extension, mandelbrot fractal, Marshall McLuhan, microbiome, music of the spheres, negative emissions, Nick Bostrom, Norbert Wiener, paperclip maximiser, pattern recognition, peer-to-peer, planetary scale, RAND corporation, random walk, recommendation engine, self-driving car, SETI@home, shareholder value, Silicon Valley, Silicon Valley ideology, speech recognition, statistical model, surveillance capitalism, techno-determinism, technological determinism, technoutopianism, the long tail, the scientific method, The Soul of a New Machine, theory of mind, traveling salesman, trolley problem, Turing complete, Turing machine, Turing test, UNCLOS, undersea cable, urban planning, Von Neumann architecture, wikimedia commons, zero-sum game

Samuel Gibbs, ‘Elon Musk: Regulate AI to Combat “Existential Threat” Before It’s Too Late’, The Guardian, 17 July 2017; https://www.theguardian.com/technology/2017/jul/17/elon-musk-regulation-ai-combat-existential-threat-tesla-spacex-ceo. 11. Nick Statt, ‘Bill Gates is Worried about Artificial Intelligence Too’, CNET, 28 January 2015; https://www.cnet.com/news/bill-gates-is-worried-about-artificial-intelligence-too/. 12. Sam Shead, ‘DeepMind’s Elusive Third Cofounder is the Man Making Sure that Machines Stay On Our Side’, Business Insider, 26 January 2017; https://www.businessinsider.com/shane-legg-google-deepmind-third-cofounder-artificial-intelligence-2017-1. 13. Charlie Stross, ‘Invaders from Mars’, Charlie’s Diary, 10 December 2010; http://www.antipope.org/charlie/blog-static/2010/12/invaders-from-mars.html. 14.

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Yet Elon Musk, creator of PayPal and owner of Tesla and SpaceX, believes that AI is the ‘biggest existential threat’ to humanity.10 Bill Gates, the founder of Microsoft – whose Azure AI platform keeps Shell’s oil platforms humming – has said he doesn’t understand why people are not more concerned about its development.11 Even Shane Legg, co-founder of the Google-owned AI company DeepMind – best known for beating the best human players at the game of Go – has gone on the record to state that ‘I think human extinction will probably occur, and technology will likely play a part in this.’ He wasn’t talking about oil: he was talking about AI.12 These fears aren’t so surprising. After all, the captains of digital industry, the beneficiaries of the vast wealth that technology generates, have the most to lose in being replaced by super-intelligent AI.

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Many of those working directly on AI at Facebook and Google and other Silicon Valley corporations are more than aware of the potential, existential threats of super-intelligence. As we’ve seen, some of the most celebrated people in tech, from Bill Gates and Elon Musk to Shane Legg, the founder of Google’s DeepMind, have expressed concerns about its emergence. But their response is a technological one: we must engineer AI to be ‘friendly’, embedding into its programming the necessary safeguards and procedures to ensure that it is never a threat to human life and well-being. This approach seems both wildly optimistic and worryingly naive.

AIQ: How People and Machines Are Smarter Together by Nick Polson, James Scott

Abraham Wald, Air France Flight 447, Albert Einstein, algorithmic bias, Amazon Web Services, Atul Gawande, autonomous vehicles, availability heuristic, basic income, Bayesian statistics, Big Tech, Black Lives Matter, Bletchley Park, business cycle, Cepheid variable, Checklist Manifesto, cloud computing, combinatorial explosion, computer age, computer vision, Daniel Kahneman / Amos Tversky, data science, deep learning, DeepMind, Donald Trump, Douglas Hofstadter, Edward Charles Pickering, Elon Musk, epigenetics, fake news, Flash crash, Grace Hopper, Gödel, Escher, Bach, Hans Moravec, Harvard Computers: women astronomers, Higgs boson, index fund, information security, Isaac Newton, John von Neumann, late fees, low earth orbit, Lyft, machine translation, Magellanic Cloud, mass incarceration, Moneyball by Michael Lewis explains big data, Moravec's paradox, more computing power than Apollo, natural language processing, Netflix Prize, North Sea oil, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, p-value, pattern recognition, Pierre-Simon Laplace, ransomware, recommendation engine, Ronald Reagan, Salesforce, self-driving car, sentiment analysis, side project, Silicon Valley, Skype, smart cities, speech recognition, statistical model, survivorship bias, systems thinking, the scientific method, Thomas Bayes, Uber for X, uber lyft, universal basic income, Watson beat the top human players on Jeopardy!, young professional