DeepMind

Rule of the Robots: How Artificial Intelligence Will Transform Everything
by Martin Ford
Published 13 Sep 2021

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
Published 15 Mar 2021

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
Published 14 Oct 2019

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
Published 4 Sep 2023

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
Published 5 Mar 2019

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
Published 7 Mar 2019

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
Published 16 Nov 2018

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
Published 2 Nov 2018

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
Published 16 Jun 2021

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.

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

BACK TO NOTE REFERENCE 81 Carl Engelking, “The AI That Dominated Humans in Go Is Already Obsolete,” Discover, October 18, 2017, https://www.discovermagazine.com/technology/the-ai-that-dominated-humans-in-go-is-already-obsolete; DeepMind, “AlphaGo China,” DeepMind, accessed November 20, 2021, https://deepmind.com/alphago-china; DeepMind, “AlphaGo Zero: Starting from Scratch.” BACK TO NOTE REFERENCE 82 DeepMind, “AlphaGo Zero: Starting from Scratch.” BACK TO NOTE REFERENCE 83 David Silver et al., “AlphaZero: Shedding New Light on Chess, Shogi, and Go,” DeepMind, December 6, 2018, https://deepmind.com/blog/article/alphazero-shedding-new-light-grand-games-chess-shogi-and-go. BACK TO NOTE REFERENCE 84 Julian Schrittwiese et al., “MuZero: Mastering Go, Chess, Shogi and Atari Without Rules,” DeepMind, December 23, 2020, https://deepmind.com/blog/article/muzero-mastering-go-chess-shogi-and-atari-without-rules.

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BACK TO NOTE REFERENCE 15 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; Matt Reynolds, “DeepMind’s AI Is Getting Closer to Its First Big Real-World Application,” Wired, January 15, 2020, https://www.wired.co.uk/article/deepmind-protein-folding-alphafold. BACK TO NOTE REFERENCE 16 For some more detailed explainers of AlphaFold 2 and the scientific paper describing it, see “AlphaFold: The Making of a Scientific Breakthrough,” DeepMind, YouTube video, November 30, 2020, https://www.youtube.com/watch?

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BACK TO NOTE REFERENCE 76 Nick Bostrom, “Nick Bostrom The Intelligence Explosion Hypothesis eDay 2012,” eAcast55, YouTube video, August 9, 2015, https://www.youtube.com/watch?v=VFE-96XA92w. BACK TO NOTE REFERENCE 77 DeepMind, “AlphaGo,” DeepMind, accessed November 20, 2021, https://deepmind.com/research/case-studies/alphago-the-story-so-far. BACK TO NOTE REFERENCE 78 DeepMind, “AlphaGo.” BACK TO NOTE REFERENCE 79 For an engaging account of the significance of the Deep Blue–Kasparov match, see Mark Robert Anderson, “Twenty Years On from Deep Blue vs. Kasparov: How a Chess Match Started the Big Data Revolution,” The Conversation, May 11, 2017, https://theconversation.com/twenty-years-on-from-deep-blue-vs-kasparov-how-a-chess-match-started-the-big-data-revolution-76882.

Four Battlegrounds
by Paul Scharre
Published 18 Jan 2023

Brown, Measuring the Algorithmic Efficiency of Neural Networks (arXiv.org, n.d.), https://arxiv.org/pdf/2005.04305.pdf. 298compute efficiency for both training and inference: Hernandez and Brown, Measuring the Algorithmic Efficiency of Neural Networks, 9–10; Radosvet Desislavov et al., Compute and Energy Consumption Trends in Deep Learning Inference (arXiv.org, September 12, 2021), https://arxiv.org/pdf/2109.05472.pdf. 298progress in algorithmic efficiency: Katja Grace, Algorithmic Progress in Six Domains (technical report no. 2013-3, Machine Intelligence Research Institute, 2013), https://intelligence.org/files/AlgorithmicProgress.pdf. 298compute-heavy models much more accessible: Desislavov et al., Compute and Energy Consumption Trends in Deep Learning Inference. 298ASIC optimized for deep learning: “Cloud TPU,” Google Cloud, n.d., https://cloud.google.com/tpu; “Cloud Tensor Processing Units (TPUs),” Google Cloud, n.d., 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
Published 29 Oct 2018

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
Published 1 Jan 2019

Erlich, Y., et al., 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.

The Age of AI: And Our Human Future
by Henry A Kissinger , Eric Schmidt and Daniel Huttenlocher
Published 2 Nov 2021

This may call for an AI-age definition of freedom of speech and expression that distinguishes between utterances originated by humans and utterances generated by machines. Advances in AI for scientific discovery have also continued to accelerate. In the summer of 2021, Deep Mind released AlphaFold2, the successor to AlphaFold, which predicts the 3D structure of proteins from their amino acid sequence (see chapter 6). AlphaFold2 and work done at the Baker Lab at the University of Washington were named by Science as the “2021 Breakthrough of the Year,”8 generating a new DeepMind database of protein structures containing nearly one million proteins as of spring 2022, with plans to grow it to nearly one hundred million (hundreds of times more than the number of structures that have been experimentally determined).

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Raphaël Millière (@raphamilliere), “I asked GPT-3 to write a response to the philosophical essays written about it…” July 31, 2020, 5:24 a.m., https://twitter.com/raphamilliere/status/1289129723310886912/photo/1; Justin Weinberg, “Update: Some Replies by GPT-3,” Daily Nous, July 30, 2020, https://dailynous.com/2020/07/30/philosophers-gpt-3/#gpt3replies. 6. Richard Evans and Jim Gao, “DeepMind AI Reduces Google Data Centre Cooling Bill by 40%,” DeepMind blog, July 20, 2016, https://deepmind.com/blog/article/deepmind-ai-reduces-google-data-centre-cooling-bill-40. 7. Will Roper, “AI Just Controlled a Military Plane for the First Time Ever,” Popular Mechanics, December 16, 2020, https://www.popularmechanics.com/military/aviation/a34978872/artificial-intelligence-controls-u2-spy-plane-air-force-exclusive.

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Admittedly, the kind of AI underlying AlphaZero—machine learning in which algorithms are trained on deep neural networks—has limitations of its own. But in an increasing number of applications, machines are devising solutions that seem beyond the scope of human imagination. In 2016, a subdivision of DeepMind, DeepMind Applied, developed an AI (that ran on many of the same principles as AlphaZero) to optimize the cooling of Google’s temperature-sensitive data centers. Although some of the world’s best engineers had already tackled the problem, DeepMind’s AI program further optimized cooling, reducing energy expenditures by an additional 40 percent—a massive improvement over human performance.6 When AI is applied to achieve comparable breakthroughs in diverse fields of endeavor, the world will inevitably change.

Artificial Whiteness
by Yarden Katz

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
Published 10 Aug 2016

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
Published 5 Oct 2020

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.

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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
Published 18 Jun 2018

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.

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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.

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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 and Erik Brynjolfsson
Published 26 Jun 2017

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
Published 28 Jul 2015

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.

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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
Published 2 Nov 2021

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.

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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.

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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 and Tom Rivett-Carnac
Published 25 Feb 2020

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
Published 7 Oct 2019

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.

The Myth of Artificial Intelligence: Why Computers Can't Think the Way We Do
by Erik J. Larson
Published 5 Apr 2021

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 otherwise. 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 of finding the best algorithms to solve various aspects of the game—all 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 decades 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 wasn’t involved simply wasn’t factually accurate.”6 A more succinct way of putting this is that the DeepMind team used human inferences—namely, abductive ones—to design the system to successfully accomplish its task. These inferences were supplied from outside the inductive framework. SURPRISE!

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W., 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 Czechoslovakia, 60–61 Dartmouth Conference (Dartmouth Summer Research Project on Artificial Intelligence; 1956), 50–51 data: big data, 142–146; observations turned into, 291n12 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 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 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.

Hello World: Being Human in the Age of Algorithms
by Hannah Fry
Published 17 Sep 2018

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.

What Algorithms Want: Imagination in the Age of Computing
by Ed Finn
Published 10 Mar 2017

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
Published 23 Apr 2018

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
Published 24 Sep 2018

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
Published 17 Jul 2016

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
Published 14 Sep 2018

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
Published 14 Oct 2018

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.

How to Spend a Trillion Dollars
by Rowan Hooper
Published 15 Jan 2020

AI with a working memory, able to apply something learned in one context to use in another, has been demonstrated at Deep-Mind. Complex reasoning is one of the things that humans can do as a matter of course. It means we can respond correctly when we are presented with statements such as ‘Replicants are afraid of Blade Runners. Rachael is a replicant. What is Rachael afraid of?’ Or we can look at the London Underground map and tell someone how to get from Old Street to Putney. It’s something that computers have had trouble with, but which DeepMind is starting to tackle with a neural network-style computer that has access to a short-term memory.12 It’s a small step towards human-like thinking, and it’s this sort of success that encourages DeepMind that the neural network approach is the route to get there.

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DOI: 10.1038/s41591-018-0335-9 4 Hannah Devlin (2020) ‘AI systems claiming to read emotions pose discrimination risks’. www.theguardian.com/technology/2020/feb/16/ai-systems-claiming-to-read-emotions-pose-discrimination-risks 5 Yilun Wang and Michal Kosinski (2017) ‘Deep neural networks are more accurate than humans at detecting sexual orientation from facial images’. Journal of Personality and Social Psychology 114(2), 246–257. DOI: 10.17605/OSF.IO/ZN79K 6 See DeepMind: https://deepmind.com/blog/safety-first-ai-autonomous-data-centre-cooling-and-industrial-control/ 7 Hal Hodson (2016) ‘Revealed: Google AI has access to huge haul of NHS patient data’. www.newscientist.com/article/2086454-revealed-google-ai-has-access-to-huge-haul-of-nhs-patient-data/ 8 Frank Arute et al. (2019) ‘Quantum supremacy using a programmable superconducting processor’.

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First, though, we’ll join the race to build a digital, computer-based entity capable of human-level flexible thinking. $ $ $ OUR GOAL IS WHAT IS CALLED artificial general intelligence (AGI). The general is the thing. There are accomplished AI systems already in operation, but their skills are non-transferrable. One of the world’s leading AI firms is DeepMind, which is owned by Google. It created a computer program called AlphaZero, which became the greatest chess player of all time when it was given the rules of the game and played itself over and over again, hundreds of millions of times. AlphaZero is phenomenal, a breakthrough AI, but it can’t tell you if it looks like raining.

The Deep Learning Revolution (The MIT Press)
by Terrence J. Sejnowski
Published 27 Sep 2018

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
Published 31 Aug 2022

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.

Prediction Machines: The Simple Economics of Artificial Intelligence
by Ajay Agrawal , Joshua Gans and Avi Goldfarb
Published 16 Apr 2018

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.

Possible Minds: Twenty-Five Ways of Looking at AI
by John Brockman
Published 19 Feb 2019

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.

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

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
Published 9 Oct 2017

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.

Code Dependent: Living in the Shadow of AI
by Madhumita Murgia
Published 20 Mar 2024

These companies have developed generative AI models that run in accordance with a constitution of sorts – a set of ethical rules, compiled internally by the companies – that their AI software is supposed to adhere to. For instance, ethics researchers at Google DeepMind, the AI research arm of the search giant, published a paper defining its own set of rules,2 which aimed for ‘helpful, correct and harmless’ dialogue. Anthropic’s constitution3 draws from DeepMind’s principles, as well as sources like the UN Declaration of Human Rights, Apple’s terms of service, and so-called ‘non-Western perspectives’ (without specifying which ones). All the companies have warned that their ethical rules are works in progress, and do not wholly reflect humanity’s values.

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He also plays the role of matchmaker between what Stephen Jay Gould famously described as the non-overlapping magisteria – leaders of faith on the one hand and technology on the other. Paolo held meetings with IBM’s vice-president John Kelly, Mustafa Suleyman, a former co-founder of Alphabet-owned AI company Google DeepMind, and Norberto Andrade, who heads AI ethics policy at Meta, to facilitate an exchange of ideas on what is considered ‘ethical’ in the design and deployment of the emerging technology. He was also instrumental in advising the Pope and his council on AI’s potential dangers. While he believed AI had the power to produce another technological revolution, his concern was that it could usurp the power of workers, and the decision-making power of human beings.

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I spent two days in Bletchley Park, the UK hub for codebreakers during the Second World War, last November, where state representatives of two dozen nations, from India, Brazil and Nigeria to China, the United States and the European Union, came together with leaders of all the major AI companies including OpenAI, Google DeepMind and Microsoft to discuss these very issues. Who would be held responsible for the mistakes of artificial intelligence? How would artificial intelligence change the ways in which human beings communicate, learn and consume information? How would the technology affect our behaviour, our beliefs and our consequent actions?

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

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
Published 31 Mar 2019

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.

Scary Smart: The Future of Artificial Intelligence and How You Can Save Our World
by Mo Gawdat
Published 29 Sep 2021

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.

Framers: Human Advantage in an Age of Technology and Turmoil
by Kenneth Cukier , Viktor Mayer-Schönberger and Francis de Véricourt
Published 10 May 2021

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.

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

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
Published 1 Mar 2018

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
Published 13 Mar 2017

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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They are versatile, powerful, and scalable, making them ideal to tackle large and highly complex Machine Learning tasks, such as classifying billions of images (e.g., 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
Published 30 Sep 2019

“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 and Jeff Howe
Published 6 Dec 2016

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.

…

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.

…

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

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.

…

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.”

…

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 and Marlene Jia
Published 1 Jun 2018

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 and H. James Wilson
Published 15 Jan 2018

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
Published 29 May 2017

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.

…

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.

…

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 and Michelle Estes
Published 28 Feb 2015

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”.

…

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.

…

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
Published 4 Apr 2018

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.

…

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
Published 11 Sep 2023

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.

…

“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.

…

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
Published 28 Jun 2021

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/.

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

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

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

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

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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.

The Driver in the Driverless Car: How Our Technology Choices Will Create the Future
by Vivek Wadhwa and Alex Salkever
Published 2 Apr 2017

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.

What to Think About Machines That Think: Today's Leading Thinkers on the Age of Machine Intelligence
by John Brockman
Published 5 Oct 2015

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.

Life After Google: The Fall of Big Data and the Rise of the Blockchain Economy
by George Gilder
Published 16 Jul 2018

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.

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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.

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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.

Novacene: The Coming Age of Hyperintelligence
by James Lovelock
Published 27 Aug 2019

Thanks to the wonders of the age of fire, we have taken the first step. We now stand at a critical moment in this process, the moment when the Anthropocene gives way to the Novacene. The fate of the knowing cosmos hangs upon our response. PART THREE Into the Novacene 15 AlphaGo In October 2015 AlphaGo, a computer program developed by Google DeepMind, beat a professional Go player. At first glance you may have shrugged and thought, ‘So what?’ Ever since 1997, when IBM's computer Deep Blue beat Garry Kasparov, the greatest chess player of all time, we have known that computers play these sorts of brain games better than humans. The first reason you'd be wrong to shrug is pretty obvious.

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It did this much faster than any human player, but to play Go you need more than this one-dimensional approach. AlphaGo used two systems – machine-learning and tree-searching – which combined human input with the machine's ability to teach itself. This was an enormous step forward, but an even bigger one followed. In 2017 DeepMind announced two successors: AlphaGo Zero and AlphaZero, neither of which used human input. The computer simply played against itself. AlphaZero turned itself into a superhuman chess, Go and Shogi (otherwise known as Japanese chess) player within twenty-four hours. Remarkably, AlphaGo searched a mere 80,000 positions per second when playing chess; the best conventional program, Stockfish, searched 70 million.

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(Rossum's Universal Robots) 91, 114 capitalism, global 68 carbon 109 compounds 72 carbon dioxide 5, 10, 11, 57, 64, 105, 110 interglacial levels 55 possible implications of over-reducing levels 56 chalk 64 chlorofluorocarbons (CFCs) 38, 69, 71 cities 40, 50–53 Clarke, Arthur C. 59 climate change global warming 54, 57–8, 60–62, 65, 111 Paris Conference on (2016) 55 possible implications of over-reducing carbon dioxide levels 56 Clynes, Manfred 29 coal 33–5 communication cyborg 99–101 and evolution of speech 96–9 Marconi and electronic information transfer 128 telepathic 100–101 computer code 95 computer programs 79–80, 82, 83, 84 design limitations, lacking intuitive awareness 92 computing systems 92 based on an adaptive neural network 113–14 evolution of adaptive 114 parallel 82 PC chips 92 cosmos age of 3–4 and anthropic principle 25–7, 75, 89, 123 awakening to consciousness 3, 23, 26–8, 121–3 conventional scientific view of 15 dependence of the knowing cosmos on human survival 23–4 Grand Unification epoch 39 information as fundamental property of 26, 75, 87–8 purpose 26–8, 123 size of 3 cyborgs 29–30, 88–9, 101–3, 106, 119–20, 123 and Asimov's three laws of robotics 94 communication 99–101 cooperation with humans 102–3, 104–5, 106–8, 110, 115 and Earth's temperature 106–8, 115 emergence of 85–6, 118–20 evolution 29–30, 94–5, 101, 118, 123 human diplomacy and life with 118–20 as masters over humans 118–20, 123 parallel processing 82 and the quantum world 102 self-written code 94–5 space travel possibilities 108–9 telepathy 100–101 and war 112–17 Daisyworld 13 ‘Data’ (Star Trek android) 93 DeepMind (AI technology company) 79, 80 Deep Blue (computer) 79–80 deep learning technology 82 Delhi 50 diamond 109 chips 44, 109 diesel 72 digital technology 82–3 dimethyl sulphide 60 DNA 109, 127 dolphin intelligence 23 drones 91, 101, 113, 114, 116 Earth/Gaia age of 3, 4, 57–8 in Anthropocene 37–40; see also Anthropocene and asteroid strikes 6–8, 58–9, 63, 65–6 beginning of life on 3, 129–30 capacity to know itself 130 cooling mechanisms 11, 15, 30, 64, 66, 105 feedback loops 65 Gaia theory 12–13, 14–17, 26–7, 106 geoengineering 106–8 glaciation 55 Great Dying 6–7 in Novacene 30, 106–11; see also Novacene as an ocean planet 59–60, 64, 105–6; see also oceans population growth 67 radiation of excessive heat 11, 15, 30 temperature, see temperature of the Earth ecomodernism 67–9 The Economist 112–13 ecosystems 7, 19 Einstein, Albert 20, 21, 102 electrical power 70 electromagnetic pulses (EMPs) 114 electron capture detector (ECD) 38 electronic life 85, 105, 109, 114, 118 cyborgs, see cyborgs exoplanet possibilities of 9–10 European Geophysical Union 17 evolution 27, 28, 129–30 of adaptive computer systems 114 Anthropocene 35–6, 43, 70 chance, necessity and 85 cybernetic 29–30, 94–5, 101, 118, 123 and entry to the Novacene 83–6 of the eye 97 by intentional selection 43, 84, 86, 88–9 by natural selection 3–4, 70, 114; see also natural selection of nesting insects, and city life 51–2 Novacene 29–30, 94–5, 101, 110–11 from self-written code 94–5 of speech 96–9 exoplanets 3, 9–10, 121–2 extremophiles 62, 106 eyes, evolution of 97 Faraday, Michael 70 feedback loops 65 feminine intuition 18, 20 Fermi, Enrico 121 Feynman, Richard 102 First World War 45 fossil fuels 49 Freedman, Sir Lawrence 48 Frege, Gottlob 16 Freud, Sigmund 90 Gaia, see Earth/Gaia galaxies 3, 27 Gatling, Richard, rotary cannon 45 geoengineering 106–8 glaciation 55 global warming 54, 57–8, 60–62, 65, 111 Go (game) 79–80, 84, 88 God 24, 26, 68 Goldilocks Zone 10–11 graphene 109 greenhouse effect 5, 12, 60–61, 107 Greens, the 57, 71, 72 Guernica bombing 45, 48 Hamilton, Clive 68 Hansen, James 63 Hardy, Thomas 124 Havel, Václav 26–7 Hawking, Frank 62–3 Hawking, Isobel Eileen 63 Hawking, Stephen 63 Heaviside, Oliver 128 helium 11 Hiroshima 46 Hooke, Robert 33 human race age of species, Homo sapiens 3 aloneness of 3–5, 121, 122 and the Anthropocene, see Anthropocene at edge of extinction 6–13 guilt feelings over achievements 56 as prime understanders of cosmos 5 temperature tolerance 62, 63–4 hunter-gatherers 21, 67, 125 hydrogen 11, 63–4, 110 Indonesia 7 industrial pollution 37–8 Industrial Revolution 34–5, 37, 70 information 21, 74–5, 87 and anthropic principle 26, 75, 89, 123 capture and storage of 28, 74–5 communicating, see communication conversion of sunlight into 28, 39, 74–5, 87 cyborg retrieval of 101 as a fundamental property of the universe 26, 75, 87–8 junk information 111 Marconi and electronic information transfer 128 maximum transmission rate 81 and neurons 81 theory 88 unconscious 14; see also intuition units of 88, 89 instinct 17, 19, 20, 93 see also intuition intelligence/intelligent life 3, 4, 26, 102–3 AI, see artificial intelligence of animal species 23, 51–2 and cosmic purpose 26–8 cybernetic, see cyborgs distinguishing feature of human intelligence 23 dolphin intelligence 23 electronic, see artificial intelligence; cyborgs; electronic life humanoid ideas of intelligent beings 90–92, 93–4 intelligence/intelligent life (Cont.) hyperintelligence 29, 117, 122 natural selection for 27 social intelligence of bees 51–2 intentional selection 43, 84, 86, 88–9 intuition 13, 14, 18–20, 22, 38; see also instinct AI 80 computer design limitations, lacking intuitive awareness 92 denigration of 20, 99 feminine 18, 20 and invention 38, 99 and parallel processing 92–3 and telepathy 100–101 iodine 60 Jakarta 50 Jefferson, Thomas 52 Jet Propulsion Laboratory, California 47–8 junk information 111 Kasparov, Garry 79–80 Kline, Nathan 29 Laki volcano, Iceland 40 Laplace, Pierre-Simon 18–19 Latour, Bruno 17 LAWS (lethal autonomous weapons systems) 115–17 Lidwell, Owen 62 life chance, necessity and the appearance of 85 and Earth's temperature 11, 15, 30, 62, 63–4, 105–6 electronic, see cyborgs; electronic life evolution of, see evolution on exoplanets 3, 9–10, 121–2 intelligent, see intelligence/intelligent life longevity of life forms 4 Novacene 86, 88–9; see also cyborgs; electronic life spotting life on another planet 127 and zone of habitability/Goldilocks Zone 10–11 limestone 64 linear logic, see logic, linear logic, linear 13, 14, 15, 16, 18, 21, 93, 94, 100, 102 Lotka, Alfred 19 Lovelace, Ada 83 Lovelock, Nell 124 Lovelock, Sandy 125 Lovelock, Tom 124–5 Lovelock family 124–5 Luftwaffe 45 Lynas, Mark 67–8, 69 Marconi, Guglielmo 128–9 Mars 6, 8–9, 10, 59, 108 Mariner mission 126–7 The Matrix 108 Maxwell, James Clerk 16 megacities 40, 50 memory 120 Mercury 10, 21, 22 methane 65, 72 methyl iodide 60 Monod, Jacques 85 Moon 6, 58, 126–7 soft landing on 25, 126 Moore, Gordon 43 Moore's Law 43–4, 82–3, 86 Morton, Oliver: The Planet Remade 107 multiverse theory 26 Mumford, Lewis 45 Musk, Elon 115 Nagasaki 46 NASA 24–5, 126–7 natural selection 3–4, 70, 84, 88, 98, 114 for intelligence 27 neurons 81 new atheists 27 Newcomen, Thomas 33, 128 steam engine 34–6, 87, 124 Newton, Isaac 18–19 laws of planetary motion 21 Novacene and AlphaZero 80, 82 and autonomous weapons 115–17 and the conscious cosmos 121–3 cyborgs, see cyborgs and engineering 83–6; see also cyborgs evolution 29–30, 94–5, 101, 110–11 and Gaia 30, 106–11 likely duration 39 and Marconi 128–9 and Moore's Law 43–4, 82–3, 86 rise of 30, 80, 82–6 speech and writing delaying emergence of 98–9 nuclear power/energy 46, 48–9, 61, 73 nuclear weapons 7, 46–9, 114 oceans 55, 59–60, 63, 64, 107, 110, 129 original sin 56 Orwell, George 4 oxygen 28, 35, 48, 63–4, 108, 109, 110 packaging 72 Palaeocene/Eocene Thermal Maximum 65 parallel processing 82, 92–3 Paris Conference on Climate Change (2016) 55 permafrost 65 petrol 72 Philosophical Transactions of the Royal Society 19 photosynthesis 28, 39, 87, 109 Planck, Max 18–19 plastics 71–2 Poincaré, Henri 18–19 polar ice caps 65 pollution 54, 55, 74 industrial 37–8 radioactive 46 Popper, Karl 16 population growth 67 quantum theory 26, 96, 102 radioactivity 46 railways 41–2 re-wilding 72 reforestation 72 religion 24; see also God green 69 and original sin 56 Rhodes, Richard 46 robots 90, 91, 93–4 rocket speed 42 Roswell incident 121 Russell, Bertrand 16 Second World War 46, 112 selenium 60 Shanghai 50 Shannon, Claude 88 Shelley, Mary: Frankenstein 7 shipbuilding 33–4 Siberian Traps 6–7 silicon 109 chips 43–4 Silverstein, Abe 126 Socrates 20 solar power 73 see also Sun/solar energy solar system 4, 17 speech, evolution of 96–9 stars 3, 4, 27, 121 main sequence 4, 11, 13, 105; see also Sun/solar energy steam engines 70 Newcomen's 34–6, 87, 124 Watt's governor 15–16 Stockfish (computer program) 80 Stoermer, Eugene 37 sulphur 60 Sun/solar energy 4–5, 28, 35, 39, 48, 75; see also sunlight heat emission, and Earth's temperature 5, 10–13, 105, 106–7, 111 sunlight 5, 30, 35, 61 conversion into information 28, 39, 74–5, 87 and the Industrial Revolution 34, 35 and photosynthesis 28, 87 supercritical steam 63, 64 Szilard, Leo 46 Tambora, Mount 7 telegraphy, wireless 128 telepathy 100–101 Tellus journal 17 temperature of the Earth 5, 55–6, 57–66 critical upper limit for life 62, 63–4, 105 current average 65 and cyborgs 106–8, 115 and extremophiles 62 Gaia's cooling mechanisms 11, 15, 30, 64, 66, 105 global warming 54, 57–8 and greenhouse effect 5, 12, 60–61, 107 and human skin cells 62 and life 11, 15, 30, 62, 63–4, 105–6 Palaeocene/Eocene Thermal Maximum 65 possible implications of over-reducing carbon dioxide levels 56 and radiation of excessive heat 11, 15, 30, 60, 107–8 and sea temperature 60, 64 and the Sun 5, 10–13, 105, 106–7, 111 supercritical state 63–4 and water vapour 60–61, 107 Tennyson, Alfred, Lord 130 thinking and anthropic principle 25–7, 89 intuitive, see intuition logical, see logic, linear Tipler, Frank (with John Barrow): The Anthropic Cosmological Principle 24, 25–6, 27, 123 Tokyo, Greater 50 trains 42 transport 42–3 trench warfare 45 Tsar Bomba 46 UFOs 121 unconscious mind 19, 20 see also intuition Venus 10, 64 volcanic events 66 devastating 6–7, 63 Laki 40 Vulcan 21–2 warfare 45–9, 54; see also nuclear weapons and cyborgs 112–17 water vapour 60–61, 107 Watson, Andrew 13 Watt, James 15, 70 Watt governor 15–16 White, Gilbert 39, 41 The Natural History of Selborne 39–40, 41 Wilson, Edward O. 51 wind power 73 Wittgenstein, Ludwig 16, 96 Wood, Lowell 106 Wordsworth, William 42, 54 zone of habitability 10–11

The Inevitable: Understanding the 12 Technological Forces That Will Shape Our Future
by Kevin Kelly
Published 6 Jun 2016

, 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.

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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.

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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
Published 4 Sep 2019

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
Published 5 Sep 2022

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
Published 21 Sep 2015

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
Published 14 Oct 2020

‘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.

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, 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?

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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
Published 6 Apr 2022

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.

Exponential Organizations: Why New Organizations Are Ten Times Better, Faster, and Cheaper Than Yours (And What to Do About It)
by Salim Ismail and Yuri van Geest
Published 17 Oct 2014

The contest ended early, in September 2009, when one of the 44,014 valid submissions achieved the goal and was awarded the prize. Deep Learning is a new and exciting subset of Machine Learning based on neural net technology. It allows a machine to discover new patterns without being exposed to any historical or training data. Leading startups in this space are DeepMind, bought by Google in early 2014 for $500 million, back when DeepMind had just thirteen employees, and Vicarious, funded with investment from Elon Musk, Jeff Bezos and Mark Zuckerberg. Twitter, Baidu, Microsoft and Facebook are also heavily invested in this area. Deep Learning algorithms rely on discovery and self-indexing, and operate in much the same way that a baby learns first sounds, then words, then sentences and even languages.

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To implement algorithms, ExOs need to follow four steps: Gather: The algorithmic process starts with harnessing data, which is gathered via sensors or humans, or imported from public datasets. Organize: The next step is to organize the data, a process known as ETL (extract, transform and load). Apply: Once the data is accessible, machine learning tools such as Hadoop and Pivotal, or even (open source) deep learning algorithms like DeepMind, Vicarious and SkyMind, extract insights, identify trends and tune new algorithms. Expose: The final step is exposing the data, as if it were an open platform. Open data and APIs can be used to enable an ExO’s community to develop valuable services, new functionalities and innovation layered on top of the platform by remixing the ExO’s data with their own.

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This is known as ETL (extract, transform and load). Apply: Once the data is accessible, algorithms such as machine or deep learning extract insights, identify trends and tune new algorithms. These are realized via tools such as Hadoop and Pivotal, or even (open source) deep learning algorithms like DeepMind or Skymind. Expose: The final step is exposing the data in the form of an open platform. Open data and APIs can be used such that an ExO’s community develops valuable services, new functionalities and innovations layered on top of the platform by remixing published data with their own. Examples of companies that have successfully exposed their data this way are the Ford Company, Uber, IBM Watson, Twitter and Facebook.

AIQ: How People and Machines Are Smarter Together
by Nick Polson and James Scott
Published 14 May 2018

Alex Brokaw, “This Startup Uses Machine Learning and Satellite Imagery to Predict Crop Yields,” The Verge, August 4, 2016, https://www.theverge.com/2016/8/4/12369494/descartes-artificial-intelligence-crop-predictions-usda. 13.  Sam Shead, “Google’s DeepMind Wants to Cut 10% Off the Entire UK’s Energy Bill,” Business Insider, March 13, 2017, http://www.businessinsider.com/google-deepmind-wants-to-cut-ten-percent-off-entire-uk-energy-bill-using-artificial-intelligence-2017-3. 14.  “The Women Missing from the Silver Screen and the Technology Used to Find Them,” Google.com, https://www.google.com/intl/en/about/main/gender-equality-films/.

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One research lab at ETH Zurich, for example, has developed an AI algorithm for grading the severity of inflammatory bowel disease from an abdominal MRI.46 Another lab at Memorial Sloan Kettering Cancer Center has built a system for classifying renal-cell carcinoma from digital microscope slides.47 And Moorfields Eye Hospital in London recently partnered with Google DeepMind to analyze over a million images from eye scans. The result was a neural network capable of automatically detecting signs of eye disease, like diabetic retinopathy and macular degeneration.48 Hardware companies have also responded to the exploding demand for AI-powered medical imaging. The chipmaker Nvidia, for example, is mostly known for its high-end computer graphics cards (GPUs) for gamers and filmmakers.

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Correctional Offender Management Profiling for Alternative Sanctions (COMPAS) Craven, John credit cards digital assistants and fraud Crimean War criminal justice system cucumbers data, missing data accumulation, pace of data mining data science anomaly detection and assumptions and democracy and feature engineering health care and imputation institutional commitment and legacy of Florence Nightingale lurking variable pattern recognition and personalization and prediction rules and user-based collaborative filtering data sets anomalies in assumptions and bias in, bias out ImageNet Visual Recognition Challenge massive pattern recognition and privacy sharing databases compilers and health care natural language processing Netflix smart cities de Moivre’s equation (square-root rule) decision-making anomaly detection and human voting deep learning corn yields and electricity demands and gender portrayals in film and honeybees and prediction rules and privacy and Descartes Labs Dickens, Charles Christmas Carol, A Martin Chuzzlewit digital assistants Alexa (Amazon) algorithms and Google Home medicine and speech recognition and DiMaggio, Joe Dole, Bob Duke University early-warning systems Earth Echo, Amazon e-commerce Eggo, Rosalind Einstein, Albert energy industry Facebook advertisers anomaly detection “data for gossip” bargain data sets data storage image classification and recognition market dominance pattern-recognition system personalization presidential election of 2016 and targeted marketing Facebook Messenger fake news financial industry Bayes’s rule and investing gambling strategy indexing strategy Fitbit Ford, Henry Formula 1 racing Fowler, Samuel Lemuel Friedman, Milton Friends (television series) Gawande, Atul: The Checklist Manifesto Geena Davis Institute on Gender in Media gender bias in films stereotypes word vectors and Google anomaly detection data sets data storage image classification Inception (neural-network model) market dominance pattern-recognition system personalization search engine self-driving car speech recognition TensorFlow word2vec model Google Google DeepMind Google Doodle Google Flu Trends Google Home Google Ngram Viewer Google Translate Google Voice Gould, Stephen Jay GPS technology Great Andromeda Nebula. See also astronomy Great Recoinage (1696) Green, Jane Greenblatt, Joel Gresham’s law Guest, William “Bull Dog” Hall, John Harvard Computers (math team) Harvard Mark I HBO health care and medicine AI and contraception failure rates Crohnology data-science legacy of Florence Nightingale data sharing future trends General Practice Research Database (U.K.)

Vassal State
by Angus Hanton
Published 25 Mar 2024

In recent years, Google has snapped up the UK companies Dataform and Redux and, perhaps most controversially, the AI innovator DeepMind, bought in 2014. As UK investor and SongKick co-founder Ian Hogarth has written, the talent pool for serious AI development is small. In 2018 he estimated that there are perhaps 700 people who can contribute to the leading edge of research. ‘I find it hard to believe,’ he wrote, ‘that the UK would not be better off were DeepMind still an independent company. How much would Google sell DeepMind for today? $5 billion? $10 billion? $50 billion? It’s hard to imagine Google selling DeepMind to Amazon, or Tencent or Facebook at almost any price.’21 Google paid just £400 million, with no questions asked by the UK government.

Succeeding With AI: How to Make AI Work for Your Business
by Veljko Krunic
Published 29 Mar 2020

[Cited 2018 Jun 21.] Available from: https://en.wikipedia.org/w/index.php?title=AlphaGo_versus _Lee_Sedol&oldid=846917953 DeepMind. AlphaGo. DeepMind. [Cited 2018 Jul 2.] Available from: https:// deepmind.com/research/alphago/ Wikimedia Foundation. AlphaGo. Wikipedia. [Cited 2019 Jul 10.] Available from: https://en.wikipedia.org/w/index.php?title=AlphaGo The AlphaStar Team. AlphaStar: Mastering the real-time strategy game StarCraft II. DeepMind. [Cited 2019 Sep 9.] Available from: https://deepmind.com/blog/ article/alphastar-mastering-real-time-strategy-game-starcraft-ii Caruana R, Simard P, Weinberger K, LeCun Y.

Machine Translation
by Thierry Poibeau
Published 14 Sep 2017

But, as outlined in Goodfellow et al. (2016, p. 13): “the modern term ‘deep learning’ goes beyond the neuroscientific perspective on the current breed of machine learning models. It appeals to a more general principle of learning multiple levels of composition, which can be applied in machine learning frameworks that are not necessarily neurally inspired.” This approach has received extensive press coverage. This was particularly the case in March 2016, when Google Deepmind’s system AlphaGo—based on deep learning—beat the world champion in the game of Go. This approach is especially efficient in complex environments such as Go, where it is impossible to systematically explore all the possible combinations due to combinatorial explosion (i.e., there are very quickly too many possibilities to be able to explore all of them systematically).

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See also Artificial dialogue Coordination, 175 Corpus alignment, 91–108 Cross-language information retrieval, 238–239 Cryptography, 49, 52, 56, 58–60 Cryptology. See Cryptography CSLi, 232, 236 Cultural hegemony, 168, 250–251 Czech, 210, 213 DARPA, 200–203, 209, 259 Database access, 241 Date expressions, 115, 152, 160 Deceptive cognate, 11, 261 Decoder, 141, 144, 185, 186, 190 Deep learning, 34–35, 37, 170, 181–195, 228, 234, 247, 253–255 Deepmind, 182 Defense industry, 77, 88, 173, 232–233, 235 De Firmas-Périés, Arman-Charles-Daniel, 41 De Maimieux, Joseph, 41 Descartes, René, 40–42 Determiner, 133, 215 Dialogue. See Artificial dialogue Dictionary definition, 18, 176–177 Direct comparability. See Evaluation measure and test Direct machine translation, 25–27, 33, 62–64, 68, 124, 156, 158–159 Directorate General for Translation (European institution), 230, 274 Direct transfer.

The Precipice: Existential Risk and the Future of Humanity
by Toby Ord
Published 24 Mar 2020

And that the people of the future may be even more powerless to protect themselves from the risks we impose than the dispossessed of our own time. Addressing these risks has now become the central focus of my work: both researching the challenges we face, and advising groups such as the UK Prime Minister’s Office, the World Economic Forum and DeepMind on how they can best address these challenges. Over time, I’ve seen a growing recognition of these risks, and of the need for concerted action. To allow this book to reach a diverse readership, I’ve been ruthless in stripping out the jargon, needless technical detail and defensive qualifications typical of academic writing (my own included).

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Steady incremental progress took chess from amateur play in 1957 all the way to superhuman level in 1997, and substantially beyond.77 Getting there required a vast amount of specialist human knowledge of chess strategy. In 2017, deep learning was applied to chess with impressive results. A team of researchers at the AI company DeepMind created AlphaZero: a neural network–based system that learned to play chess from scratch. It went from novice to grand master in just four hours.78 In less than the time it takes a professional to play two games, it discovered strategic knowledge that had taken humans centuries to unearth, playing beyond the level of the best humans or traditional programs.

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For example, Stuart Russell, a professor at the University of California, Berkeley, and author of the most popular and widely respected textbook in AI, has strongly warned of the existential risk from AGI. He has gone so far as to set up the Center for Human-Compatible AI, to work on the alignment problem.104 In industry, Shane Legg (Chief Scientist at DeepMind) has warned of the existential dangers and helped to develop the field of alignment research.105 Indeed many other leading figures from the early days of AI to the present have made similar statements.106 There is actually less disagreement here than first appears. The main points of those who downplay the risks are that (1) we likely have decades left before AI matches or exceeds human abilities, and (2) attempting to immediately regulate research in AI would be a great mistake.

Coders: The Making of a New Tribe and the Remaking of the World
by Clive Thompson
Published 26 Mar 2019

< Chapter 9 > Cucumbers, Skynet, and Rise of AI It started with the ancient Chinese board game Go and ended with cucumbers. In the fall of 2015, we had another one of those Skynet-like moments when a form of artificial intelligence utterly destroys a human. In this case, it involved “AlphaGo”—software designed by DeepMind, a subsidiary of the Google empire—playing a wickedly great game of Go. To test their AI, DeepMind had arranged for it to play against Fan Hui, the European Go champion. It was no contest: The computer won 5 games out of 5. A few months later, AlphaGo fought Lee Sedol, an even more elite player—and again, AlphaGo dominated, 4 to 1. AlphaGo was so good at the game partly because it incorporated “deep learning,” a hot new neural-net technique that let the computer analyze millions of Go games and, on its own, build up a model of how the game worked; feed any board with Go positions into the model, and it could, in conjunction with a more traditional “Monte Carlo” algorithm, then predict a future move.

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We’re not even clear when that might happen. Is it even possible to make a machine that could, on its own, imbibe and grasp all the forms of knowledge that are out there? Today’s AI seems impressive, but it has zero serious reasoning ability, or even a semantic understanding of what things are. DeepMind’s AlphaGo can slaughter anyone at that game, but it doesn’t really understand what Go is. Google Translate can expertly map the sentence “The cat is annoyed that you haven’t fed it” onto a French version that, statistically, means the same thing. But it doesn’t grasp the meaning of “cat” or “annoyed” or “fed.”

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“We believe that Google should not be in the business of war,” the letter stated. The response was electric: In under a day, a thousand Google staffers had signed it. By early April 2018, it had a staggering 3,000 signatures. Some of the firm’s highest AI talent was hotly opposed to military work: When Google had bought the elite AI lab DeepMind in 2014, its heads had insisted that none of its inventions ever be used for weaponry. Google tried to manage the staff revolt by holding all-hands meetings where employees could discuss their dismay about the Maven program. But “leadership got hammered,” as Kim notes: At one extra-long meeting, a woman who’d been at Google for thirteen years said, “I have been working with you for so long.

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

They can help lawyers and paralegals sift through thousands of documents to find the relevant precedents for a court case. They can turn natural-language instructions into computer code. They can even compose new music that sounds eerily like Johann Sebastian Bach and write (dull) newspaper articles. In 2016 the AI company DeepMind released AlphaGo, which went on to beat one of the two best Go players in the world. The chess program AlphaZero, capable of defeating any chess master, followed one year later. Remarkably, this was a self-taught program and reached a superhuman level after only nine hours of playing against itself.

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When all is said and done, the newfound enthusiasm about AI seems an intensification of the same optimism about technology, regardless of whether it focuses on the automation, surveillance, and disempowerment of ordinary people that had already engulfed the digital world. Yet these concerns are not taken seriously by most tech leaders. We are continuously told that AI will bring good. If it creates disruptions, those problems are short-term, inevitable, and easily rectified. If it is creating losers, the solution is more AI. For example, DeepMind’s cofounder, Demis Hassabis, not only thinks that AI “is going to be the most important technology ever invented,” but he is also confident that “by deepening our capacity to ask how and why, AI will advance the frontiers of knowledge and unlock whole new avenues of scientific discovery, improving the lives of billions of people.”

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Once the problem of recognizing cats in a picture is “solved,” we can move on to doing the same for more complex image-recognition tasks or to seemingly unrelated problems, such as determining the meaning of sentences in a foreign language. The potential, therefore, is for truly pervasive use of AI in the economy and in our lives—for good but often also for bad. In the extreme, the aim becomes the development of completely autonomous, general intelligence, which can do everything that humans can do. In the words of DeepMind cofounder and CEO Demis Hassabis, the objective is “solving intelligence, and then using that to solve everything else.” But is this the best way to develop digital technologies? This question typically remains unasked. Third and more problematically, this approach has pushed the field even further in the direction of automation.

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

The pixels on the screen are provided to the agent as percepts, along with a hardwired score of the game so far. ALE was used by the DeepMind team to implement DQN learning and verify the generality of their system on a wide variety of games (Mnih et al., 2015). DeepMind in turn open-sourced several agent platforms, including the DeepMind Lab (Beattie et al., 2016), the AI Safety Gridworlds (Leike et al., 2017), the Unity game platform (Juliani et al., 2018), and the DM Control Suite (Tassa et al., 2018). Blizzard released the StarCraft II Learning Environment (SC2LE), to which DeepMind added the PySC2 component for machine learning in Python (Vinyals et al., 2017a).

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The first major successful demonstration of deep RL was DeepMind's Atari-playing agent, DQN (Mnih et al., 2013). Different copies of this agent were trained to play each of several different Atari video games, and demonstrated skills such as shooting alien spaceships, bouncing balls with paddles, and driving simulated racing cars. In each case, the agent learned a Q-function from raw image data with the reward signal being the game score. Subsequent work has produced deep RL systems that play at a superhuman level on the majority of the 57 different Atari games. DeepMind’s ALPHAGO system also used deep RL to defeat the best human players at the game of Go (see Chapter 6).

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Book cover The cover depicts the final position from the decisive game 6 of the 1997 chess match in which the program Deep Blue defeated Garry Kasparov (playing Black), making this the first time a computer had beaten a world champion in a chess match. Kasparov is shown at the top. To his right is a pivotal position from the second game of the historic Go match between former world champion Lee Sedol and DeepMind’s ALPHAG Oprogram. Move 37 by ALPHAG Oviolated centuries of Go orthodoxy and was immediately seen by human experts as an embarrassing mistake, but it turned out to be a winning move. At top left is an Atlas humanoid robot built by Boston Dynamics. A depiction of a self-driving car sensing its environment appears between Ada Lovelace, the world’s first computer programmer, and Alan Turing, whose fundamental work defined artificial intelligence.

Seeking SRE: Conversations About Running Production Systems at Scale
by David N. Blank-Edelman
Published 16 Sep 2018

Success Stories There are a few areas of enterprise IT for which AI has and will have a significant impact: Log analysis Capacity planning Infrastructure scaling Cost management Performance tuning Energy efficiency Security Recently, Google started managing data center cooling through DeepMind. In one instance, it managed to reduce the amount of energy used by 40 percent, as illustrated in Figure 18-19. Figure 18-19. Reduction of 40% spent on data center energy using DeepMind (source: https://deepmind.com/blog/deepmind-ai-reduces-google-data-centre-cooling-bill-40) It accomplished this by using the historical sensor data, such as temperatures, power, pump speeds, setpoints, and so on, that were already collected by thousands of units in the data center.

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This outcome got everyone thinking that machines like Deep Blue would solve very important problems. In 2015, nearly 20 years later, the ancient Chinese game Go, which has many more possible moves than chess, was won by DeepMind,4 using a program called AlphaGo, via the application of deep reinforcement learning, which is a much different approach than using search algorithms. Table 18-1 compares the two machines and their methodologies. Table 18-1. The two machines and their methodologies Deep Blue; Chess; May 1997 DeepMind; AlphaGo; October 2015 Brute force Search Algorithm Developer: IBM Adversary: Garry Kasparov Deep learning Machine learning Developer: Google Adversary: Fan Hui But the first game that gripped the world’s attention in AI was checkers, with the pioneer Arthur Samuel, who coined the term “machine learning” back in 1959.

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He is also a passionate advocate of free software, digital rights, and is a frequent speaker at IT events. 1 Ben Treynor Sloss, Google Engineering. 2 Russell, S. J. and Peter Norvig. Artificial Intelligence — A Modern Approach. Upper Saddle River, NJ: Pearson Education, 2003, Chapter 2. 3 Definition proposed by Tom Mitchell in 1998, Machine Learning Research. 4 DeepMind Technologies is a British artificial intelligence company founded in September 2010. It was acquired by Google in 2014. 5 According to a report from IBM, “10 Key Marketing Trends For 2017,” every day we create 2.5 quintillion bytes of data and 90% of the data today has been created in the past two years alone.

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

“Distinguishing AI-generated text, images and audio from human generated will become extremely difficult,” says Mustafa Suleyman, a cofounder of DeepMind and till recently head of AI policy at Google, as the “transformers” revolution accelerates the power of AI.43 As a consequence, a large number of white-collar jobs using advanced levels of cognition will become obsolete. Humans won’t know that their counterparts are machines. When I met Demis Hassabis—the other cofounder of DeepMind—he compared the coming singularity to super intelligence that resembles ten thousand Einsteins solving any problem of science, medicine, technology, biology, or knowledge at the same time and in parallel.

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A revolutionary approach deployed messenger RNA, or mRNA, that teaches cells how to mobilize our bodies’ immune responses. For anything resembling a happy ending to happen, computers poised to displace us must come to our rescue. We must hope that very rapid development of vaccines will defend us against new viruses. I marvel at an accelerating pace of biomedical discoveries. In 2020, DeepMind’s AlphaFold solved the protein-folding problem that perplexed experts for half a century. It augurs well for accelerating progress against other diseases. Success would improve accessibility and lower costs for prevention, diagnosis, and treatment of all sorts of diseases. Breakthroughs on climate change could deliver cascading benefits.

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Do not suppose that creativity requires people. The elusive spark of human ingenuity faces digital competition. To beat world chess champion Garry Kasparov multiple times in 1997, IBM Deep Blue devised inventive strategies. Yet that was just an opening gambit compared to Deep Mind, a self-teaching algorithm. In 2016, a Deep Mind computer christened AlphaGo mastered a game with more possible moves than there are atoms in the universe. “It studies games that humans have played, it knows the rules and then it comes up with creative moves,” Wired editor in chief Nicholas Thompson told PBS Frontline.4 In a much-touted contest, AlphaGo outplayed the reigning world Go champion Lee Sedol in four out of five tries.

Beginners: The Joy and Transformative Power of Lifelong Learning
by Tom Vanderbilt
Published 5 Jan 2021

In the eyes of the psychologist Anders Ericsson, the man behind the now-familiar, often-misunderstood ten-thousand-hour rule, she was engaging in “deliberate practice.” I, on the other hand, was settling for “mindless repetition,” trying to get better through brute force, without tangible goals. I was trying, in a way, to play like AlphaZero, DeepMind’s celebrated artificial intelligence engine. Given no more than the basic rules of chess, AlphaZero had mastered the game after playing itself forty-four million times.* It learned as it went along the whole way through, without the aid of a coach, becoming the most formidable opponent in the world.

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*3 As Martin Amis has argued about chess, “Nowhere in sport, perhaps nowhere in human activity, is the gap between the tryer and the expert so astronomical.” *4 Although it also has been suggested as an acronym for “beginning of one’s tour.” *5 AlphaGo Zero, the artificial intelligence engine developed by DeepMind to teach itself the strategy game Go, was seen, early in its learning process, to focus “greedily on capturing stones, much like a human beginner.” David Silver et al., “Mastering the Game of Go Without Human Knowledge,” Nature, Oct. 19, 2017, 354–59. *6 The episode brings to mind, of course, Hans Christian Andersen’s famous tale “The Emperor’s New Clothes

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Horgan and David Morgan, “Chess Expertise in Children,” Applied Cognitive Psychology 4, no. 2 (1990): 109–28. It learned as it went: See James Somers, “How Artificial-Intelligence Program AlphaZero Mastered Its Games,” New Yorker, Dec. 3, 2018. aid of a coach: This point was made by the DeepMind researcher Matthew Lai in Matthew Sadler and Natasha Regan, Game Changer (Alkmaar, Neth.: New in Chess, 2019), 92. “If you want to improve”: Anders Ericsson and Robert Pool, Peak: Secrets from the New Science of Expertise (Boston: Houghton Mifflin Harcourt, 2016). The studies were typically small: For a comprehensive overview of the literature, see Fernand Gobet and Guillermo Campitelli, “Educational Benefits of Chess Instruction: A Critical Review,” in Chess and Education: Selected Essays from the Koltanowski Conference, ed.

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

In the first three and a half decades of my AI journey, artificial intelligence as a field of inquiry was essentially confined to academia, with few successful commercial adaptations. AI’s practical applications once evolved slowly. In the past five years, however, AI has become the world’s hottest technology. A stunning turning point came in 2016 when AlphaGo, a machine built by DeepMind engineers, defeated Lee Sedol in a five-round Go contest known as the Google DeepMind Challenge Match. Go is a board game more complex than chess by one million trillion trillion trillion trillion times. Also, in contrast to chess, the game of Go is believed by its millions of enthusiastic fans to require true intelligence, wisdom, and Zen-like intellectual refinement.

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AI can greatly accelerate the speed and reduce the cost of drug and vaccine discovery. For determining protein folding (step 2), in 2020, DeepMind developed AlphaFold 2, which is AI’s greatest achievement for science to date. Proteins are the building blocks of life, yet one aspect of proteins that has remained a mystery is how a sequence of amino acids will fold into a 3D structure to carry out life’s tasks. This is a problem with profound scientific and medical implications and appears well-suited for deep learning. DeepMind’s AlphaFold, trained on a large database of previously discovered 3D protein structures, has demonstrated that it is able to simulate the 3D structure of unseen proteins with similar accuracy to traditional techniques (such as cryo-electron microscopy, mentioned on page 156), which are expensive and can take years for each protein.

Hit Refresh: The Quest to Rediscover Microsoft's Soul and Imagine a Better Future for Everyone
by Satya Nadella , Greg Shaw and Jill Tracie Nichols
Published 25 Sep 2017

The following year Deep Blue went a giant step further when it defeated Russian chess legend Garry Kasparov in an entire six-game match. It was stunning to see a computer win a contest in a domain long regarded as representing the pinnacle of human intelligence. By 2011, IBM Watson had defeated two masters of the game show Jeopardy!, and in 2016 Google DeepMind’s AlphaGo outplayed Lee Se-dol, a South Korean master of Go, the ancient, complex strategy game played with stones on a grid of lines, usually nineteen by nineteen. Make no mistake, these are tremendous science and engineering feats. But the future holds far greater promise than computers beating humans at games.

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See also United Kingdom British Raj, 16, 186–87 broadband infrastructure, 225 Buddha, Gautama, 9 Burgum, Doug, 47–48 cable TV, 30 Cairo, 214, 218 cameras, 150 Canada, 230 cancer, 142, 159, 214 Candidate, The (film), 75 capabilities, 122–23, 141 capitalism, 237–38 late-stage, 221 Capossela, Chris, 3, 71, 81–82 Carnegie Mellon, 3 Carney, Susan L., 177 Carroll, Pete, 4 Case, Anne, 236 Cavium Networks, 20 CD-ROM, 28 CEO as curator of culture, 100, 241 “disease,” 92 panoramic view of, 118 cerebral palsy, 8–10 Chang, Emily, 129 charter city, 229 Cheng, Lili, 197 chess, 198–99 Chik, Joy, 58 child exploitation, 190 Chile, 223, 230 China, 86, 195, 220, 222, 229, 232, 236 chip design, 25 CIA, 169 Cisco, 174 civil liberties, 172–73 civil rights, 24 civil society, 179 Civil War, 188 clarity, 119 Clayton, Steve, 155 client/server era, 45 climate change, 142, 214 Clinton, Hillary, 230 cloud, 13, 41–47, 49, 51–62, 68, 70, 73, 81, 88, 110, 125, 129, 131, 137, 140, 150, 164, 166, 172, 180–81, 186, 189–92, 216, 219, 223–25, 228 cloud-first mission and, 57–58, 70, 76, 79, 83 public, 42–43, 57 Cloud for Global Good, 240–41 Codapalooza, 104 cognition, 89, 150, 152–53 Cohen, Leonard, 10 collaboration, 88, 102–3, 106–8, 126, 135, 163–64, 166, 200 collaborative robots (co-bots), 204 collective IQ, 142, 143 Colombia, 78 Columbia University, 165 Comin, Diego, 216–17, 226 commitment, shared, 77, 119 Common (hip hop artist), 71 Common Objects in Context challenge, 151 communication, 76–77 Compaq, 29 comparative advantage, 222, 228 competition, internal, 52 competitive zeal, 38–39, 70–71, 102 competitors, 39 partnerships and, 78, 125–38 complexity, 25, 224 computers early, 21–22, 24–26 future platforms, 110–11 programs by, 153–54 computing power, massive, 150–51 Conard, Edward, 220 concepts, 122–23, 141 consistency, 77–78, 182 Constitution Today, The (Amar), 186–87 constraints, 119 construction companies, 153 consumers, 49–50, 222 context, shared, 56–57 Continental Congress, 185 Continuum, 73 Convent of Jesus and Mary (India), 19 Cook, Tim, 177 cook stoves, 43 coolness, 75–76 core business, 142 Cortana, 125, 152, 156–58, 195, 201 Couchbase company, 58 counterintuitive strategy, 56–57 Coupland, Douglas, 74 Courtois, Jean-Philippe, 82 courts, 184–85 Covington and Burling lawyers, 3 Cranium games, 7 creativity, 58, 101, 119, 201, 207, 242 credit rating, 43, 204–5 Creed (film), 44–45 cricket, 18–22, 31, 35–40, 115 Cross-country Historical Adoption of Technology (CHAT), 217 culture bias and, 205 “live site first,” 61 three Cs and, 122–23, 141 transforming, 2, 11, 16, 40, 76–78, 81–82, 84, 90–92, 98–103, 105, 108–10, 113–18, 120, 122–23, 241–42 Culture (Eagleton), 91 Curiosity (Mars rover), 144 customer needs, 42, 59, 73, 80, 83, 88, 99, 101–2, 108, 126, 138 customization, 151 cybersecurity, 171, 190 cyberworld, rules for, 184 data, 60, 151 data analytics, 50 databases, 26 Data General company, 68 data management, 54 data platform, 59 data security, 175–76, 188–89 Deaton, Angus, 236 Deep Blue, 198–99 deep neural networks, 153 Delbene, Kurt, 3, 81–82 Delhi, India, 19, 31, 37 Dell, 63, 87, 127, 129–30 Dell, Michael, 129 democracy, 180 democratization, 4, 13, 69, 127, 148, 151–52 Deng Xiaoping, 229 Depardieu, Gerard, 33 design, 50, 69, 141, 239 desktop software, 27 Detroit, 15, 225, 233 developed economies, 99–100 share of world income, 236 developing economies, 99–100, 217, 225 device management solutions, 58 digital assistants, 142, 156–58, 195–98, 201 digital cable, 28 digital evidence, 191–92 Digital Geneva Convention, 171–72 digital ink, 142 digital literacy, 226–27 digital publishing laws, 185 digital transformation, 70, 126–27, 132, 235 dignity, 205 disabilities, 103, 200 disaster relief, 44 Disney, 150 disruption, 13 distributed systems, 49 diversity, 101–2, 108, 111–17, 205–6, 238, 241 Donne, John, 57 drones, 209, 226 Drucker, Peter, 90 dual users, 79 Dubai, 214, 228 Duke University, 3 Dupzyk, Kevin, 147 D-Wave, 160 Dweck, Carol, 92 dynamic learning, 100 Dynamics, 121 Dynamics 365, 152 dyslexia, 44, 103–4 Eagleton, Terry, 91 earthquakes, 44 EA Sports, 127 economic growth, 211–34 economic inequality, 12, 207–8, 214, 219–21, 225, 227, 236–41 Edge browser, 104 education, 42–44, 78, 97, 104, 106–7, 142, 145, 206–7, 224, 226–28, 234, 236–38 Egypt, 218–19, 223, 225 E-health companies, 222–23 8080 microprocessor, 21 elasticity, 49 electrical engineering (EE), 21–22 elevator and escalator business, 60 Elop, Stephen, 64, 72 email, 27, 169–73, 176 EMC, 129 emotion, 89, 197, 201 emotional intelligence (EQ), 158, 198 empathy, 6–12, 16, 40, 42–43, 93, 101, 133–34, 149, 157, 182, 197, 201, 204, 206, 226, 239, 241 employee resource groups (ERGs), 116–17 employees, 66–68, 75, 138 diversity and, 101, 111–17 empowerment and, 79–80, 126 global summit of, 86–87 hackathon, 10–11 talent development and, 117–18 empowerment, 87–88, 98–99, 106, 108–10, 126 encryption, 161–62, 175, 192–93 energy, generating across company, 119 energy costs, 237 Engelbart, Doug, 142 Engelbart’s Law, 142–43 engineers, 108–9 Enlightiks, 222–23 Enterprise Business, 81 entertainment industry, 126 ethics, 195-210, 239 Europe, 193 Excel, 121 experimental physicists, 162–64 eye-gaze tracking, 10 Facebook, 15, 44, 51, 125, 144, 174, 200, 222 failures, overcoming, 92, 111 Fairfax Financial Holdings, 20 fairness, 236 Federal Bureau of Investigation (FBI), 170, 177–78, 189 Federal Communications Commission (FCC), 28 fear of unknown, 110–11 feedback loop, 53 fertilizer, 164 Feynman, Richard, 160 fiefdoms, 52 field-programmable gate arrays (FPGAs), 161 Fields Medal, 162 firefighters, 43, 56 First Amendment, 185, 190 Flash, 136 focus, 135–36, 138 Foley, Mary Jo, 52 Ford Motor Company, 64 foreign direct investment, 219, 225, 229 Foreign Intelligence Surveillance Act (FISA), 173 Fourth Amendment, 185–88, 190, 193 France, 223, 236 Franco, James, 169 Franklin, Benjamin, 186 Freedman, Michael, 162, 166 free speech, 170–72, 175, 179, 185, 190, 238 Fukushima nuclear plant, 44 G20 nations, 219 Galaxy Explorer, 148 game theory, 123–24 Gandhi, Mohandas Mahatma, 16 Gartner Inc., 145 Gates, Bill, 4, 12, 21, 28, 64, 46, 67–69, 73–75, 87, 91, 127, 146, 183, 203 Gavasker, Sunil, 36 GE, 3, 126–27, 237 Gelernter, David, 143, 183 Geneva Convention, Fourth (1949), 171 Georgia Pacific, 29 Germany, 220, 223, 227–36 Gervais, Michael, 4–5 Gini, Corrado, 219 Gini coefficient, 219–21 GLEAM, 117 Gleason, Steve, 10–11 global competitiveness, 78–79, 100–102, 215 global information, policy and, 191 globalization, 222, 227, 235–37 global maxima, 221–22 goals, 90, 136 Goethe, J.W. von, 155 Go (game), 199 Goldman Sachs, 3 Google, 26, 45, 70–72, 76, 127, 160, 173–74, 200 partnership with, 125, 130–32 Google DeepMind, 199 Google Glass, 145 Gordon, Robert, 234 Gosling, James, 26 government, 138, 160 cybersecurity and, 171–79 economic growth and, 12, 223–24, 226–28 policy and, 189–92, 223–28 surveillance and, 173–76, 181 Grace Hopper, 111–14 graph coloring, 25 graphical user interfaces (GUI), 26–27 graphics-processing unit (GPU), 161 Great Convergence, the (Baldwin), 236 Great Recession (2008), 46, 212 Greece, 43 Green Card (film), 33 Guardians of Peace, 169 Gutenberg Bible, 152 Guthrie, Scott, 3, 58, 60, 82, 171 H1B visa, 32–33 habeas corpus, 188 Haber, Fritz, 165 Haber process, 165 hackathon, 103–5 hackers, 169–70, 177, 189, 193 Hacknado, 104 Halo, 156 Hamaker, Jon, 157 haptics, 148 Harvard Business Review, 118 Harvard College, 3 Harvey Mudd College, 112 Hawking, Stephen, 13 Hazelwood, Charles, 180 head-mounted computers, 144–45 healthcare, 41–42, 44, 142, 155–56, 159, 164, 198, 218, 223, 225, 237 Healthcare.gov website, 3, 81, 238 Heckerman, David, 158 Hewlett Packard, 63, 87, 127, 129 hierarchy, 101 Himalayas, 19 Hindus, 19 HIV/AIDS, 159, 164 Hobijn, Bart, 217 Hoffman, Reid, 232, 233 Hogan, Kathleen, 3, 80–82, 84 Holder, Eric, 173–74 Hollywood, 159 HoloLens, 69, 89, 125, 144–49, 236 home improvement, 149 Hong Kong, 229 Hood, Amy (CFO), 3, 5, 82, 90 Horvitz, Eric, 154, 208 hospitals, 42, 78, 145, 153, 223 Hosseini, Professor, 23 Huang, Xuedong, 151 human capital, 223, 226 humanistic approach, 204 human language recognition, 150–51, 154–55 human performance, augmented by technology, 142–43, 201 human rights, 186 Hussain, Mumtaz, 36, 37 hybrid computing, 89 Hyderabad, 19, 36–37, 92 Hyderabad Public School (HPS), 19–20, 22, 37–38, 136 hyper-scale, cloud-first services, 50 hypertext, 142 IBM, 1, 160, 174, 198 IBM Watson, 199–200 ideas, 16, 42 Illustrator, 136 image processing, 24 images, moving, 109 Imagine Cup competition, 149 Immelt, Jeff, 237 Immigration and Naturalization Act (1965), 24, 32–33 import taxes, 216 inclusiveness, 101–2, 108, 111, 113–17, 202, 206, 238 independent software vendor (ISV), 26 India, 6, 12, 17–22, 35–37, 170, 186–87, 222–23, 236 immigration from, 22–26, 32–33, 114–15 independence and, 16–17, 24 Indian Administrative Service (IAS), 16–17, 31 Indian Constitution, 187 Indian Institutes of Technology (IIT), 21, 24 Indian Premier League, 36 IndiaStack, 222–23 indigenous peoples, 78 Indonesia, 223, 225 industrial policy, 222 Industrial Revolution, 215 Fourth or future, 12, 239 information platforms, 206 information technology, 191 Infosys, 222 infrastructure, 88–89, 152–53, 213 innovation, 1–2, 40, 56, 58, 68, 76, 102, 111, 120, 123, 142, 212, 214, 220, 224, 234 innovator’s dilemma, 141–42 insurance industry, 60 Intel, 21, 45, 160, 161 intellectual property, 230 intelligence, 13, 88–89, 126, 150, 154–55, 160, 169, 173, 239 intelligence communities, 173 intensity of use, 217, 219, 221, 224–26 International Congress of the International Mathematical Union, 162 Internet, 28, 30, 48, 79, 97–98, 222 access and, 225–26, 240 security and privacy and, 172–73 Internet Explorer, 127 Internet of Things (IoT), 79, 134, 142, 228 Internet Tidal Wave, 203 Intersé, 3 Interview, The (film), 169–71 intimidation, 38 investment strategy, 90, 142 iOS devices, 59, 72, 123, 132 iPad, 70, 141 iPad Pro, 123–25 iPhone, 70, 72, 85, 121–22, 125, 177–79 Irish data center, 176, 184 Islamic State (ISIS), 177 Istanbul, 214 Jaisimha, M.L., 18, 36–37 Japan, 44, 223, 230 Japanese-American internment, 188 JAVA, 26 Jeopardy (TV show), 199 Jha, Rajesh, 82 jobs, 214, 231, 239–40.

World Without Mind: The Existential Threat of Big Tech
by Franklin Foer
Published 31 Aug 2017

Algorithms replicate the brain’s information processing and its methods for learning. Google has hired the British-born professor Geoff Hinton, who has made the greatest progress in this direction. It also acquired a London-based company called DeepMind, which created neural networks that taught themselves, without human instruction, to play video games. Because DeepMind feared the dangers of a single company possessing such powerful algorithms, it insisted that Google never permit its work to be militarized or sold to intelligence services. How deeply does Google believe in the singularity? Hardly everyone in the company agrees with Kurzweil’s vision.

…

Cigarettes” (Orwell), 213–14 Borah, William, 190 Bourdieu, Pierre, 218–19 Bowen, William, 171–72 Brand, Stewart, 12–25, 56, 87, 177, 205–7 Brandeis, Louis, 190–94, 203, 218 Brin, Sergey, 1, 37–38, 50, 52, 212 BuzzFeed, 74, 137–39, 145–48, 151–52 Calico, 53 Cecil lion story, 148 Ceruzzi, Paul, 16 Chartbeat, 144–45, 212 Clinton administration, 203 code, 34, 58, 68, 73–74, 84, 200 Coleridge, Samuel, 163 collaboration, 2–3, 13, 26, 29, 156–57, 160 communal connection, 21–27, 55, 65, 177–79 communes, 18–23, 206–7 competition, 29, 186, 202–3 with journalism, 144–45 in the marketplace, 84, 174, 178, 184, 188, 191–92, 231 and monopolies, 5, 11, 30 and tech giants, 3, 12, 31, 55, 103 computer science, 22, 33–35, 42, 59, 68–71, 73–74, 80 computers, 8, 25, 38, 101, 109 and algorithms, 67–70, 74, 229 and cooperation/connection, 25–27 as copying machine, 85–86 and creativity, 76–77 early era of, 15–17, 20–22, 25–28, 33–34, 43–46, 57, 68, 74 and human transformation, 2, 13–14, 28, 33, 38 and neural networks, 52–53 personal, 20–22, 28 progress in, 47–48 Comte, Auguste, 61, 63 conformism, 5, 13, 60, 156, 178, 206, 208, 231 Consumer Financial Protection Bureau, 200 copyright laws, 84–85, 90, 157–60, 163–66 counterculture, 12–24, 56, 205–8 Cowley, Malcolm, 169 creativity, 76–77, 85, 101, 105, 156–62, 173, 230 Credit Suisse, 152–53 CrowdTangle, 147 culture definition of, 218–20 degradation of, 92, 210 Dallas Morning News, 196 data, 97, 218, 220 collection of, 8, 33, 69, 83, 186–87, 200, 211, 224, 229 exploitation of, 82–83, 123–25, 200, 229 and the media, 139–40, 145–50 ownership of, 200–201 patterns in, 69–71, 75–76, 187, 231 power of, 186–88, 200–201 protection of, 200–204 sets of, 74–75, 77, 123, 160 tracking of, 82, 138, 145, 171, 187, 201, 224, 230 See also algorithms; surveillance (of users) DeepMind, 53 Democrats, 116–17, 141, 199 Denton, Nick, 146 Descartes, René, 39–43, 47 Diamandis, Peter, 48 Dickens, Charles, 164–65 digital age, 43, 67, 224, 229 disruptive agents, 59, 93, 101, 176, 199. See also specific names Doctorow, Cory, 85–86 dot-com crash, 185–86 Economist, 191 Eisenhower, Dwight, 99, 109 Eliot, T.

The Ethical Algorithm: The Science of Socially Aware Algorithm Design
by Michael Kearns and Aaron Roth
Published 3 Oct 2019

And many high-profile people have. Stephen Hawking said that superintelligent AI “could spell the end of the human race.” Elon Musk views artificial intelligence as “our greatest existential threat.” And Google DeepMind cofounder Shane Legg has said that he thinks that artificial intelligence poses “the number one risk for this century.” In fact, when Google negotiated the purchase of DeepMind in 2014 for $400 million, one of the conditions of the sale was that Google would set up an AI ethics board. All of this makes for good press, but in this section, we want to consider some of the arguments that are causing an increasingly respectable minority of scientists to be seriously worried about AI risk.

…

See lending and creditworthiness crime, 14–15, 62, 92–93 crowdsourcing, 104 cryptography, 31–34, 37 Cuddy, Amy, 141–42 cultural biases, 57 Dalenius, Tore, 35 data administrators, 45–47 data analysis procedures, 164 data collection, 58 dating preferences, 94–97, 100–101 decision-making process, 3–4, 11, 190–91 decision trees, 154–55, 159–60, 164, 173 deep learning algorithms, 133, 146–47 DeepMind, 179 deep neural networks, 174–75 defection, 99–100, 115, 128 demographics, 7, 193 derived models, 6 diagnostics, 27–29 dietary research, 143–45, 158 differential privacy to combat overfitting data, 167 commercial deployment of, 47–49 and correlation equilibrium, 114–15 described, 36–39 design of ethical algorithms, 193, 195 and differing notions of fairness, 85 and embarrassing polls, 40, 43–45 and fairness vs. accuracy of models, 63 and game-theoretic algorithm design, 135 limitations of, 50–56 and trust in data administrators, 45–47 Dijkstra’s algorithm, 104, 109 diminishing marginal returns, 186–88 disability status, 86–89 discrimination and algorithmic violations of fairness and privacy, 96 and data collection bias, 90–93 and “fairness gerrymandering,” 86–89 and fairness issues in machine learning, 65–66 and fairness vs. accuracy of models, 63 and game-theoretic algorithm design, 134–35 and “merit” in algorithmic fairness, 75 and recent efforts to address machine learning issues, 15 and self-play in machine learning, 132–33 and statistical parity, 69 and supervised machine learning, 64 and unique challenges of algorithms, 7 and user preferences, 96–97.

Rage Inside the Machine: The Prejudice of Algorithms, and How to Stop the Internet Making Bigots of Us All
by Robert Elliott Smith
Published 26 Jun 2019

Plus, through wishful mnemonics, we’re describing elements of algorithms with ambitious metaphors because we only wish they were capable of those sophisticated human abilities. Intuition is a word that has been used to describe one of the most popularly lauded AI triumphs of recent years, Google’s AlphaGo. Created by the Google subsidiary DeepMind, AlphaGo is a program that was designed to play the 2500-year-old Chinese board game Go, in which two players alternate placing white and black stones on the intersections of a nineteen by nineteen grid. The winner of the game is the player who captures the largest territory of the board, based on various scoring rules that evaluate the territories occupied by the stones.14 Although it has simple elements and rules, Go is considered one of the most intellectually challenging games ever devised, with a complexity that dwarfs Chess.

…

As a result, it has become widely accepted that not only does AlphaGo possess the elusive quality of intuition, computers can now mimic abilities that were heretofore considered the most prized aspects of humanity. The Atlantic enthusiastically reported that the important thing to take away from the match between AlphaGo and Sedol was: not that DeepMind’s AI can learn to conquer Go, but that by extension it can learn to conquer anything easier than Go—which amounts to a lot of things. The ways in which we might apply these revolutionary advances in machine learning—in machines’ ability to mimic human creativity and intuition—are virtually endless.22 Emphatic memes like this, which tie algorithms to words like ‘intuition’, are tailor-made to be picked up by other algorithms and spread in popular consciousness.

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See Social Credit System Chomsky, Noam, here, here, here, here, here Chomsky’s hierarchy, here, here Clairmont, Claire, here, here Clairmont, Mary Jane, here CLT (Central Limit Theorem), here, here, here combinatorics, here complex systems, here, here, here, here, here, here, here computational creativity, here, here conjunction fallacy, here connectionism, here, here, here conviction narratives, here, here Correctional Offender Management Profiling for Alternative Sanctions (COMPAS), here craniology, here, here Crawford, Kate, here creativity, here, here, here, here Crick, Francis, here Crutchfield, James P., here, here Dalí, Salvador, here, here, here, here Dalmatian, here, here, here, here Darrow, Clarence, here Darwin, Charles, here, here, here, here, here, here, here, here, here, here, here, here, here, here Darwin, Erasmus, here, here, here, here Davenport, Charles Benedict, here, here Da Vinci, Leonardo, here Dawkins, Richard, here, here Deb, Kalyan, here, here DeepMind, here Defense Advanced Research Projects Agency (ARPA/DARPA), here Deliveroo, here, here de Prony, Gaspard, here, here Descartes, Rene, here, here Dickens, Charles, here Dike, Bruce, here, here, here diversity, here, here, here, here, here, here, here, here, here, here, here, here, here, here, here divided states, here, here edge of chaos, here, here Edwards, John, here Edwards, Mary, here, here, here, here Eliza, here emergent behaviours, here, here ENIAC, here, here entropy, here equilibrium, here, here, here, here, here ERO.

Radical Uncertainty: Decision-Making for an Unknowable Future
by Mervyn King and John Kay
Published 5 Mar 2020

It is the means by which many believe that eventually all mysteries will become soluble puzzles. DeepMind, an AI company, put together a program which beat the reigning champions of Go – a game which has more potential combinations of board positions than there are atoms in the universe. It did so by allowing the computer to create a massive database of games constructed by playing against itself. DeepMind’s computer did not need access to any historic data. But this was possible only because Go is a problem which, although immensely complex, is comprehensively and precisely defined by its rules. The DeepMind computer which taught itself to play Go had access to the rules of Go and knew, at the end of each of the many thousands of games which it played with itself, which side had won.

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INDEX 10 (film, 1979), 97 737 Max aircraft, 228 9/11 terror attacks, 7 , 74–6 , 202 , 230 Abbottabad raid (2011), 9–10 , 20 , 26 , 44 , 71 , 102 , 118–19 , 120 , 174–5 ; reference narrative of, 122–3 , 277 , 298 ; role of luck in, 262–3 ; and unhelpful probabilities, 8–19 , 326 abductive reasoning, 138 , 147 , 211 , 388 , 398 ABN AMRO, 257 Abraham (biblical character), 206 Abrahams, Harold, 273 Abramovich, Roman, 265 accountancy, 409 aeronautics, 227–8 , 352–6 , 383 Agdestein, Simen, 273 AIDS, 57 , 230 , 375–6 Airbus A380, 40 , 274–6 , 408 Akerlof, George, 250–1 , 252 , 253 , 254 , 382 Alchian, Armen, 158 alien invasion narratives, 295–6 Allais, Maurice, 134–5 , 136 , 137 , 437 , 440–3 Allen, Bill, 227–8 Allen, Paul, 28 , 29 Altair desktop, 28 Amazon, 289 , 309 Anderson, Roy, 375 ant colonies, 173 anthropology, 160 , 189–91 , 193–4 , 215–16 antibiotics, 40 , 45 , 284 , 429 Antz (film, 1998), 274 apocalyptic narratives, 331–2 , 335 , 358–62 Appiah, Anthony, 117–18 Apple, 29–30 , 31 , 169 , 309 Applegarth, Adam, 311 arbitrage, 308 Archilochus (Greek poet), 222 Aristotle, 137 , 147 , 303 Arrow, Kenneth, 254 , 343–5 , 440 artificial intelligence (AI), xvi , 39 , 135 , 150 , 173–4 , 175–6 , 185–6 , 387 ; the ‘singularity’, 176–7 Ashtabula rail bridge disaster (1876), 33 Asimov, Isaac, 303 asteroid strikes, 32 , 71–2 , 238 , 402 astrology, 394 astronomical laws, 18–19 , 35 , 70 , 373–4 , 388 , 389 , 391–2 , 394 AT&T, 28 auction theory, 255–7 Austen, Jane, 217 , 224–5 , 383 autism, 394 , 411 aviation, commercial, 23–4 , 40 , 227–8 , 274–6 , 315 , 383 , 414 axiomatic rationality: Allais disputes theory, 134–5 , 136 , 137 ; Arrow– Debreu world, 343–5 ; assumption of transitivity, 437 ; and Becker, 114 , 381–2 ; and behavioural economics, 116 , 135–6 , 141–9 , 154–5 , 167–8 , 386–7 , 401 ; capital asset pricing model (CAPM), 307–8 , 309 , 320 , 332 ; completeness axiom, 437–8 ; consistency of choice axiom, 108–9 , 110–11 ; continuity axiom, 438–40 ; definition of rationality, 133–4 , 137 , 436 ; definition of risk, 305 , 307 , 334 , 420–1 ; efficient market hypothesis, 252 , 254 , 308–9 , 318 , 320 , 332 , 336–7 ; efficient portfolio model, 307–8 , 309 , 318 , 320 , 332–4 , 366 ; and evolutionary rationality, 16 , 152–3 , 154–5 , 157 , 158 , 166–7 , 171–2 , 386–7 , 407 ; and ‘expectations’ concept, 97–8 , 102–3 , 121–2 , 341–2 ; extended to decision-making under uncertainty, xv , 40–2 , 110–14 , 133–7 , 257–9 , 420–1 ; and Friedman, 73–4 , 111–12 , 113–14 , 125 , 257–9 , 307 , 399–400 , 420 , 437 ; hegemony of over radical uncertainty, 40–2 , 110–14 ; implausibility of assumptions, xiv–xv , 16 , 41–4 , 47 , 74–84 , 85–105 , 107–9 , 111 , 116–22 , 344–9 , 435–44 ; independence axiom, 440–4 ; as limited to small worlds, 170 , 309–10 , 320–1 , 342–9 , 382 , 400 , 421 ; and Lucas, 36 , 92 , 93 , 338–9 , 341 , 345 , 346 ; and Markowitz, 307 , 308 , 309–10 , 318 , 322 , 333 ; maximising behaviour, 310 ; ‘pignistic probability’, 78–84 , 438 ; and Popperian falsificationism, 259–60 ; Prescott’s comparison with engineering, 352–6 ; ‘rational expectations theory, 342–5 , 346–50 ; and Samuelson, xv , 42 , 110–11 , 436 ; and Savage, 111–14 , 125 , 257–9 , 309 , 345 , 400 , 435 , 437 , 442–3 ; shocks and shifts discourse, 42 , 346 , 347 , 348 , 406–7 ; Simon’s work on, 134 , 136 , 149–53 ; triumph of probabilistic reasoning, 15–16 , 20 , 72–84 , 110–14 ; Value at risk models (VaR), 366–8 , 405 , 424 ; von Neumann–Morgenstern axioms, 111 , 133 , 435–44 ; see also maximising behaviour Ballmer, Steve, 30 , 227 Bank of England, xiii , 45 , 103–5 , 286 , 311 Barclays Bank, 257 Barings Bank, 411 Basel regulations, 310 , 311 Bay of Pigs fiasco (1961), 278–9 Bayes, Reverend Thomas, 60–3 , 66–7 , 70 , 71 , 358 , 431 Beane, Billy, 273 Bear Stearns, 158–9 Becker, Gary, 114 , 381–2 Beckham, David, 267–8 , 269 , 270 , 272–3 , 414 behavioural economics, 116 , 145–8 , 154 , 386–7 ; and Allais paradox, 442 ; ‘availability heuristic’, 144–5 ; biases in human behaviour, 16 , 136 , 141–8 , 154 , 162 , 165 , 167–8 , 170–1 , 175–6 , 184 , 401 ; and evolutionary science, 154–5 , 165 ; Kahneman’s dual systems, 170–1 , 172 , 271 ; Kahneman–Tversky experiments, 141–7 , 152 , 215 ; ‘noise’ (randomness), 175–6 ; nudge theory, 148–9 Bentham, Jeremy, 110 Berkshire Hathaway, 153 , 319 , 324 , 325–6 Berlin, Isaiah, 222 Bernoulli, Daniel, 114–16 , 199 Bernoulli, Nicolaus, 199 , 442 Bertrand, Joseph, 70 Bezos, Jeff, 289 big data, 208 , 327 , 388–90 billiard players, 257–8 bin Laden, Osama, 7 , 8–10 , 21 , 44 , 71 , 118–19 , 120 , 122–3 , 262–3 , 326 Bismarck, Otto von, 161 Bitcoin, 96 , 316 Black Death, 32 , 39–40 BlackBerry, 30 , 31 blackjack, 38 Blackstone, Sir William, 213 BNP Paribas, 5 , 6 BOAC, 23–4 Boas, Franz, 193 Boeing, 24 , 227–8 Boer War, 168 Bolt, Usain, 273 bonobos, 161–2 , 178 Borges, Jorge Luis, 391 Borodino, battle of (1812), 3–4 , 433 Bortkiewicz, Ladislaus, 235–6 Bower, Tom, 169–70 Bowral cricket team, New South Wales, 264 Box, George, 393 Boycott, Geoffrey, 264–5 Bradman, Don, 237 , 264 Brahe, Tycho, 388–9 Brånemark, Per-Ingvar, 387 , 388 Branson, Richard, 169–70 Brearley, Michael, 140–1 , 264–5 Breslau (now Wrocław), 56 Brexit referendum (June 2016), 241–2 ; lies told during, 404 bridge collapses, 33 , 341 Brownian motion, 37 Brunelleschi, Filippo, 143 , 147 Buffett, Warren, 83 , 152 , 179 , 319–20 , 324 , 335 , 336–7 Burns, Robert, 253 Bush, George W., 295 , 407 , 412 business cycles, 347 business history (academic discipline), 286 business schools, 318 business strategy: approach in 1970s, 183 ; approach in 1980s, 181–2 ; aspirations confused with, 181–2 , 183–4 ; business plans, 223–4 , 228 ; collections of capabilities, 274–7 ; and the computer industry, 27–31 ; corporate takeovers, 256–7 ; Lampert at Sears, 287–9 , 292 ; Henry Mintzberg on, 296 , 410 ; motivational proselytisation, 182–3 , 184 ; quantification mistaken for understanding, 180–1 , 183 ; and reference narratives, 286–90 , 296–7 ; risk maps, 297 ; Rumelt’s MBA classes, 10 , 178–80 ; Shell’s scenario planning, 223 , 295 ; Sloan at General Motors, 286–7 ; strategy weekends, 180–3 , 194 , 296 , 407 ; three common errors, 183–4 ; vision or mission statements, 181–2 , 184 Buxton, Jedediah, 225 Calas, Jean, 199 California, 48–9 Cambridge Growth Project, 340 Canadian fishing industry, 368–9 , 370 , 423 , 424 cancer, screening for, 66–7 Candler, Graham, 352 , 353–6 , 399 Cardiff City Football Club, 265 Carlsen, Magnus, 175 , 273 Carnegie, Andrew, 427 Carnegie Mellon University, 135 Carré, Dr Matt, 267–8 Carroll, Lewis, Through the Looking-Glass , 93–4 , 218 , 344 , 346 ; ‘Jabberwocky’, 91–2 , 94 , 217 Carron works (near Falkirk), 253 Carter, Jimmy, 8 , 119 , 120 , 123 , 262–3 cartography, 391 Casio, 27 , 31 Castro, Fidel, 278–9 cave paintings, 216 central banks, 5 , 7 , 95 , 96 , 103–5 , 285–6 , 348–9 , 350 , 351 , 356–7 Central Pacific Railroad, 48 Centre for the Study of Existential Risk, 39 Chabris, Christopher, 140 Challenger disaster (1986), 373 , 374 Chamberlain, Neville, 24–5 Chandler, Alfred, Strategy and Structure , 286 Chariots of Fire (film, 1981), 273 Charles II, King, 383 Chelsea Football Club, 265 chess, 173 , 174 , 175 , 266 , 273 , 346 Chicago economists, 36 , 72–4 , 86 , 92 , 111–14 , 133–7 , 158 , 257–8 , 307 , 342–3 , 381–2 Chicago Mercantile Exchange, 423 chimpanzees, 161–2 , 178 , 274 China, 4–5 , 419–20 , 430 cholera, 283 Churchill, Winston: character of, 25–6 , 168 , 169 , 170 ; fondness for gambling, 81 , 168 ; as hedgehog not fox, 222 ; on Montgomery, 293 ; restores gold standard (1925), 25–6 , 269 ; The Second World War , 187 ; Second World War leadership, 24–5 , 26 , 119 , 167 , 168–9 , 170 , 184 , 187 , 266 , 269 Citibank, 255 Civil War, American, 188 , 266 , 290 Clapham, John, 253 Clark, Sally, 197–8 , 200 , 202 , 204 , 206 Clausewitz, Carl von, On War , 433 climate systems, 101–2 Club of Rome, 361 , 362 Coase, Ronald, 286 , 342 Cochran, Johnnie, 198 , 217 Cochrane, John, 93 coffee houses, 55–6 cognitive illusions, 141–2 Cohen, Jonathan, 206–7 Colbert, Jean-Baptiste, 411 Cold War, 293–4 , 306–7 Collier, Paul, 276–7 Columbia disaster (2003), 373 Columbia University, 117 , 118 , 120 Columbus, Christopher, 4 , 21 Colyvan, Mark, 225 Comet aircraft, 23–4 , 228 communication: communicative rationality, 172 , 267–77 , 279–82 , 412 , 414–16 ; and decision-making, 17 , 231 , 272–7 , 279–82 , 398–9 , 408 , 412 , 413–17 , 432 ; eusociality, 172–3 , 274 ; and good doctors, 185 , 398–9 ; human capacity for, 159 , 161 , 162 , 172–3 , 216 , 272–7 , 408 ; and ill-defined concepts, 98–9 ; and intelligibility, 98 ; language, 98 , 99–100 , 159 , 162 , 173 , 226 ; linguistic ambiguity, 98–100 ; and reasoning, 265–8 , 269–77 ; and the smartphone, 30 ; the ‘wisdom of crowds’, 47 , 413–14 Community Reinvestment Act (USA, 1977), 207 comparative advantage model, 249–50 , 251–2 , 253 computer technologies, 27–31 , 173–4 , 175–7 , 185–6 , 227 , 411 ; big data, 208 , 327 , 388–90 ; CAPTCHA text, 387 ; dotcom boom, 228 ; and economic models, 339–40 ; machine learning, 208 Condit, Phil, 228 Condorcet, Nicolas de, 199–200 consumer price index, 330 , 331 conviction narrative theory, 227–30 Corinthians (New Testament), 402 corporate takeovers, 256–7 corporations, large, 27–31 , 122 , 123 , 286–90 , 408–10 , 412 , 415 Cosmides, Leda, 165 Cretaceous–Paleogene extinction, 32 , 39 , 71–2 Crick, Francis, 156 cricket, 140–1 , 237 , 263–5 crime novels, classic, 218 crosswords, 218 crypto-currencies, 96 , 316 Csikszentmihalyi, Mihaly, 140 , 264 Cuba, 278–80 ; Cuban Missile Crisis, 279–81 , 299 , 412 Custer, George, 293 Cutty Sark (whisky producer), 325 Daily Express , 242–3 , 244 Damasio, Antonio, 171 Dardanelles expedition (1915), 25 Darwin, Charles, 156 , 157 Davenport, Thomas, 374 Dawkins, Richard, 156 de Havilland company, 23–4 Debreu, Gerard, 254 , 343–4 decision theory, xvi ; critiques of ‘American school’, 133–7 ; definition of rationality, 133–4 ; derived from deductive reasoning, 138 ; Ellsberg’s ‘ambiguity aversion’, 135 ; expected utility , 111–14 , 115–18 , 124–5 , 127 , 128 – 30 , 135 , 400 , 435–44 ; hegemony of optimisation, 40–2 , 110–14 ; as unable to solve mysteries, 34 , 44 , 47 ; and work of Savage, 442–3 decision-making under uncertainty: and adaptation, 102 , 401 ; Allais paradox, 133–7 , 437 , 440–3 ; axiomatic approach extended to, xv , 40–2 , 110–14 , 133–7 , 257–9 , 420–1 ; ‘bounded rationality concept, 149–53 ; as collaborative process, 17 , 155 , 162 , 176 , 411–15 , 431–2 ; and communication, 17 , 231 , 272–7 , 279–82 , 398–9 , 408 , 412 , 413–17 , 432 ; communicative rationality, 172 , 267–77 , 279–82 , 412 , 414–16 ; completeness axiom, 437–8 ; continuity axiom, 438–40 ; Cuban Missile Crisis, 279–81 , 299 , 412 ; ‘decision weights’ concept, 121 ; disasters attributed to chance, 266–7 ; doctors, 184–6 , 194 , 398–9 ; and emotions, 227–9 , 411 ; ‘evidence-based policy’, 404 , 405 ; excessive attention to prior probabilities, 184–5 , 210 ; expected utility , 111–14 , 115–18 , 124–5 , 127 , 128–30 , 135 , 400 , 435–44 ; first-rate decision-makers, 285 ; framing of problems, 261 , 362 , 398–400 ; good strategies for radical uncertainty, 423–5 ; and hindsight, 263 ; independence axiom, 440–4 ; judgement as unavoidable, 176 ; Klein’s ‘primed recognition decision-making’, 399 ; Gary Klein’s work on, 151–2 , 167 ; and luck, 263–6 ; practical decision-making, 22–6 , 46–7 , 48–9 , 81–2 , 151 , 171–2 , 176–7 , 255 , 332 , 383 , 395–6 , 398–9 ; and practical knowledge, 22–6 , 195 , 255 , 352 , 382–8 , 395–6 , 405 , 414–15 , 431 ; and prior opinions, 179–80 , 184–5 , 210 ; ‘prospect theory’, 121 ; public sector processes, 183 , 355 , 415 ; puzzle– mystery distinction, 20–4 , 32–4 , 48–9 , 64–8 , 100 , 155 , 173–7 , 218 , 249 , 398 , 400–1 ; qualities needed for success, 179–80 ; reasoning as not decision-making, 268–71 ; and ‘resulting’, 265–7 ; ‘risk as feelings’ perspective, 128–9 , 310 ; robustness and resilience, 123 , 294–8 , 332 , 335 , 374 , 423–5 ; and role of economists, 397–401 ; Rumelt’s ‘diagnosis’, 184–5 , 194–5 ; ‘satisficing’ (’good enough’ outcomes), 150 , 167 , 175 , 415 , 416 ; search for a workable solution, 151–2 , 167 ; by securities traders, 268–9 ; ‘shock’ and ‘shift’ labels, 42 , 346 , 347 , 348 , 406–7 ; simple heuristics, rules of thumb, 152 ; and statistical discrimination, 207–9 , 415 ; triumph of probabilistic reasoning, 20 , 40–2 , 72–84 , 110–14 ; von Neumann– Morgenstern axioms, 111 , 133 , 435–44 ; see also business strategy deductive reasoning, 137–8 , 147 , 235 , 388 , 389 , 398 Deep Blue, 175 DeepMind, 173–4 The Deer Hunter (film, 1978), 438 democracy, representative, 292 , 319 , 414 demographic issues, 253 , 358–61 , 362–3 ; EU migration models, 369–70 , 372 Denmark, 426 , 427 , 428 , 430 dentistry, 387–8 , 394 Derek, Bo, 97 dermatologists, 88–9 Digital Equipment Corporation (DEC), 27 , 31 dinosaurs, extinction of, 32 , 39 , 71–2 , 383 , 402 division of labour, 161 , 162 , 172–3 , 216 , 249 DNA, 156 , 198 , 201 , 204 ‘domino theory’, 281 Donoghue, Denis, 226 dotcom boom, 316 , 402 Doyle, Arthur Conan, 34 , 224–5 , 253 Drapers Company, 328 Drescher, Melvin, 248–9 Drucker, Peter, Concept of the Corporation (1946), 286 , 287 Duhem–Quine hypothesis, 259–60 Duke, Annie, 263 , 268 , 273 Dulles, John Foster, 293 Dutch tulip craze (1630s), 315 Dyson, Frank, 259 earthquakes, 237–8 , 239 Eco, Umberto, The Name of the Rose , 204 Econometrica , 134 econometrics, 134 , 340–1 , 346 , 356 economic models: of 1950s and 1960s, 339–40 ; Akerlof model, 250–1 , 252 , 253 , 254 ; ‘analogue economies’ of Lucas, 345 , 346 ; artificial/complex, xiv–xv , 21 , 92–3 , 94 ; ‘asymmetric information’ model, 250–1 , 254–5 ; capital asset pricing model (CAPM), 307–8 , 309 , 320 , 332 ; comparative advantage model, 249–50 , 251–2 , 253 ; cost-benefit analysis obsession, 404 ; diversification of risk, 304–5 , 307–9 , 317–18 , 334–7 ; econometric models, 340–1 , 346 , 356 ; economic rent model, 253–4 ; efficient market hypothesis, 252 , 254 , 308–9 , 318 , 320 , 332 , 336–7 ; efficient portfolio model, 307–8 , 309 , 318 , 320 , 332–4 , 366 ; failure over 2007–08 crisis, xv , 6–7 , 260 , 311–12 , 319 , 339 , 349–50 , 357 , 367–8 , 399 , 407 , 423–4 ; falsificationist argument, 259–60 ; forecasting models, 7 , 15–16 , 68 , 96 , 102–5 , 347–50 , 403–4 ; Goldman Sachs risk models, 6–7 , 9 , 68 , 202 , 246–7 ; ‘grand auction’ of Arrow and Debreu, 343–5 ; inadequacy of forecasting models, 347–50 , 353–4 , 403–4 ; invented numbers in, 312–13 , 320 , 363–4 , 365 , 371 , 373 , 404 , 405 , 423 ; Keynesian, 339–40 ; Lucas critique, 341 , 348 , 354 ; Malthus’ population growth model, 253 , 358–61 , 362–3 ; misuse/abuse of, 312–13 , 320 , 371–4 , 405 ; need for, 404–5 ; need for pluralism of, 276–7 ; pension models, 312–13 , 328–9 , 405 , 423 , 424 ; pre-crisis risk models, 6–7 , 9 , 68 , 202 , 246–7 , 260 , 311–12 , 319 , 320–1 , 339 ; purpose of, 346 ; quest for large-world model, 392 ; ‘rational expectations theory, 342–5 , 346–50 ; real business cycle theory, 348 , 352–4 ; role of incentives, 408–9 ; ‘shift’ label, 406–7 ; ‘shock’ label, 346–7 , 348 , 406–7 ; ‘training base’ (historical data series), 406 ; Value at risk models (VaR), 366–8 , 405 , 424 ; Viniar problem (problem of model failure), 6–7 , 58 , 68 , 109 , 150 , 176 , 202 , 241 , 242 , 246–7 , 331 , 366–8 ; ‘wind tunnel’ models, 309 , 339 , 392 ; winner’s curse model, 256–7 ; World Economic Outlook, 349 ; see also axiomatic rationality; maximising behaviour; optimising behaviour; small world models Economic Policy Symposium, Jackson Hole, 317–18 economics: adverse selection process, 250–1 , 327 ; aggregate output and GDP, 95 ; ambiguity of variables/concepts, 95–6 , 99–100 ; appeal of probability theory, 42–3 ; ‘bubbles’, 315–16 ; business cycles, 45–6 , 347 ; Chicago School, 36 , 72–4 , 86 , 92 , 111–14 , 133–7 , 158 , 257–8 , 307 , 342–3 , 381–2 ; data as essential, 388–90 ; division of labour, 161 , 162 , 172–3 , 216 , 249 ; and evolutionary mechanisms, 158–9 ; ‘expectations’ concept, 97–8 , 102–3 , 121–2 , 341–2 ; forecasts and future planning as necessary, 103 ; framing of problems, 261 , 362 , 398–400 ; ‘grand auction’ of Arrow and Debreu, 343–5 ; hegemony of optimisation, 40–2 , 110 – 14 ; Hicks–Samuelson axioms, 435–6 ; market fundamentalism, 220 ; market price equilibrium, 254 , 343–4 , 381–2 ; markets as necessarily incomplete, 344 , 345 , 349 ; Marshall’s definition of, 381 , 382 ; as ‘non-stationary’, 16 , 35–6 , 45–6 , 102 , 236 , 339–41 , 349 , 350 , 394–6 ; oil shock (1973), 223 ; Phillips curve, 340 ; and ‘physics envy’, 387 , 388 ; and power laws, 238–9 ; as practical knowledge, 381 , 382–3 , 385–8 , 398 , 399 , 405 ; public role of the social scientist, 397–401 ; reciprocity in a modern economy, 191–2 , 328–9 ; and reflexivity, 35–6 , 309 , 394 ; risk and volatility, 124–5 , 310 , 333 , 335–6 , 421–3 ; Romer’s ‘mathiness’, 93–4 , 95 ; shift or structural break, 236 ; Adam Smith’s ‘invisible hand’, 163 , 254 , 343 ; social context of, 17 ; sources of data, 389 , 390 ; surge in national income since 1800, 161 ; systems as non-linear, 102 ; teaching’s emphasis on quantitative methods, 389 ; validity of research findings, 245 ‘Economists Free Ride, Does Anyone Else?’

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

It was difficult to overplay something as inherently dramatic as the potential destruction of the entire human race, which is of course the main reason why the media—a category from which I did not presume to exclude myself—was so drawn to this whole business in the first place. What Stuart was willing to say, however, was that human-level AI had come, in recent years, to seem “more imminent than it used to.” Developments in machine learning like those spearheaded by DeepMind, the London-based AI start-up acquired by Google in 2014, seemed to him to mark an acceleration in the advancement toward something transformative. (Not long before I met Stuart, DeepMind had released a video demonstrating the result of an experiment in which an artificial neural network was set the task of maximizing its score in the classic Atari arcade game Breakout, in which the player controls a paddle at the bottom of a screen, with which they must break through a wall by bouncing a ball off it and thereby breaking its bricks.

…

I would open up Twitter or Facebook, and my timelines—flows of information that were themselves controlled by the tidal force of hidden algorithms—would contain a strange and unsettling story about the ceding of some or other human territory to machine intelligence. I read that a musical was about to open in London’s West End, with a story and music and words all written entirely by an AI software called Android Lloyd Webber. I read that an AI called AlphaGo—also the work of Google’s DeepMind—had beaten a human grandmaster of Go, an ancient Chinese strategy board game that was exponentially more complex, in terms of possible moves, than chess. I read that a book written by a computer program had made it through the first stage of a Japanese literary award open to works written by both humans and AIs, and I thought of the professional futurist I had talked to in the pub in Bloomsbury after Anders’s talk, and his suggestion that works of literature would come increasingly to be written by machines.

The Future Is Faster Than You Think: How Converging Technologies Are Transforming Business, Industries, and Our Lives
by Peter H. Diamandis and Steven Kotler
Published 28 Jan 2020

(Author note: Peter’s VC firm is an investor.) researchers only managed to find about five new drug targets a year: Reinhard Renneberg, Biotechnology for Beginners (Academic Press, 2016), p. 281. a biannual competition was created: See: http://predictioncenter.org/. AlphaFold: Read the DeepMind blog about AlphaFold here: https://deepmind.com/blog/alphafold/. Chapter Ten: The Future of Longevity The Nine Horsemen of Our Apocalypse Francis Collins: Dr. Francis Collins shared the stage with Peterat at a 2018 event hosted by the Cura Foundation. Watch the conversation on Longevity and the Morality of Extreme Life Extension here: https://www.youtube.com/watch?

…

But a protein with merely a hundred amino acids (a rather small protein) can produce a googol-cubed worth of potential shapes—that’s a one followed by three hundred zeroes. This is also why protein-folding has long been considered a really hard problem for a really big supercomputer. Back in 1994, to monitor this supercomputer protein-folding progress, a biannual competition was created. Until 2018, success was fairly rare. But then the creators of DeepMind turned their neural networks loose on the problem. They created an AI that mines enormous datasets to determine the most likely distance between a protein’s base pairs and the angles of their chemical bonds—aka, the basics of protein folding. They called it AlphaFold. On its first foray into the competition, contestant AIs were given forty-three protein-folding problems to solve.

…

L., 228 Anikeeva, Polina, 254 Annals of Internal Medicine, 159 anti-Semitism, 238, 239 Apeel Sciences, 203–4 Apollo program, 73 Appallicious, 235 Apple, 29, 35, 52, 100, 127, 157 Apple Watch, 41–42 aquaculture, 225 arthritis, 176 artificial intelligence (AI), 8, 10, 33–37, 85 AI machine built by, 36 and availability of capital, 75 BCIs and, 141–42 big data and, 33–34 convergence of VR and, 148–50 crowdsurance and, 186–87 customer service and, 102–3 drug development and, 165–67 employment and, 69, 229 entertainment content and, 130, 131–32 existential risks and, 233 finance industry and, 194–96 healthcare and, 36, 158, 161–63 human collaboration with, 47, 130, 162, 229 investing and, 195 real estate industry and, 197–98 saved time and, 72 service economy and, 34–35 shopping and, 100–106 Singularity in, 76 smart objects and, 60 workforce retraining and, 230 ASIMO (humanoid robot), 45 Atlantic, 89–90, 97, 98 Atlas (robot), 46 augmented reality (AR), 52, 86, 118, 119–20 contact lenses and, 139, 140 entertainment content and, 139–42 Autodesk, 103 automatic teller machines (ATMs), 228 automobiles, see cars autonomous cars, 12–16, 26, 221 data gathering by, 14 insurance and, 184–85 prime real estate redefined by, 199 ridesharing and, 14–16, 19 saved time and, 15 sensors and, 43 Uber and, 4 AVA (Autodesk Virtual Assistant), 103 avatars, 24–26 Babbage, Charles, 88 Babylonians, insurance invented by, 183 Baidu, 121–22 Bailenson, Jeremy, 52, 148 VR demonstration by, 49–50 balloons, as network connections, 40 Bangladesh, 192 banking, see finance industry Barnard, Matt, 205 batteries, 10–11, 218–20, 222 BBC, 246 Bell, Alexander Graham, 38 Benjamin, Simon, 30 Ben-Joseph, Eran, 16 Best Buy, 107 Beta Technologies, 154 Better Angels of Our Nature, The (Pinker), 262 Betterment, 195 Beyond Verbal, 102–3 Bezos, Jeff, 4, 176 space colonization and, 250–51, 253 big data, 85 advertising and, 118 AI and, 33–34 Binging with Babish (YouTube program), 128 bin Salman, Mohammed, 77 BioCarbon Engineering, 224, 227 biodiversity crisis, 48, 207, 212, 223–27 Bionaut Labs, 162 biotechnology, 8, 65–68, 75 3–D printing and, 54–55 Bitcoin, 31, 56–57 bKash, 192 Blockbuster, 126 blockchain, 56–61, 75, 193, 194 as bridge between virtual and physical world, 59–60 content creation and, 129 contracts and, 58 crowdsurance and, 187 definition of, 57 ICOs and, 75–76 IDs and, 58 international money transfers and, 58 land ownership and, 58 third party eliminated by, 57 blood, young, rejuvenating effects of, 90, 178–79 Blue Moon Lunar Lander, 251 Blue Origin, 251 BMW, 219, 222, 229 Body Labs, 114 Boeing, 48 BOLD (Diamandis and Kotler), xi, 7, 16, 31, 53, 235 Bold Capital Partners (BCP), xii, 266 Bombfell, 114 Boomerang Nebula, 27 Boring Company, 18–19 Boston Dynamics, 46 Bostrom, Nick, 230–31 Boy in the Plastic Bubble, The (TV movie), 6 brain: global and exponential environment as challenge to, 11–12, 22–24 pleasure chemistry of, 246–49 and thinking about the future, 21–22 brain-computer interfaces (BCIs), 255–57 AI and, 141–42 convergence and, 255 EEG-based, 256 brain implants, 81–82 BrainNet, 141 brain-to-brain communication, 256–57 Brand, Stewart, 232 Branson, Richard, 17 Brazil, 217 Breakthrough Energy Ventures, 220 Brooks, Avery, 7 Bubble Boy disease (severe combined immunodeficiency), 65, 66 Buck Institute for Research on Aging, 172–73 Burkina Faso, 160 business, convergence and, 23, 181–200 new models for, 83–87, 111–13 see also specific businesses Business Insider, 97 BusinessWeek, 42, 248 Byron, Lady, 87 Byron, Lord, 87 Cabela’s, 112 Caenorhabditis elegans (roundworm), 172–73 Calico, 89–90, 173–74 California, renewable energy projects in, 218–19 California, University of: at Berkeley, 131 at San Diego, 239 Cameron, Geoffrey, 237–38 cancer, 89, 90, 162, 164–65 rapamycin and, 175 regenerative medicines and, 177 capital, availability of, 73–77 Carbeck, Jeff, 62 carbon dioxide (CO2) emissions, 215–16 see also greenhouse gasses Carbon Disclosure Project, 217 Carbon Majors Database, 215 cardiac disease, 89 car makers, impact of ridesharing on, 15–16 Carnegie Mellon University, 62–63, 122 CAR-NK therapy, 164–65 cars: age of, 12–13 electric, 10, 16–17, 221–23 flying, see flying cars self-driving, see autonomous cars cars, ownership of: cost of, 5 ridesharing vs., 14–15, 26 CAR-T (chimeric antigen receptor T-cell) therapy, 164 cash, disappearance of, 195–96 Celgene, 163, 164 Cell, 178 cells, immunological, 164 cellular medicine, 163–65 cellular senescence, 171 Celularity, 90–91, 164 change, acceleration of, 69–91 convergence and, see convergence exponential technologies and, see exponential technologies ChargePoint, 223 chatbots, 33, 36, 37 Chen, Yan, 71 Chile, 217 China, 192 electric cars in, 221 renewable energy projects in, 216 unbanked population of, 194–95 Xiaoice chatbot in, 33, 36, 37 Choose Your Own Adventure (film), 138 Christensen, Clayton, 87 chromosomes, 170 Chung, Anshe, 248 Church, George, 159 cities: floating, 199–200 innovation and, 82–83, 244 migration to, 243–45 productivity and, 244 smart, 235, 245 sustainability of, 244–45 Clean Air Task Force, 218 Climate Central, 241 climate change, 44, 48, 199–200, 207, 212–13, 215–18, 223 greenhouse gases and, 206, 207, 215–16, 221, 226 migration and, 211, 241–42 Netherlands and, 232 Clinton, Bill, 213 closed-loop economies, 85 clothing, 3–D printed, 109 Cluep, 137 coal, 216–17 Coca-Cola, 213–14 coffee houses, rise of, 82 cognitive function, brain implants and, 81–82 cognitive psychology, 136 collaboration, 227, 230, 240 human/AI, 47, 130, 162, 229 see also hive-mind collaborations collective consciousness, see hive-mind collaboration Collins, Francis, 169, 172–73 Collins, Marc, 200 communication, brain-to-brain, 256–57 communications technologies: and economic paradigm shifts, 98 in rise and fall of Sears, 98 computer-aided design, 11 computers, computing: affective, 136–38 demonetization and, 78 emotionally intelligent, 103 3–D printing and, 54 computer simulations, 11, 17–18 Congress, US, mail delivery and, 96–97 connectivity, see networks consciousness, collective, see hive-mind collaboration construction industry, 55 contact lenses, AR and, 139, 140 content, entertainment: AI and, 130, 131–32 AR and, 139–42 brain-computer interfaces and, 141–42 deepfakes and, 131–32 democratization of, 131–32 immersive, 132–35 new forms of, 130–38 new venues for, 138–42 sensory input in, 134–35 user-generated, 127–30 contracts, blockchain and, 58 convergence, 8–9, 68 affective computing and, 136–38 BCIs and, 255 business and, 23, 181–200 disruptive innovation and, 9 environmental threats and, 226–27 existential risks and, 235–36 finance industry and, 189–96 flying cars and, 9–12 food industry and, 201–8 healthcare and, 68, 89, 154–55 Hyperloop and, 17–18 insurance industry and, 183–89 longevity and, 169, 173, 179 real estate industry and, 196–200 renewable energy and, 217–18 robotics and, 48 secondary forces unleashed by, 22–24, 69–70; see also capital, availability of; demonetization; genius, nurturing of; longevity; networks; time, saving of of technologies and markets, 127 3–D printing and, 54–55 transportation revolution and, 21 of VR and AI, 148–50 Cook, Tim, 140, 155 Cooking with Dog (YouTube program), 128 Cooper, Al, 249 coral reefs, 223–25 Cortana, 132 Costa Rica, renewable energy in, 217 CRISPR, 67, 68, 154, 160 crowd economy, 84 crowdfunding, 73–75, 84 crowdlending, 194–95 crowdsurance, 183, 185–87 cryptocurrencies, 31, 56–57, 59, 190 ICOs and, 75–76 customer-designed products, 3–D printing and, 110–11 customer service, AI and, 102–3 cutin, 203–4 Daimler Financial Services, 103, 221 dairy products, animal-free, 208 Dakota Pipeline, 190 DARPA (Defense Advanced Research Projects Agency), 256 Grand Challenge of, 13 Robotics Challenge of, 45–46 DART project, 233 data mining, see big data Daugherty, Paul, 229 David Rubenstein Show, The, 77 decentralized autonomous organizations, 85–86 deception, exponential technologies and, 31, 32, 33, 39, 215 Deep Blue computer, 28, 35–36 deep brain stimulators, 253–55 deepfake technology, 122–23, 131–32 DeepMind, 167 Defense Department, US, 36 deforestation, 48, 206, 207, 223, 224, 226 de Grey, Aubrey, 173 dematerialization, 31, 32, 200 democratization, xi, 31, 32, 108 of content creation, 128, 131–32 of healthcare, 162 real estate industry and, 200 demonetization, xi, 31–32, 15, 77–79 autonomous-car ridesharing and, 15 real estate industry and, 200 Denmark, 196 Department for International Development (UK), 191 “Destination 2028,” 112, 115 diabetes, 171, 175 diagnostics, personal, 156–58 Diamond Age, The (Stephenson), 149 digital assistants, see AI assistants digital currency, see cryptocurrencies digital mimicry, 121–22 see also deepfake technology digital technology, 31 and availability of capital, 73–77 Digital Trends, 122 digital world, boundaries between physical world and, 118–20 disaster relief, drones and, 48 discount pricing, Sears as pioneer of, 96, 98 discrimination, VR in combatting of, 52 disease, 41, 213 early detection of, 158, 159 disruption: exponential technologies and, 31, 32, 33, 215 innovation as, 9 distributed autonomous organizations (DAOs), 103 distributed electric propulsion (DEP), 10 DNA, 65, 66–67 Domino’s Robotic Unit (DRU), 106 dopamine, 246–47 double-spending problem, 56, 57 Dracula myth, 178 Dragon TV, 33 Dreamscape, 135 Drexler, K.

The Autonomous Revolution: Reclaiming the Future We’ve Sold to Machines
by William Davidow and Michael Malone
Published 18 Feb 2020

This is precisely what has been happening in the past few decades. In 1997, Deep Blue, a chess-playing computer developed by IBM, beat the Russian grandmaster Garry Kasparov in a six-game match.10 Kasparov said that he had sensed a thinking presence inside his computer opponent. Then, in 2016, Google DeepMind’s artificial-intelligence program, AlphaGo, defeated Lee Sedol, a Go champion, 4–1. Go is a more difficult game for a computer to play than chess, and AlphaGo’s victory is perhaps the best harbinger of what is to come. While Deep Blue relied on hard-coded functions written by human experts for its decision-making processes, AlphaGo used neural networks and reinforcement learning.

…

The Economist recently reported that certain computers, trained to play a number of games, have been able to come up with viable strategies for playing different games they have never seen before.12 The semiconductor industry has only just begun to unleash the power of Moore’s Law (the regular doubling of semiconductor chip performance) on neural networks. Google’s DeepMind system used Nvidia’s newly announced P100 chip, containing 1.5 billion transistors, to power its system. The chip enabled Google to build neural networks that were five times deeper than in the past—and the deeper the networks, the more intelligent the behavior.13 The key point here is that neural network systems will benefit from high rates of progress in the semiconductor industry.

Ten Lessons for a Post-Pandemic World
by Fareed Zakaria
Published 5 Oct 2020

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

Cloudmoney: Cash, Cards, Crypto, and the War for Our Wallets
by Brett Scott
Published 4 Jul 2022

That was the 1980s, though. Now these cultures are merging. One illustration of this is the migration of staff between Big Finance and Big Tech – for instance, a friend of mine used to work at J. P. Morgan as a quantitative analyst, where he calculated the prices of financial contracts. Now he works at DeepMind, Google’s AI research unit, investigating how to create AI that can be applied to any situation. This blending of finance and tech is also visible in the hybridisation of the two industries in the realm of fintech. It is an industry that exemplifies the ambiguous yet close relationship between the two worlds.

…

, 49, 72 ‘Cashfree and Proud’, 40 Cashless Catalyst, 127–8 Cashless Challenge, 40 cashless society, 2, 5, 10, 15, 38, 64, 81, 83, 84, 251 inevitability, 10–12, 121–33, 260–61 Cashless Way, 37 casinos, 66–9, 70–71, 83, 236 categorisation, 109, 113–14, 162, 167 Catholicism, 131, 212 Cayman Islands, 111 censorship, 33, 116–18, 250 central banks, 36, 42–5, 51, 84, 254 data surveillance, 115 digital currencies (CBDC), 242–5, 254, 255 international transfers, 79 transfers, 73–4 centralisation of power, 15, 180–83 centralised–decentralised model, 136 Chama, 130 charging up, 22–5 chatbots, 146–8 Chaum, David, 106–7, 117, 183 cheques, 89 Chicago Mercantile Exchange, 158 China, 2, 7, 18, 33, 74–5, 79, 114–15, 254 CBDC plans, 245, 254–5 facial recognition in, 150 leviathan complex, 178 People’s Bank of China, 79, 242 Social Credit System, 115, 245 choice, 124–6, 251 Christianity, 154, 175–6, 212 Christl, Wolfie, 109 cigarettes, 181 Circles, 260 Citigroup, 1, 37, 109, 132, 150, 227 City of London, 6, 135 class, see social class Cleo, 146 climate change, 226 cloakrooms, 66–9, 70–71 cloud, 30 cloudmoney, 82 Coca-Cola, 31, 131 cocaine, 98 code is law, 223, 224 Coinbase, 233 collateralised debt obligations, 26 colonialism, 55, 97, 175–6, 178, 239 Commerzbank Tower, Frankfurt, 18–20, 143, 156 computer boys, 158 conductivity, 179, 249 ConsenSys, 229 conservatism, 7, 131, 155, 184, 192–3, 211 see also right-wing politics consortium blockchains, 231, 233 conspiracy theories, 261–2 constitutional monarchies, 56 consumers, 25 contactless payments, 13, 31, 37–8, 91, 125, 127 core, 28 corporate personhood, 147 Corruption Perceptions Index, 43 counterfeiting, 60–61 countertradability, 209–10, 213, 256–7 Covid-19 pandemic, 2, 10, 16, 34, 36, 181, 249, 254 ATM use, 36 cash and, 2, 34, 40–41, 249, 261 conspiracy theories, 261 Cracked Labs, 109 credit cards, 39, 91, 109 credit creation of bank-money, 70, 72 credit default swap market, 232 credit expansion, 168–9 credit ratings, 17, 114, 160, 162–3, 167, 168, 170 crime cash and, 36, 42–3, 45, 81, 112 cybercrime, 32 financial crime, 111–12 marijuana industry, 102 trust and, 93 Crypto Sex Toys, 13 crypto-anarchists, 183 Cryptocannabis Salon, 101–2 cryptocurrencies, 13–15, 16, 101–2, 103, 184–5, 187–246, 254–60 alt-coins, 217–18 as commodity, 206–10, 213–14, 217, 246, 256 countertradability, 209–10, 213, 256–7 decentralisation and, 14, 15, 189–94, 196, 230, 234, 255, 258 forks, 214, 217 millenarianism and, 212, 213 mutual credit systems and, 260 oligopolies and, 229–33, 246 politics and, 191–3, 211–12, 215–17, 225–6 smart contracts, 220–24, 258 stablecoins, 233–41, 245–6, 255 Currency Conference (2017), 60 Curse of Cash, The (Rogoff), 93 Cyber Monday, 86 cyberattacks, 32, 48 cybercrime, 34 cyberpunk genre, 10 cypherpunk movement, 106, 183–5, 216–17 Dahabshiil, 116 DAI, 235 dark market, 216–17, 259 Dark Wallet, 216 data, 2, 8, 10, 33, 39, 104–19, 156–72 AI analysis, 108, 153–72 banking sector and, 108–9 Big Brother and, 113–15 categorisation, 109, 113–14, 162 panopticon effect and, 118–19, 172 payments censorship and, 116–18 predictive systems and, 105 states and, 110–12, 114–15 Data Bank Society, The (Warner), 106 data centres, 3, 4, 5, 30, 32, 34, 35, 47, 73, 76–7, 149 Davos, Switzerland, 11 debit cards, 39 Decathlon, 40–41 decentralisation, 14, 15, 189–94, 196, 230, 234, 255, 258–60 decentralised autonomous organisations (DAOs), 221–4, 258 DECODE, 236 DeepMind, 8 DeFi (decentralised finance), 258 Delft University of Technology, 31 demand, 29 demonetisations, 43, 44, 93 deposits, 66–7, 69 derivatives, 6, 18, 21, 26, 27, 160 Desparte, Dante, 238 Diamond, Robert ‘Bob’, 38 Diem, 241, 244 DigiCash, 106, 183 digital footprint, 169 disruption, 8, 9, 14, 32, 140–43 distributed ledger technology (DLT), 229–46, 258 Dogecoin, 13, 218 dollar system, 80, 182, 210, 233–6, 239, 240 double spending, 182, 194 doublethink, 143 Dow Chemical, 24 Drakensberg Mountains, 3–4 Dridex, 32 drones, 11 drug dealers, 96 Dubai, United Arab Emirates, 248 Dylan, Robert ‘Bob’, 90 e-commerce, 40, 77 East India Company, 178 eBay, 109, 113 ecological activism, 7 economic syncretism, 175–6 Ecuador, 240 Egypt, 116 El Salvador, 98, 208 elderly people, 126 electricity, 247 Elwartowski, Chad, 216 Emili, Geronimo, 37 employees, 25 enclosure, 86 Enlightenment (c. 1637–1789), 252 enterprise blockchains, 231 Enterprise Ethereum Alliance, 233 entrepreneurs, 1, 15, 129, 155 equivocation fallacies, 85 Erica, 147 Ethereum, 219–24, 257–8 Ethereum Classic, 224 European Union, 14, 37, 42, 254 Central Bank, 51, 74, 79, 242 DECODE project, 236 Eurozone, 51, 74, 79 Evans, Mel, 144 exiting, 39, 48, 61, 63, 68, 83 Experian, 163 F-16 fighter jets, 153 Facebook, 7, 38, 105, 150, 166, 198, 255, 262 Libra, 236–41, 245 Messenger, 237 facial recognition, 10, 138, 150, 181, 245 far-left politics, 7, 215 far-right politics, 7, 14, 215, 225–6, 261–2 fascism, 7, 14, 226 Federal Bureau of Investigation (FBI), 111 Federal Reserve, 32, 35, 36, 234, 242 federated frontline, 136–8, 147 fees, 39, 57, 91, 94 feminism, 226 fiat money, 51–2, 56, 192, 193 Fidor, 142 Financial Crimes Enforcement Network, 111 financial crisis (2008), 6, 8, 17–18, 26–7, 96, 184, 232, 248 financial inclusion, 37, 39, 93–9, 130–32, 167, 238, 262 fingerprints, 150 Fink, Stanley, 38 fintech, 8, 41–2, 140–43 first-world problems, 154 fitness centres, 17 fixed money supplies, 191–3 Floored (2009 film), 158 Florentine Republic (1115–1569), 135, 159 Follow the Money, 112 Fourth Industrial Revolution, 11 fractional reserve banking, 70 France cashless payments strategy, 43 Frankfurt, Germany, 18–20, 143, 156, 248 frogs, slow-boiling, 104 futurism, 1, 12, 86, 122–3, 250, 252 gambling, 105 game theory, 220 Gap, 131 Gates, William ‘Bill’, 44–5, 261–2 GCHQ, 112 Generation Z, 86, 140 gentrification, 128–33 Germany, 7, 18 Bundesbank, 35, 47 cash thresholds, 42–3 Corruption Perceptions Index, 43 Frankfurt, 18–20, 143, 156 honesty boxes in, 91 get-rich-quick investments, 26 Getty Images, 80 giant parable, 52–6, 63–4, 188 global matrix, 12 Gmail, 203 gold, 192–3, 207, 214 Goldman Sachs, 38, 150, 157, 158, 230 Golumbia, David, 225 Google, 2, 5, 7, 262 Cashe, 150 data, 105, 108 DeepMind, 8 Gmail, 203 Maps, 4 Mastercard deal, 109 Pay, 1, 78, 125 Singularity University, 153–6, 252–3 Trends, 84 USAID and, 128, 178 Grassroots Economics, 260 Greece, 42, 43, 62, 131 Green Dot, 150 Greenpeace, 116 growth, 123, 126–7, 249 hackers, 6–7, 101, 184 Hacktivist Village, 101 Halkbank, 131 Handmaid’s Tale, The (Atwood), 117 Hansen, Tyler, 101–2 Harvard University, 47, 93 hawala systems, 179 ‘Here Today.

…

, 49, 72 ‘Cashfree and Proud’, 40 Cashless Catalyst, 127–8 Cashless Challenge, 40 cashless society, 2, 5, 10, 15, 38, 64, 81, 83, 84, 251 inevitability, 10–12, 121–33, 260–61 Cashless Way, 37 casinos, 66–9, 70–71, 83, 236 categorisation, 109, 113–14, 162, 167 Catholicism, 131, 212 Cayman Islands, 111 censorship, 33, 116–18, 250 central banks, 36, 42–5, 51, 84, 254 data surveillance, 115 digital currencies (CBDC), 242–5, 254, 255 international transfers, 79 transfers, 73–4 centralisation of power, 15, 180–83 centralised–decentralised model, 136 Chama, 130 charging up, 22–5 chatbots, 146–8 Chaum, David, 106–7, 117, 183 cheques, 89 Chicago Mercantile Exchange, 158 China, 2, 7, 18, 33, 74–5, 79, 114–15, 254 CBDC plans, 245, 254–5 facial recognition in, 150 leviathan complex, 178 People’s Bank of China, 79, 242 Social Credit System, 115, 245 choice, 124–6, 251 Christianity, 154, 175–6, 212 Christl, Wolfie, 109 cigarettes, 181 Circles, 260 Citigroup, 1, 37, 109, 132, 150, 227 City of London, 6, 135 class, see social class Cleo, 146 climate change, 226 cloakrooms, 66–9, 70–71 cloud, 30 cloudmoney, 82 Coca-Cola, 31, 131 cocaine, 98 code is law, 223, 224 Coinbase, 233 collateralised debt obligations, 26 colonialism, 55, 97, 175–6, 178, 239 Commerzbank Tower, Frankfurt, 18–20, 143, 156 computer boys, 158 conductivity, 179, 249 ConsenSys, 229 conservatism, 7, 131, 155, 184, 192–3, 211 see also right-wing politics consortium blockchains, 231, 233 conspiracy theories, 261–2 constitutional monarchies, 56 consumers, 25 contactless payments, 13, 31, 37–8, 91, 125, 127 core, 28 corporate personhood, 147 Corruption Perceptions Index, 43 counterfeiting, 60–61 countertradability, 209–10, 213, 256–7 Covid-19 pandemic, 2, 10, 16, 34, 36, 181, 249, 254 ATM use, 36 cash and, 2, 34, 40–41, 249, 261 conspiracy theories, 261 Cracked Labs, 109 credit cards, 39, 91, 109 credit creation of bank-money, 70, 72 credit default swap market, 232 credit expansion, 168–9 credit ratings, 17, 114, 160, 162–3, 167, 168, 170 crime cash and, 36, 42–3, 45, 81, 112 cybercrime, 32 financial crime, 111–12 marijuana industry, 102 trust and, 93 Crypto Sex Toys, 13 crypto-anarchists, 183 Cryptocannabis Salon, 101–2 cryptocurrencies, 13–15, 16, 101–2, 103, 184–5, 187–246, 254–60 alt-coins, 217–18 as commodity, 206–10, 213–14, 217, 246, 256 countertradability, 209–10, 213, 256–7 decentralisation and, 14, 15, 189–94, 196, 230, 234, 255, 258 forks, 214, 217 millenarianism and, 212, 213 mutual credit systems and, 260 oligopolies and, 229–33, 246 politics and, 191–3, 211–12, 215–17, 225–6 smart contracts, 220–24, 258 stablecoins, 233–41, 245–6, 255 Currency Conference (2017), 60 Curse of Cash, The (Rogoff), 93 Cyber Monday, 86 cyberattacks, 32, 48 cybercrime, 34 cyberpunk genre, 10 cypherpunk movement, 106, 183–5, 216–17 Dahabshiil, 116 DAI, 235 dark market, 216–17, 259 Dark Wallet, 216 data, 2, 8, 10, 33, 39, 104–19, 156–72 AI analysis, 108, 153–72 banking sector and, 108–9 Big Brother and, 113–15 categorisation, 109, 113–14, 162 panopticon effect and, 118–19, 172 payments censorship and, 116–18 predictive systems and, 105 states and, 110–12, 114–15 Data Bank Society, The (Warner), 106 data centres, 3, 4, 5, 30, 32, 34, 35, 47, 73, 76–7, 149 Davos, Switzerland, 11 debit cards, 39 Decathlon, 40–41 decentralisation, 14, 15, 189–94, 196, 230, 234, 255, 258–60 decentralised autonomous organisations (DAOs), 221–4, 258 DECODE, 236 DeepMind, 8 DeFi (decentralised finance), 258 Delft University of Technology, 31 demand, 29 demonetisations, 43, 44, 93 deposits, 66–7, 69 derivatives, 6, 18, 21, 26, 27, 160 Desparte, Dante, 238 Diamond, Robert ‘Bob’, 38 Diem, 241, 244 DigiCash, 106, 183 digital footprint, 169 disruption, 8, 9, 14, 32, 140–43 distributed ledger technology (DLT), 229–46, 258 Dogecoin, 13, 218 dollar system, 80, 182, 210, 233–6, 239, 240 double spending, 182, 194 doublethink, 143 Dow Chemical, 24 Drakensberg Mountains, 3–4 Dridex, 32 drones, 11 drug dealers, 96 Dubai, United Arab Emirates, 248 Dylan, Robert ‘Bob’, 90 e-commerce, 40, 77 East India Company, 178 eBay, 109, 113 ecological activism, 7 economic syncretism, 175–6 Ecuador, 240 Egypt, 116 El Salvador, 98, 208 elderly people, 126 electricity, 247 Elwartowski, Chad, 216 Emili, Geronimo, 37 employees, 25 enclosure, 86 Enlightenment (c. 1637–1789), 252 enterprise blockchains, 231 Enterprise Ethereum Alliance, 233 entrepreneurs, 1, 15, 129, 155 equivocation fallacies, 85 Erica, 147 Ethereum, 219–24, 257–8 Ethereum Classic, 224 European Union, 14, 37, 42, 254 Central Bank, 51, 74, 79, 242 DECODE project, 236 Eurozone, 51, 74, 79 Evans, Mel, 144 exiting, 39, 48, 61, 63, 68, 83 Experian, 163 F-16 fighter jets, 153 Facebook, 7, 38, 105, 150, 166, 198, 255, 262 Libra, 236–41, 245 Messenger, 237 facial recognition, 10, 138, 150, 181, 245 far-left politics, 7, 215 far-right politics, 7, 14, 215, 225–6, 261–2 fascism, 7, 14, 226 Federal Bureau of Investigation (FBI), 111 Federal Reserve, 32, 35, 36, 234, 242 federated frontline, 136–8, 147 fees, 39, 57, 91, 94 feminism, 226 fiat money, 51–2, 56, 192, 193 Fidor, 142 Financial Crimes Enforcement Network, 111 financial crisis (2008), 6, 8, 17–18, 26–7, 96, 184, 232, 248 financial inclusion, 37, 39, 93–9, 130–32, 167, 238, 262 fingerprints, 150 Fink, Stanley, 38 fintech, 8, 41–2, 140–43 first-world problems, 154 fitness centres, 17 fixed money supplies, 191–3 Floored (2009 film), 158 Florentine Republic (1115–1569), 135, 159 Follow the Money, 112 Fourth Industrial Revolution, 11 fractional reserve banking, 70 France cashless payments strategy, 43 Frankfurt, Germany, 18–20, 143, 156, 248 frogs, slow-boiling, 104 futurism, 1, 12, 86, 122–3, 250, 252 gambling, 105 game theory, 220 Gap, 131 Gates, William ‘Bill’, 44–5, 261–2 GCHQ, 112 Generation Z, 86, 140 gentrification, 128–33 Germany, 7, 18 Bundesbank, 35, 47 cash thresholds, 42–3 Corruption Perceptions Index, 43 Frankfurt, 18–20, 143, 156 honesty boxes in, 91 get-rich-quick investments, 26 Getty Images, 80 giant parable, 52–6, 63–4, 188 global matrix, 12 Gmail, 203 gold, 192–3, 207, 214 Goldman Sachs, 38, 150, 157, 158, 230 Golumbia, David, 225 Google, 2, 5, 7, 262 Cashe, 150 data, 105, 108 DeepMind, 8 Gmail, 203 Maps, 4 Mastercard deal, 109 Pay, 1, 78, 125 Singularity University, 153–6, 252–3 Trends, 84 USAID and, 128, 178 Grassroots Economics, 260 Greece, 42, 43, 62, 131 Green Dot, 150 Greenpeace, 116 growth, 123, 126–7, 249 hackers, 6–7, 101, 184 Hacktivist Village, 101 Halkbank, 131 Handmaid’s Tale, The (Atwood), 117 Hansen, Tyler, 101–2 Harvard University, 47, 93 hawala systems, 179 ‘Here Today.

People, Power, and Profits: Progressive Capitalism for an Age of Discontent
by Joseph E. Stiglitz
Published 22 Apr 2019

See, e.g., Stiglitz, Freefall; Commission of Experts on Reforms of the International Monetary and Financial System appointed by the President of the United Nations General Assembly, The Stiglitz Report: Reforming the International Monetary and Financial Systems in the Wake of the Global Crisis (New York: The New Press, 2010); Simon Johnson and James Kwak, 13 Bankers: The Wall Street Takeover and the Next Financial Meltdown (New York: Random House, 2010); and Rana Foroohar, Makers and Takers: How Wall Street Destroyed Main Street (New York: Crown, 2016). CHAPTER 6: THE CHALLENGE OF NEW TECHNOLOGIES 1.Google’s Go-playing computer program AlphaGo, developed by the tech giant’s AI company, DeepMind, beat Go world champion Lee Se-dol in March 2016. See Choe Sang-Hun, “Google’s Computer Program Beats Lee Se-dol in Go Tournament,” New York Times, Mar. 15, 2016. A year and a half later, Google announced the release of a program with even larger AI capabilities. See Sarah Knapton, “AlphaGo Zero: Google DeepMind Supercomputer Learns 3,000 Years of Human Knowledge in 40 Days,” Telegraph, Oct. 18, 2017. 2.Robert J. Gordon, The Rise and Fall of American Growth: The US Standard of Living since the Civil War (Princeton: Princeton University Press, 2016).

…

I have been involved in a number of antitrust suits, trying to preserve competition in the American economy, and the insights of Keith Leffler, Michael Cragg, David Hutchings, and Andrew Abere have been invaluable. My understanding of the role these market imperfections have in labor markets has been enhanced by Mark Stelzner and Alan Krueger. The discussions of new technologies have been particularly influenced by my coauthor Anton Korinek; on artificial intelligence, by Erik Brynjolfsson, Shane Legg of DeepMind, Mark Sagar of Soul Machines, and a dinner on AI at the Royal Society after my lecture there on the subject of work and AI. Yochai Benkler, Julia Angwin, and Zeynep Tüfekçi have contributed to my understanding of the special issues posed by disinformation. As I return to the issues of globalization, I need to thank Dani Rodrik as well as Danny Quah, Rohinton Medhora, and Mari Pangestu; and on the role of globalization in tax avoidance, Mark Pieth and the Independent Commission for Reform of International Corporate Taxation, chaired by José Antonio Ocampo, on which I serve.

…

Federal Election Commission, 166, 169–70, 172 class warfare, 6 climate change and attacks on truth, 20 and intergenerational justice, 204–5 markets’ failure to address, xxiii money’s effects on debate, 20 Clinton, Bill, and administration, xiii, 4, 5, 168, 238, 242 Clinton, Hillary, 4, 6 coal companies, 20 Cold War, end of, 28; See also Communism, collapse of collective action, 138–56 balancing with individualism, 139 circumstances requiring, 140–42 government failures, 148–52 increasing need for government action, 152–55 in preamble to Constitution, 138–39 regulation as, 143–48 collective judgments, 262–63n20 college, income inequality and, 200 Comcast, 147 Communications Decency Act, 320n32 Communism, collapse of, 3, 28 comparative advantage, 82–84 competition market concentration, 55–56 market failures, 23 in marketplace of ideas, 75–76 market power, 57–60 power vs., 22 competitive equilibrium model, 47, 280n1 Comprehensive and Progressive Agreement for Trans-Pacific Partnership, 306n25 compulsion, power of, 155 confirmatory bias, 225 conflicts of interest, 70, 72, 124 Congress antitrust laws, 51, 68 and Great Recession, 39, 215 and lobbyists, See lobbyists and money in politics, 171–72 and Obamacare, 213 and regulatory process, 145–46 Supreme Court nominations, 166 and USTR, 100 conservatism, embracing change vs., 226–28 Constitution of the United States collective action reference in preamble, 138–39 economic changes since writing of, 227 “General Welfare” in Preamble, 242 individual liberties vs. collective interest in, 229 and minority rights, 6 as product of reasoning and argumentation, 229 three-fifths clause, 161 consumer demand, See demand consumer surplus, 64 cooperatives, 245 Copenhagen Agreement, 207 copyright extensions, 74 Copyright Term Extension Act (1998), 74 corporate taxes, 108, 206, 269n44 corporate tax rates, globalization and, 84–85 corporate welfare, 107 corporations and labor force participation, 182 and money in politics, 172–73 as people, 169–70 rights as endowed by the State, 172 corruption, 50 cost-benefit analysis, 146, 204–5 Council of Economic Advisers (CEA), xii credit, 102, 145, 186, 220 credit cards, 59–60, 70, 105 credit default swaps, 106 credit unions, 245 culture, economic behavior and, 30 customer targeting, 125–26 cybersecurity, 127–28 cybertheft, 308n35 Daraprim, 296n72 data exclusivity, 288n40 data ownership, 129–30 Deaton, Angus, 41–42 debt, 220; See also credit DeepMind, 315n1 defense contractors, 173 deficits, See budget deficits deglobalization, 92 deindustrialization early days of, xix effect on average citizens, 4, 21 facilitating transition to postindustrial world, 186–88 failure to manage, xxvi in Gary, Indiana, xi globalization and, 4, 79, 87 place-based policies and, 188 deliberation, 228–29 demand automation and, 120 and job creation, 268n41 Keynesian economics and, xv market power’s effect on, 63 demand for labor, technological suppression of, 122 democracy, 159–78 agenda for reducing power of money in politics, 171–74 curbing the influence of wealth on, 176–78 fragility of norms and institutions, 230–36 inequality as threat to, 27–28 maintaining system of checks and balances, 163–67 need for a new movement, 174–76 new technologies’ threat to, 131–35 and power of money, 167–70 as shared value, 228 suppression by minority, xx Trump’s disdain for, xvii voting reforms, 161–63 democratic institutions, fragility of, 230–36 Democratic Party gerrymandering’s effect on, 159 and Great Recession, 152 need for reinvention of, 175 popular support for, 6 renewal of, 242 and voter disenfranchisement, 162 demographics, xx, 181 “deplorables,” 4 deregulation, 25, 105, 143–44, 152, 239; See also supply-side economics derivatives, 80, 88, 106–7, 144 Detroit, Michigan, 188 Dickens, Charles, 12 Digital Millennium Copyright Act, 320–21n32 disadvantage, intergenerational transmission of, 199–201 disclosure laws, 171 discourse, governance and, 11 discrimination, 201–4; See also gender discrimination; racial discrimination by banks, 115 and economics texts, 23 forms of, 202 under GI Bill, 210 and inequality, 40–41, 198–99 and labor force participation, 183 means of addressing, 203–4 and myths about affirmative action, 225 reducing to improve economy, 201–4 diseases of despair, 42–43 disenfranchisement, 27, 161–62 disintermediation, 109 Disney, 65, 74 dispute resolution, 56–57, 309n40 Dodd–Frank Wall Street Reform and Consumer Protection Act, 70, 102, 107 driverless cars, 118 drug overdoses, 42 Durbin Amendment, 70 East Asia, 149 economic justice historical perspectives, 241–42 intergenerational justice, 204–5 racial justice and, 176, 203–4 tax system and, 205–8 economics, assumptions about individuals in, 29–30, 223 economic segregation, 200 economies of scale, 72 economies of scope, 347–48n15 economy and collective action, 153–54 decent jobs with good working conditions, 192–97 deterioration since early 1980s, 32–46 failure’s effect on individuals and society, 29–31 failure since late 1980s, 3–5 government involvement in, 141–42, 150–55 intergenerational transmission of advantage/disadvantage, 199–201 reducing discrimination in, 201–4 restoring fairness to tax system, 205–8 restoring growth and productivity, 181–86 restoring justice across generations, 204–5 restoring opportunity and social justice, 197–201 social protection, 188–91 “sugar high” from Trump’s tax cut, 236–38 transition to postindustrial world, 186–88 education equalizing opportunity of, xxv–xxvi, 219–20 improving access to, 203 returns on government investment in, 232 taxation and, 25 undermining of institutions, 233–34 Eggers, Dave, 128 Eisenhower, Dwight, and administration, 210 elderly, labor force growth and, 181–82 election of 1992, 4 election of 2000, 165–66 election of 2012, 159, 178 election of 2016, xix, 132, 178 elections, campaign spending in, 171–73 elite control of economy by, 5–6 and distrust in government, 151 and 2008 financial crisis, 5 promises of growth from market liberalization, 21–22 rules written by, 230 employers, market power over workers, 64–67 employment, See full employment; jobs; labor force participation End of History, The (Fukuyama), 3 Enlightenment, the, 10–12 attack on ideals of, 14–22 and standard of living, 264n24 environment carbon tax, 194, 206–7 and collective action, 153 economic growth and, 176 economists’ failure to address, 34 markets’ failure to protect, 24 and true economic health, 34 environmental justice, economic justice and, 176 Environmental Protection Agency (EPA), 267n38 epistemology, 10, 234 equality as basis for well-running economy, xxiv–xxv economic agenda for, xxvii as shared value, 228 Equifax, 130 equity value, rents as portion of, 54 ethnic discrimination, 201–4 Europe data regulation, 128–29 globalization, 81 infrastructure investment, 195–96 privacy protections, 135 trade agreements favoring, 80 unity against Trump, 235 European Investment Bank, 195–96 evergreening, 60 excess profits, as rent, 54 exchange rate, 89, 307n28, 307n32 exploitation in current economy, 26 in economics texts, 23 financial sector and, 113 market power and, 47–78 reducing, 197 as source of wealth, 144–45 wealth creation vs., 34 and wealth redistribution, 50 exports, See globalization; trade wars Facebook anticompetitive practices, 70 and Big Data, 123, 124, 127–28 competition for ad revenue, 56 and conflicts of interest, 124 market power in relaxed antitrust environment, 62 as natural monopoly, 134 and preemptive mergers, 60, 73 reducing market power of, 124 regulation of advertising on, 132 fact-checking, 132, 177 “Fading American Dream, The” (Opportunity Insights report), 44–45 “fake news,” 167 family leave, 197 Farhi, Emmanuel, 62 farmers, Great Depression and, 120 fascism, 15–16, 18, 235 Federal Communication Commission (FCC), 147 Federal Reserve Board, 70, 112 Federal Reserve System, 121, 214–15 Federal Trade Commission, 69 fees bank profits from, 105, 110 credit card, 60, 70, 105 for mergers and acquisitions, 108 mortgages and, 107, 218 “originate-to-distribute” banking model, 110 private retirement accounts and, 215 fiduciary standard, 314n21, 347n10 finance (financial sector); See also banks and American crisis, 101–16 contagion of maladies to rest of economy, 112 disintermediation, 109 dysfunctional economy created by, 105–9 gambling by, 106–8 and government guarantees, 110–11 history of dysfunctionality, 109–12 as microcosm of larger economy, 113 mortgage reform opposed by, 216–18 private vs. social interests, 111–12 and public option, 215–16 shortsightedness of, 104–5 stopping societal harm created by, 103–5 and trade agreements, 80 financial crisis (2008), 101; See also Great Recession bank bailout, See bank bailout [2008] China and, 95 deregulation and, 25, 143–44 as failure of capitalism, 3 government response to, 5 housing and, 216 as man-made failure, 153–54 market liberalization and, 4 and moral turpitude of bankers, 7 regulation in response to, 101–2 as symptomatic of larger economic failures, 32–33 and unsustainable growth, 35 financial liberalization, See market liberalization First National Bank, 101 “fiscal paradises,” 85–86 fiscal policy, 121, 194–96 fiscal responsibility, 237 food industry, 182 forced retirement, 181–82 Ford Motor Company, 120 Fox News, 18, 133, 167, 177 fractional reserve banking, 110–11 fraud, 103, 105, 216, 217 freedom, regulation and, 144 free-rider problem, 67, 155–56, 225–26 Friedman, Milton, 68, 314–15n22 FUD (fear, uncertainty, and doubt), 58 Fukuyama, Francis, 3, 259n1 full employment, 83, 193–94, 196–97 Galbraith, John K., 67 gambling, by banks, 106–7, 207 Garland, Merrick, 166–67 Gates, Bill, 5, 117 GDP elites and, 22 as false measure of prosperity, 33, 227 financial sector’s increasing portion of, 109 Geithner, Tim, 102 gender discrimination, 41, 200–204 gene patents, 74–75 general welfare, 242–47 generic medicines, 60, 89 genetically modified food (GMO), 88 genetics, 126–27 George, Henry, 206 Germany, 132, 152 gerrymandering, 6, 159, 162 GI Bill, 210 Gilded Age, 12, 246 Glass-Steagall Act, 315n25, 341n39 globalization, 79–100 budget deficits and trade imbalances, 90 collective action to address, 154–55 effect on average citizens, 4, 21 in era of AI, 135 failure to manage, xxvi false premises about, 97–98 and global cooperation in 21st century, 92–97 and intellectual property, 88–89 and internet legal frameworks, 135 and low-skilled workers, 21, 82, 86, 267n39 and market power, 61 pain of, 82–87 and protectionism, 89–92 and 21st-century trade agreements, 87–89 and tax revenue, 84–86 technology vs., 86–87 and trade wars, 93–94 value systems and, 94–97 GMO (genetically modified food), 88 Goebbels, Joseph, 266n35 Goldman Sachs, 104 Google AlphaGo, 315n1 antipoaching conspiracy, 65 and Big Data, 123, 127, 128 conflicts of interest, 124 European restrictions on data use, 129 gaming of tax laws by, 85 market power, 56, 58, 62, 128 and preemptive mergers, 60 Gordon, Robert, 118–19 Gore, Al, 6 government, 138–56 assumption of mortgage risk, 107 Chicago School’s view of, 68–69 debate over role of, 150–52 and educational system, 220 failure of, 148–52 in finance, 115–16 and fractional reserve banking, 111 and Great Depression, 120 hiring of workers by, 196–97 increasing need for, 152–55 interventions during economic downturns, 23, 120 lack of trust in, 151 lending guarantees, 110–11 managing technological change, 122–23 and need for collective action, 140–42 and political reform, xxvi pre-distribution/redistribution by, xxv in progressive agenda, 243–44 public–private partnerships, 142 regulation and rules, 143–48 restoring growth and social justice, 179–208 social protection by, 231 government bonds, 215 Great Britain, wealth from colonialism, 9 Great Depression, xiii, xxii, 13, 23, 120 “great moderation,” 32 Great Recession, xxvi; See also financial crisis (2008) deregulation and, 25 diseases of despair, 42 elites and, 151 employment recovery after, 193 inadequate fiscal stimulus after, 121 as market failure, 23 pace of recovery from, 39–40 productivity growth after, 37 and retirement incomes, 214–15 weak social safety net and, 190 Greenspan, Alan, 112 Gross Fixed Capital Formation, 271n4 gross investment, 271n4 growth after 2008 financial crisis, 103 in China, 95 decline since 1980, 35–37 economic agenda for, xxvii failure of financial sector to support, 115 and inequality, 19 international living standard comparisons, 35–37 knowledge and, 183–86 labor force, 181–82 market power as inimical to, 62–64 in post-1970s US economy, 32 restoring, 181–86 taxation and, 25 guaranteed jobs, 196–97 Harvard University, 16 Hastert Rule, 333n31 health inequality in, 41–43 and labor force participation, 182 health care and American exceptionalism, 211–12 improving access to services, 203 public option, 210–11 in UK and Europe, 13 universal access to, 212–13 hedonic pricing, 347n13 higher education, 219–20; See also universities Hispanic Americans, 41 hi-tech companies, 54, 56, 60, 73 Hitler, Adolf, 152, 266n35 Hobbes, Thomas, 12 home ownership, 216–18 hours worked per week, US ranking among developed economies, 36–37 House of Representatives, 6, 159 housing, as barrier to finding new jobs, 186 housing bubble, 21 housing finance, 216–18 human capital index (World Bank), 36 Human Development Index, 36 Human Genome Project, 126 hurricanes, 207 IA (intelligence-assisting) innovations, 119 identity, capitalism’s effect on, xxvi ideology, science replaced by, 20 immigrants/immigration, 16, 181, 185 imports, See globalization; trade wars incarceration, 161, 163, 193, 201, 202 incentive payments for teachers, 201 voting reform and, 162–63 income; See also wages average US pretax income (1974-2014), 33t universal basic income, 190–91 income inequality, 37, 177, 200, 206 income of capital, 53 India, guaranteed jobs in, 196–97 individualism, 139, 225–26 individual mandate, 212, 213 industrial policies, 187 industrial revolution, 9, 12, 264–65n24 inequality; See also income inequality; wealth inequality benefits of reducing, xxiv–xxv and current politics, 246 in early years after WWII, xix economists’ failure to address, 33 education system as perpetuator of, 219 and election of 2016, xix–xxi and excess profits, 49 and financial system design, 198 growth of, xii–xiii, 37–45 in health, 41–43 in opportunity, 44–45 in race, ethnicity, and gender, 40–41 and 2017 tax bill, 236–37 technology’s effect on, 122–23 in 19th and early 20th century, 12–13 20th-century attempts to address, 13–14 tolerance of, 19 infrastructure European Investment Bank and, 195–96 fiscal policy and, 195 government employment and, 196–97 public–private partnerships, 142 returns on investment in, 195, 232 taxation and, 25 and 2017 tax bill, 183 inheritance tax, 20 inherited wealth, 43, 278n38 innovation intellectual property rights and, 74–75 market power and, 57–60, 63–64 net neutrality and, 148 regulation and, 134 slowing pace of, 118–19 and unemployment, 120, 121 innovation economy, 153–54 insecurity, social protection to address, 188–91 Instagram, 70, 73, 124 institutions fragility of, 230–36 in progressive agenda, 245 undermining of, 231–33 insurance companies, 125 Intel, 65 intellectual property rights (IPR) China and, 95–96 globalization and, 88–89, 99 and stifling of innovation, 74–75 and technological change, 122 in trade agreements, 80, 89 intelligence-assisting (IA) innovations, 119 interest rates, 83, 110, 215 intergenerational justice, 204–5 intergenerational transmission of advantage/disadvantage, xxv–xxvi, 199–201, 219 intermediation, 105, 106 Internal Revenue Service (IRS), 217 International Monetary Fund, xix internet, 58, 147 Internet Explorer, 58 inversions, 302n10 investment buybacks vs., 109 corporate tax cuts and, 269n44 and intergenerational justice, 204 long-term, 106 weakening by monopoly power, 63 “invisible hand,” 76 iPhone, 139 IPR, See intellectual property rights Ireland, 108 IRS (Internal Revenue Service), 217 Italy, 133 IT sector, 54; See also hi-tech companies Jackson, Andrew, 101, 241 Janus v.

Industry 4.0: The Industrial Internet of Things
by Alasdair Gilchrist
Published 27 Jun 2016

Presently the state of machine learning and artificial intelligence is defined by the latest innovations. In November 2015, Google launched its machine learning system called TensorFlow. Interest in deep learning continues to gain momentum, especially following Google’s purchase of DeepMind Technologies, which has since been renamed Google DeepMind. In February 2015, DeepMind scientists revealed how a computer had taught itself to play almost 50 video games, by figuring out what to do through deep neural networks and reinforcement learning. Watson, developed by IBM, was the first commercially available cognitive computing offering.

Nervous States: Democracy and the Decline of Reason
by William Davies
Published 26 Feb 2019

The achievement of institutions such as the Royal Society was to entrench a culture of promise-making and promise-keeping within its highly select bunch of members, and then to communicate and publish this reliably. Can a computer make a promise? This is an intriguing philosophical question. If Google DeepMind were to take data on 100 million “promises” that had been made (perhaps legal contracts, informal agreements via email, videos of people “shaking on a deal,” friends promising to be somewhere at a certain time) and feed it to an AI, what would it make of it all? Would it understand? In a manner of speaking, it would.

…

A/B testing, 199 Acorn, 152 ad hominem attacks, 27, 124, 195 addiction, 83, 105, 116–17, 172–3, 186–7, 225 advertising, 14, 139–41, 143, 148, 178, 190, 192, 199, 219, 220 aerial bombing, 19, 125, 135, 138, 143, 180 Affectiva, 188 affective computing, 12, 141, 188 Agent Orange, 205 Alabama, United States, 154 alcoholism, 100, 115, 117 algorithms, 150, 169, 185, 188–9 Alsace, 90 alt-right, 15, 22, 50, 131, 174, 196, 209 alternative facts, 3 Amazon, 150, 173, 175, 185, 186, 187, 192, 199, 201 American Association for the Advancement of Science, 24 American Civil War (1861–5), 105, 142 American Pain Relief Society, 107 anaesthetics, 104, 142 Anderson, Benedict, 87 Anthropocene, 206, 213, 215, 216 antibiotics, 205 antitrust laws, 220 Appalachia, 90, 100 Apple, 156, 185, 187 Arab Spring (2011), 123 Arendt, Hannah, xiv, 19, 23, 26, 53, 219 Aristotle, 35, 95–6 arrogance, 39, 47, 50 artificial intelligence (AI), 12–13, 140–41, 183, 216–17 artificial video footage, 15 Ashby, Ross, 181 asymmetrical war, 146 atheism, 34, 35, 209 attention economy, 21 austerity, 100–101, 225 Australia, 103 Australian, 192 Austria, 14, 60, 128, 153–75 Austria-Hungary (1867–1918), 153–4, 159 authoritarian values, 92–4, 101–2, 108, 114, 118–19, 211–12 autocracy, 16, 20, 202 Babis, Andrej, 26 Bacon, Francis, 34, 35, 95, 97 Bank of England, 32, 33, 55, 64 Banks, Aaron, 26 Bannon, Steve, 21, 22, 60–61 Bayh–Dole Act (1980), 152 Beck Depression Inventory, 107 Berlusconi, Silvio, 202 Bernays, Edward, 14–15, 16, 143 “Beyond the Pleasure Principle” (Freud), 110 Bezos, Jeff, 150, 173 Big Data, 185–93, 198–201 Big Government, 65 Big Science, 180 Bilbao, Spain, 84 bills of mortality, 68–71, 75, 79–80, 81, 127 Birmingham, West Midlands, 85 Black Lives Matter, 10, 225 Blackpool, Lancashire, 100 blind peer reviewing, 48, 139, 195 Blitz (1940–41), 119, 143, 180 blue sky research, 133 body politic, 92–119 Bologna, Italy, 96 bookkeeping, 47, 49, 54 Booth, Charles, 74 Boston, Massachusetts, 48 Boyle, Robert, 48–50, 51–2 BP oil spill (2010), 89 brainwashing, 178 Breitbart, 22, 174 Brexit (2016–), xiv, 23 and education, 85 and elites, 33, 50, 61 and inequality, 61, 77 and NHS, 93 and opinion polling, 80–81 as self-harm, 44, 146 and statistics, 61 Unite for Europe march, 23 Vote Leave, 50, 93 British Futures, 65 Brooks, Rosa, 216 bullying, 113 Bureau of Labor, 74 Bush, George Herbert Walker, 77 Bush, George Walker, 77, 136 cadaverous research, 96, 98 call-out culture, 195 Calvinism, 35 Cambridge, Cambridgeshire, 85 University, 84, 151 Cambridge Analytica, 175, 191, 196, 199 Cameron, David, 33, 73, 100 cancer, 105 Capital in the Twenty-First Century (Piketty), 74 capital punishment, 92, 118 car accidents, 112–13 cargo-cult science, 50 Carney, Mark, 33 cartography, 59 Case, Anne, 99–100, 102, 115 Catholicism, 34 Cato Institute, 158 Cavendish, William, 3rd Earl of Devonshire, 34 Central Intelligence Agency (CIA), 3, 136, 151, 199 Center for Policy Studies, 164 chappe system, 129, 182 Charles II, King of England, Scotland, and Ireland, 34, 68, 73 Charlottesville attack (2017), 20 Chelsea, London, 100 Chevillet, Mark, 176 Chicago School, 160 China, 13, 15, 103, 145, 207 chloroform, 104 cholera, 130 Chongqing, China, 13 chronic pain, 102, 105, 106, 109 see also pain Churchill, Winston, 138 citizen science, 215, 216 civil rights movements, 21, 194 civilians, 43, 143, 204 von Clausewitz, Carl, 128–35, 141–7, 152 and defeat, 144–6 and emotion, 141–6, 197 and great leaders, 146–7, 156, 180–81 and intelligence, 134–5, 180–81 and Napoleon, 128–30, 133, 146–7 and soldiers, number of, 133–4 war, definition of, 130, 141, 193 climate change, 26, 50, 165, 205–7, 213–16 Climate Mobilization, 213–14 climate-gate (2009), 195 Clinton, Hillary, 27, 63, 77, 99, 197, 214 Clinton, William “Bill,” 77 coal mining, 90 cognitive behavioral therapy, 107 Cold War, 132, 133, 135–6, 137, 180, 182–4, 185, 223 and disruption, 204–5 intelligence agencies, 183 McCarthyism (1947–56), 137 nuclear weapons, 135, 180 scenting, 135–6 Semi-Automatic Ground Environment (SAGE), 180, 182, 200 space race, 137 and telepathy, 177–8 colonialism, 59–61, 224 commercial intelligence, 152 conscription, 127 Conservative Party, 80, 154, 160, 163, 166 Constitution of Liberty, The (Hayek), 160 consumer culture, 90, 104, 139 contraceptive pill, 94 Conway, Kellyanne, 3, 5 coordination, 148 Corbyn, Jeremy, 5, 6, 65, 80, 81, 197, 221 corporal punishment, 92 creative class, 84, 151 Cromwell, Oliver, 57, 59, 73 crop failures, 56 Crutzen, Paul, 206 culture war, xvii Cummings, Dominic, 50 currency, 166, 168 cutting, 115 cyber warfare, xii, 42, 43, 123, 126, 200, 212 Czech Republic, 103 Daily Mail, ix Damasio, Antonio, 208 Darwin, Charles, 8, 140, 142, 157, 171, 174, 179 Dash, 187 data, 49, 55, 57–8, 135, 151, 185–93, 198–201 Dawkins, Richard, 207, 209 death, 37, 44–5, 66–7, 91–101 and authoritarian values, 92–4, 101–2, 211, 224 bills of mortality, 68–71, 75, 79–80, 81, 89, 127 and Descartes, 37, 91 and Hobbes, 44–5, 67, 91, 98–9, 110, 151, 184 immortality, 149, 183–4, 224, 226 life expectancy, 62, 68–71, 72, 92, 100–101, 115, 224 suicide, 100, 101, 115 and Thiel, 149, 151 death penalty, 92, 118 Deaton, Angus, 99–100, 102, 115 DeepMind, 218 Defense Advanced Research Projects Agency (DARPA), 176, 178 Delingpole, James, 22 demagogues, 11, 145, 146, 207 Democratic Party, 77, 79, 85 Denmark, 34, 151 depression, 103, 107 derivatives, 168, 172 Descartes, René, xiii, 36–9, 57, 147 and body, 36–8, 91, 96–7, 98, 104 and doubt, 36–8, 39, 46, 52 and dualism, 36–8, 39, 86, 94, 131, 139–40, 179, 186, 223 and nature, 37, 38, 86, 203 and pain, 104, 105 Descartes’ Error (Damasio), 208 Devonshire, Earl of, see Cavendish, William digital divide, 184 direct democracy, 202 disempowerment, 20, 22, 106, 113–19 disruption, 18, 20, 146, 147, 151, 171, 175 dog whistle politics, 200 Donors Trust, 165 Dorling, Danny, 100 Downs Survey (1655), 57, 59, 73 doxing, 195 drone warfare, 43, 194 drug abuse, 43, 100, 105, 115–16, 131, 172–3 Du Bois, William Edward Burghardt, 74 Dugan, Regina, 176–7 Dunkirk evacuation (1940), 119 e-democracy, 184 Echo, 187 ecocide, 205 Economic Calculation in the Socialist Commonwealth (Mises), 154, 166 economics, 59, 153–75 Economist, 85, 99 education, 85, 90–91 electroencephalography (EEG), 140 Elizabethan era (1558–1603), 51 embodied knowledge, 162 emotion and advertising, 14 artificial intelligence, 12–13, 140–41 and crowd-based politics, 4, 5, 8, 9, 10, 15, 16, 21, 23–7 Darwin’s analysis, 8, 140 Descartes on, 94, 131 and experts, 53, 60, 64, 66, 90 fear, 11–12, 16–22, 34, 40–45, 52, 60, 142 Hobbes on, 39, 41 James’ analysis, 140 and markets, 168, 175 moral, 21 and nationalism, 71, 210 pain, 102–19 sentiment analysis, xiii, 12–13, 140, 188 and war, 124–6, 142 empathy, 5, 12, 65, 102, 104, 109, 112, 118, 177, 179, 197 engagement, 7, 219 England Bank of England founded (1694), 55 bills of mortality, 68–71, 75, 79–80, 81, 89, 127 civil servants, 54 Civil War (1642–51), 33–4, 45, 53 Elizabethan era (1558–1603), 51 Great Fire of London (1666), 67 hospitals, 57 Irish War (1649–53), 59 national debt, 55 Parliament, 54, 55 plagues, 67–71, 75, 79–80, 81, 89, 127 Royal Society, 48–52, 56, 68, 86, 208, 218 tax collection, 54 Treasury, 54 see also United Kingdom English Defense League, ix entrepreneurship, 149, 156, 162 environment, 21, 26, 50, 61, 86, 165, 204–7, 213–16 climate change, 26, 50, 165, 205–7, 213–16 flying insects, decline of, 205, 215 Environmental Protection Agency, 23 ether, 104 European Commission, 60 European Space Agency, 175 European Union (EU), xiv, 22, 60 Brexit (2016–), see under Brexit and elites, 60, 145, 202 euro, 60, 78 Greek bailout (2015), 31 immigration, 60 and nationalism, 60, 145, 146 quantitative easing, 31 refugee crisis (2015–), 60, 225 Unite for Europe march (2017), 23 Exeter, Devon, 85 experts and crowd-based politics, 5, 6, 23, 25, 27 Hayek on, 162–4, 170 and representative democracy, 7 and statistics, 62–91 and technocracy, 53–61, 78, 87, 89, 90 trust in, 25–33, 63–4, 66, 74–5, 77–9, 170, 202 violence of, 59–61 Expression of the Emotions in Man and Animal, The (Darwin), 8, 140 Exxon, 165 Facebook, xvi, 15, 201 advertising, 190, 192, 199, 219, 220 data mining, 49, 185, 189, 190, 191, 192, 198, 219 and dog whistle politics, 200 and emotional artificial intelligence, 140 as engagement machine, 219 and fake news, 199 and haptics, 176, 182 and oligarchy, 174 and psychological profiling, 124 and Russia, 199 and sentiment analysis, 188 and telepathy, 176–8, 181, 185, 186 and Thiel, 149, 150 and unity, 197–8 weaponization of, 18 facial recognition, 13, 188–9 failed states, 42 fake news, 8, 15, 199 Farage, Nigel, 65 fascism, 154, 203, 209 fear, 11–12, 16–22, 34, 40–45, 52, 60, 142 Federal Bureau of Investigation (FBI), 137 Federal Reserve, 33 feeling, definition of, xii feminism, 66, 194 Fifth Amendment, 44 fight or flight, 111, 114 Financial Times, 15 first past the post, 13 First World War, see World War I Fitbit, 187 fixed currency exchange rates, 166 Florida, Richard, 84 flu, 67, 191 flying insects, 205, 215 France censuses, 66, 73 conscription introduced (1793), 127 Front National, 27, 61, 79, 87, 92 Hobbes in (1640–51), 33–4, 41–2 Le Bon’s crowd psychology, 8–12, 13, 15, 16, 20, 24, 25, 38 life expectancy, 101 Napoleonic Wars (1803–15), see Napoleonic Wars Paris climate accord (2015), 205, 207 Paris Commune (1871), 8 Prussian War (1870–71), 8, 142 Revolution (1789–99), xv, 71, 126–9, 141, 142, 144, 204 statistics agency established (1800), 72 unemployment, 83 Franklin, Benjamin, 66 free markets, 26, 79, 84, 88, 154–75 free speech, 22, 113, 194, 208, 209, 224 free will, 16 Freud, Sigmund, 9, 14, 44, 107, 109–10, 111, 112, 114, 139 Friedman, Milton, 160, 163, 166 Front National, 27, 61, 79, 87, 92, 101–2 full spectrum warfare, 43 functional magnetic resonance imaging (fMRI), 140 futurists, 168 Galen, 95–6 Galilei, Galileo, 35 gambling, 116–17 game theory, 132 gaming, 193–4 Gandhi, Mohandas, 224 gate control theory, 106 Gates, Sylvester James “Jim,” 24 Gavotti, Giulio, 143 geek humor, 193 Gehry, Frank, 84 Geller, Uri, 178 geometry, 35, 49, 57, 59, 203 Gerasimov, Valery, 123, 125, 126, 130 Germany, 34, 72, 137, 205, 215 gig economy, 173 global financial crisis (2007–9), 5, 29–32, 53, 218 austerity, 100–101 bailouts, 29–32, 40, 42 and gross domestic product (GDP), 76 as “heart attack,” 57 and Obama administration, 158 and quantitative easing, 31–2, 222 and securitization of loans, 218–19 and statistics, 53, 65 and suicide, 101 and unemployment, 82 globalization, 21, 78, 84, 145, 146 Gonzales, Alberto, 136 Google, xvi, 174, 182, 185, 186, 191, 192 DeepMind, 218 Maps, 182 Transparency Project, 198 Government Accountability Office, 29 Graunt, John, 67–9, 73, 75, 79–80, 81, 85, 89, 127, 167 Great Fire of London (1666), 67 great leaders, 146–8 Great Recession (2007–13), 76, 82, 101 Greece, 5, 31, 101 Greenpeace, 10 Grenfell Tower fire (2017), 10 Grillo, Beppe, 26 gross domestic product (GDP), 62, 65, 71, 75–9, 82, 87, 138 guerrillas, 128, 146, 194, 196 Haldane, Andrew, 32 haptics, 176, 182 Harvey, William, 34, 35, 38, 57, 96, 97 hate speech, 42 von Hayek, Friedrich, 159–73, 219 health, 92–119, 224 hedge funds, 173, 174 hedonism, 70, 224 helicopter money, 222 Heritage Foundation, 164, 214 heroin, 105, 117 heroism and disruption, 18, 146 and genius, 218 and Hobbes, 44, 151 and Napoleonic Wars, 87, 127, 142 and nationalism, 87, 119, 210 and pain, 212 and protection, 202–3 and technocracy, 101 and technology, 127 Heyer, Heather, 20 Hiroshima atomic bombing (1945), 206 Hobbes, Thomas, xiii, xvi, 33–6, 38–45, 67, 147 on arrogance, 39, 47, 50, 125 and body, 96, 98–9 and Boyle, 49, 50, 51 on civil society, 42, 119 and death, 44–5, 67, 69–70, 91, 98–9, 110, 151, 184 on equality, 89 on fear, 40–45, 52, 67, 125 France, exile in (1640–51), 33–4, 41 on geometry, 35, 38, 49, 56, 57 and heroism, 44, 151 on language, 38–9 natural philosophy, 35–6 and nature, 38, 50 and Petty, 56, 57, 58 on promises, 39–42, 45, 148, 217–18 and Royal Society, 49, 50, 51 on senses, 38, 49, 147 and sovereign/state, 40–45, 46, 52, 53, 54, 60, 67, 73, 126, 166, 217, 220 on “state of nature,” 40, 133, 206, 217 war and peace, separation of, 40–45, 54, 60, 73, 125–6, 131, 201, 212 Hobsbawm, Eric, 87, 147 Hochschild, Arlie Russell, 221 holistic remedies, 95, 97 Holland, see under Netherlands homeopathy, 95 Homer, xiv Hungary, 20, 60, 87, 146 hysteria, 139 IBM, 179 identity politics, 208, 209 Iglesias Turrión, Pablo, 5 imagined communities, 87 immigration, 60, 63, 65, 79, 87, 145 immortality, 149, 183–4, 224 in-jokes, 193 individual autonomy, 16 Industrial Revolution, 133, 206 inequality, 59, 61, 62, 76, 77, 83, 85, 88–90 inflation, 62, 76, 78, 82 infographics, 75 information theory, 147 information war, 43, 196 insurance, 59 intellectual property, 150 intelligence, 132–9 intensity, 79–83 International Association for the Study of Pain, 106 International Monetary Fund (IMF), 64, 78 Internet, 184–201, 219 IP addresses, 193 Iraq War (2003–11), 74, 132 Ireland, 57, 73 Irish Republican Army (IRA), 43 “Is This How You Feel?

…

A/B testing, 199 Acorn, 152 ad hominem attacks, 27, 124, 195 addiction, 83, 105, 116–17, 172–3, 186–7, 225 advertising, 14, 139–41, 143, 148, 178, 190, 192, 199, 219, 220 aerial bombing, 19, 125, 135, 138, 143, 180 Affectiva, 188 affective computing, 12, 141, 188 Agent Orange, 205 Alabama, United States, 154 alcoholism, 100, 115, 117 algorithms, 150, 169, 185, 188–9 Alsace, 90 alt-right, 15, 22, 50, 131, 174, 196, 209 alternative facts, 3 Amazon, 150, 173, 175, 185, 186, 187, 192, 199, 201 American Association for the Advancement of Science, 24 American Civil War (1861–5), 105, 142 American Pain Relief Society, 107 anaesthetics, 104, 142 Anderson, Benedict, 87 Anthropocene, 206, 213, 215, 216 antibiotics, 205 antitrust laws, 220 Appalachia, 90, 100 Apple, 156, 185, 187 Arab Spring (2011), 123 Arendt, Hannah, xiv, 19, 23, 26, 53, 219 Aristotle, 35, 95–6 arrogance, 39, 47, 50 artificial intelligence (AI), 12–13, 140–41, 183, 216–17 artificial video footage, 15 Ashby, Ross, 181 asymmetrical war, 146 atheism, 34, 35, 209 attention economy, 21 austerity, 100–101, 225 Australia, 103 Australian, 192 Austria, 14, 60, 128, 153–75 Austria-Hungary (1867–1918), 153–4, 159 authoritarian values, 92–4, 101–2, 108, 114, 118–19, 211–12 autocracy, 16, 20, 202 Babis, Andrej, 26 Bacon, Francis, 34, 35, 95, 97 Bank of England, 32, 33, 55, 64 Banks, Aaron, 26 Bannon, Steve, 21, 22, 60–61 Bayh–Dole Act (1980), 152 Beck Depression Inventory, 107 Berlusconi, Silvio, 202 Bernays, Edward, 14–15, 16, 143 “Beyond the Pleasure Principle” (Freud), 110 Bezos, Jeff, 150, 173 Big Data, 185–93, 198–201 Big Government, 65 Big Science, 180 Bilbao, Spain, 84 bills of mortality, 68–71, 75, 79–80, 81, 127 Birmingham, West Midlands, 85 Black Lives Matter, 10, 225 Blackpool, Lancashire, 100 blind peer reviewing, 48, 139, 195 Blitz (1940–41), 119, 143, 180 blue sky research, 133 body politic, 92–119 Bologna, Italy, 96 bookkeeping, 47, 49, 54 Booth, Charles, 74 Boston, Massachusetts, 48 Boyle, Robert, 48–50, 51–2 BP oil spill (2010), 89 brainwashing, 178 Breitbart, 22, 174 Brexit (2016–), xiv, 23 and education, 85 and elites, 33, 50, 61 and inequality, 61, 77 and NHS, 93 and opinion polling, 80–81 as self-harm, 44, 146 and statistics, 61 Unite for Europe march, 23 Vote Leave, 50, 93 British Futures, 65 Brooks, Rosa, 216 bullying, 113 Bureau of Labor, 74 Bush, George Herbert Walker, 77 Bush, George Walker, 77, 136 cadaverous research, 96, 98 call-out culture, 195 Calvinism, 35 Cambridge, Cambridgeshire, 85 University, 84, 151 Cambridge Analytica, 175, 191, 196, 199 Cameron, David, 33, 73, 100 cancer, 105 Capital in the Twenty-First Century (Piketty), 74 capital punishment, 92, 118 car accidents, 112–13 cargo-cult science, 50 Carney, Mark, 33 cartography, 59 Case, Anne, 99–100, 102, 115 Catholicism, 34 Cato Institute, 158 Cavendish, William, 3rd Earl of Devonshire, 34 Central Intelligence Agency (CIA), 3, 136, 151, 199 Center for Policy Studies, 164 chappe system, 129, 182 Charles II, King of England, Scotland, and Ireland, 34, 68, 73 Charlottesville attack (2017), 20 Chelsea, London, 100 Chevillet, Mark, 176 Chicago School, 160 China, 13, 15, 103, 145, 207 chloroform, 104 cholera, 130 Chongqing, China, 13 chronic pain, 102, 105, 106, 109 see also pain Churchill, Winston, 138 citizen science, 215, 216 civil rights movements, 21, 194 civilians, 43, 143, 204 von Clausewitz, Carl, 128–35, 141–7, 152 and defeat, 144–6 and emotion, 141–6, 197 and great leaders, 146–7, 156, 180–81 and intelligence, 134–5, 180–81 and Napoleon, 128–30, 133, 146–7 and soldiers, number of, 133–4 war, definition of, 130, 141, 193 climate change, 26, 50, 165, 205–7, 213–16 Climate Mobilization, 213–14 climate-gate (2009), 195 Clinton, Hillary, 27, 63, 77, 99, 197, 214 Clinton, William “Bill,” 77 coal mining, 90 cognitive behavioral therapy, 107 Cold War, 132, 133, 135–6, 137, 180, 182–4, 185, 223 and disruption, 204–5 intelligence agencies, 183 McCarthyism (1947–56), 137 nuclear weapons, 135, 180 scenting, 135–6 Semi-Automatic Ground Environment (SAGE), 180, 182, 200 space race, 137 and telepathy, 177–8 colonialism, 59–61, 224 commercial intelligence, 152 conscription, 127 Conservative Party, 80, 154, 160, 163, 166 Constitution of Liberty, The (Hayek), 160 consumer culture, 90, 104, 139 contraceptive pill, 94 Conway, Kellyanne, 3, 5 coordination, 148 Corbyn, Jeremy, 5, 6, 65, 80, 81, 197, 221 corporal punishment, 92 creative class, 84, 151 Cromwell, Oliver, 57, 59, 73 crop failures, 56 Crutzen, Paul, 206 culture war, xvii Cummings, Dominic, 50 currency, 166, 168 cutting, 115 cyber warfare, xii, 42, 43, 123, 126, 200, 212 Czech Republic, 103 Daily Mail, ix Damasio, Antonio, 208 Darwin, Charles, 8, 140, 142, 157, 171, 174, 179 Dash, 187 data, 49, 55, 57–8, 135, 151, 185–93, 198–201 Dawkins, Richard, 207, 209 death, 37, 44–5, 66–7, 91–101 and authoritarian values, 92–4, 101–2, 211, 224 bills of mortality, 68–71, 75, 79–80, 81, 89, 127 and Descartes, 37, 91 and Hobbes, 44–5, 67, 91, 98–9, 110, 151, 184 immortality, 149, 183–4, 224, 226 life expectancy, 62, 68–71, 72, 92, 100–101, 115, 224 suicide, 100, 101, 115 and Thiel, 149, 151 death penalty, 92, 118 Deaton, Angus, 99–100, 102, 115 DeepMind, 218 Defense Advanced Research Projects Agency (DARPA), 176, 178 Delingpole, James, 22 demagogues, 11, 145, 146, 207 Democratic Party, 77, 79, 85 Denmark, 34, 151 depression, 103, 107 derivatives, 168, 172 Descartes, René, xiii, 36–9, 57, 147 and body, 36–8, 91, 96–7, 98, 104 and doubt, 36–8, 39, 46, 52 and dualism, 36–8, 39, 86, 94, 131, 139–40, 179, 186, 223 and nature, 37, 38, 86, 203 and pain, 104, 105 Descartes’ Error (Damasio), 208 Devonshire, Earl of, see Cavendish, William digital divide, 184 direct democracy, 202 disempowerment, 20, 22, 106, 113–19 disruption, 18, 20, 146, 147, 151, 171, 175 dog whistle politics, 200 Donors Trust, 165 Dorling, Danny, 100 Downs Survey (1655), 57, 59, 73 doxing, 195 drone warfare, 43, 194 drug abuse, 43, 100, 105, 115–16, 131, 172–3 Du Bois, William Edward Burghardt, 74 Dugan, Regina, 176–7 Dunkirk evacuation (1940), 119 e-democracy, 184 Echo, 187 ecocide, 205 Economic Calculation in the Socialist Commonwealth (Mises), 154, 166 economics, 59, 153–75 Economist, 85, 99 education, 85, 90–91 electroencephalography (EEG), 140 Elizabethan era (1558–1603), 51 embodied knowledge, 162 emotion and advertising, 14 artificial intelligence, 12–13, 140–41 and crowd-based politics, 4, 5, 8, 9, 10, 15, 16, 21, 23–7 Darwin’s analysis, 8, 140 Descartes on, 94, 131 and experts, 53, 60, 64, 66, 90 fear, 11–12, 16–22, 34, 40–45, 52, 60, 142 Hobbes on, 39, 41 James’ analysis, 140 and markets, 168, 175 moral, 21 and nationalism, 71, 210 pain, 102–19 sentiment analysis, xiii, 12–13, 140, 188 and war, 124–6, 142 empathy, 5, 12, 65, 102, 104, 109, 112, 118, 177, 179, 197 engagement, 7, 219 England Bank of England founded (1694), 55 bills of mortality, 68–71, 75, 79–80, 81, 89, 127 civil servants, 54 Civil War (1642–51), 33–4, 45, 53 Elizabethan era (1558–1603), 51 Great Fire of London (1666), 67 hospitals, 57 Irish War (1649–53), 59 national debt, 55 Parliament, 54, 55 plagues, 67–71, 75, 79–80, 81, 89, 127 Royal Society, 48–52, 56, 68, 86, 208, 218 tax collection, 54 Treasury, 54 see also United Kingdom English Defense League, ix entrepreneurship, 149, 156, 162 environment, 21, 26, 50, 61, 86, 165, 204–7, 213–16 climate change, 26, 50, 165, 205–7, 213–16 flying insects, decline of, 205, 215 Environmental Protection Agency, 23 ether, 104 European Commission, 60 European Space Agency, 175 European Union (EU), xiv, 22, 60 Brexit (2016–), see under Brexit and elites, 60, 145, 202 euro, 60, 78 Greek bailout (2015), 31 immigration, 60 and nationalism, 60, 145, 146 quantitative easing, 31 refugee crisis (2015–), 60, 225 Unite for Europe march (2017), 23 Exeter, Devon, 85 experts and crowd-based politics, 5, 6, 23, 25, 27 Hayek on, 162–4, 170 and representative democracy, 7 and statistics, 62–91 and technocracy, 53–61, 78, 87, 89, 90 trust in, 25–33, 63–4, 66, 74–5, 77–9, 170, 202 violence of, 59–61 Expression of the Emotions in Man and Animal, The (Darwin), 8, 140 Exxon, 165 Facebook, xvi, 15, 201 advertising, 190, 192, 199, 219, 220 data mining, 49, 185, 189, 190, 191, 192, 198, 219 and dog whistle politics, 200 and emotional artificial intelligence, 140 as engagement machine, 219 and fake news, 199 and haptics, 176, 182 and oligarchy, 174 and psychological profiling, 124 and Russia, 199 and sentiment analysis, 188 and telepathy, 176–8, 181, 185, 186 and Thiel, 149, 150 and unity, 197–8 weaponization of, 18 facial recognition, 13, 188–9 failed states, 42 fake news, 8, 15, 199 Farage, Nigel, 65 fascism, 154, 203, 209 fear, 11–12, 16–22, 34, 40–45, 52, 60, 142 Federal Bureau of Investigation (FBI), 137 Federal Reserve, 33 feeling, definition of, xii feminism, 66, 194 Fifth Amendment, 44 fight or flight, 111, 114 Financial Times, 15 first past the post, 13 First World War, see World War I Fitbit, 187 fixed currency exchange rates, 166 Florida, Richard, 84 flu, 67, 191 flying insects, 205, 215 France censuses, 66, 73 conscription introduced (1793), 127 Front National, 27, 61, 79, 87, 92 Hobbes in (1640–51), 33–4, 41–2 Le Bon’s crowd psychology, 8–12, 13, 15, 16, 20, 24, 25, 38 life expectancy, 101 Napoleonic Wars (1803–15), see Napoleonic Wars Paris climate accord (2015), 205, 207 Paris Commune (1871), 8 Prussian War (1870–71), 8, 142 Revolution (1789–99), xv, 71, 126–9, 141, 142, 144, 204 statistics agency established (1800), 72 unemployment, 83 Franklin, Benjamin, 66 free markets, 26, 79, 84, 88, 154–75 free speech, 22, 113, 194, 208, 209, 224 free will, 16 Freud, Sigmund, 9, 14, 44, 107, 109–10, 111, 112, 114, 139 Friedman, Milton, 160, 163, 166 Front National, 27, 61, 79, 87, 92, 101–2 full spectrum warfare, 43 functional magnetic resonance imaging (fMRI), 140 futurists, 168 Galen, 95–6 Galilei, Galileo, 35 gambling, 116–17 game theory, 132 gaming, 193–4 Gandhi, Mohandas, 224 gate control theory, 106 Gates, Sylvester James “Jim,” 24 Gavotti, Giulio, 143 geek humor, 193 Gehry, Frank, 84 Geller, Uri, 178 geometry, 35, 49, 57, 59, 203 Gerasimov, Valery, 123, 125, 126, 130 Germany, 34, 72, 137, 205, 215 gig economy, 173 global financial crisis (2007–9), 5, 29–32, 53, 218 austerity, 100–101 bailouts, 29–32, 40, 42 and gross domestic product (GDP), 76 as “heart attack,” 57 and Obama administration, 158 and quantitative easing, 31–2, 222 and securitization of loans, 218–19 and statistics, 53, 65 and suicide, 101 and unemployment, 82 globalization, 21, 78, 84, 145, 146 Gonzales, Alberto, 136 Google, xvi, 174, 182, 185, 186, 191, 192 DeepMind, 218 Maps, 182 Transparency Project, 198 Government Accountability Office, 29 Graunt, John, 67–9, 73, 75, 79–80, 81, 85, 89, 127, 167 Great Fire of London (1666), 67 great leaders, 146–8 Great Recession (2007–13), 76, 82, 101 Greece, 5, 31, 101 Greenpeace, 10 Grenfell Tower fire (2017), 10 Grillo, Beppe, 26 gross domestic product (GDP), 62, 65, 71, 75–9, 82, 87, 138 guerrillas, 128, 146, 194, 196 Haldane, Andrew, 32 haptics, 176, 182 Harvey, William, 34, 35, 38, 57, 96, 97 hate speech, 42 von Hayek, Friedrich, 159–73, 219 health, 92–119, 224 hedge funds, 173, 174 hedonism, 70, 224 helicopter money, 222 Heritage Foundation, 164, 214 heroin, 105, 117 heroism and disruption, 18, 146 and genius, 218 and Hobbes, 44, 151 and Napoleonic Wars, 87, 127, 142 and nationalism, 87, 119, 210 and pain, 212 and protection, 202–3 and technocracy, 101 and technology, 127 Heyer, Heather, 20 Hiroshima atomic bombing (1945), 206 Hobbes, Thomas, xiii, xvi, 33–6, 38–45, 67, 147 on arrogance, 39, 47, 50, 125 and body, 96, 98–9 and Boyle, 49, 50, 51 on civil society, 42, 119 and death, 44–5, 67, 69–70, 91, 98–9, 110, 151, 184 on equality, 89 on fear, 40–45, 52, 67, 125 France, exile in (1640–51), 33–4, 41 on geometry, 35, 38, 49, 56, 57 and heroism, 44, 151 on language, 38–9 natural philosophy, 35–6 and nature, 38, 50 and Petty, 56, 57, 58 on promises, 39–42, 45, 148, 217–18 and Royal Society, 49, 50, 51 on senses, 38, 49, 147 and sovereign/state, 40–45, 46, 52, 53, 54, 60, 67, 73, 126, 166, 217, 220 on “state of nature,” 40, 133, 206, 217 war and peace, separation of, 40–45, 54, 60, 73, 125–6, 131, 201, 212 Hobsbawm, Eric, 87, 147 Hochschild, Arlie Russell, 221 holistic remedies, 95, 97 Holland, see under Netherlands homeopathy, 95 Homer, xiv Hungary, 20, 60, 87, 146 hysteria, 139 IBM, 179 identity politics, 208, 209 Iglesias Turrión, Pablo, 5 imagined communities, 87 immigration, 60, 63, 65, 79, 87, 145 immortality, 149, 183–4, 224 in-jokes, 193 individual autonomy, 16 Industrial Revolution, 133, 206 inequality, 59, 61, 62, 76, 77, 83, 85, 88–90 inflation, 62, 76, 78, 82 infographics, 75 information theory, 147 information war, 43, 196 insurance, 59 intellectual property, 150 intelligence, 132–9 intensity, 79–83 International Association for the Study of Pain, 106 International Monetary Fund (IMF), 64, 78 Internet, 184–201, 219 IP addresses, 193 Iraq War (2003–11), 74, 132 Ireland, 57, 73 Irish Republican Army (IRA), 43 “Is This How You Feel?

Ghost Work: How to Stop Silicon Valley From Building a New Global Underclass
by Mary L. Gray and Siddharth Suri
Published 6 May 2019

Five months later, AlphaGo fell to its progeny, AlphaGo Zero. But, lest we be too impressed, it’s important to keep in mind that the rules of go are fixed and fully formalized and it is played in a closed environment where only the two players’ actions determine the outcome. AlphaGo and AlphaGo Zero’s human programmers at the Google-backed company DeepMind gave the programs clear definitions of winning versus losing. Winning go is about foreseeing the long-term consequences of one’s actions as one plays them out against those of an opponent.15 So AlphaGo was trained on billions of board positions using a large database of games between human experts, as well as games against itself, allowing it to learn what constitutes a better move or a stronger board position.16 AlphaGo Zero was then steeped in all of those prior experiences by playing against AlphaGo, a mirror image of self.

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Alic, and Howard Wial, New Rules for a New Economy: Employment and Opportunity in Post-Industrial America (Ithaca, NY: ILR Press, 2000); Chris Brenner, Work in the New Economy: Flexible Labor Markets in Silicon Valley, Information Age Series (Malden, MA: Wiley-Blackwell, 2002). [back] 14. Scott Hartley, The Fuzzy and the Techie: Why the Liberal Arts Will Rule the Digital World (Boston: Houghton Mifflin Harcourt, 2017). Hartley focuses on the case of AlphaGo. Both AlphaGo and AlphaGo Zero were the brainchildren of DeepMind, a London-based research lab acquired by Google in 2014. [back] 15. Tom Dietterich, personal conversation, April 13, 2018. Noted AI researcher Dietterich put it this way: the version of AlphaGo that defeated Ke Jie was “told” the rules of go (in the sense that it could invoke code to compute all legal moves for any board state and it was given the definitions of winning and losing).

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See benefits; wages computers access to, 85, 122, 236 n26 algorithmic cruelty in, 67–69, 85–91 as executors of code, xiv–xv humans as, 39, 51–53, 54, 57 limitations of, 170–71, 231 n41 outsourcing, rise of, 54–56 consumer action, 193–94 content moderation, ix, x–xii, xxi, 19, 183 Contingent and Alternative Employment Arrangements, xxiv contingent work, xxii, xxiv, 8, 44, 46, 51, 53–55, 58–61 contract (temporary) work Amazon.com hiring of, 1–2 classification of, 57–63, 144–47 vs full-time work, 45–50, 159–60, 172–73, 185, 187–88 reliance on, 39 transaction costs, 68–69 See also on-demand employment corporate culture, transaction costs, 73 corporate firewalls, 16–21 cost-of-living allowance (COLA), 47 costs/expenses of employees, 39, 54 hiring, 32 outsourcing and, 55 platform fees, 144–47 shared workspaces, 180–81 social consequences, 68–69 transaction costs. see transaction costs up-front costs for workers, 108 See also double bottom line Craigslist, 4, 27, 32 creativity dependence on, xii, 31, 147, 170–71, 192 humans vs CPUs, xiv, 176, 231 n41 LeadGenius, 22 need for, 21, 161, 177–78 CrowdFlower, xv, 13, 34–35, 144–45 crowdsourcing. See on-demand employment crowdwork, xv D Danelle, 114–15 Daqri, 167–68 deadlines, artificial, 77 deaf and hard-of-hearing communities, xxix, 28, 152, 225 n29 DeepMind, xx, 220 n14 degrees. See college education demographics, on-demand employment Amara, 29 LeadGenius, 23–24, 224 n27 MTurk, 3–4, 10, 11, 126 UHRS, 18, 19 Upwork, 169 Department of Labor, 11, 168 design flaws, 91–93 Diane, 78–79 Dietterich, Tom, xx–xxi, 220 n15 Digital Divide, 162 disability captioning for, xxix, 28, 152–55, 225 n29 on-demand work perceived as, xxx employment, 113–17, 175 insurance for, 60 laws pertaining to, 237 n35 discrimination APIs, 172 collaboration, 135–37 digital access, 161–62 glass ceilings, 113–17 marital status, 53–54 skin color, 226 n3 slavery, 40–41, 226 n2 See also women disenfranchisement, 86 Disney, scheduling, 100 “dollars for dicks,” x DoorDash, 157–58, 162, 189 double bottom line, 140–65 Amara and, 153–55 defined, 141 by design, 148–52, 240 n9 Good Work Code, 156–58 overview of, 140–43 peer-to-peer sharing company, 155–56 platform cooperatives, 158–59 shortcomings of, 159–63 vs single bottom line, 144–47 social entrepreneurship and, 147 tragedy of the commons, 164–65 driver-partners (Uber), 145–46, 240 n5 Dynamo, 136–37 E Economic Policy Institute, xxv education college, xxix, 50, 97, 98, 101, 190 recommendations for, 190 requirement of, 10, 161–62 skill development, 110–13 for women, 114 See also training empathy, 184–85 employees.

More: The 10,000-Year Rise of the World Economy
by Philip Coggan
Published 6 Feb 2020

Gordon, The Rise and Fall of American Growth, op. cit. 20. Amie Gordon and Tom Rawstorne, “Traffic is slower than a horse drawn carriage”, Daily Mail, October 16th 2016 21. Andrew McAfee and Erik Brynjolfsson, Machine, Platform, Crowd: Harnessing Our Digital Future 22. “DeepMind AI reduces Google data centre cooling bill by 40%”, https://deepmind.com/blog/deepmind-ai-reduces-google-data-centre-cooling-bill-40/ 23. Nathan Rosenberg, Exploring the Black Box: Technology, Economics, and History 24. Ami Sedghi, “Facebook: 10 years of social networking, in numbers”, The Guardian, February 4th 2014 25. Source: https://www.statista.com/statistics/264810/number-of-monthly-active-facebook-users-worldwide/ 26.

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Technology is reducing coordination costs through search engines, cheap communication networks and free information. That allows companies to outsource tasks to the cheapest and most efficient providers. Artificial intelligence can be used to create designs that humans would not devise on their own; when a neural network called DeepMind was asked to come up with a system for cooling a data centre, energy use fell 40%.22 The debate on the economic impact of the internet is not easily settled. It is true that past technologies were slow to have an effect; it was 60 years after the Wright brothers flew before people commonly took commercial flights.

Talk to Me: How Voice Computing Will Transform the Way We Live, Work, and Think
by James Vlahos
Published 1 Mar 2019

If it wasn’t already hard enough to get a computer to say things properly in English, the big tech companies are rushing to expand into new markets globally. Siri now speaks more than twenty languages, each presenting its own complexities of pronunciation and inflection. So you can probably guess at the more automated and improved technique for synthesizing voices that the tech world has recently rushed to embrace: deep learning. DeepMind’s WaveNet technology, which was released to developers in 2018 and helps the Google Assistant to speak, is parametric synthesis on steroids. Once WaveNet knows what to say, it synthesizes waveforms and assembles them into words at a rate of up to 24,000 samples per second of speech. Apple, in turn, rolled out new neural-network-backed voice options for Siri in August 2017.

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See voice computing conversational doppelgängers, 14, 187–88 conversation design, 119, 127–28, 134 Cope, David, 108 Corrado, Greg, 106 Cortana Alexa and, 213 gender and, 130 inappropriate and hateful speech and, 240, 241–43 as lifelike entity, 12 personality of, 10–11, 117–18, 119–24, 128–29, 132–33 platform war and, 8, 281, 282 release of, 8, 49–50 responsibility for content and, 219 on smart speaker, 213, 281 XiaoIce compared to, 182–83 Cosmic Call, 285 Cross, Greg, 271–72 crowdturfers, 217 Crowther, William and Pat, 78–79, 98 “The Crying Shame of Robot Nannies” (Sharkey and Sharkey), 235 Curley, John, 155–56 Curry, Amanda, 146–47 customer-service bots, xiii, 52–53, 57–58, 132 Cyc, 161–62 Czech Technical University, 143–45, 153, 156, 159 D Dadbot continued improvement of, 267–68, 276–77 on Facebook Messenger, 261, 266 interactions with, 261–65, 268–69 oral history used to build, 251–53, 262, 276–77 plans for and building of, 253–61 PullString and, 253, 256, 259, 260–61, 267 Daimler Financial Services, 271 DARPA (Defense Advanced Research Projects Agency), 22–23 The Da Vinci Code (Brown), 199 Debevec, Paul, 273 deception, 194 deep learning ASR and, 97–98 definition of, 90 image recognition and, 94–95, 103 natural-language generation and, 104–9 natural-language understanding and, 98–103 potential of, 93 speech synthesis and, 113–15 DeepMind, 113–14 Defense Advanced Research Projects Agency (DARPA), 22–23 Descartes, René, 65 Deschanel, Zooey, 46 dialogue systems, 72 The Diamond Age (Stephenson), 180 digital humans, 270–76 Dino, 234–35 disambiguation, 99–100 distributional semantics, 102 DNNresearch (Deep Neural Net Research), 94 Dolly Rekord, 170 DolphinAttack scenario, 230 Domingos, Pedro, 161–62 Domino’s, 57, 110, 114 Doodles (Google), 124–25 doppelgängers, conversational, 14, 187–88 Doppler, Project, 41–45 Dostert, Léon, 71–72 Dudley, Homer, 70 Dungeons & Dragons, 78 Duplex, 116 E Eastern Mediterranean Public Health Network, 246 eavesdropping, 222–34 by accident, 226–27 in the future, 232–34 by the government or hackers, 13, 227–30 to improve quality, 225–26 police investigations and, 222–24 in science fiction, 13 scrutinizing technologies for, 249 that begs for action, 230–32 Echo (Amazon).

Work in the Future The Automation Revolution-Palgrave MacMillan (2019)
by Robert Skidelsky Nan Craig
Published 15 Mar 2020

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

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Strong, 99 Artisans, 12, 29, 38, 74, 93, 94 Attitudes to work, 1, 4, 53–62, 73, 75 Aubrey, 184 Austria, 68, 196 Authenticity, 116 Authority, 120, 165 Automation restrictions on, 95 speed of, 21, 137 task automation vs job automation, 92, 93, 110, 141 Autonomous cars, 114, 115, 118 Autor, David, 59, 126 Autor Levy Murnane (ALM) hypothesis, 126–128, 131 B Bailey, Olivia, 180 Bairoch, Paul, 44, 46 Banking (automation of ), 87, 147 Bargaining, 68, 70, 177, 181, 182, 184, 185 Bastani, Aaron, 179n2 Beckert, Sven, 44 Berger, Thor, 95 Bessen, James, 4 Blumenbach, Wenzel, 41 Bosch, Gerhard, 179 Bostrom, Nick, 112, 113 Bourgeois household, 39 Brain and AI, 113 analagous to computer, 100, 103, 104, 115 Brown, William, 185 Bullshit jobs psychological effects, 162 Bureaucracy, 169 C Capitalism, 12, 17, 28, 53, 57, 58, 61, 75, 135, 159 Capper, Phillip, 127, 128 Care work, 3, 48, 75, 117, 178 Carlyle, Thomas, 28 Catholic, 74 Central Europe, 38, 40 Centralisation, 69, 175, 176 Chalmers, David, 103 Chatbots, 91 Chen, Chinchih, 95 Chess (and AI), 112 China, 95, 135 Christian (view of work), 74, 75, 161, 166 Clark, A, 60 Class, 13–15, 17, 30, 39, 43, 46, 47, 118, 159, 160, 162, 165, 172 Classical economics, 54, 55 Climate change, 30, 198 Cloud computing, 139, 140 Coase, Ronald, 70 Coats, David, 184, 185 Collective bargaining, 68, 181, 182, 185 Communism, 13, 57, 58, 61 Competition, 12, 16–18, 39, 91, 94, 112, 115, 119, 139, 140, 152, 199 Index Computational Creativity, 109, 115, 120, 121 Computer aided design (CAD), 34, 35 Computer programming, 100, 116 Computer revolution, 90, 94, 95, 99 Computers, 20, 34, 84, 86, 90, 92–94, 99–107, 110, 111, 115, 116, 120, 131, 134, 146, 147, 151, 197 Consciousness of AI, 110–111 the hard problem, 103 of humans, 105 objective vs. subjective, 102, 103 Consumerism/consumer society, 30, 74, 161, 194 Consumption, 3, 5, 12, 13, 16, 19, 38, 41, 56, 59, 61, 62, 66, 85, 88, 166, 176, 192, 194, 197, 199 Contested concepts, 120 Cooperatives, 40, 61, 69 Craftsmanship, 3, 11, 35, 36, 39, 194 Craig, Nan, 4, 179 Creative work, 3, 48, 74 Creativity, 3, 5, 57, 91, 105–107, 110, 120, 121, 193–195 D D’Arcy, Conor, 177 Data, 2, 84, 92, 107, 129, 130, 137–140, 146, 149, 150, 153, 178, 191, 197, 198 Davies, W.H., 31 De Spiegelaere, Stan, 181, 183 205 Deep Blue, 91, 112, 129, 130 Dekker, Fabian, 180 Deliveroo, 136 Demand effects on automation, 4, 21, 86 elasticity, 86 of work, 4, 13, 15, 16, 76, 158, 164, 180, 199 Democracy, 28 Denmark, 68, 177, 180 Dennett, Daniel, 100, 102, 103 Developing countries, 145 Digital economy, 5, 19, 125–132, 140 Digital revolution, 70 Division of labour, 11, 35, 38, 43, 44, 55 Donkin, Richard, 3 Dosi, Giovanni, 192, 195 Do what you love, 73, 74, 76 Dreyfus, Herbert, 100 E Economics, 1, 4, 5, 7, 10, 12, 14, 15, 18, 29, 30, 53–62 Economic view of work, 53–62 Education, 41, 42, 48, 67–69, 126, 131, 169, 171, 196, 197 Efficiency, 5, 16, 75, 159, 168, 184 Empathy, 106, 107 Employment law, 68 rates, 67, 68, 70 English East India Company, 44 Entrepreneurs, 29, 70, 77, 190, 192, 197, 199 Environment, 25, 31, 56, 70, 87, 91, 109, 111, 113, 120, 178, 198 206 Index Equality of opportunity, 69 of outcome, 69 social, 163 Ethics of AI, 6, 110, 119, 145–153, 197 stagnation of, 151–152 of work, 28 Exit, 69 Experience, 36, 61, 85, 90, 94, 99–105, 116, 119, 189, 190 F Facebook, 136–141, 161 Factory system, 29–30 Families, 3, 26, 29, 37–48, 75, 76, 138, 159, 162, 178, 196 Feminist (arguments about work), 79 Finance, 48, 87, 170, 197 Fire, harnessing/discovery of, 29 Firestone, Shulamith, 159 Firms, 16, 17, 68, 70, 85, 87, 133, 148, 149, 151, 152, 168, 169, 172, 190 Flexicurity, 68 Ford, Henry, 30 Ford, Martin, 2, 59, 106 France, 4, 6, 66–70, 177, 181, 182 Franklin, Benjamin, 28 Freeman, Chris, 192 French Revolution, 43 Frey, Carl Benedikt, 4, 180 Friedman, Milton, 171 Fuzzy matching, 148, 149 G Galbraith, JK, 66 GDP, 19, 178 Gender, 38, 43, 44, 48, 151, 178 Gendered division of labour, 38, 43, 44 Germany, 6, 177, 180–182, 196 Gig economy, 27, 184 Globalisation, 20, 30, 90, 95 Google Google Cloud, 140 Google Home, 140 Google Maps, 35 Google Translate, 106 Google DeepMind, 112, 119 Gorz, A., 59 Graeber, David, 6, 76, 157, 161, 168 Greek ideas of work, 74 Growth, 2, 6, 7, 12, 25, 27, 30, 31, 55, 69, 75, 85, 86, 88, 110, 126, 128, 130, 135, 169, 176, 180, 183, 185, 190, 192, 198, 200 H Happiness, 5, 62, 195 Harrop, Andrew, 180 Hassabis, Demis, 119 Hayden, Anders, 182, 183 Healthcare, 3, 87, 94, 117, 165, 197 Heterodox economics, 54, 56, 62 Hierarchy, 46, 48, 55, 69, 170 High-skilled jobs, 128, 134 Homejoy, 135 Homo economicus, 56, 57 Homo laborans, 3 Homo ludens, 3 Household economy, 4, 38–40, 45, 47 Housewives, 42, 43, 46, 47 Housework, 39, 40, 42, 44, 47 Hunter-gatherers, 11, 26, 27, 30 Index I Idleness, 54 India, 44–47 Industrial Revolution, 2, 4, 14, 29, 37, 75, 93, 94, 175, 177, 190, 191 Inequality, 67–69, 86, 87, 192, 193, 199, 200 Informal economy, 47 Information technology, 86, 161 Infrastructure digital, 140 physical, 103 Innovation, 6, 10, 14, 16, 18, 34, 67, 69, 189–199 process innovation vs. product innovation, 16, 18, 190–191, 195 International Labour Organisation (ILO), 193 Internet of Things, 139, 191 Investment in capital, 114 in skills, 70 J Japan, 117 Jensen, C, 55 Job guarantee, 172 Jobs, Steve, 73 Journalism automation of, 118 clickbait, 118 Juries, algorithmic selection of, 150, 153 K Karstgen, Jack, 196 Kasparov, Garry, 91, 112, 129, 130 207 Katz, Lawrence, 198 Kennedy, John F., 160 Keune, Maarten, 180 Keynes, John Maynard, 6, 9, 11, 27, 60, 61, 160, 161, 176 King, Martin Luther, 171 Knowledge (tacit vs. explicit), 127 Komlosy, Andrea, 4, 75 Kubrick, Stanley, 26 Kurzweil, Raymond, 101, 103, 104 Kuznets, Simon, 190 L Labour, 3, 10, 11, 13–16, 18–21, 29, 34–36, 38, 43–46, 55, 59, 65–70, 73–76, 85–87, 89, 90, 93, 94, 96, 114, 125, 126, 128, 130, 131, 141, 158, 165, 176–180, 183–184, 189, 190, 192–196, 199–200 Labour market polarisation, 67, 70, 126 Labour markets, 67, 68, 70, 87, 90, 96, 125, 126, 128, 130, 131, 141, 178, 183–184, 189, 192, 193, 195, 196, 199–200 Labour-saving effect, 86 Lall, Sanjaya, 193 Language translation, 105, 106 Latent Damage Act 1986, 127 Law automation of, 145, 152, 153 ethics, 145–153 Lawrence, Mathew, 177 Layton, E., 58 Le Bon, Gustave, 101 Lee, Richard, 26 Legal search/legal discovery, 148–150 208 Index Leisure, 3, 10, 11, 19, 27, 48, 55, 56, 59–62, 65, 77, 79, 117, 118, 159, 161, 178, 180, 182, 184, 191, 195 Levy, Frank, 126 List, Friedrich, 193 Love, 55, 74, 76, 99, 103, 106, 112, 118 Low-income jobs, 96 Loyalty, 69 Luddites, 2, 14, 18, 35, 59, 94, 96 Lyft, 136 M Machine learning, 59, 84, 90, 91, 96, 138, 139 Machines, 2, 5, 10, 12–15, 17, 19, 20, 35, 36, 38, 59, 84–87, 90–96, 99–103, 105–107, 109–121, 127–131, 138, 139, 145, 147, 148, 160, 168, 191 Machine vision, 120 Malthusian, 19 Man, Henrik de, 79 Management, 27, 30, 41, 69, 70 management theory/ organisational theory (see also Scientific management) Mann, Michael, 46 Manual work, 1 Manufacturing, 86, 87, 90, 94, 95, 176, 184, 198 Markets/market forces, 5, 6, 21, 38, 44–46, 67, 68, 70, 79, 85–88, 90, 96, 120, 125, 126, 128, 130, 131, 140, 141, 150, 152, 159, 164, 165, 171, 178, 183, 189–193, 195, 196, 198–200 Marx, Karl, 17, 18, 27, 56–59, 61, 62, 78 Matrimonial relationships, 37 McCormack, Win, 159 Meaning, 4, 9, 10, 19, 25, 54, 57, 58, 66, 73, 76, 78, 79, 84, 106, 116, 176, 180 Mechanisation, 15, 17, 19, 20, 192 Meckling, W., 55 Méda, Dominique, 183 Medical diagnosis (automation of ), 128, 129 Menger, Pierre-Michel, 4 Mental labour, 3 Meritocracy, 28 Middle-income jobs, 90, 93, 94 Migration, 40, 47 Minimum wage, 67, 69 Mining, 26, 38, 197 Mokyr, J., 59 Monopolies, 6, 136, 138–140 Morals/morality, 48, 77, 159, 160, 162, 164, 166, 167 Moravec’s paradox, 131 Murnane, Richard, 126 N Nagel, Thomas, 100, 102 National Living wage, 184 Needs vs.

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In addition, concerns were raised about my point that the majority of AI researchers tend to separate development of AI technology from its uses and not worry too much about ethical issues. I emphasized that while this is currently the case, things are changing, with technology leaders such as Demis Hassabis from Google Deep Mind promoting ethical usage of AI, and ethics courses being given to computing students. Later, we returned to the question of who makes the decisions about AI usage, and discussed whether this is likely to come from the bottom up, for example from community or consumer groups, and I confessed to being dubious about this.

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

Digital never forgets, and cybernetics makes sure that everything eventually comes back. Even if they can outrun all that, there’s one force that the tech titans almost universally fear more than any other: artificial intelligence. In January 2015, when Elon Musk, Stephen Hawking, and Google’s director of research, Peter Norvig, joined the founders of AI companies including DeepMind and Vicarious in signing an open letter about the frightening potential for artificial intelligence to end the human race, I wasn’t sure how to react. Other than Hawking, these men were mostly industry developers and salesmen, and had histories of overstating the abilities of their technologies.

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That may not be interpreted as a threat to their interests.” The bigger the billionaire, the greater the fear, and the countermeasures. Elon Musk told a 2014 audience at MIT that by experimenting with AI, Larry Page and his friends at Google are “summoning the demon .” In a now famous Vanity Fair account of a conversation between Elon Musk and DeepMind creator Demis Hassabis, Musk explained that one of the reasons he intended to colonize Mars was “so that we’ll have a bolt-hole if AI goes rogue and turns on humanity.” Similarly, Musk has been developing a neural net apparatus that can be lasered onto our brains, which would potentially allow us to compete with a superintelligent rogue AI that turns against us.

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

For me, in 1998, that thing was PayPal (my college roommate cofounded the company, and I bought some early shares); in 2004, it was Facebook (my then boss made the first outside investment in the social network, and I worked as a junior associate on part of that deal). Later, I made personal investments in SpaceX, Lyft, Palantir, and DeepMind, which are not all household names, though they have succeeded well enough. But these companies were exceptions, very rare ones. I mention them less to establish my credibility as a prognosticator than to show the value of socially funded innovation (every company I mentioned was built on technologies pioneered by government grants or research) and, most important, to show the overwhelming importance of luck in a stagnating economy.

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They will become our helpers, then possibly our competitors, and we have no real plan. In the 1990s, the threat did not seem credible, and inaction then might have been excusable. But AI, which had been a joke for years, constantly failing to live up to its promises, has begun to exceed even more optimistic forecasts. In 2016, DeepMind’s AlphaGo program beat a human master at Go 4–1, an achievement many thought unlikely to occur before 2025. Because of the flexible way AlphaGo learns, and the enormous difficulty of the game it was playing (Go is to chess what chess is to checkers), an AI that can win at Go is something we need to take seriously.

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* It’s doubtful that a malevolent Skynet will be the author of catastrophe; more likely, AIs responsible for essential systems like power plants, autonomous weapons, dams, and so on will make mistakes that could unleash catastrophe. Then again, the possibility of a rogue supercomputer is not zero, though it remains distant. * Full disclosure: I invested in DeepMind personally in its earlier years; the company was then acquired by Google, in which I now hold stock. Wall Street has long dismissed Google’s side projects like self-driving cars and AI as money sinks, but Google has a thoughtful plan and one you may not be fully comfortable with. Google (in the verb sense; may as well start there) “self-driving car,” “AlphaGo,” and “Android Marketshare” and you’ll get a sense for the future Google might have in mind.

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

AI witnessed a comeback in the last decade, primarily because the world woke up to the realization that deep learning by machines is possible to the level where they can actually perform many tasks better than humans. Where did this wake-up call come from? From a simple game called Go. In 2016, AlphaGo, a program developed by Google DeepMind, beat the human world champion in the Chinese board game, Go. This was a surprise to many, as Go – because of its complexity – was considered the territory of human, not AI, victors. In a decade where our human desire to connect globally, execute tasks faster, and accumulate massive amounts of data, was omnipresent, such deep learning capabilities were, of course, quickly embraced.

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If this is the case, then it is no surprise that the availability and possibility of implementing intelligent machines and their learning algorithms will have a significant impact on how work will be executed and experienced. This reality is hard to deny because the facts seem to be there. As mentioned earlier, Google’s DeepMind autonomous AI beat the world’s best Go-player, and recently Alibaba’s algorithms have been shown to be superior to humans in the basic skills of reading and comprehension.⁹ If such basic human skills can be left to machines and those machines possess the ability to learn, what then will the future look like?

Spike: The Virus vs The People - The Inside Story
by Jeremy Farrar and Anjana Ahuja
Published 15 Jan 2021

Adding in the chief scientists and other emissaries for various government departments, plus the devolved nations, I would guess that SAGE has between 200 and 300 people to call on in total, although there were rarely more than 20 to 30 in attendance (plus others dialling in on often ropey lines). I do not recall Treasury officials at the meetings I attended. Outsiders were occasionally invited in; at one meeting I sat next to Demis Hassabis, a researcher who cofounded artificial intelligence start-up DeepMind. The SAGE meetings mostly took place in a basement at 10 Victoria Street in Westminster, which also houses the Government Office for Science. We would go through security and head downstairs through corridors with peeling paint. Then we would swipe our security passes to access a waiting area, strewn with unwashed cups that looked like they had been there for weeks.

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Harries said publicly that the UK did not need to follow the WHO’s advice (that countries should ‘test, test, test’) because it did not apply to high-income countries. In 2021, she was appointed chief executive of the UK Health Security Agency. Demis Hassabis A former child chess prodigy, neuroscientist, games designer and entrepreneur, and co-founder of artificial intelligence start-up DeepMind. Hassabis attended the SAGE meeting on 18 March 2020, where he expressed alarm at the way the epidemic was unfolding. Richard Hatchett CEO of CEPI (the Coalition for Epidemic Preparedness Innovations) and a former White House adviser (during the H1N1 outbreak of 2009). He concluded a funding agreement with Stéphane Bancel of Moderna in January 2020 and shortly afterward began to include Jeremy Farrar in US emails on the emerging threat and on vaccine development.

The War on Normal People: The Truth About America's Disappearing Jobs and Why Universal Basic Income Is Our Future
by Andrew Yang
Published 2 Apr 2018

Go is a 3,000-year-old Chinese game with theoretically infinite moves. In order to beat the world’s best go players, an AI would need to use something resembling judgment and creativity in addition to pure computation. In 2015 Google’s DeepMind beat the world’s best go player and then did it again in 2017 against other world champions. Go champions looked at the DeepMind strategies and said that it used moves and tactics no one had ever seen before. New kinds of AI are emerging that can do much of what we now consider intelligent and creative. You might have heard the term “machine learning,” which is an application of AI in which you give machines access to data and let them learn for themselves what the best methods are.

Hands-On Machine Learning With Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
by Aurelien Geron
Published 14 Aug 2019

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 May 2017 when it beat the world champion Ke Jie 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.

Escape From Model Land: How Mathematical Models Can Lead Us Astray and What We Can Do About It
by Erica Thompson
Published 6 Dec 2022

Nor can humans directly use the strategy of Deep Blue’s successor. AlphaZero, constructed by engineers at Google’s DeepMind, searches for good strategies by playing huge numbers of games against itself and assigning estimated probabilities of winning to each move. Although in the real world we cannot possibly think through all the consequences of even the simplest actions, in a way the development of these probabilities is somewhat like the formation of a simple conviction narrative. David Silver and colleagues from DeepMind describe how the AlphaZero algorithm ‘focus[es] selectively on the most promising variations’, in the same way that we ignore the vast majority of possible futures and home in on a few that seem either most likely or most desirable.

Blitzscaling: The Lightning-Fast Path to Building Massively Valuable Companies
by Reid Hoffman and Chris Yeh
Published 14 Apr 2018

Amazon may have started as a simple online retailer with no unique technology, but today its technological prowess in cloud computing, automated logistics, and voice recognition help to maintain its dominance. In fact, the megacompanies built by blitzscaling are often the ones buying the technology innovators, much as Google bought DeepMind and Facebook bought Oculus. Technology innovation is a key factor in retaining the gains produced by business model innovation. After all, if one technology innovation can create a new market, another technology innovation can render it obsolete, seemingly overnight. While Uber has achieved massive scale, the greatest threat to its future doesn’t come in the form of direct competitors like Didi Chuxing, though these are formidable threats.

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Apple hopped from music players to smartphones to tablets, and it is no doubt spending some of its vast profits chasing the next S-curve. The premium that the public markets grant these companies also helps them use mergers and acquisitions (M&A) to jump these curves, much as Facebook did with Instagram, WhatsApp, and Oculus, and Google did with DeepMind. Of course, network effects don’t apply to every company or market, even if they are superficially similar—as many companies and their investors discovered to their chagrin during the dot-com bust, the Great Recession, and the funding slowdown of 2016. This is why the best entrepreneurs try to design innovative business models that leverage network effects.

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

This complexity is also why computers using rider/think-slow/System-2 “thinking” couldn’t match human-level performance in Go even though they beat the best humans at chess decades ago. That changed in May 2017. That’s when a computer program, called AlphaGo Master, used machine learning techniques to beat the world’s best Go player.10 The how is as amazing as the what. AlphaGo Master, owned by the leading AI company DeepMind, learned the ropes by studying 30 million board positions from 160,000 actual games. This is a bit intimidating. There are only about 26 million minutes in a human working life, so AlphaGo Master started with more than a lifetime of experience. But then things got even more daunting for human players hoping to compete with this technology.

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Third, the processes can scale up and down rapidly to deal with, for example, seasonal fluctuations in the paperwork flow; there is no need to hire and train temporary workers when you can just run the software a bit harder. In some sense, RPA is the “wave of today” when it comes to globotics automation. The “wave of tomorrow” refers to the more sophisticated systems—the Cortanas and DeepMinds of this world. These can handle a much wider range of workplace tasks. This makes them a much deeper threat to existing human jobs, but it also makes them harder to implement and thus slower to phase in. High-End White-Collar Robots Amelia, the white-collar robot we met in Chapter 1, is not just an amazingly productive service-sector worker, she is simply amazing.

The Capitalist Manifesto
by Johan Norberg
Published 14 Jun 2023

It means that large resources are not wasted on things that are similar to what we already have, but are channelled into areas for radical and subversive innovation that cannot easily be incorporated into existing business models. Something that will enrich us all more than having a second Facebook or a slightly bolder set of emojis. One way for market leaders to continue to stay on top for a while longer is to buy small, innovative companies – from YouTube and Instagram to Oculus and DeepMind. It is sometimes almost considered cheating, as if the old vampires are extending their own lives with the blood of young, vibrant startups. But this is an important division of labour. It is difficult for established companies that focus on defending old business models to be radically innovative, while new companies rarely have the knowledge of the market, the capital to invest, the ability to manage regulatory systems or the infrastructure to develop, market and sell.

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INDEX NB Page numbers in italics indicate illustrations Afghanistan, 160–61, 256 Africa, 30–35, 70, 267, 282 colonisation, 31 independence, 31–4 Sub-Saharan Africa, 30–31 AIM (AOL Instant Messenger), 170 Albania, 50 Algeria, 251 Alphabet, 179 AltaVista, 169, 174 Amazon, 169–72, 178–9 Amazon Prime, 179 Andersson, Magdalena, 8 Angola, 239 Annan, Kofi, 3 Ant Group, 227 AOL (America Online), 169–71, 174 Apple, 107–8, 159, 163, 169–73, 179 Apple TV, 179 Arab Spring, 215 Aristophanes, 73 Aristotle, 70 ARPA, 183–6 ARPANET, 184–5 Asia, 267, 282 Asp, Anette, 287 Attac, 2–3, 6 Australia, 11, 258, 267, 282, 285 Ayittey, George, 31 Bangladesh, 235 Bank for International Settlements (BIS), 144 Bankman-Fried, Sam, 153 Bao Tong, 212 Baran, Paul, 184, 186–7 Bastiat, Frédéric, 114 Beijing, China, 209 Belgium, 285 Berggren, Niclas, 62 Bergh, Andreas, 56, 103 Bezos, Jeff, 127 Biden, Joe, 76, 217 big companies, 141, 146–50, 176–7, 292 BioNTech, 177 biotechnology, 195 Björk, Nina, 263, 265, 272, 274–5, 278 BlackBerry, 174 Blair, Tony, 170 Blockbuster, 151 Blue Origin, 202 Bolivia, 47 Bolt, Beranek and Newman, 184 Bono, 4, 170 Botswana, 34–5 Boudreaux, Donald, 125 Boulevard of Broken Dreams (Lerner), 190 Brazil, 11, 29, 239, 258 Brexit, 116–18 Bullshit Jobs: A Theory (Graeber), 86, 98–9 business regulation, 139–41 Callaghan, James, 10 Canada, 102, 267, 283 Capital in the Twenty-First Century (Piketty), 128 capital income, 130–31 Carbon Engineering, 255 Cardoso, Fernando Henrique, 29 Carlson, Tucker, 146 cars, 158 Carter, Jimmy, 10 Case Deaton, Anne, 108–11, 136 Castillo, Pedro, 30 Chávez, Hugo, 43, 135 child labour, 20 child mortality, 19–20, 20 Chile, 11, 29–30 China, 5, 7, 11, 19, 24–5, 76, 78–80, 83–4, 104–7, 204–29, 239, 258 agricultural productivity, 206–7, 209 Communist Party, 182, 204–9, 211–12, 215–18, 221–3, 226–8 deindustrialization, 84 economic development, 205–29 environmental issues, 251–3, 257 exports, 209–10 industrial policy, 205, 212–13, 217, 223–4, 296 innovation strategy, 182, 192 innovation, 226–8 poverty, 213, 214 Reform and Opening Up programme, 212 state-owned companies, 208 WTO and, 205, 209, 211 China’s Leaders (Shambaugh), 215 Chirac, Jacques, 191 Chomsky, Noam, 49 Christianity, 264–5 Churchill, Winston, 135 Clark, Daniel, 87 climate change, 5–7, 230–60, 293 carbon border tariffs, 258 carbon tax, 256–7, 259 energy supplies, 233–5, 253–6, 259 greenhouse gas emissions, 231, 233–5, 238, 240–41, 244, 253–9 see also environmental issues Climeworks, 255 Clinton, Hillary, 140 Coase, Ronald, 206 Cohen, Linda, 189 communism, 2, 25–6, 241–3, 290–91 Communist Manifesto, The, 1848, 2 community, 267 Compaq, 174 Concorde, 191 Confucianism, 22, 25 Congo-Brazzaville, 30 Congo, 239 consumer culture, 160–62, 287–8 Cook, Tim, 173 cooperation, 278–9 Coopersmith, Jonathan, 188–9 Corbyn, Jeremy, 43 coronavirus see Covid-19 pandemic Council of Economic Advisers, 147, 152 Covid-19 pandemic, 8, 21, 76–81, 223, 232–3, 270 Cowen, Tyler, 154 Credit Suisse, 132–3 crony capitalism, 139–40, 291 culture wars, 12–13 Czechoslovakia, 26 Dalits, 63–4 dating profiles, 154 ‘deaths of despair’, 7, 108–10, 136, 271, 293 Deaths of Despair (Deaton and Case Deaton), 136 Deaton, Angus, 19, 108–119, 136 DeepMind, 177 degrowth, 232–5, 254–5 ‘deindustrialization’, 83–5 democracies, 26, 37, 46 Deneen, Patrick, 262–5 Deng Xiaoping, 24, 46, 205, 212–13 Denmark, 91, 285 ‘dependency theory’, 27–8 Detroit, Michigan, 87–8 dictatorships, 11, 24, 29, 32, 42–8 Digital Equipment Corporation, 174 disability-adjusted life years (DALY), 237 dishonesty, 153–6 Disney, 178 Dominican Republic, 225 Easterlin, Richard, 279 ‘Easterlin paradox’, 279–80 Easterly, William, 39 Ecclesiazusae (Aristophanes), 73 Economic Freedom of the World index, 35–7 economic freedom, 35–42, 36, 57, 58–62, 58, 77–8 Economist, The, 179, 192 education, 20, 94 Energiewende, 191, 192–3 Engels, Friedrich, 2, 277, 290–91 Enlightenment, 73 entrepreneurship, 123–4, 128–9, 152–4 ‘welfare entrepreneurs’, 197 environmental issues, 236–41, 245–52, 293 agriculture, 239–40 air pollution, 237–8 biodiversity, 238–9, 249–50 deforestation, 239 health and, 236–8, 237 plastics, 247–8 prosperity and, 245–52, 249 transportation, 250–51, 254–5 Environmental Performance Index (EPI), 248, 252 Estonia, 26 Ethiopia, 277 Europe, 22, 239, 267, 282 European Centre for International Political Economy, 79 European Union (EU), 4, 68, 79, 116, 164, 258–9 Everybody Lies (Stephens-Davidowitz), 155 Facebook, 163, 167–75, 179–80 Fallon, Brad, 192 famine, 29 Fanjul, Alfonso and José, 140 fascism, 75 Federal Communications Decency Act (USA), 174 Feldt, Kjell-Olof, 11 feudalism, 73, 75 Financial Fiasco (Norberg), 142 financial markets, 141–3 Financial Times, 8, 267 Finland, 76, 78, 268, 285 Foodora, 102 Forbes’ list, 129–30 forced technology transfers, 211 Foroohar, Rana, 8 Fortune 500 list, 151 Fortune magazine, 169 France, 79–80, 97, 159, 192, 281, 285 Fraser Institute, 35 free markets, 2–4, 6, 23, 58–62, 65–82, 83, 290–97 happiness and, 279–89, 282, 284, 286 human values and, 261–89 Friedman, Thomas, 204 ‘friendshoring’, 79 Friendster, 170 GAFAM (Google, Amazon, Facebook, Apple, Microsoft), 169–70 Gallup World Poll, 267 Gandhi, Indira, 245 Gapminder, 18 Gates, Bill, 124–7, 274 GDP (Gross Domestic Product), 5, 23, 26, 33, 35, 49–56 General Data Protection Regulation (EU GDPR), 164 generosity, 274–7 Georgia, 26, 215 Germany, 26, 84, 97, 101, 192–3, 196, 268 gig economy, 101–3 Gingrich, Newt, 191–2 Gini coefficient, 132 global financial crisis, 2008, 4–5, 142–3 global supply chains, 41–2, 58–61, 76, 81 Global Thermostat, 255 global warming see climate change globalization, 3–8, 17, 19, 80, 103–10, 117 Google, 163, 169–73, 179–80 Gorbachev, Mikhail, 215 Graeber, David, 86, 98–9 Grafström, Jonas, 240 Greece, 26, 254 Green Revolution, 239–40 green technology, 243, 251–5 Greider, Göran, 50, 241 growth, 49–57 degrowth, 232–5, 254–5 government and, 55–6 health and, 52–3 poverty and, 53–4 Guangdong, China, 207–8 Guardian, 3, 169 Halldorf, Joel, 262, 265 happiness, 279–89, 282, 284, 286 Hawkins Family Farm, 140 Hawkins, Zach, 140 Hayden, Brian, 161 Hayek, Friedrich, 66 Helm, Dieter, 193 Henrekson, Magnus, 56 Hertz, Noreena, 261, 262, 265, 268, 272, 274–5, 278 Hillbilly Elegy (Vance), 87 Hinduism, 22, 25 Hong Kong, 23, 205, 207 Horwitz, Steven, 294 housing market, 131, 142–3, 208–9 How China Became Capitalist (Wang and Coase), 206 How Innovation Works (Ridley), 188 Hsieh, Chang-Tai, 148–9 Hu Jintao, 215–16 Hugo, Victor, 25 Hume, David, 284 Hungary, 26, 283 IBM, 151 Iceland, 285 IKEA, 119, 141, 147 illiteracy, 20, 20 ‘import substitution’, 27–8 In Defence of Global Capitalism (Norberg), 3, 17, 33, 38, 42, 146, 151, 156, 169, 204, 214, 230–31 income, 22, 55, 88–96, 95, 134–5, 285, 291 low-income earners, 136–8 minimum wage, 90 wage stagnation, 89, 92–3 see also inequality India, 11, 24–5, 63–4, 70, 234, 239, 251, 258 caste system, 63–4 Indonesia, 239 industrial policy, 182, 188–203 Industrial Revolution, 22 inequality, 7, 27, 42, 54–5, 110, 131–8, 133, 285–7 happiness inequality, 131–2 income, 285–7 life expectancy and, 136–8 infant mortality, 19–20, 235, 291 Infineon, 196 inflation, 8, 10–11, 69 innovation, 65–6, 122–3, 125, 151, 181–203 government policy and, 181–203 innovation shadow, 169, 176 prizes and, 199 research, 199–200 subsidies and grants, 196–7 Instagram, 168, 177 integrity, 164 intellectual property, 41, 210–11 International Disaster Database, 235 International Union for the Conservation of Nature (IUCN), 238 internet, 162–8, 183–7 IPCC (Intergovernmental Panel on Climate Change), 231 iPhone, 107–8, 156, 159 Iran, 220 Iraq, 251 Ireland, 285 Italy, 97, 285 Jackson, Jesse, 43 Jacobs, A.

Homo Deus: A Brief History of Tomorrow
by Yuval Noah Harari
Published 1 Mar 2015

Deep Blue was given a head start by its creators, who preprogrammed it not only with the basic rules of chess, but also with detailed instructions regarding chess strategies. A new generation of AI uses machine learning to do even more remarkable and elegant things. In February 2015 a program developed by Google DeepMind learned by itself how to play forty-nine classic Atari games. One of the developers, Dr Demis Hassabis, explained that ‘the only information we gave the system was the raw pixels on the screen and the idea that it had to get a high score. And everything else it had to figure out by itself.’ The program managed to learn the rules of all the games it was presented with, from Pac-Man and Space Invaders to car racing and tennis games.

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Rebecca Morelle, ‘Google Machine Learns to Master Video Games’, BBC, 25 February 2015, accessed 12 August 2015, http://www.bbc.com/news/science-environment-31623427; Elizabeth Lopatto, ‘Google’s AI Can Learn to Play Video Games’, The Verge, 25 February 2015, accessed 12 August 2015, http://www.theverge.com/2015/2/25/8108399/google-ai-deepmind-video-games; Volodymyr Mnih et al., ‘Human-Level Control through Deep Reinforcement Learning’, Nature, 26 February 2015, accessed 12 August 2015, http://www.nature.com/nature/journal/v518/n7540/full/nature14236.html. 14. Michael Lewis, Moneyball: The Art of Winning an Unfair Game (New York: W. W.

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Goode’ 257–61, 358, 387, 388 Bible 46; animal kingdom and 76–7, 93–5; Book of Genesis 76–8, 77, Dataism and 381; 93–4, 97; composition of, research into 193–5; evolution and 102; homosexuality and 192–3, 195, 275; large-scale human cooperation and 174; Old Testament 48, 76; power of shaping story 172–3; scholars scan for knowledge 235–6; self-absorption of monotheism and 173, 174; source of authority 275–6; unique nature of humanity, promotes 76–8 biological poverty line 3–6 biotechnology 14, 43–4, 46, 98, 269, 273, 375 see also individual biotech area Bismarck, Otto von 31, 271 Black Death 6–8, 6, 7, 11, 12 Borges, Jorge Luis: ‘A Problem’ 299–300 Bostrom, Nick 327 Bowden, Mark: Black Hawk Down 255 bowhead whale song, spectrogram of 358, 358 brain: Agricultural Revolution and 156–7, 160; artificial intelligence and 278, 278; biological engineering and 44; brain–computer interfaces 48, 54, 353, 359; consciousness and 105–13, 116, 118–19, 121–4, 125; cyborg engineering and 44–5; Dataism and 368, 393, 395; free will and 282–8; happiness and 37, 38, 41; self and 294–9, 304–5; size of 131, 132; transcranial stimulators and manipulation of 287–90; two hemispheres 291–4 brands 156–7, 159–60, 159, 162 Brezhnev, Leonid 273 Brin, Sergey 28, 336 Buddhism 41, 42, 94, 95, 181, 185, 187, 221, 246, 356 Calico 24, 28 Cambodia 264 Cambridge Declaration on Consciousness, 2012 122 capitalism 28, 183, 206, 208–11, 216–17, 218–19, 251–2, 259, 273–4, 369–73, 383–6, 396 see also economics/economy Caporreto, Battle of, 1917 301 Catholic Church 147, 183; Donation of Constantine 190–2, 193; economic and technological innovations and 274; marriage and 26; papal infallibility principle 147, 190, 270–1; Protestant revolt against 185–7; religious intolerance and 198; Thirty Years War and 242, 243, 246; turns from creative into reactive force 274–5 see also Bible and Christianity Ceauçescu, Nicolae 133–4, 134, 135–6, 137 Charlie Hebdo 226 Château de Chambord, Loire Valley, France 62, 62 Chekhov Law 17, 18, 55 child mortality 10, 33, 175 childbirth, narration of 297–8, 297 China 1, 269; biotech and 336; Civil War 263; economic growth and 206, 207, 210; famine in 5, 165–6; Great Leap Forward 5, 165–6, 376; Great Wall of 49, 137–8, 178; liberalism, challenge to 267–8; pollution in 213–14; Taiping Rebellion, 1850–64 271; Three Gorges Dam, building of 163, 188, 196 Chinese river dolphin 188, 196, 395 Christianity: abortion and 189; animal welfare and 90–6; change from creative to reactive force 274–6; economic growth and 205; homosexuality and 192–3, 225–6, 275–6; immortality and 22 see also Bible and Catholic Church Chukwu 47 CIA 57, 160, 293–4 Clever Hans (horse) 128–30, 129 climate change 20, 73, 151, 213, 214–17, 376, 377, 397 Clinton, Bill 57 Clovis, King of France 227, 227 Cognitive Revolution 156, 352, 378 Cold War 17, 34, 149, 206, 266, 372 cold water experiment (Kahneman) 294–5, 338 colonoscopy study (Kahneman and Redelmeier) 296–7 Columbus, Christopher 197, 359, 380 Communism 5, 56, 57, 98, 149, 165, 166, 171, 181; cooperation and 133–7, 138; Dataism and 369, 370–3, 394, 396; economic growth and 206, 207, 208, 217, 218; liberalism, challenge to 264–6, 271–4; religion and 181, 182, 183; Second World War and 263 computers: algorithms and see algorithms; brain–computer interfaces 48, 54, 287, 353, 359; consciousness and 106, 114, 117–18, 119, 120; Dataism and 368, 375, 388, 389 Confucius 46, 267, 391–2; Analects 269, 270 Congo 9, 10, 15, 19, 168, 206, 257–61, 387, 388 consciousness: animal 106–7, 120–32; as biologically useless by-product of certain brain processes 116–17; brain and locating 105–20; computer and 117–18, 119, 120, 311–12; current scientific thinking on nature of 107–17; denying relevance of 114–16; electrochemical signatures of 118–19; intelligence decoupling from 307–50, 352, 397; manufacturing new states of 360, 362–3, 393; positive psychology and 360; Problem of Other Minds 119–20; self and 294–5; spectrum of 353–9, 359, 360; subjective experience and 110–20; techno-humanism and 352, 353–9 cooperation, intersubjective meaning and 143–51, 155–77; power of human 131–51, 155–77; revolution and 132–7; size of group and 137–43 Cope, David 324–5 credit 201–5 Crusades 146–8, 149, 150–1, 190, 227–8, 240, 305 Csikszentmihalyi, Mihaly 360 customer-services departments 317–18 cyber warfare 16, 17, 59, 309–10 Cyborg 2 (movie) 334 cyborg engineering 43, 44–5, 66, 275, 276, 310, 334 Cyrus, King of Persia 172, 173 Daoism 181, 221 Darom, Naomi 231 Darwin, Charles: evolutionary theory of 102–3, 252, 271, 372, 391; On the Origin of Species 271, 305, 367 data processing: Agricultural Revolution and 156–60; Catholic Church and 274; centralised and distributed (communism and capitalism) 370–4; consciousness and 106–7, 113, 117; democracy, challenge to 373–7; economy and 368–74; human history viewed as a single data-processing system 377–81, 388; life as 106–7, 113, 117, 368, 377–81, 397; stock exchange and 369–70; value of human experience and 387–9; writing and 157–60 see also algorithms and Dataism Dataism 366, 367–97; biological embracement of 368; birth of 367–8; computer science and 368; criticism of 393–5; economy and 368–74; humanism, attitude towards 387–8; interpretation of history and 377–80; invisible hand of the data flow, belief in the 385–7; politics and 370–4, 375–6; power/control over data 373–7; privacy and 374, 384–5; religion of 380–5; value of experience and 387–9 Dawkins, Richard 305 de Grey, Aubrey 24, 25, 27 Deadline Corporation 331 death, 21–9 see also immortality Declaration of the Rights of Man and of the Citizen, The 308–9 Deep Blue 320, 320 Deep Knowledge Ventures 322, 323 DeepMind 321 Dehaene, Stanislas 116 democracy: Dataism and 373–5, 376, 377, 380, 391, 392, 396; evolutionary humanism and 253–4, 262–3; humanist values and 226–8; liberal humanism and 248–50, 262–7, 268; technological challenge to 306, 307–9, 338–41 Dennett, Daniel 116 depression 35–6, 39, 40, 49, 54, 67, 122–4, 123, 251–2, 287, 357, 364 Descartes, René 107 diabetes 15, 27 Diagnostic and Statistical Manual of Mental Disorders (DSM) 223–4 Dinner, Ed 360 Dix, Otto 253; The War (Der Krieg) (1929–32) 244, 245, 246 DNA: in vitro fertilisation and 52–4; sequencing/testing 52–4, 143, 332–4, 336, 337, 347–8, 392; soul and 105 doctors, replacement by artificial intelligence of 315, 316–17 Donation of Constantine 190–2, 193 drones 288, 293, 309, 310, 310, 311 drugs: computer-assisted methods for research into 323; Ebola and 203; pharmacy automation and 317; psychiatric 39–41, 49, 124 Dua-Khety 175 dualism 184–5, 187 Duchamp, Marcel: Fountain 229–30, 233, 233 Ebola 2, 11, 13, 203 economics/economy: benefits of growth in 201–19; cooperation and 139–40; credit and 201–5; Dataism and 368–73, 378, 383–4, 385–6, 389, 394, 396, 397; happiness and 30, 32, 33, 34–5, 39; humanism and 230, 232, 234, 247–8, 252, 262–3, 267–8, 269, 271, 272, 273; immortality and 28; paradox of historical knowledge and 56–8; technology and 307–8, 309, 311, 313, 318–19, 327, 348, 349 education 39–40, 168–71, 231, 233, 234, 238, 247, 314, 349 Eguía, Francisco de 8 Egypt 1, 3, 67, 91, 98, 141, 142, 158–62, 170, 174–5, 176, 178–9, 206; Lake Fayum engineering project 161–2, 175, 178; life of peasant in ancient 174–5, 176; pharaohs 158–60, 159, 174, 175, 176; Revolution, 2011 137, 250; Sudan and 270 Egyptian Journalists Syndicate 226 Einstein, Albert 102, 253 electromagnetic spectrum 354, 354 Eliot, Charles W. 309 EMI (Experiments in Musical Intelligence) 324–5 Engels, Friedrich 271–2 Enki 93, 157, 323 Epicenter, Stockholm 45 Epicurus 29–30, 33, 35, 41 epilepsy 291–2 Erdoğan, Recep Tayyip 207 eugenics 52–3, 55 European Union 82, 150, 160, 250, 310–11 evolution 37–8, 43, 73–4, 75, 78, 79–83, 86–7, 89, 102–5, 110, 131, 140, 150, 203, 205, 252–3, 260, 282, 283, 297, 305, 338, 359, 360, 388, 391 evolutionary humanism 247–8, 252–7, 260–1, 262–3, 352–3 Facebook 46, 137, 340–1, 386, 387, 392, 393 famine 1–6, 19, 20, 21, 27, 32, 41, 55, 58, 166, 167, 179, 205, 209, 219, 350 famine, plague and war, end of 1–21 First World War, 1914–18 9, 14, 16, 52, 244, 245, 246, 254, 261–2, 300–2, 301, 309, 310 ‘Flash Crash’, 2010 313 fMRI scans 108, 118, 143, 160, 282, 332, 334, 355 Foucault, Michel: The History of Sexuality 275–6 France: famine in, 1692–4 3–4, 5; First World War and 9, 14, 16; founding myth of 227, 227; French Revolution 155, 308, 310–11; health care and welfare systems in 30, 31; Second World War and 164, 262–3 France, Anatole 52–3 Frederick the Great, King 141–2 free will 222–3, 230, 247, 281–90, 304, 305, 306, 338 freedom of expression 208, 382, 383 freedom of information 382, 383–4 Freudian psychology 88, 117, 223–4 Furuvik Zoo, Sweden 125–6 Future of Employment, The (Frey/Osborne) 325–6 Gandhi, Indira 264, 266 Gazzaniga, Professor Michael S. 292–3, 295 GDH (gross domestic happiness) 32 GDP (gross domestic product) 30, 32, 34, 207, 262 genetic engineering viii, 23, 25, 41, 44, 48, 50, 52–4, 212, 231, 274, 276, 286, 332–8, 347–8, 353, 359, 369 Germany 36; First World War and 14, 16, 244, 245, 246; migration crisis and 248–9, 250; Second World War and 255–6, 262–3; state pensions and social security in 31 Gilgamesh epic 93 Gillies, Harold 52 global warming 20, 213, 214–17, 376, 377, 397 God: Agricultural Revolution and 95, 96, 97; Book of Genesis and 77, 78, 93–4, 97, 98; Dataism and 381, 382, 386, 389, 390, 393; death of 67, 98, 220, 234, 261, 268; death/immortality and 21, 22, 48; defining religion and 181, 182, 183, 184; evolutionary theory and 102; hides in small print of factual statements 189–90, 195; homosexuality and 192–3, 195, 226, 276; humanism and 220, 221, 222, 224, 225, 226, 227, 228, 229, 234–7, 241, 244, 248, 261, 268, 270, 271, 272, 273, 274, 276, 305, 389, 390–1; intersubjective reality and 143–4, 145, 147–9, 172–3, 179, 181, 182, 183, 184, 189–90, 192–3, 195; Middle Ages, as source of meaning and authority in 222, 224, 227, 228, 235–7, 305; Newton myth and 97, 98; religious fundamentalism and 220, 226, 268, 351; Scientific Revolution and 96, 97, 98, 115; war narratives and 241, 244 gods: Agricultural Revolution and theist 90–6, 97, 98, 156–7; defining religion and 180, 181, 184–5; disappearance of 144–5; dualism and 184–5; Epicurus and 30; humans as (upgrade to Homo Deus) 21, 25, 43–9, 50, 55, 65, 66, 98; humanism and 98; intersubjective reality and 144–5, 150, 155, 156–7, 158–60, 161–3, 176, 178–80, 323, 352; modern covenant and 199–200; new technologies and 268–9; Scientific Revolution and 96–7, 98; spirituality and 184–5; war/famine/plague and 1, 2, 4, 7, 8, 19 Google 24, 28, 114, 114, 150, 157, 163, 275, 312, 321, 322, 330, 334–40, 341, 384, 392, 393; Google Baseline Study 335–6; Google Fit 336; Google Flu Trends 335; Google Now 343; Google Ventures 24 Gorbachev, Mikhail 372 Götze, Mario 36, 63 Greece 29–30, 132, 173, 174, 228–9, 240, 265–6, 268, 305 greenhouse gas emissions 215–16 Gregory the Great, Pope 228, 228 guilds 230 hackers 310, 313, 344, 382–3, 393 Hadassah Hospital, Jerusalem 287 Hamlet (Shakespeare) 46, 199 HaNasi, Rabbi Yehuda 94 happiness 29–43 Haraway, Donna: ‘A Cyborg Manifesto’ 275–6 Harlow, Harry 89, 90 Harris, Sam 196 Hassabis, Dr Demis 321 Hattin, Battle of, 1187 146, 147 Hayek, Friedrich 369 Heine, Steven J. 354–5 helmets: attention 287–90, 362–3, 364; ‘mind-reading’ 44–5 Henrich, Joseph 354–5 Hercules 43, 176 Herodotus 173, 174 Hinduism 90, 94, 95, 181, 184, 187, 197, 206, 261, 268, 269, 270, 348, 381 Hitler, Adolf 181, 182, 255–6, 352–3, 375 Holocaust 165, 257 Holocene 72 Holy Spirit 227, 227, 228, 228 Homo deus: Homo sapiens upgrade to 43–9, 351–66; techno-humanism and 351–66 Homo sapiens: conquer the world 69, 100–51; end famine, plague and war 1–21; give meaning to the world 153–277; happiness and 29–43; Homo deus, upgrade to 21, 43–9; immortality 21–9; loses control, 279–397; problems with predicting history of 55–64 homosexuality 120, 138–9, 192–3, 195, 225–6, 236, 275 Hong Xiuquan 271 Human Effectiveness Directorate, Ohio 288 humanism 65–7, 98, 198, 219; aesthetics and 228–9, 228, 233, 233, 241–6, 242, 245; economics and 219, 230–1, 232, 232; education system and 231, 233, 233, 234; ethics 223–6, 233; evolutionary see evolutionary humanism; formula for knowledge 237–8, 241–2; homosexuality and 225–6; liberal see liberal humanism; marriage and 223–5; modern industrial farming, justification for 98; nationalism and 248–50; politics/voting and 226–7, 232, 232, 248–50; revolution, humanist 220–77; schism within 246–57; Scientific Revolution gives birth to 96–9; socialist see socialist humanism/socialism; value of experience and 257–61; techno-humanism 351–66; war narratives and 241–6, 242, 245, 253–6; wars of religion, 1914–1991 261–7 hunter-gatherers 34, 60, 75–6, 90, 95, 96–7, 98, 140, 141, 156, 163, 169, 175, 268–9, 322, 355, 360, 361, 378 Hussein, Saddam 18, 310 IBM 315–16, 320, 330 Iliescu, Ion 136, 137 ‘imagined orders’ 142–9 see also intersubjective meaning immigration 248–50 immortality 21–9, 30, 43, 47, 50, 51, 55, 56, 64, 65, 67, 138, 179, 268, 276, 350, 394–5 in vitro fertilisation viii, 52–3 Inanna 157, 323 India: drought and famine in 3; economic growth in modern 205–8, 349; Emergency in, 1975 264, 266; Hindu revival, 19th-century 270, 271, 273; hunter-gatherers in 75–6, 96; liberalism and 264, 265; population growth rate 205–6; Spanish Flu and 9 individualism: evolutionary theory and 103–4; liberal idea of undermined by twenty-first-century science 281–306; liberal idea of undermined by twenty-first-century technology 327–46; self and 294–304, 301, 303 Industrial Revolution 57, 61, 270, 274, 318, 319, 325, 374 inequality 56, 139–43, 262, 323, 346–50, 377, 397 intelligence: animal 81, 82, 99, 127–32; artificial see artificial intelligence; cooperation and 130–1, 137; decoupling from consciousness 307–50, 352, 397; definition of 130–1; development of human 99, 130–1, 137; upgrading human 348–9, 352 see also techo-humanism; value of consciousness and 397 intelligent design 73, 102 internet: distribution of power 374, 383; Internet-of-All-Things 380, 381, 382, 388, 390, 393, 395; rapid rise of 50 intersubjective meaning 143–51, 155–77, 179, 323, 352 Iraq 3, 17, 40, 275 Islam 8, 18, 21, 22, 64, 137, 188, 196, 205, 206, 207, 221, 226, 248, 261, 268, 269, 270, 271, 274, 275, 276, 351, 392; fundamentalist 18, 196, 226, 268, 269, 270, 275, 351 see also Muslims Islamic State (IS) 275, 351 Isonzo battles, First World War 300–2, 301 Israel 48, 96, 225–6, 249 Italy 262, 300–2, 301 Jainism 94–5 Jamestown, Virginia 298 Japan 30, 31, 33, 34, 207, 246, 349 Jefferson, Thomas 31, 192, 249, 282, 305 Jeopardy!

Future Politics: Living Together in a World Transformed by Tech
by Jamie Susskind
Published 3 Sep 2018

If deployed into the theatre of war, they’d have the capacity to select targets based on certain criteria before homing in and destroying them—potentially, in due course, without intervening human decision-making.15 Games of skill and strategy are considered a good way to gauge the increasing capability of digital systems. In short, they now beat the finest human players in almost every single one, including backgammon (1979), checkers (1994), and chess, in which IBM’s Deep Blue famously defeated world champion Garry Kasparov (1997). In 2016, to general astonishment, Google DeepMind’s AI system AlphaGo defeated Korean Grandmaster Lee Sedol 4–1 at the ancient game of Go, deploying dazzling and innovative tactics in a game exponentially more complex than chess. ‘I . . . was able to get one single win,’ said Lee Sedol rather poignantly; ‘I wouldn’t exchange it for anything in the world.’16 OUP CORRECTED PROOF – FINAL, 26/05/18, SPi РЕЛИЗ ПОДГОТОВИЛА ГРУППА "What's News" VK.COM/WSNWS 32 FUTURE POLITICS A year later, a version of AlphaGo called AlphaGo Master thrashed Ke Jie, the world’s finest human player, in a 3–0 clean sweep.17 A radically more powerful version now exists, called AlphaGo Zero.

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‘Samsung is Working on Putting AI Voice Assistant Bixby in Your TV and Fridge’. Wired, 27 Jun. 2017<https://www.wired.co.uk/ article/samsung-bixby-television-refrigerator> (accessed 30 Nov. 2017). Byford, Sam.‘AlphaGo Beats Ke Jie Again to Wrap Up Three-part Match’. The Verge, 25 May 2017 <https://www.theverge.com/2017/5/25/ 15689462/alphago-ke-jie-game-2-result-google-deepmind-china> (accessed 28 Nov. 2017). Byrnes, Nanette. ‘As Goldman Embraces Automation, Even the Masters of the Universe Are Threatened’. MIT Technology Review, 7 Feb. 2017 <https://www.technologyreview.com/s/603431/as-goldman-embracesautomation-even-the-masters-of-the-universe-are-threatened/ ? s e t = 6 0 3 5 8 5 & u t m _ c o n t e n t = bu f f e rd 5 a 8 f & u t m _ m e d i u m = social&utm_source=twitter.com&utm_campaign=buffer> (accessed 1 Dec. 2017).

Heart of the Machine: Our Future in a World of Artificial Emotional Intelligence
by Richard Yonck
Published 7 Mar 2017

Continuing advances contributed to the significant gains seen by artificial intelligence during this past decade, including Facebook’s development of DeepFace, which identifies human faces in images with 97 percent accuracy. In 2012, a University of Toronto artificial intelligence team made up of Hinton and two of his students won the annual ImageNet Large Scale Visual Recognition Competition with a deep learning neural network that blew the competition away.5 More recently, Google DeepMind used deep learning to develop the Go-playing AI, AlphaGo, training it by using a database of thirty million recorded moves from expert-level games. In March 2016, AlphaGo beat the world Go grandmaster, Lee Sedol, in four out of five games. Playing Go is considered a much bigger AI challenge than playing chess.

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M., 229 FOXP2, 15 Frankenstein (Shelley), 228 Freud, Sigmund, 96 Frewen, Cindy, 170–171 Friendly AI theory, 262 “Friendly Faces of Industry,” 171 Frubber (flesh rubber), 87, 113 functional magnetic resonance imaging (fMRI), 126–127 “The Future of Social Robotics,” 171 G galvactivator, 57–58 gaming community and designer emotions, 217 Garver, Carolyn, 113 Gazzaniga, Michael, 247 Geminoids, 100–101 general intelligence, 255 general morphological analysis (GMA), 165–166 General Problem Solver (1957), 37 Georgia Institute of Technology, 120 geriatric physiotherapy rehabilitation robots, 152 Gibson, William, 171 Gigolo Joe (mecha), 233–234 global AI nanny, 262 GMA. See general morphological analysis (GMA) Goel, Asok, 120–121 Goertzel, Ben, 258–259, 262 Gogh, Vincent van, 223 Google DeepMind, 68 Google Images, 42 Gordon, Goren, 118 Gosling, Ryan, 193 Gould, Stephen Jay, 250 GPS location services, 211 Great Rift Valley, East Africa, 5–8 Groden Center, Providence, Rhode Island, 61 The Grudge, 101 Gutenberg, Johannes, 211 H habituation, 106 hackers, 157–158 hacking tools, 141 HAL 9000, 232 Hanson, David, 86–87 Hanson Robotics, 86–87, 113 haptic devices, 214–215 Harris Interactive Poll, 50 Harvard Medical School, 217 Hasbro, 200 Hauser, Kasper, 257 “Helper Bots,” 171 Henna Hotel, 87 Her (Jonze), 173, 196, 225–227, 235–236 Heraclitus, 206 Hines, Andy, 171–172 Hinton, Geoffrey, 66 hippocampus, 19, 205–206 Hjortsjö, Carl-Herman, 55 Hobbes, Thomas, 36 Homo habilis, 10, 12, 14–15 Homo hybridus, 207, 267 Homo sapiens sapiens, 16, 261, 267 Homo technologus, 207 hormones, 16, 141, 186, 187, 196, 221 HTC Vive, 189 human augmentation, 104–105, 204–205, 267 human emotional bonding, 186–188 human emulation and AI development, 252–255 human-computer interaction, 52–53 Huxley, Aldous, 229 I I-Consciousness.

Falter: Has the Human Game Begun to Play Itself Out?
by Bill McKibben
Published 15 Apr 2019

In October 2018, for instance, Stephen Hawking’s posthumous set of “last predictions” was published—his greatest fear was a “new species” of genetically engineered “superhumans” who would wipe out the rest of humanity.17 Or consider tech entrepreneur Elon Musk, who described the development of artificial intelligence as “summoning the demon.” “We need to be super careful with AI,” he recently tweeted. “Potentially more dangerous than nukes.” Musk was an early investor in DeepMind, a British AI company acquired by Google in 2014. He’d put up the money, he said, precisely so he could keep an eye on the development of artificial intelligence. (Probably a good idea, given that one of the founders of the company once remarked, “I think human extinction will probably occur, and technology will likely play a part in this.”)18 “I have exposure to the very cutting-edge AI, and I think people should be really concerned about it,” Musk told the National Governors Association in the summer of 2017.

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See also solidarity Competitive Enterprise Institute (CEI) computers COMTY gene ConocoPhillips Consumer Reports continental flood basalt Cooler Heads Coalition cooperative ownership Copenhagen climate conference coral reefs Corinth corn Corporation Commission Côte d’Ivoire Council of Economic Advisers Cowen, Tyler Crack in Creation, A (Doudna) Craigslist Credit Suisse Cretaceous extinction Crick, Francis CRISPR cryogenics Csikszentmihalyi, Mihaly cyanobacteria Cyberselfish (Borsook) cystic fibrosis Dakota Access Pipeline Dalio, Ray Dankert, Don Dante Alighieri Dark Money (Mayer) Darnovsky, Marcy Davison, Richard Day, Dorothy dead zones Death Valley Deaton, Angus Deccan Traps DeepMind Defense of the Ancients (video game) Delhi DeMille, Cecil B. democracy Democratic Party democratic socialism Denmark depression desertification designer babies Devon Energy Devonian extinction Diamond, Jared Dickens, Charles dinosaurs disease DNA. See also genetic engineering dopamine receptor D4 Doudna, Jennifer Dougie mice Dr.

Amazon: How the World’s Most Relentless Retailer Will Continue to Revolutionize Commerce
by Natalie Berg and Miya Knights
Published 28 Jan 2019

‘With the likes of more traditional retailers facing closures, innovation needs to be in the spotlight more than ever’, he said. He rightly highlighted that the field of AI is developing incredibly quickly. Amazon’s recommendation system runs on a totally machine learning-based architecture, so its suggestions on what to buy, watch or read next are ‘incredibly smart’, and Google’s DeepMind division is now giving its AI algorithms an ‘imagination’ so that it can predict how a certain situation will evolve and make decisions. ‘This leads to more conversions and upselling across the business, as well as giving Amazon insight on how to price its products for its customers, and how much stock to hold’, he added.

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(and) 242–45 basic principles for retailers’ co-existence with Amazon 244 regulation and legislation 243 Connected Home 46 Connell, B (CEO, Target) 226 Co-op 209 see also Italy and Deliveroo delivery service 102 Costco 46, 181, 217 Cummins, M (CEO, Pointy) 172 Darvall, M (director of marketing and communications,Whistl) 215–16 Debenhams 81, 193, 194 definition(s) of showrooming 174 webrooming 168 Dhaliwal, T (MD, Iceland) 116 Diewald, G (head of Ikea US food operations) 189 digital automation and customer experience 165–85 see also ROBO and ZMOT the digital customer experience 176–83 see also subject entry location as a proxy for relevance 170–73, 173 research online, buy offline 167–70 the store as a showroom 174–76 see also definition(s) the digital customer experience (and) 176–83 see also robots digital points of purchase 179–80 the human touch, importance of 180–81 intelligent space 177–79 from self-checkout to no checkout 182–83 Dixons Carphone (Currys PC World) 188 membership scheme for use of washing machines, etc 201 drones 238 see also JD.com Prime Air 151 Dunn, A (CEO, Bonobos, 2016) 75 East, M (former M&S executive) 116 eBay 36, 216–17 and Shutl 217 e-commerce, growth of 48 Edison, T: quoted on failure 11 end of pure-play e-commerce: Amazon’s transition to bricks and mortar retailing 62–86 Amazon makes it move 77, 80–82, 78–79, 80 clicks chasing bricks – the end of online shopping 71–77 O2O: incentives for getting physical 72–75 cost of customer acquisition 74–75 shipping costs 73–74 O2O: who and how 75–77 key drivers of convergence of physical and digital retail (and) 66–71 click, collect and return 69–70 pervasive computing: shopping without stores or screens 70–71 role of mobile: frictionless, personalized experience 67–69 role of mobile: knowledge is power 66–67 next-generation retail: quest for omnichannel 63–66 electronic shelf labels (ESLs) 177–79 The Everything Store 6, 29 see also Stone, B Facebook 45, 76 Marketplace 213 Messenger purchasing bot 179 Payments 213 Fear of Missing Out (FOMO) 55 FedEx 224–25, 229 figures Amazon operating margin by segment 19 Amazon opened first checkout-free store, Amazon Go (2018) 109 Amazon’s first-ever bricks and mortar retail concept, Amazon Books, 2015 80 the flywheel: the key to Amazon’s success 7 growing complexity of fulfilling e-commerce customer orders 211 growing importance of services: Amazon net sales by business segment 18 Market Capitalization: Select US Retailers (7 June 2018) 6 new fulfilment options driving heightened complexity in retail supply chains 210 online-only is no longer enough: Amazon acquired Whole Foods Market (2017) 108 playing the long games: Amazon sales vs profits 12 top reasons why US consumers begin their product searches on Amazon 173 France (and) 2, 113 see also Auchan and Carrefour Amazon and Fauchon and Monoprix 103 ‘click and drive’ 208 Monoprix 236 frugality 9, 122 at Amazon, Mercadona and Walmart 9 Furphy, T 94 Galloway, S (NYU professor) 14 Generation Z 54 Germany (and) 2, 35, 209, 232 Amazon and Feneberg 103 H&M ‘Take Care’ service 49 Metro 191 retailer HIT Sütterlin 180 Rossman drugstore chain 236 unions call for strikes over Amazon workers’ pay rates (2013) 229 Gilboa, D (co-founder Warby Parker) 75 Gimeno, D (Chairman, El Corte Ingles) 52 Glass, D (CEO, Walmart) 50 global shipping market, worth of 230 Goldman Sachs 13 and independent factors correlating to online grocery adoption & profitability 88 Google 1, 14, 19, 45, 66, 76, 115, 154, 179 Assistant 157, 160 Checkout 213 DeepMind 159 Express 157, 160, 217 Home 153, 157 Knowledge Panel 171, 172 Maps 172, 177 Nest heating thermostat controller 155 Play 213 Search 157 See What’s In Store (SWIS) 171–72 Shopping Actions 157 What Amazon Can’t Do (WACD) 171 and ‘zero moment of truth’ (ZMOT) 171, 172 Great Recession 48, 122 Gurr, D (Amazon UK Country Manager UK, (2018) 21, 29, 44, 64 Ham, P 94 Hamleys: Moscow store mini-theme park 196 Han, L (General Manager of International Supply Chain, JD Logistics) 235 Harkaway, N 222 Herbrich, R (Amazon, director of machine learning) 150 Herrington, D 94 Home Depot 2, 157, 172 online returns instore 70 Huang, C (founder and CEO of Boxed, 2018) 71 Ikea (and) 71 acquires TaskRabbit (2018) 202 mobile AR 175 Place app 175 India 31, 116 see also Prime Video and Walmart Amazon Stella Flex service tested in 232 Instacart 89, 112–13, 119, 157, 216, 219, 224, 236 Sprouts teamed with 103 Intel and RealSense technology for ESLs (2018) 178 intelligence software: trialled by The Hershey Company, Pepsi and Walmart 178 Internet of Things (IoT) 70, 96 Italy 16, 209 see also Carrefour Co-op’s ‘store of the future’ in 191 James, S (Boots CEO) 55 Japan (and) 2, 35 Prime Video 31 Tokyo 102 Uniqlo 175–76 JD.com (and) 182–83, 230 7fresh 112, 183 BingoBox 182 Europe–China freight train (2018) 235 Logistics 235 online retail: opening 1000 stores a day in China 63 use of drones 238 John Lewis (and) see also Nickolds, P co-working space 193 customers staying overnight 187 ‘discovery room’ 200 Jones, G (CEO, Borders) 47 Kaness, M (CEO, Modcloth) 76 Kenney, M 190 Khan, L 242, 243 Kiva Systems 94, 151, 223 see also robots Kohl’s 2, 70, 81, 193, 233 Kopalle, Professor P 151 Kroger 2, 19, 46, 114–15, 208 see also case studies HomeChef 116 ‘Scan, Bag, Go’ 214–15 smart shelf solution 178 Kwon, E (former executive Amazon fashion) 127 Ladd, B 13, 115, 219 see also case studies Landry, S (VP, Amazon Prime Now) 218 the last-mile infrastructure 222–41 see also Amazon Amazon as a carrier 231–32 fulfilment by Amazon 232–33 growing IT infrastructure 226–29 last-mile labour 223–26 race for the last mile 233–36 real estate demand 229–31 remote innovation 236–38 Leahy, Sir T 62 Lebow, V 54, 122 see also articles/papers legislation (US) and calls for legislation to be rewritten and regulation of tech giants 243 Tax Act (2017) 16 Lego 195 allows building in-store 196–97 AR kiosks in stores (2010) and X app 175 Leung, L (Prime Director) 29 Levy, H P 147 Lidl 33, 51, 122, 209 Limp, D (Amazon Digital Devices SVP) 153 Liu, R (JD.com founder/chief executive) 182 lockers/collection lockers 74, 90, 112, 209–10, 233 emmasbox (Germany) 209 Lore, M (co-founder of Quidsi; CEO Walmart domestic e-commerce operations) 76–77, 97, 224, 235, 236 loyalty schemes 32–33 Ma, J (founder, Alibaba) 63 McAllister, I (Director of Alexa International) 10, 19 McBride, B (ASOS Chairman, former Amazon UK boss) 9 Mackey, J (Whole Foods Market CEO and Co-Founder) 107, 110 McDonalds McDelivery 218 in Walmart stores 189 McMillon, D (CEO, Walmart, 2017) 87, 89, 107 Macy’s 52, 69, 71, 172, 177, 193 New York store as ‘World’s Largest Store’ 50 Mahaney, M (RBC Capital Managing Director/analyst) 14, 111 Mansell, K (Chairman, President and CEO of Kohl) 233 Marks & Spencer (M&S) 49, 81, 193, 196 delivery service partnership with Gophr 102 Marseglia, M (Director, Amazon Prime) 101 Mastandrea, M 94 Mathrani, S (CEO of GGP) 49 Mehta, A (CEO, Instacart) 113 MercadoLibre as Latin America’s answer to eBay 36 Metrick, M (president, Saks Fifth Avenue) 190 Microsoft 19, 115 Bing 173 checkout-less store concept 182 Millennials 122, 144, 157 Miller, B (Miller Value Partners) 13 Millerberg, S (managing partner, One Click Retail) 158 Misener, P (Amazon VP for Global Innovation) 10 Mochet, J P (CEO of convenience banners, Casino Group, 2018) 192 Morrisons 102, 209, 217, 236 Mothercare 195, 196 Motley Fool 15 see also Bowman, J Mountz, M 94 Mulligan, J (chief operating officer, Target) 225–26 Musk, E 194 near-field communications (NFC) technology 178–79 Newemann, A (CEO WeWork) 192 Next 188 and pizza and prosecco bars instore 190 Nickolds, P (MD, John Lewis, 2017) 64 Nike 103 selling on Amazon 127 Nordstrom, E (Co-President, Nordstrom, 2017) 45 Nordstrom 135, 193 Local (launched 2017) 199 Ocado 19, 112–15, 135 see also Clarke, P and Steiner, T and Alexa 157 deal with Casino Groupe (2017) 113 Smart Platform 113 Olsavsky, B (Amazon CFO, 2018) 124 One Click Retail 90, 123, 129, 155, 158 online to offline (O2O) 63 capabilities 216 incentives for getting physical 72–75 who and how 75–77 Ovide, S (Bloomberg) 47, 119, 154 Park, D (co-founder, Tuft & Needle) 81 PayPal 45, 137, 213–14 Peapod 87 see also Bienkowski, C and ‘Ask Peapod’ skill for Alexa 156–58 Penner, G (Walmart Chairman, 2017) 77 Perrine, A (Amazon General Manager, 2018) 29 polls see reports Price, Lord M (former Waitrose MD) 51 Prime (and) 11, 14, 20, 92, 112, 121, 137, 153, 174, 210, 215, 217, 218, 222, 227 see also Prime 2.0; Prime Air; Prime ecosystem and Prime Now AmazonFresh 34 AmazonFresh Pickup 37 Day 32, 136, 147 Fresh Add-on 237 members 2 Pantry 34, 100–101, 226, 227 Video 30–31 Wardrobe 128, 226 Prime 2.0 (and) 38–40 ‘Invent and Simplify’ Leadership Principle 143 looking to new demographics for growth 39 more bells and whistles 38 more fee hikes 40 Prime Wardrobe (2017) 38–39 Prime Air 151 development centres U~S, Austria, France, Israel 238 first autonomous drone delivery 238 Prime ecosystem: redefining loyalty for today’s modern shopper (and) 28–40 advantages for Amazon 33–35 going global 35–36, 35–36 integrating Prime at point of sale 38 Prime 2.0 38–40 see also subject entry Prime as loyalty programme?

Money: Vintage Minis
by Yuval Noah Harari
Published 5 Apr 2018

On 10 February 1996, IBM’s Deep Blue defeated world chess champion Garry Kasparov, laying to rest that particular claim for human pre-eminence. Deep Blue was given a head start by its creators, who preprogrammed it not only with the basic rules of chess, but also with detailed instructions regarding chess strategies. A new generation of AI prefers machine learning to human advice. In February 2015 a program developed by Google DeepMind learned by itself how to play forty-nine classic Atari games, from Pac-Man to car racing. It then played most of them as well as or better than humans, sometimes coming up with strategies that never occur to human players. Shortly afterwards AI scored an even more sensational success, when Google’s AlphaGo software taught itself how to play Go, an ancient Chinese strategy board game significantly more complex than chess.

Deep Thinking: Where Machine Intelligence Ends and Human Creativity Begins
by Garry Kasparov
Published 1 May 2017

The nineteen-by-nineteen Go board with its 361 black and white stones is too big of a matrix to crack by brute force, too subtle to be decided by the tactical blunders that define human losses to computers at chess. In that 1990 article on Go as a new target for AI, a team of Go programmers said they were roughly twenty years behind chess. This turned out to be remarkably accurate. In 2016, nineteen years after my loss to Deep Blue, the Google-backed AI project DeepMind and its Go-playing offshoot AlphaGo defeated the world’s top Go player, Lee Sedol. More importantly, and also as predicted, the methods used to create AlphaGo were more interesting as an AI project than anything that had produced the top chess machines. It uses machine learning and neural networks to teach itself how to play better, as well as other sophisticated techniques beyond the usual alpha-beta search.

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(early 1990s), 120 Copenhagen (1993), 125 descriptions, 5, 37, 63, 73, 89, 119–120, 125, 133, 151 development/play problems and interventions (1990s), 128, 129–130, 135 end of chess career/dismantling, 3, 217–219 gamesmanship and, 184 origins, 39 team/IBM research facility, 125 World Computer Chess Championship (1995), 128–129, 130–131 Deep Blue/Kasparov IBM PR/impacts, 149, 153–154, 155 match/rematch negotiations, 128, 154, 160–161 Deep Blue/Kasparov match (1996/Philadelphia) analysis, 136–137, 139, 140–141, 145, 148 descriptions, 135–136, 139, 140–141, 142–145, 147–148 draw offer and, 145–147 IBM impacts, 149 PR/rematch and, 150–151 predictions, 132, 148 scene/hype description, 133–135, 145, 162 “sharp positions” and, 144, 145 significance of first game, 114, 141 sponsors/prize fund, 132 technical problems/distractions, 135, 142, 144–145 Deep Blue/Kasparov rematch (1997/New York City) analysis, 187–188, 189–190, 193, 194–195, 204, 212 analyzing game two (Kasparov/team), 187–188, 189–190 anti-computer strategy and, 168, 171, 172, 173, 176, 181, 182, 187, 190, 193, 210, 213, 217 Deep Blue changes in game two, 186, 187–188 Deep Blue ending chess playing/dismantling, 3, 217–219 Deep Blue improvements/GMs on team, 155, 156, 158–159, 164, 165, 167–168, 181, 202, 203, 207 Deep Blue rating and, 159 Deep Blue rook move/meaning and, 177–179, 180–181 Deep Blue team attitude change, 161–164, 166–167, 169, 184–185, 196, 219–220 Deep Blue team ethics/questions and, 184–185, 200–202, 216–217, 218, 220 Deep Blue team secrecy agreement/tricking and spying on Kasparov, 184–185, 200–202 Deep Blue’s logs/printouts and, 195–196, 202, 210, 211–212, 219 Deep Blue’s previous games data and, 162–163 descriptions, 2, 5, 75, 171–172, 173–176, 177–178, 182, 185–189, 193–194, 195, 203–204, 208, 209–211, 212, 213–214, 215 disputes/handling and, 169, 195–196 drawing of lots, 170, 171 game six/mythology, 213–217, 218–219 “human intervention” and, 196, 198 human/machine differences, 166, 168, 169, 177, 182–184, 186, 203–204, 210 Kasparov after match/challenge, 214–215 Kasparov/future IBM collaboration and, 162 Kasparov giving credit to Deep Blue, 212, 214 Kasparov resigning game two/draw, 190–193, 201, 217 Kasparov’s evaluation/confidence before, 154–155, 156, 158–159, 161 Kasparov’s strategy and, 168, 170, 171, 172, 173, 176, 181–182, 187, 190, 193, 210, 213, 214, 217 Deep Blue/Kasparov rematch (1997/New York City), continued media and, 2, 3, 167, 171, 179–180 predictions, 170 press conferences after games/match, 189, 194, 195–196, 212, 214–215, 218 prize fund, 160–161 rules/schedule, 162–163, 168–169 scene/conditions, 165–167 tablebases and, 204–205, 208 technical problems/distractions and, 169, 176, 177, 187–188, 198–200, 208–210 Deep Junior, 37, 67, 207, 208, 254 Deep Thought Deep Blue name change, 104–105, 125 development/descriptions, 39, 67, 69, 90–91, 104–105, 106–108, 120, 128–129, 133, 180, 254 Kasparov and, 104, 107–112, 114, 115–116, 122, 130, 132, 139–140, 154, 162 playing history, 92, 95, 125 See also Deep Blue Deep Thunder, 155 DeepMind machine, 75 DeFirmian, Nick, 202 Denker, Arnold, 90, 105 depression and decision making, 239 Der Spiegel, 15, 23 Descartes, 225 Doctorow, Cory, 223 Dokhoian, Yuri, 106, 133, 167, 178, 182, 190, 200 Donskoy, Mikhail, 73, 74 Drosophila of AI, 74, 230, 234 Duchamp, Marcel, 14 economic theory and human rational behavior, 239 education creativity/innovation and, 234–235 obsolete methods and, 234–235 technology/automation and, 43, 44 Einstein, Albert, 14–15, 80 Eisenhower, President/administration, 43, 45, 97 Enron scandal, 200 ethics corporation ethics, 200 Deep Blue team ethics/questions and (rematch 1997), 184–185, 200–202, 216–217, 218, 220 questions with machine crashes, 169, 176, 177, 187–188, 198–200, 208–210 Fedorowicz (Fedorovich), John, 202 Ferrucci, Dave, 70–71, 72, 104, 251 Feynman, Richard, 152, 153 Fischer, Bobby chess and, 20, 21, 22, 66, 92, 109, 166, 183, 197, 231, 232 ending chess career/health decline, 20, 21–22 Spassky match/disputes, 3, 22, 93–94, 167, 197 Franklin, Benjamin, 4 freestyle tournament chess/results, 246–247 Friedel, Frederic Kasparov/chess and, 48–49, 57–59, 115, 122, 131, 133, 142, 160, 178–179, 180, 190, 194, 204, 218–219 Kasparov visiting/Hopper game and, 57–58 Fritz, 39, 86, 115, 120, 122–123, 126, 127–128, 129, 130–131, 139, 142, 163, 165, 169, 178, 179, 180, 199, 236, 243, 254 From Russia with Love (movie), 16–17 “gambler’s fallacy”/”Monte Carlo fallacy,” 239–240 Game and Playe of the Chesse, 11 Gates, Bill, 65, 95 Gerstner, Lou, 126, 155, 209, 210, 218 Giuliani, Rudy, 131 Gladwell, Malcolm, 82–83, 84, 233 Go game/machines, 74–75, 104, 121 Gödel, Escher, Bach: An Eternal Golden Braid (Hofstadter), 103 Goldin, Ian, 252 Google, 6, 61, 71, 75, 102, 103, 104, 117, 151, 225, 247 Google Home, 118 Google Translate, 99–100, 101, 102 Gorbachev, Mikhail, 94 Gravity’s Rainbow (Pynchon), 217 Greenblatt, Richard, 55–56 Greengard, Mig, 219 Guinness, Alec, 61 Harry Potter movies, 16 Hawking, Stephen, 9, 14–15 Hitchhiker’s Guide to the Galaxy, The (Adams), 69 HiTech machine, 39, 89, 90–91, 98, 105, 108 Hoane, Joe, 125, 130, 160, 167, 214 Hofstadter, Douglas, 103–104 Horowitz, I.

System Error: Where Big Tech Went Wrong and How We Can Reboot
by Rob Reich , Mehran Sahami and Jeremy M. Weinstein
Published 6 Sep 2021

Then, in 1997, front-page stories in newspapers around the world announced that IBM’s Deep Blue computer had “unseated humanity” by defeating the reigning chess champion, Garry Kasparov. In 2011, IBM’s Watson system, built to play the TV quiz show Jeopardy, soundly defeated the two former all-time winners, Brad Rutter and Ken Jennings. Final score: Rutter with $21,600, Jennings with $24,000, and Watson with $77,147. In 2017, scientists from Google’s DeepMind group used machine learning to build a program that would go on to beat Ke Jie, the number one Go player in the world. Until that time, many game players believed that Go, a game that has far more than a billion billion billion more board configurations than chess, was beyond the scope of world-championship, or even expert-level, play by a computer.

…

Since Russell published his open letter, more than three thousand individuals and organizations have indicated their support for an autonomous weapons ban and signed a pledge not to “support the development, manufacture, trade, or use of lethal autonomous weapons.” The signatories include major names in technology, including Elon Musk (SpaceX and Tesla), Jeff Dean (the head of Google AI), and Martha Pollack (the president of Cornell University), and leading organizations, such as Google DeepMind. A campaign to ban killer robots has gone global, and thirty member countries of the United Nations have explicitly endorsed the call for a ban. The United States is not among them. Neither is China nor Russia. Maybe autonomous weapons are a relatively easy case for strict limits on automation.

21 Lessons for the 21st Century
by Yuval Noah Harari
Published 29 Aug 2018

, Artificial Intelligence 199–200 (2013), 93–105. 18 ‘Google’s AlphaZero Destroys Stockfish in 100-Game Match’, Chess.com, 6 December 2017; David Silver et al., ‘Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm’, arXiv (2017), https://arxiv.org/pdf/1712.01815.pdf; see also Sarah Knapton, ‘Entire Human Chess Knowledge Learned and Surpassed by DeepMind’s AlphaZero in Four Hours’, Telegraph, 6 December 2017. 19 Cowen, Average is Over, op. cit.; Tyler Cowen, ‘What are humans still good for? The turning point in freestyle chess may be approaching’, Marginal Revolution, 5 November 2013. 20 Maddalaine Ansell, ‘Jobs for Life Are a Thing of the Past.

Infinite Powers: How Calculus Reveals the Secrets of the Universe
by Steven Strogatz
Published 31 Mar 2019

The current generation of the world’s strongest chess programs, with intimidating names like Stockfish and Komodo, still play in this inhuman style. They like to capture material. They defend like iron. But although they are far stronger than any human player, they are not creative or insightful. All that changed with the rise of machine learning. On December 5, 2017, the DeepMind team at Google stunned the chess world with its announcement of a deep-learning program called AlphaZero. The program taught itself chess by playing millions of games against itself and learning from its mistakes. In a matter of hours, it became the best chess player in history. Not only could it easily defeat all the best human masters (it didn’t even bother to try), it crushed the reigning computer world champion of chess.

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See intuition and creativity CT scanning, 265–69, 289 cuneiform, 1 Cureau de La Chambre, Marin, 116 curvature, 299–300 curves Archimedes on, 47–48 area of, 168–69, 176–79, 209–11 equations, 96–97 interpolation, 163 Kepler and, 87 nonlinear equations, 149–54 slope of, 142, 206–9 smoothness, 153, 163–64 struggle with, xviii three central problems of, 144–46 D data compression, 107–13 day length example, 108–12, 154–59 De Analysi (Newton), 196, 199, 200 De Methodis (Newton), 196, 197, 201 decay and exponential growth, 137–39, 220–24, 251 decimals, 9–10, 91, 92, 189, 193, 295–97 Declaration of Independence, xxi–xxii, 239 Deep Blue, 291–92 DeepMind, 292 dependent variables, 124, 141, 147, 242 derivatives day length example, 154–59 vs differentials, 206–9 instantaneous speed, 159–66 integrals and, 168–69 linear relationships, 146–49 nonlinear relationships, 149–54 purpose and types of, 141–44 sine waves, 256–59 slope and, 177–79 symbol for, 143 Descartes, René analytic geometry, 101–3 background, 99–100 on curved arcs, 168 Dioptrics, 115–16 Discourse on Method, 99, 101 Fermat rivalry, 98–99, 116 Geometry, 119 legacy of, 93, 188 lenses, 87 tangents, 119–20 unknowns and constants, 92 xy plane, 96–97 Description of the Wonderful Rule of Logarithms (Napier), 133 determinism, 277–79, 280 Deuflhard, Peter, 53–55 diameter, of a circle, 30 Differential Analyzer, 286 differential calculus, 89–121 aircraft engineering, 244–47 algebra and geometry convergence, 93–96, 98 analytic geometry, 101–3 derivatives vs differentials, 206–9 Descartes-Fermat rivalry, 98–101 Fermat’s contributions to, 120–21 fundamental theorem, 209–11 infinitesimals, 205–6 vs integral calculus, 89, 185–86 Leibniz and, 201–208 Newton and, 184–85 optimization problems, 103–7 ordinary vs partial equations, 242–44 origins of, 59, 68–69 overview of, vii–viii, xx–xxi partial equations, applications of, 247–48 as phase of calculus, xv–xvi sine law, 117–18 dimensions, four or more, 287–91 Dioptrics (Descartes), 115–16 Dirac, Paul, xiv, 297–98 Discourse on Method (Descartes), 99, 101 Discourses and Mathematical Demonstrations Concerning Two New Sciences (Galilei), 65 discrete vs continuous systems, 16–21, 241 distance function, 170 DNA, 273–76 double intersection, 106, 111, 119 DreamWorks, 51, 52–53 Dyson, Freeman, 285 E Earth as center of universe, 60–65 free falling objects, 173, 233 GPS, 76, 299–300 greenhouse effect, 249 Kepler on, 79 moon’s orbit, 232–33 navigation and longitude, 75 Newton and, 229, 235–36 period of, 84–85 retrograde motion, 62 tunneling phenomenon, 22 two-body problem, 237–38 eight decimal places, 295–97 Einstein, Albert, xiii, xxii, 77, 287, 289, 297, 299–301 Electric and Musical Industries (EMI), 268 electronic synthesizers, 255 Elements (Euclid), 32, 188, 236 ellipses equations for, 97 planetary motion, 81–82, 83, 87, 234 as slice of cone, 35 ENIAC, 286 Enlightenment period, 238–40 equations.

The Man From the Future: The Visionary Life of John Von Neumann
by Ananyo Bhattacharya
Published 6 Oct 2021

The answer is that neurons do not fire one after the other, but do their work simultaneously: they are not serial, like von Neumann architecture computers, but parallel – massively so. It was a lasting insight. The artificial neural networks that power today’s best-performing artificial intelligence systems, like those of Google’s DeepMind, are also a kind of parallel processor: they seem to ‘learn’ in a somewhat similar way to the human brain – altering the various weights of each artificial neuron until they can perform a particular task. This was the first time anyone had so clearly compared brains and computers. ‘Prior to von Neumann,’ says inventor and futurologist Ray Kurzweil, ‘the fields of computer science and neuroscience were two islands with no bridge between them.’97 Some believe it should have stayed that way.

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Jack 121–2, 307n37 Copenhagen 58, 76 Copenhagen interpretation 46, 48–9, 53, 54 critique of 46–8 history of 296n43 inadequacies of 58–60 Courant, Richard 63–4 Coy, Wolfgang 111 Crick, Francis 230, 231 Critchfield, Charles 83 Cuban Missile Crisis 221 Czechoslovakia, Nazi annexation of the Sudetenland 76 Dantzig, George 191–2, 193, 317n21 Darwinian Marxism 225 Davis, Martin 116, 198 Dawkins, Richard 179, 181, 257 de Broglie, Louis 33, 54–5 Debreu, Gérard 151 decomposable games 171–2 DeepMind 275 defence budgets 188 Defense in Atomic War (von Neumann) 221 delay-lines 124–5 Delbrück, Max 226–7, 231 Delicate Balance of Terror, The (Wohlstetter) 215–16 depth charges 188 Descartes, René 229 determinism 52 DeWitt, Bryce 58 Dick, Philip K. xii, 225 ‘Autofac’ 231–2, 232, 233, 261, 263 Dieks, Dennis 54 differential analysers 107–8 digital cosmogenesis 245–6 Dirac, Paul 36–7, 52, 60 Dirac delta function 37, 38–9 DNA 62, 227, 230–, 231 Douglas, Donald 185–6, Douglas Aircraft Company 185–6, 187 Dr Strangelove (film) xiii, 215, 219 Dresher, Melvin 204–8 Drexler, Eric 261, 268–9 du Bois-Reymond, Emil 20 duels, mathematics of 194–67 Dulles, John Foster 210, 222 Dyson, Freeman xiv, 12–13, 37, 61, 96, 231, 253, 263–4 Dyson, George 1398 Eckart, Carl 280 Eckert, J.

The Contrarian: Peter Thiel and Silicon Valley's Pursuit of Power
by Max Chafkin
Published 14 Sep 2021

As impressive as this entrepreneurial resume might be, Thiel has been even more influential as an investor and backroom deal maker. He leads the so-called PayPal Mafia, an informal network of interlocking financial and personal relationships that dates back to the late 1990s. This group includes Elon Musk, plus the founders of YouTube, Yelp, and LinkedIn. They would provide the capital to Airbnb, Lyft, Spotify, Stripe, DeepMind—now better known as Google’s world-leading artificial intelligence project—and, of course, to Facebook. In doing so, Thiel and his friends helped transform what was once a regional business hub—on par with Boston and a few other midsized American metro areas—into the undisputed engine of America’s economy and culture.

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Glenn, 15 capital gains taxes, 212, 213, 330 capitalism, xiii, xviii, 32, 177, 288 Capitol attack, 322–23 captcha, 78 Carlson, Tucker, viii, 199, 232, 289, 332 Carr, David, 98–99, 124 Carson, Ben, 224 Carter, Jimmy, 250 Casper, Gerhard, 42 Cathedral, the, 177, 186 Cato Institute, 140, 141, 192 Cato Unbound, 140, 176 Catz, Safra, 271–72 CBS This Morning, 193 CDC (Centers for Disease Control), 308, 311 Cernovich, Mike, 202, 231, 255 Chamber of Commerce, 150 Chan, Priscilla, 299, 303 Chan Zuckerberg Initiative, 298 Chapman, Lizette, 233 Chapo Trap House, 287 Charlottesville, Unite the Right rally in, 272 Chen, Steve, 105 Chenault, Ken, 298 Cheney, Dick, 116 chess, 7–8, 17, 18, 21–23, 43 Chevron, 250 China, 249, 259, 261–62, 288, 299, 307, 320, 328 Facebook and, 298–99 Google and, 288–89, 321 Chirwa, Dawn, 30 Chmieliauskas, Alfredas, 215, 217–21 Christchurch earthquake, 209 Christians, Christianity, 2, 20, 32, 82, 327 Christie, Chris, 182 CIA, 114, 116, 117, 122, 125, 154, 218, 285, 289 Cisco, 257, 258, 260 City Journal, 164 Claremont Conservative, 199 Claremont Institute, 198 Claremont McKenna College, 198–99 Clarium Capital, 96, 100–105, 117, 119, 121, 125, 128–34, 137–39, 145–46, 165, 183, 195, 203, 211, 215, 274, 313 Clark, Jim, 48 Clarke, Arthur C., 8 Clean Air Act, 250 Clearview, 268, 296–97, 318, 333 Clem, Heather, 196 Cleveland, Ohio, 2–6 climate change, 120–21, 176, 177, 251–52, 261, 264 Clinton, Bill, 47, 63, 139, 211, 213, 264 sexual assault claims against, 243, 246 Clinton, Hillary, xi, xv, 211–12, 237, 238, 241, 245–47, 255, 259, 270, 276, 283 Shelton case and, 243 Closing of the American Mind, The (Bloom), 30 Club for Growth Action, 184 CNBC, 244, 289–90 CNN, viii, 212, 247, 300, 303, 317 Coca-Cola, 221 Cohan, William, 212 Cohen, Stephen, 113, 114, 319 Cohler, Matt, 107 Cold War, 33, 112, 144 Collins, Francis, 264–66, 311 Collison, Patrick, 331 communism, 3, 15, 211, 265, 280, 288 Compaq, 56, 223–24 competitive governance, 140 Cone, Sarah, 331 Confinity, 51, 58, 66, 67, 69 Conservative Political Action Conference, 177 conservatives, conservatism, 37, 60, 114, 128, 144, 198, 287–89 Facebook and, viii–xi, 245–46, 298, 300, 303–4 gays and, 177 at Google, 277–79 at Stanford, xii, 14–15, 30–31, 33 of Thiel, ix, xi–xii, 17, 24, 30–31, 41, 120 Contract with America, 213 Cook, John, 228, 229 Cook, Tim, 129, 257, 258, 260–61, 299 Cooper, Anderson, 128, 129 Cornell Review, 177 Coulter, Ann, 177, 286 COVID-19 pandemic, 265, 305–17, 319, 320, 326–30 Facebook and, 309, 313 Palantir and, 310–11, 318, 320–21 Silicon Valley and, 308–9 Trump and, 307, 311, 313–17, 319 Cowen, Tyler, 192–93, 208, 250 Cox, Christopher, 42, 83 Craigslist, 98 Cranston, Alan, 17 Credit Suisse Financial Products, 39, 43 Crowe, William, 271 Cruz, Ted, 184–85, 199, 221, 224–26, 236, 237, 321, 322, 332, 333 cryonics, 23, 101 cryptography, 50–51 Cryptonomicon (Stephenson), 52–53 Cuban, Mark, 188 currency, 302 Customs and Border Protection (CBP), 267, 285–86 Daily Beast, 314 Daily Caller, 199, 225, 226, 232 Daily Stormer, 203, 204 Daily Wire, 304 Dalai Lama, 146 Damore, James, 277–79, 281, 295–96 Danforth, John, 331 Danzeisen, Matt, 127, 207, 209, 214, 303 parenthood of, 302, 330 Thiel’s marriage to, 272 Dartmouth Review, 31, 61 Dash, Anil, 230 data mining, xiii, 116, 285 Daulerio, A. J., 196, 227 Davidson, James Dale, 175 The Sovereign Individual, 175, 208–9 DCGS, 147, 216–17, 234–35, 284 DealBook conference, 326 DeAnna, Kevin, 203 DeepMind, xiii deep state, 192–93 Defense Advanced Research Projects Agency (DARPA), 145, 333 Defense Department, 114, 145–46, 149, 288, 310 Defense Intelligence Agency (DIA), 149 de Grey, Aubrey, 138, 139, 326, 327 DeMartino, Anthony, 283 democracy, 14, 32, 112, 140, 141, 176, 182, 192, 250, 303, 318, 321, 322 Democrats, Democratic Party, 47, 94, 179, 197, 220, 281, 301, 306, 313, 333 “Atari,” 94 Facebook and, 299, 300, 302–3 Deng, Wendi, x Denny, Simon, 305–6 Denton, Nick, 123–29, 194–96, 200, 201, 227–33 Deploraball, 255 Dershowitz, Alan, 198 Details, 173, 175 Dhillon, Harmeet, 279 Dickinson, Pax, 202 Dietrick, Heather, 201 Digg, 118 Dimon, Jamie, 118 disruption, 77, 313 Diversity Myth, The (Thiel and Sacks), 40–42, 47, 53, 145, 202, 252, 344n DNA sequencing, 168 Doherty, Bran, 181 Donnelly, Sally, 283 Doohan, James, 59 dot-com era, 48, 68, 73, 80, 84, 85, 88, 95, 98, 118, 292 Dowd, Maureen, 266 Downs, Jim, 243 Drange, Matt, 230 drones, 152, 288 Dropbox, 298 Drudge Report, viii drug legalization, 178–79, 259 D’Souza, Aron, 166, 193–95, 198, 201 D’Souza, Dinesh, 31, 35, 42, 61, 99 Duke, David, 31 Dungeons & Dragons (D&D), 1–2, 8, 306 Earnhardt, Dale, Jr., 299 Eastwood, Clint, 182 eBay Billpoint and, 56, 65, 90 PayPal and, 56, 59, 64–66, 70, 80–81, 84–85, 147, 274 PayPal acquired by, xii, 76, 88–91, 105, 108 Eden, William, 331 Edmondson, James Larry, 38 education, higher, xvi, 158, 160–62, 191–92, 335 Edwards, John, 177 Eisenberg, Jesse, 159 Eisman, Steve, 132 Electric Kool-Aid Acid Test, The (Wolfe), 162 Elevation Partners, 76 Ellis, Bret Easton, 25 Ellis, Curt, 251 Ellison, Larry, 68, 188, 221 Emergent Ventures, 192 Endorse Liberty, 179–81 EPA (Environmental Protection Agency), 250, 251 Epstein, Marcus, 203 ESPN, 99 Esquire, 144 extropianism, 23 Facebook, viii, ix, xiii, 77, 105–9, 112, 119, 134, 135, 141, 159, 162–64, 180, 182, 213, 234, 245, 259, 264, 268, 271, 276–77, 279, 280, 282, 285, 291–304, 317 Cambridge Analytica scandal and, 219–20 China and, 298–99 conservative opinions and, viii–xi, 245–46, 298, 300, 303–4 COVID pandemic and, 309, 313 Democrats and, 299, 300, 302–3 IPO of, 292, 294 Luckey at, 296 and 2008 US presidential election, 135 and 2016 US presidential election, 299, 323 Russia and, 245, 299 Trump and, 220, 245–46, 299–300, 302–4, 323 users’ sharing of information on, 297 Fairchild Semiconductor, 143–44 Falwell, Jerry, Jr., 237 Fast Company, 135 Fathom Radiant, 168 FBI, 79, 80, 114, 149, 289 FCC, 249 FDA (Food and Drug Administration), xvii, 181–82, 249, 253–54, 308, 316, 327 Federalist Society, 33, 170, 250 Federal Reserve, 133, 178, 183 Federation for American Immigration Reform (FAIR), 139, 266 feminism, 36, 202 Ferguson, Niall, 280–81 Fidelity, 211 Fieldlink, 50–51 1517 Fund, 169 financial crisis of 2008, 131–33, 145, 311, 313 Great Recession following, 104, 132, 157, 178 Financial Times, 124 Fincher, David, 159 Finish, The (Bowden), 152–53 Fiorina, Carly, 221, 223–25 Fischer, Bobby, 7, 22 Flatiron Health, 253 Flickr, 118 Flooz, 56, 68, 72 Flynn, Michael, 148–49, 235, 283–84 Forbes, 154, 215, 230 Ford, Henry, 270 formalism, 176 Fortune, 121, 192, 223, 231 Foster, Jodie, 128 Foster City, Calif., 1–2, 6–7, 10 Founders Fund, 119–21, 126, 138, 160, 162–64, 167, 168, 170, 173, 180, 189, 211, 214, 234, 248, 249, 269, 282, 285, 293, 297, 309, 310, 319, 330 Founder’s Paradox, The (Denny), 305–6 Fountainhead, The (Rand), 176 Fox News, x, 179, 247–48, 286, 289, 332 Free Forever PAC, 315 Frieden, Tom, 311 Friedman, Milton, 137 Friedman, Patri, 136–37, 169, 174, 176 Friedman, Thomas, 189 Friendster, 105 Frisson, 97–99, 108, 210 From Poop to Gold (Jones), 180 FTC, 249, 281 FWD.us, 263 Gaetz, Matt, 302 gambling, 81–83 Gamergate, 204 GameStop, 330 Garner, Eric, 187 Gates, Bill, 68 Gausebeck, David, 78 Gawker Media, xiv–xvi, xviii, 122, 123–24, 126–30, 133, 134, 137, 153, 184, 189, 193–98, 200–202, 228–33, 239, 277, 279, 287, 326, 334 Hogan’s suit against, xv, 195–97, 201, 227–34 Valleywag, 121, 123, 124, 126–29, 134, 140–42, 189 gay community, 34, 40–42, 125, 177 AIDS and, 32, 34, 40 conservatives in, 177 gay marriage, 177, 179, 199, 240 gay rights, 40–41, 177, 184, 186, 259, 314 homophobia and, 32–35, 40, 126, 128 outing and, 128, 129 Thiel’s sexual orientation, xviii, 41, 98, 104, 125–29, 134, 138, 239, 241, 243 Gelernter, David, 252–53 Genentech, 163 General Society of Mechanics and Tradesmen, 192 Genius Grants for Geeks, 160 Germany, 3 Gettings, Nathan, 113, 114 Ghostnet, 146, 153 Gibney, Bruce, 163 Gibson, Michael, 164, 165, 169, 174 Giesea, Jeff, 43, 200–201, 204, 255, 278, 288 gig workers, 189, 190 Gingrich, Newt, 213 Gionet, Tim, 255 Girard, René, 19–20, 42, 111 GitHub, 286 Gizmodo, viii Glitch, 230 globalization, 112, 131, 189, 209, 225, 259, 298 Goliath (Stoller), 329–30 Goldin, David, 227 Goldman Sachs, 185 Goldwater, Barry, 15, 60–61, 287 Google, xii, xiv, xvi, 55, 57, 98, 123, 133, 136, 137, 145, 169, 180, 188, 190, 191, 234, 245, 259, 261, 263, 274–81, 288–90, 295, 300, 318, 328 artificial intelligence project of, xiii, 280, 288 China and, 288–89, 321 conservatives at, 277–79 Damore at, 277–79, 281, 295–96 Defense Department and, 288 Hawley’s antitrust investigation of, 279–80 indexing of websites by, 297 monopoly of, 274–77 Palantir and, 289, 290 Places, 274 Trump and, 276 Gopnik, Adam, 124 GOProud, 177 Gore, Al, 63, 94 Gorka, Sebastian, 332 Gorshkov, Vasiliy, 80 Gorsuch, Neil, 314 Gotham, 116 GotNews, 199 Government Accountability Office, 213 Gowalla, 164 Graeber, David, 192 Greatest Trade Ever, The (Zuckerman), 132 Great Recession, 104, 132, 157, 178 Greenwald, Glenn, 150 Grigoriadis, Vanessa, 124 growth hacking, 61, 78, 271 Gruender, Raymond, 82 Guardian, 154, 230 guns, 184 Habermas, Jürgen, 115 Hacker News, 170–71 Hagel, Chuck, 271 Haines, Avril, 333 Halcyon Molecular, 138, 167–68 Haley, Nikki, 182 Hamerton-Kelly, Robert, 19–20, 111 Hamilton College, 334–36 Happer, William, 251–52 Harder, Charles, 195–97, 228, 229 Harmon, Jeffrey, 180 Harper’s, 176 Harrington, Kevin, 101, 255, 256, 283 Harris, Andy, 265 Harris, Kamala, 300, 304 Harry Potter and the Methods of Rationality, 174–75 Harvard Business School, 192 Harvard Crimson, 108 Harvard University, 107–8, 191, 308 Hastings, Reed, 295, 296, 298 Hawley, Josh, 279–80, 288, 301, 321–23, 331–33 Hayek, Friedrich, 68 HBGary, 150–51 Health and Human Services (HHS) Department, 311, 318, 320 Hellman, Martin, 50–51, 54, 172 Hello, 167 Heritage Foundation, viii Hewlett-Packard (HP), 223–24 Heyer, Heather, 272 Hillbilly Elegy (Vance), 288, 332 Hitler, Adolf, 251–52, 255, 270 Hitler Youth, 30 Ho, Ralph, 101 Hoffman, Reid, 23–24, 42, 65, 67, 71, 76, 85, 107, 108, 171, 280, 333 Hogan, Hulk (Terry Bollea), xv, 182, 195–97, 201, 227–34 Holiday, Ryan, 193, 297–98 Holocaust, 203, 251–52, 255 Hoover, Herbert, 14, 33 Hoover Institution, 14, 15, 316 Houston, Drew, 298 Howery, Ken, 53, 101, 119 How Google Works (Schmidt), 54 HP, 144 HuffPost, 204 Hughes, Chris, 135 Hume, Hamish, 234, 258 Hunter, Duncan, 149, 216, 217 Hunter, Duncan, Sr., 149 Hurley, Chad, 105 Hurley, Doug, 310 Hurricane Katrina, 209 Hurston, Zora Neale, 25, 26 Hyde, Marina, 230 IBM, 257 Iger, Bob, 264 Igor, 79, 112–14 Illiberal Education (D’Souza), 31, 35, 42 Immelt, Jeff, 264 immigration, 112, 139–40, 185, 225, 259, 261, 263, 271, 298, 313, 315 Customs and Border Protection, 267, 285–86 Palantir and, 266–68, 285–87, 290, 318 and separation of families at border, 285–86 Trump and, xii, xiii, 226, 244, 247, 260–68, 272, 285–86, 309, 314 visas and, see visas Immigration and Customs Enforcement (ICE), 267, 268, 286, 287, 290, 318 Inc., xv, 157 incels, 41 Inception, 118–19, 215 Independent Institute, 42, 82 indeterminate optimism, 171 Ingraham, Laura, 31 initial public offerings (IPOs), 46 In-Q-Tel, 116 Instagram, 296, 300–301 Intel, 144, 163, 249, 257 Intellectual Dark Web, 278, 282, 319 Intelligence Advisory Board, 271–72 intelligence work, 114, 117, 148–49, 217 Intercollegiate Studies Institute, 25, 42 International Space Station, 310 Iran, 116 Iraq War, 135, 146, 148, 178, 199, 216, 247, 284, 303 IRAs, 212–13, 313 IRS, viii, 213, 214 ISIS, 311 Islam, see Muslims, Islam Ivanov, Alexey, 80 Jackson, Candice, 243 Jackson, Eric, 53, 121 Jackson, Jesse, 31–32, 47 Jackson, Michael, 26–27, 35 Japanese Americans, 266 Jews, 252, 255, 270, 321 Holocaust and, 203, 251–52, 255 Jobs, Steve, 8, 75–77, 124, 144, 262, 331, 334, 335 Stanford University address of, 334 John M.

The Price of Tomorrow: Why Deflation Is the Key to an Abundant Future
by Jeff Booth
Published 14 Jan 2020

The game is said to have up to 10780 playing positions—that is, a number of playing positions so large that it would be written as a 1 with 780 zeros following it. Until 2014, even top AI researchers believed top human competitors would beat computers for years to come because of the complexity of the game and the fact that algorithms had to compare every move, which required enormous compute power. But in 2016, Google’s DeepMind program AlphaGo beat one of the top players in the world, Lee Sedol, in a match that made history. AlphaGo’s program was based on deep learning, which was “trained” using thousands of human amateur and professional games. It made history not only because it was the first time a computer beat a top Go master, but also because of the way it did so.

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

An AI that learns to make video recommendations at a world-class level generally can’t be repurposed to audit financial statements or filter email spam. And so far, AI has fared poorly at what is called “transfer learning”—using information gained while solving one problem to do something else. (The exceptions to this rule are deep learning algorithms like AlphaZero, the AI built by Google’s DeepMind, which recently taught itself to play chess and Go at a world-class level in a matter of hours by playing against itself millions of times. But even AlphaZero is limited to the world of games—it couldn’t, for example, unclog a sink.) Humans, by contrast, are great connectors. We spot a problem in one area of our life and use information we learned doing something completely different to fix it.

Big Data and the Welfare State: How the Information Revolution Threatens Social Solidarity
by Torben Iversen and Philipp Rehm
Published 18 May 2022

In one project called Verily, the self-proclaimed aim is to “accelerate precision health and medicine by integrating state of the art testing, longitudinal monitoring and participant engagement.”7 One longitudinal monitoring device is “Study Watch,” which shares real-time health data with a cloudbased database (Apple Watch works similarly). Combined with data from the NHS, AI can be used to diagnose and predict a broad range of illnesses including eye disease, diabetes, kidney disease, Parkinson’s, heart failure, and multiple sclerosis. Verily is part of Google Health, which comprises two related initiatives: DeepMind and Calico. Microsoft has created a parallel health initiative called HealthVault, and Amazon Care offers both virtual and in-person AI-assisted healthcare. The potential application of such data by insurance companies is obvious. In the extreme, it could render broad swaths of the population 5 See www.genome.gov/about-genomics/fact-sheets/DNA-Sequencing-Costs-Data, last accessed June 3, 2021) [https://perma.cc/5KW5-AAJV]). 6 Unlike complete gene sequencing, genotyping requires that variants of genes are identified in advance. 7 https://verily.com/blog/better-data-better-care/ (last accessed June 3, 2021) [https://perma .cc/FAB7-KZHX].

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https://doi.org/10.1017/9781009151405.008 Published online by Cambridge University Press Index 23andMe, 62 401(k) plans, 33, 64 actuarial science: fair premiums and, 2, 17, 68, 72, 89, 99–100, 190–191; historical perspective on, 45, 48–49, 65, 68; labor markets and, 160, 163n2, 177, 179, 184; prices and, 2; private markets and, 72, 81, 83, 89, 99–100; sound principles of, 10; tables for, 45, 49, 65; trackers and, 3 adverse selection: Akerlof on, 6; credit markets and, 112; gatekeeping and, 193; historical perspective on, 13, 45–50, 54, 65, 67; life expectancy and, 45; opting out and, 30, 54, 199; partisanship and, 37; pooled equilibrium and, 40–42; private information and, 40–42; private markets and, 72, 82n17, 83, 88; regulation and, 37–38; risk and, 1–2, 4, 6, 13, 30, 34, 45– 46, 49–50, 54, 65, 67, 72, 82, 112, 199, 202; theoretical model and, 30; time inconsistency and, 30, 34 Affordable Care Act (ACA), 11, 50n2, 60– 61, 63, 91, 94, 97 Ahlquist, John S., 109 AIA Australia, 79–80 AIDS, 86 Akerlof, George A., 6, 12, 19, 23–25, 27, 29, 190, 196 algorithms, 10, 12, 81, 93, 116n7, 119, 202 “Alliance for Sweden” campaign, 184 Alphabet, 5, 62 Amazon, 5, 62 American National Election Survey (ANES), 97–99 Ansell, Ben W., 109 Apple, 5, 62, 79 Arndt, Christoph, 182–183 artificial intelligence (AI), 5, 27, 62, 81–82, 202 Australia, 80, 90, 102, 107 Austria, 80, 90, 102, 107, 147 autocorrelation, 87 automobiles, 3 bad state, 20–21, 25n9, 40, 41, 112n5, 114, 142n27, 143–145, 190 bankruptcy, 31, 33, 46, 74, 96 banks: default and, 116, 132, 197; Fannie Mae and, 65, 109, 116–117, 121; financial crises and, 14, 61, 65, 116n7; Freddie Mac and, 65, 109, 116–117, 119–130, 140n25, 197; governmentsponsored enterprises (GSEs) and, 116– 121; loans and, 65, 105, 116, 131–132, 202; mortgages and, 131 (see also mortgages); small-town, 105 Barr, Nicholas, 12, 19, 24–25 Bayesianism, 15n1, 26, 56, 113, 143, 160, 164 Besley, Timothy, 93 Beveridge model, 53 Big Data: consequences of, 5; differential risk pools and, 63; financialization and, 138; informed patients and, 22; poor people and, 138; private markets and, 13, 219 https://doi.org/10.1017/9781009151405.009 Published online by Cambridge University Press 220 Index 63, 191; uncertainty reduction by, 13; utility and, 119; variety of available data, 108 Big Tech, 81, 201 Bildt government, 11, 177 Bismarckian system, 52–53, 58, 67, 191, 199–201 Blue Cross Blue Shield, 1, 49–50, 60 Boadway, Robin, 19 Bradley, David, 188 budget constraints, 20, 36n20, 42–43 burial insurance, 47–48 Busemeyer, Marius R., 39–40 Bush, George W., 17 Calico, 62 Canada, 90, 102, 107 CAT scans, 1 Clareto, 77–78 Clinton, Bill, 116 Code on Genetics, 93 coercion, 15, 25 collective bargaining, 64, 159, 195 commercial banks, 116–117, 131n14 commercial insurance: customer exclusions and, 193; digitalization and, 76; mutual aid societies (MASs) and, 45–50, 54–55, 67; unemployment insurance funds (UIFs) and, 66 Comparative Political Data Set, 102 Comparative Study of Electoral Systems (CSES), 176 COVID-19 pandemic, 61, 74, 77, 86n20, 100, 189 credible information, 28, 38, 39 credit guarantee schemes (CGSs), 115 credit markets: access to, 105–106; adverse selection and, 112; banks and, 105 (see also banks); collective bargaining and, 64, 159, 193; default and, 108–120, 128, 131–136, 141–146, 196–197; democracy and, 105, 117; discretionary income and, 100, 105, 108–115, 118, 138, 140, 142, 196; education and, 7, 33, 110, 115, 138, 141; empirical applications and, 196– 198; employment and, 108, 133–135; FICO scores and, 121–130, 149, 151– 158; flat-rate benefits and, 37, 114–115, 132, 144–146; Germany and, 107, 131, 135n23, 147; Gini coefficient and, 121– 127, 129, 138; government-sponsored enterprises (GSEs) and, 116–121; historical perspective on, 64–65; homeownership and, 108, 116, 131–140; inequality and, 106–115, 118–131, 138, 140, 144, 196–198; information and, 64– 65, 112–113; interest rates and, 105, 108, 111–132, 138–144, 152, 156; liquidity and, 109; loans and, 118–119 (see also loans); middle class and, 106; model for, 110–117, 141–144; mortgages and, 131 (see also mortgages); partisanship and, 118; pensions and, 64–65, 114, 131n14, 135n20, 141; Placebo outcomes and, 126–127, 146, 156–157; poor people and, 115, 133–140, 196; poverty and, 115; redistribution and, 109, 115, 124, 128, 144; reform and, 116–117, 120, 131– 137, 140; regression analysis and, 125– 126, 127, 130, 146, 147–158; regulation and, 14, 109–111, 115–131, 138, 140; rich people and, 133–137, 140, 196; risk and, 105, 108–120, 128–146; savings and loans (S&Ls), 116–117; segmentation and, 40, 159, 192; Single Family LoanLevel Dataset and, 121; subsidies and, 109, 116, 118, 131n14, 138, 139, 144; taxes and, 114–115, 139, 144; transfers and, 109, 114–115, 144; unemployment and, 108–109, 131–138; United States and, 106–107, 109, 117, 121, 124, 131, 139–140; wealth and, 108, 110, 111n2, 133, 140; welfare and, 105, 108–115, 131–138, 140 credit reports, 76 crime, 21n4 CT scans, 27, 83 deductibles, 17, 50, 195 DeepMind, 62 default: credit markets and, 108–120, 128, 131–136, 141–146, 196–197; flat-rate benefits and, 144–146; historical perspective on, 63; income relationship and, 146; information and, 7; private markets and, 80; theoretical model and, 17 democracy: asymmetric information and, 22–25; credit markets and, 105, 117; future issues, 199; historical perspective on, 51–52, 56, 63–64, 67–68; inequality and, 12, 70, 188; intergenerational https://doi.org/10.1017/9781009151405.009 Published online by Cambridge University Press Index transfers and, 190; labor markets and, 163, 183; market failure and, 19–30; mutual aid societies (MASs) and, 16; private markets and, 13, 70, 73, 89, 100; rich people and, 2, 73, 183; social protection and, 2, 56; symmetric information and, 25–29; theoretical model and, 16, 19–20, 30, 32nn15–16, 33; transfers and, 16, 30, 67, 190; uncertainty and, 8; welfare and, 8 Denmark, 8–9, 90, 92, 102, 107, 109, 117, 147, 183, 193, 197–198 Department of Motor Vehicles, 75 destitution, 45, 67 diagnostics, 10, 27, 49, 62, 81, 83–88, 94, 100, 193 digitalization, 76–79 disability, 34, 38, 44, 63, 75, 139, 197 Discovery Limited, 79 discretionary income: credit markets and, 100, 105, 108–115, 118, 138, 140, 142, 196; risk and, 100, 105, 108–115, 138; welfare and, 110–111 discrimination, 2, 5, 35, 38–39, 63, 81, 88, 93–94, 100, 116, 199, 202 “double payment” problem, 9, 13, 37, 39, 68, 89, 92, 94–95, 100, 198–199 education: additional schooling, 107; advantages of, 200; credit markets and, 7, 33, 110, 115, 138, 141; double-payment problem and, 9; employment and, 11, 33, 60, 66, 69, 159, 161–162, 165, 174, 179, 183–184, 192, 197–198; health and, 9, 60, 66, 84, 93, 95–96, 159, 192–193, 197; income and, 9, 11, 17, 33, 60, 64, 69, 92–96, 110, 115, 138, 141, 161, 174, 192, 197; labor markets and, 159, 161– 162, 165, 167, 174, 179, 183–184, 198; mutual aid societies (MASs) and, 192; private markets and, 84, 92–95, 96n24; rich people and, 9, 40, 60, 92, 95; risk and, 7, 11, 17, 33, 40, 60, 66, 69, 84, 93, 115, 138, 141, 159, 161–162, 165, 174, 179, 183–184, 192, 197n3, 198; social media and, 196; unemployment and, 11, 60, 66, 159, 161–162, 165, 174, 179, 183–184, 192, 197n3, 198 elasticity, 111 elderly: health and, 2, 7, 18, 29, 34, 96–97, 99; higher expenses of, 195; insecurity 221 and, 8, 18; labor markets and, 159, 171, 173, 185–186; market feasibility and, 16– 18, 30–35; Medicare and, 2, 7, 9, 17, 59– 60, 96–99, 133; mutual aid societies (MASs) and, 44, 47–49, 55; old-age insurance and, 4–5, 13, 31, 159; pensions and, 56 (see also pensions); poverty and, 46–47; private markets and, 18, 96–97; public spending on, 29n13; time inconsistency and, 7, 16–18, 30–35, 47, 56, 89, 96, 193; welfare and, 4, 7–8, 13– 14, 18, 33, 53–54, 58, 105, 188, 193, 199 electronic health records (EHRs), 76–79 empirical implications of theoretical models (EITM) approach, 201 Employee Retirement Income Security Act, 50n2, 60–61 employment: credit markets and, 108, 133– 135; education and, 11, 33, 60, 66, 69, 159, 161–162, 165, 174, 179, 183–184, 192, 197–198; health insurance and, 2, 4– 5, 10, 13, 18, 20, 34–35, 44, 50–51, 55, 58, 60, 66, 159–160, 191–192, 197; historical perspective on, 50, 58, 66, 69n9; homeownership and, 134–137; insiders vs. outsiders and, 66; Job Pact and, 182; labor market risks and, 159, 162, 165, 167, 174n9, 179–181, 184; Law on Employment Protection, 180; mobility and, 49, 66, 68, 189, 191–192, 200; mutual aid societies (MASs) and, 48– 49 (see also mutual aid societies (MASs); retirement and, 33 (see also retirement); sickness pay and, 44, 48; unemployment insurance funds (UIFs) and, 11, 14, 66, 177–184, 192, 198–199 employment protection, 159, 162, 180 Equitable Life Assurance Society, 49 error correction model (ECM), 87, 103 Esping-Andersen, Gosta, 52, 199 European Observatory on Health Systems and Policies (EOHSP), 93 European Social Survey (ESS), 174, 176, 186–187 Fair Housing Act, 12, 116n7 Fannie Mae, 65, 109, 116–117, 121 Federal Housing Administration (FHA), 117 FICO score: Gini coefficient and, 121–127, 129, 138; interest rates and, 121–130; loans and, 121–130, 149, 151–158 https://doi.org/10.1017/9781009151405.009 Published online by Cambridge University Press 222 Index financial crises, 14, 61, 65, 116n7 financialization, 7, 14, 16, 33, 65, 106–110, 115, 138–139 Finland, 90, 102, 107, 147 Fitbit, 79 flat-rate benefits, 37, 114–115, 132, 144–146 Food and Drug Administration, 62 Foote, Christopher, 120–121, 131 Fordism, 47, 106, 162 fragmentation: information revolution and, 58–67; labor markets and, 50n2; political polarization and, 2; risk pools and, 2, 12, 188; solidarity and, 58–67; unemployment insurance and, 11–12 France, 80, 90, 102, 107, 147 fraternal sciences, 47, 52 Freddie Mac, 65, 109, 116–130, 140n25, 197 Friedman, Rachel, 15n1, 19, 191 funded systems: adverse selection and, 45; information and, 18; intergenerational transfers and, 7; pension systems, 7, 17, 33, 53, 55, 58, 64, 193, 201; retirement and, 16, 33, 45, 64, 96; transfers and, 7, 16, 47, 64, 96 GDP, 64, 70, 83, 86n21, 87–91, 104, 139 Generali, 80 genetics, 18, 38, 62–63, 81–88, 93–94, 191, 193 German General Social Survey (ALLBUS), 171–172, 173, 185–186 German Socio-Economic Panel (GSOEP), 134–137, 165–172 Germany, 195; credit markets and, 107, 131, 135n23, 147; Hartz reforms and, 14, 65, 131–137, 140, 198; health insurance and, 17; health savings plans and, 7, 33; labor markets and, 165–172, 173, 185– 186, 198; private markets and, 80, 89n23, 90, 91, 96n25, 102; unemployment and, 14, 65, 165, 168– 173, 185–186, 198 Ghent system, 177, 179–180, 182, 184, 198 Gingrich, Jane R., 59 Gini coefficient, 121–127, 129, 138 Goering, John, 116n7 good state, 20–21, 25n9, 40, 41, 112n5, 114, 142n27, 143–144 Google, 62, 73 Gordon, Robert, 49 Gottlieb, Daniel, 48, 50n3 government-sponsored enterprises (GSEs), 116–121 GPS, 3 Great Depression, 30, 46, 117, 189 Grogan, Colleen M., 99 group plans, 49–50 Hacker, Jacob, 60 Hall, John, 93 Hariri, Jacob Gerner, 109 Harsanyi, John, 15–16 Hartz IV reforms, 14, 65, 131–137, 140, 198 health: data devices and, 62–63; diagnostics and, 10, 27, 49, 62, 81, 83–88, 94, 100, 193; disease, 10, 44, 62, 67, 79, 84, 86– 87, 100–102; education and, 9, 60, 66, 84, 93, 95–96, 159, 192–193, 197; elderly and, 2, 7, 18, 29, 34, 96–97, 99; genetics and, 18, 38, 62–63, 81–88, 93– 94, 191, 193; rich people and, 2, 4, 8–9, 58, 60, 91, 95, 193; younger generation and, 4, 6–7, 13, 17–18, 30–31, 48, 56, 67, 86, 92, 96, 101, 193–195 Healthcare NExt, 81 Health Information Technology and Economic and Clinical Health Act, 76 health insurance: Affordable Care Act (ACA), 11, 50n2, 60–61, 63, 91, 94, 97; artificial intelligence (AI) and, 81–82; choice between public/private, 94–99; electronic health records (EHRs), 76–79; empirical applications and, 193–196; employment and, 2, 4–5, 10, 13, 18, 20, 34–35, 44, 50–51, 55, 58, 60, 66, 159– 160, 191–192, 197; guaranty associations and, 33; historical perspective on, 44, 49– 51, 55, 58, 60–64; illness and, 8, 13–14, 20, 25, 48, 62–63, 75, 96, 108, 110, 171, 173, 185–186, 188; information and, 4– 8, 11, 13, 60–64, 192–196; laboratories and, 81, 83, 87; labor markets and, 159; medical data and, 75; Medical Information Bureau (MIB) and, 72n4, 75, 78–79; prescription databases and, 75, 77; private markets and, 70–102, 104, 201; Republican Party and, 94; as second largest insurance, 70; segmentation and, 70; supplementary private, 88–94; https://doi.org/10.1017/9781009151405.009 Published online by Cambridge University Press Index theoretical model and, 17–19, 33–37; trackers and, 76, 79–81, 100; underwriting and, 17, 92–94, 100; voluntary private, 63, 89–93 Health Insurance Portability and Accountability Act (HIPAA), 63n8, 78 health savings plans, 7, 17, 33, 96, 195 HealthVault, 62 Hicks, Timothy, 59, 197n3 high information, 8, 10, 25–27, 38, 56–58, 64, 82n17, 200 Home Mortgage Disclosure Act (HMDA), 120n10 homeownership: credit markets and, 108, 116, 131–140; employment and, 134– 137; GSEOP and, 134–137; Hartz IV reforms and, 14; mortgages and, 106 (see also mortgages); private markets and, 93; Sample Survey of Income and Expenditure (EVS) and, 134–135, 137; stratified rates of, 198; subsidies and, 131, 138–139, 197; VPHI and, 93; welfare and, 131–138 homophily, 164 housing, 11–12, 115–117, 121, 132–133, 138–141, 192 Human API portal, 77–78 human genome, 62, 81, 83 IBM, 62 Ignacio Conde-Ruiz, J., 53 illness, 8, 13–14, 20, 25, 48, 62–63, 75, 96, 108, 110, 171, 173, 185–186, 188 immigrants, 46, 167 individual retirement accounts (IRAs), 47, 64, 193 industrialization, 17, 96n25; deindustrialization and, 12, 30, 179, 188– 189; health insurance and, 195; historical perspective on, 44, 49, 51, 56; knowledge economy and, 192; middle class and, 6, 15, 51, 53–54; mutual aid societies (MASs) and, 44, 190; uncertainty and, 189; urbanization and, 189 inequality: credit markets and, 106–115, 118–131, 138, 140, 144, 196–198; democracy and, 12, 70, 188; future issues and, 201; Hartz IV reforms and, 14; historical perspective on, 59–61, 64–65; increased, 2, 7, 14, 16, 19, 33, 59, 61, 64– 65, 70–71, 100, 106, 108–113, 118, 128, 223 130, 138, 140, 188–189, 197–198, 201; information and, 2, 5, 7, 12, 14; labor markets and, 198; mortgages and, 119– 131; private markets and, 70–71, 82, 92, 100; reduction of, 92, 112, 118, 138, 188–189, 198; regulation and, 119–131; risk and, 2, 7, 12, 14, 19, 33, 59–61, 65, 82, 92, 100, 108, 111–114, 130, 138, 144, 188–189, 196–198, 201; segmentation and, 59, 61, 188–189, 196; taxes and, 19, 60, 100, 188–189; theoretical model and, 16, 19, 33 information: actuarial science and, 49 (see also actuarial science); asymmetric, 2–4, 8, 15, 20–27, 38, 39, 55, 56, 63, 74, 82n17, 160, 190, 199, 202; Big Data, 5, 13, 22, 63, 108, 119, 138, 191; credible, 28, 38, 39; credit markets and, 64–65, 112–113; Department of Motor Vehicles and, 75; diagnostics and, 10, 27, 49, 62, 81–88, 94, 100, 193; division of insurance pools and, 5–10; electronic health records (EHRs) and, 76–79; funded systems and, 18; health insurance and, 4–8, 11, 13, 60–64, 192–196; high, 8, 10, 25–27, 38, 56–58, 64, 82n17, 200; human genome, 62, 81, 83; incomplete, 2, 12, 18, 29, 55, 66; inequality and, 2, 5, 7, 12, 14, 119–131; laboratories and, 81, 83, 87; labor markets and, 160–165; life insurance and, 4–7, 10, 13, 72–73, 82– 88, 101–103, 104, 193–193; loans and, 112–113, 118–119; low, 8, 10, 14, 18, 25–26, 28, 38, 39, 56, 57, 67, 199; market failure and, 6, 9, 19–30, 190; market feasibility and, 16–19, 30–37, 46, 58, 160, 199; Medical Information Bureau (MIB) and, 72n4, 75, 78–79; Moore’s Law and, 61–62, 83n18; mortgages and, 119–131; mutual aid societies (MASs) and, 6, 8, 10–13, 199; ownership of, 202; pensions and, 64–65; preferences and, 18–19, 35–37; prescription databases and, 75, 77; privacy and, 10, 26–29, 40–42, 63, 78, 94, 202; regulation and, 2, 14, 18, 38, 63– 65, 70, 73, 81, 87–89, 93–94, 100, 110, 117–131, 140, 199, 202; revolution in, 2, 4, 13, 35, 39, 55, 58–73, 82, 88, 94, 100, 108, 188, 201; risk and, 1–15, 18–30, 35– 37, 160–165; segmentation and, 2, 5–6, https://doi.org/10.1017/9781009151405.009 Published online by Cambridge University Press 224 Index 8, 11–14, 16, 18, 58–59, 66–67, 70, 89, 94, 159, 162, 165, 177, 180, 188–189, 192, 196; social insurance and, 2–13, 189–190, 193, 198; social solidarity and, 53–60; symmetric, 20, 25–29, 39, 55, 82n17; trackers and, 3–4, 29, 79–80, 191; uncertainty and, 16 (see also uncertainty); underwriting and, 74–75; unemployment insurance and, 65–67, 183; welfare and, 2–14 information and communication technology (ICT), 4, 8, 119, 131, 189 integration, 2, 5 interest rates: changing, 17; credit markets and, 105, 108, 111–132, 138–144, 152, 156; Denmark and, 198; equalization of, 65; FICO scores and, 121–130; Gini coefficient and, 121–127, 129, 138; mortgages, 14, 65, 116–124, 128, 138– 140, 197; segmentation and, 52, 58, 70 International Monetary Fund (IMF), 106, 107 Ireland, 90, 102, 107, 147 ISCO, 174 Italy, 90, 102, 107, 147, 203 Japan, 58, 66, 90, 91, 102, 107 Jawbone, 79 Job Pact, 182 John Hancock Life Insurance, 4, 29, 77–78, 80 Kaiser Family Foundation Poll, 99 Keen, Michael, 19 Korpi, Walter, 53, 193 laboratories, 81, 83, 87 labor markets: actuarial approach and, 160, 163n2, 177, 179, 184; collective bargaining and, 64, 159, 193; democracy and, 163, 183; disability and, 34, 38, 44, 63, 75, 139, 197; education and, 159, 161–162, 165, 167, 174, 179, 183–184, 198; elderly and, 159, 171, 173, 185– 186; empirical applications and, 198– 199; employment protection, 159, 162, 180; fragmentation and, 50n2; Germany and, 165–172, 173, 185–186, 198; Ghent system and, 177, 179–180, 182, 184, 198; health insurance and, 159; inequality and, 198; information and, 160–165; Law on Employment Protection, 180; market failure and, 184; partisanship and, 177, 183; poor people and, 160, 176; preferences and, 14, 66, 160, 163, 165– 177; public system and, 165, 177, 182– 183; redistribution and, 172, 174–176, 183, 186–187; reform and, 165, 177– 182, 198; regression analysis and, 166, 172, 173, 185–186; regulation and, 159; segmentation and, 14, 50, 67, 159, 162, 165, 177, 180, 182, 188, 192, 198; social insurance and, 159–160, 163, 177; subsidies and, 182, 185; Swedish unemployment insurance and, 177–183; taxes and, 159, 177, 180, 181; uncertainty and, 160, 163n2; unemployment protection, 46, 159, 164, 197n3; unions and, 159, 161, 164, 174, 177–184, 200; United States and, 66; voters and, 163–164, 184; wage protection, 159 Latin America, 66 Law on Employment Protection, 180 layoffs, 110 legal issues: clerical marriage, 44; discrimination, 116; intergenerational contracts and, 31; social media, 81; symmetric information and, 26 Lexis Nexis Risk Classifier, 76 life expectancy: adverse selection and, 45; historical perspective on, 45, 48–51; increased data on, 10, 45; predicting, 18, 193; premiums and, 17; private markets and, 72, 83–87; risk and, 34 life insurance: artificial intelligence (AI) and, 81–82; commercialization of, 45–50, 54– 55, 67; credit reports and, 76; Department of Motor Vehicles and, 75; diagnostics and, 10, 27, 49, 62, 81, 83– 88, 94, 100, 193; division of insurance pools and, 5, 7; electronic health records (EHRs) and, 76–79; empirical applications and, 193–196; funded plans and, 7, 16–18, 33, 45, 48, 53, 58, 96, 193–195; guaranty associations and, 33; historical perspective on, 44–49, 55, 58, 63; information and, 4–7, 10, 13, 72–73, 82–88, 101–103, 104, 193–193; laboratories and, 81, 83, 87; Lexis Nexis Risk Classifier and, 76; market penetration of, 82–88, 101–103, 104; https://doi.org/10.1017/9781009151405.009 Published online by Cambridge University Press Index Medical Information Bureau (MIB) and, 72n4, 75, 78–79; micro-targeted products and, 73; permanent, 72; prescription databases and, 75, 77; private markets and, 70–94, 100–102, 103–104; purpose of, 71–72; theoretical model and, 16–17, 29, 33–38; trackers and, 76, 79–81, 100; underwriting and, 71, 73–82, 87–88, 100–101 liquidity, 109 loans: access to, 65, 105–106, 110; bank, 65, 105, 116, 131–132, 202; credit markets and, 118–119; default and, 108 (see also default); discretionary income and, 100, 105, 108–115, 118, 138, 140, 142, 196; FICO scores and, 121–130, 149, 151–158; flat-rate benefits and, 37, 114–115, 132, 144–146; Gini coefficient and, 121–127, 129, 138; Hartz reform and, 65, 132; inequality and, 119–131; information and, 112–113, 118–119; interest rates and, 110 (see also interest rates); liquidity and, 109; model for, 110– 117, 141–144; mortgages, 110 (see also mortgages); private markets and, 83; regulation and, 115–131; risk and, 65, 100, 105, 108–109, 111–117, 130, 132, 138, 141–142, 196, 202; Single Family Loan-Level Dataset and, 121; welfare and, 110–111, 113–115, 131–138 loan-to-value ratio, 124, 131, 156 Loewenstein, Lara, 120–121, 131 low information, 8, 10, 14, 18, 25–26, 28, 38, 39, 56, 57, 67, 199 lump sum payments, 33, 35–36, 114 McFadden pseudo R-squared measure, 166–172, 173, 185–186 Maclaurin, Colin, 45 market failure: asymmetric information and, 22–25; classic framework for, 19– 30; democracy and, 19–30; historical perspective on, 53, 57, 67; information and, 6, 9, 190; labor markets and, 184; mutual aid societies (MASs) and, 6, 67; private markets and, 94; redistribution and, 6, 12, 67, 191, 200; symmetric information and, 25–29; theoretical model and, 12, 15, 18–20, 29 market feasibility, 160, 199; historical perspective on, 46, 58; information and, 225 16–18, 30–35; preferences and, 18–19, 35–37; time inconsistency and, 16–18, 30–35 market-mediated funded systems, 201 Medicaid, 8, 10, 60, 68, 96–99, 193 Medical Information Bureau (MIB), 72n4, 75, 78–79 Medical Literature Analysis and Retrieval System Online, 84 Medical Subject Headings (MeSH), 84 Medicare, 2, 7, 9, 17, 59–60, 96–99, 193 Meltzer-Richard model, 114 Microsoft, 5, 62, 81 micro-tracking, 3 middle class: credit markets and, 106; education and, 199; industry and, 6, 15, 51, 53–54; mortgages and, 65, 106; preferences of, 59, 196, 200; private markets and, 69, 71, 92, 97, 200; theoretical model and, 5; universal public system and, 30; voters, 32, 51, 61, 193; welfare and, 6, 8, 13, 15, 54, 68–69, 193– 195, 199 Misfit, 79 MLC On Track, 80 mobility, 49, 66, 68, 189, 191–192, 200 Moore’s Law, 61–62, 83n18 moral hazard, 10, 45, 48, 184, 198 mortality: artificial intelligence (AI) and, 81–82; Lexis Nexis Risk Classifier and, 76; life expectancy and, 10, 17, 34, 45, 48–51, 72, 83–87, 193; private markets and, 72, 75–76, 79, 81, 84, 86, 101–102 mortgages: credit markets and, 106, 109– 140, 146, 147; FICO scores and, 121– 130, 149, 151–158; Gini coefficient and, 121–127, 129, 138; Home Mortgage Disclosure Act (HMDA) and, 120n10; inequality and, 119–131; information and, 119–131; interest rates and, 14, 65, 116–124, 128, 138–140, 197; middle class and, 65, 106; private markets and, 198; redlining and, 11, 116, 202; regulation and, 14, 65, 109, 115, 117– 131, 138, 140, 197; risk and, 14, 65, 109, 115–117, 120, 128, 132, 134–138, 197, 202; Single Family Loan-Level Dataset and, 121; underwriting, 120, 207–208 Motor Vehicle Reports, 75 MRI scans, 1, 27, 83 Murray, Charles, 51–52 https://doi.org/10.1017/9781009151405.009 Published online by Cambridge University Press 226 Index mutual aid societies (MASs): asymmetric information and, 23, 25, 199; burial insurance, 47–48; commercialization of, 45–50, 54–55, 67; democracy and, 16; destitution and, 45, 67; double bind of, 48, 50, 54, 67, 190; dues to, 46; education and, 192; elderly and, 44, 47–49, 55; Equitable Life Assurance Society, 49; failure of, 12; heyday of, 51; historical perspective on, 10–11, 13, 44–57, 65, 67, 192; immigrants and, 46; increase of, 44; industrialization and, 44; information and, 6, 8, 10–13, 199; limitations of, 6; market failure and, 6, 67; New England Mutual Life Insurance Company, 49; New York Life Insurance Company, 49; as partial solution, 190; protections by, 44; role of, 11; Scottish Presbyterian Widows Fund, 44–46, 49, 83, 193; sickness pay and, 44, 48; solidarity and, 46, 53–58; taxes and, 47; theoretical model and, 15–16, 23, 25, 32; timeinconsistency and, 6–7, 16, 32, 45, 47– 48, 54, 56, 199; transfers and, 6, 48, 57– 58; unions and, 192; United States and, 44, 46, 49, 55; welfare and, 6, 8, 10, 12– 13, 15–16, 25, 48, 51–52, 54, 56; widespread use of, 44 National Human Genome Research Institute, 62 National Laboratory of Medicine, 84 Netherlands, 90, 91, 102, 107, 147 New England Mutual Life Insurance Company, 49 New York Life Insurance Company, 49 New York State Department of Financial Services, 80–81 New Zealand, 90, 102, 107 Norway, 90, 92, 102, 107, 147 Obama, Barack, 76, 81, 90 occupational unemployment rates (OURs), 174n0 OECD Health Statistics, 89, 101 opting out: adverse selection and, 30, 54, 199; Akerlof and, 24; Bismarckian system and, 53; cost of, 19, 29; deterrents against, 9; private markets and, 25; privileged, 15; public system and, 8–9, 15, 19, 24–25, 30, 37, 54, 57, 59, 64, 71, 89n23, 94–96; segmentation and, 8; selfinsurance and, 11–12, 20–22, 50n2, 51, 57, 60, 67, 73, 93, 190; theoretical model and, 15, 19, 24–25, 29–30, 37, 41 Oscar Health Insurance, 80 Palme, Joakim, 53, 193 Park, Sunggeun (Ethan), 99 participation, 9, 67, 95, 102, 184 partisanship: adverse selection and, 37; coercion and, 6; Comparative Political Data Set and, 102; credit markets and, 118; historical perspective on, 59; labor markets and, 177, 183; preferences and, 12, 19, 59, 200; private markets and, 71, 92, 94, 97, 101–102, 103–104, 195; regulation and, 37–38; theoretical model and, 37–38; welfare and, 12 pay-as-you-go (PAYG) systems: credible government commitment to, 7; historical perspective on, 46–48, 53–58, 64, 67; market-mediated funded systems and, 201; private markets and, 96; redistribution and, 16, 18, 32, 53, 64, 67; subsidies and, 18, 67; time-inconsistency and, 16, 31–35, 47, 56, 96, 191, 193; transfers and, 16, 47, 55, 191; voters and, 193; welfare and, 16, 18, 33, 48, 53, 193; younger generation and, 16, 18, 31, 33, 47–48, 56, 64, 67, 96, 193 pay-how-you-drive (PHYD), 3 pensions: credit-based insurance and, 64– 65; credit markets and, 64–65, 114, 131n14, 135n20, 141; funded systems and, 7, 17, 33, 53, 55, 58, 64, 193, 201; historical perspective on, 51, 53–60, 64– 65; information and, 64–65; marketmediated funded systems and, 201; PAYG and, 31 (see also pay-as-you-go (PAYG) systems); private markets and, 18, 36, 70, 82; taxes and, 19, 31 “piggy bank”, 12, 24 Placebo outcomes, 126–127, 148, 156–157 “Politics of Medicaid, The: Most Americans Are Connected to the Program, Support Its Expansion, and Do Not View It as Stigmatizing” (Grogan and Park), 99 Ponzi schemes, 48 poor people: attitudinal gap and, 176; becoming, 7; cost of insurance and, 30; credit markets and, 115, 133–140, 196; https://doi.org/10.1017/9781009151405.009 Published online by Cambridge University Press Index labor markets and, 160, 176; Medicaid and, 8, 10, 60, 68, 96–99, 193; Medicare and, 2, 7, 9, 17, 59–60, 96–99, 193; private markets and, 96, 98, 100; support of by rich people, 4; transfers and, 7–8, 55, 115, 200; welfare and, 68 (see also welfare) Portugal, 90, 102, 147 Potential Years of Life Lost (PYLL), 86–87, 101–102, 104 poverty: credit markets and, 115; destitution, 45, 67; elderly and, 46–47; fear of, 7–8; historical perspective on, 46– 47, 55, 67–68; insurance against, 7–9, 193; private markets and, 71, 96, 99; transfers and, 13 Precision Medicine Initiative, 81 preferences: bifurcation of, 30, 163; constrained, 9, 59, 199; divergence in, 192; first-best, 9, 95; formation of, 35– 37; increased information and, 18–19, 35–37; labor markets and, 14, 66, 160, 163, 165–177; market feasibility and, 18– 19, 35–37; mass, 18; middle class, 59, 196, 200; partisan, 12, 19, 59, 200; polarization of, 2, 9, 12, 14, 16, 20, 37, 39, 66–67, 163, 169, 172, 176, 184; policy, 11–12, 14, 19, 26, 37, 67, 172, 184; political, 160, 163–177, 184, 186; private markets and, 95, 96n24, 99; public spending and, 18, 37, 59, 95, 192; redistribution, 12, 16, 18, 21n4, 35, 172, 174, 200, 203; risk and, 2, 12, 14, 16, 18, 21, 26, 30, 35, 37, 39, 57, 59, 66–67, 160, 163–176, 184, 192, 199–200, 203; shaping, 19, 59, 66, 160, 200; uncertainty and, 16, 26, 66, 199; welfare and, 2, 9, 12, 18, 21, 30, 37, 39, 68, 203 prescription databases, 75, 77 Preston, Ian, 93 price discrimination, 38 price nondiscrimination, 39 privacy, 10, 26–29, 40–42, 63, 78, 94, 202 private markets: actuarial approach and, 72, 81, 83, 89, 99–100; adverse selection and, 72, 82n17, 83, 88; Big Data and, 13, 63, 191; democracy and, 13, 70, 73, 89, 100; education and, 84, 92–95, 96n24; elderly and, 18, 96–97; Germany and, 80, 89n23, 90, 91, 96n25, 102; health insurance and, 70–102, 104, 201; homeownership and, 227 93; inequality and, 70–71, 82, 92, 100; life expectancy and, 72, 83–87; life insurance and, 70–94, 100–102, 103– 104; market failure and, 94; middle class and, 69, 71, 92, 97, 200; mortality and, 72, 75–76, 79, 81, 84, 86, 101–102; mortgages and, 198; opting out and, 25; partisanship and, 71, 92, 94, 97, 101– 102, 103–104, 195; pay-as-you-go (PAYG) systems and, 96; pensions and, 18, 36, 70, 82; poor people and, 96, 98, 100; poverty and, 71, 96, 99; preferences and, 95, 96n24, 99; public system and, 71, 82, 91–97, 100; reform and, 89–92; regression analysis and, 83; regulation and, 19, 37–38, 70, 73, 80–81, 87–94, 97, 100, 102; risk and, 70–100; segmentation and, 2, 5, 8, 11, 13–14, 18, 40, 53, 58–59, 63, 67, 70, 89, 94, 165, 180, 196; social insurance and, 70, 96; subsidies and, 94; taxes and, 89, 92, 100; time-inconsistency and, 71, 89, 96–99; top-up plans and, 9, 89, 179–182, 195; transfers and, 80n15, 81, 96; uncertainty and, 101; unemployment and, 4; United States and, 8, 18, 44, 51, 70, 74, 77–84, 89n23, 90, 91–99, 102–103, 195; voters and, 101; wealth and, 70, 97, 100; welfare and, 19, 37–38, 70; younger generation and, 84–86, 92, 96–97, 101 professional associations, 49, 66, 159, 161, 164, 179 Profeta, Paola, 53 Prudential, 80 Prussia, 44 Przeworski, Adam, 19 public spending, 29n13, 37, 68, 139, 145, 192, 199 public system: historical perspective on, 54, 57, 59, 63–64; information and, 8–9; labor markets and, 165, 177, 182–183; left’s support for, 19, 37–38; opting out and, 8–9, 15, 19, 24–25, 30, 37, 54, 57, 59, 64, 71, 89n23, 94–96; private markets and, 71, 82, 91–97, 100; taxes and, 9, 15, 19, 25, 31, 37, 39, 54, 60, 195, 200; theoretical model and, 15–20, 24– 25, 28–30, 35, 37–40; top-up plans and, 9, 36, 89, 179–182, 195; uncertainty and, 8, 15–16, 30, 61, 67 Putnam, Robert, 203 https://doi.org/10.1017/9781009151405.009 Published online by Cambridge University Press 228 Index Qualcomm, 80 Rawls, John, 8, 15n1, 54, 67 recessions, 46, 189 reciprocity, 46, 203 Recovery Act, 76 redistribution: credit markets and, 109, 115, 124, 128, 144; division of insurance pools and, 5; historical perspective on, 46, 53, 58, 60, 64, 67; intergenerational, 32, 67; labor markets and, 172, 174–176, 183, 186–187; literature on, 21n4, 189; lumpsum benefits and, 36; market failure and, 6, 12, 67, 191, 200; pay-as-you-go (PAYG) systems, 16, 18, 32, 53, 64, 67; preferences and, 12, 16, 18, 21n4, 35, 172, 174, 200, 203; risk, 5, 17, 30, 38, 53, 58, 60, 172, 174–176, 183, 186–187, 197, 200; time-inconsistency and, 30; transfers and, 16, 30, 64, 109, 144, 188– 189, 200; welfare and, 6, 12, 16, 18, 21, 36, 38, 53, 56, 58, 68, 115, 188, 191, 197, 203; younger generation and, 16, 30, 64 redlining, 11, 116, 202 reform, 201; credit markets and, 116–117, 120, 131–137, 140; Hartz IV, 14, 65, 131–137, 140, 198; historical perspective on, 65, 67; labor markets and, 165, 177– 182, 198; private markets and, 89–92; regulation and, 14, 18, 65, 89, 117; Scottish Reformation, 44; unemployment, 14, 29, 65, 67, 131–137, 165, 177–182, 198; voters and, 18, 29 regression analysis: credit markets and, 125–126, 127, 130, 146, 147–158; discontinuity results and, 148; labor markets and, 166, 172, 173, 185–186; private markets and, 83 regulation: adverse selection and, 37–38; constraints from, 2, 63, 68, 94, 111; credit markets and, 14, 109–111, 115–131, 138, 140; historical perspective on, 50, 60–65, 68; inequality and, 119–131; of information, 2, 14, 18, 38, 63–65, 70, 73, 81, 87–89, 93–94, 100, 110, 117– 131, 140, 199, 202; labor markets and, 159; loans and, 115–131; mortgage markets and, 14, 65, 109, 115, 117–131, 138, 140, 197; partisanship and, 37–38; private markets and, 19, 37–38, 70, 73, 80–81, 87–94, 97, 100, 102; redistribution and, 172, 174–176, 183, 186–187; reform and, 14, 18, 65, 89, 117; risk and, 2, 14, 18–19, 33, 42, 50, 60–61, 64–65, 70, 73, 81, 89, 94, 109, 115–120, 130–131, 138, 140, 159, 195, 197, 199, 202; role of, 37–38, 115–118; segmentation and, 2, 6, 11–14, 16, 18, 40, 50, 52–53, 58–67, 70, 89, 94, 159, 162, 165, 177, 180, 188–189, 192–193, 196, 198; tax, 19, 50, 63, 115, 195, 199; trackers and, 80–81; welfare and, 37–38 Reinfeldt, Fredrick, 92, 177 Republican Party, 94 retirement: adverse selection and, 45; Employee Retirement Income Security Act and, 50n2, 60–61; funded systems and, 16, 33, 45, 64, 96; individual retirement accounts (IRAs), 47, 64, 193; pensions and, 64–65 (see also pensions); Social Security, 47, 67 rich people: attitudinal gap and, 176; credit markets and, 133–137, 140, 196; democracy and, 2, 73, 183; education and, 9, 40, 60, 92, 95; health and, 2, 4, 8– 9, 58, 60, 91, 95, 193; self-insurance and, 11–12, 20–22, 50n2, 51, 57, 60, 67, 73, 93, 190; selfinsuring by, 12, 22; support of poor by, 4 risk: adverse selection and, 1–2, 4, 6, 13, 30, 34, 45–46, 49–50, 54, 65, 67, 72, 82, 112, 199, 202; Akerlof model and, 6, 12, 19, 23–25, 27, 29, 190, 196; argument synopsis on, 189–192; average, 16, 22– 23, 24n7, 25n8, 28, 38, 54–55, 57, 59, 163n2; aversion to, 20, 22n6, 29n14, 36– 37, 41–42, 54, 56; credit markets and, 105, 108–120, 128–146; default, 144–146 (see also default); discretionary income and, 100, 105, 108–115, 138, 196; distribution of, 5, 16–17, 29–30, 38, 53–60, 108, 112, 128n13, 132, 140, 183, 189, 191, 197–200; education and, 7, 11, 17, 33, 40, 60, 66, 69, 84, 93, 115, 138, 141, 159, 161–162, 165, 174, 179, 183– 184, 192, 197n3, 198; flat-rate benefits and, 144–146; historical perspective on, 44–69; inequality and, 2, 7, 12, 14, 19, 33, 59–61, 65, 82, 92, 100, 108, 111–114, https://doi.org/10.1017/9781009151405.009 Published online by Cambridge University Press Index 130, 138, 144, 188–189, 196–198, 201; information and, 1–15, 18–30, 35– 37, 160–165; labor markets and, 159– 185; Lexis Nexis Risk Classifier and, 76; life expectancy and, 34; loans and, 65, 105, 108–109, 111–112, 115–117, 130, 132, 141–142, 202; market failure and, 184 (see also market failure); medical data and, 75; moral hazard and, 10, 45, 48, 184, 198; mortgages and, 14, 65, 109, 115–117, 120, 128, 132, 134–138, 197, 202; pooling of, 1–16, 19, 22–29, 38–42, 50–51, 54–55, 58–68, 72, 128, 159–160, 171, 177, 179–180, 184–185, 188, 191, 200–203; preferences and, 2, 12, 14, 16, 18, 21, 26, 30, 35, 37, 39, 57, 59, 66–67, 160, 163–176, 184, 192, 199–200, 203; private markets and, 70–100; redistribution and, 5, 17, 30, 38, 53, 58, 60, 197, 200; regulation and, 2, 14, 18–19, 33, 42, 50, 60–61, 64–65, 70, 73, 81, 89, 94, 109, 115–120, 130–131, 138, 140, 159, 195, 197, 199, 202; segmentation and, 189; subsidies and, 1, 4, 11, 17–18, 23, 25, 28, 30, 54, 61, 67, 109, 116, 118, 185, 192, 197, 199; theoretical model and, 15–43; timeinconsistency and, 7, 30–35, 45, 47, 54, 56, 89, 96, 191, 199; traditional classification of, 3; uncertainty and, 8, 13, 16, 26, 30, 36, 56, 61, 66–67, 160, 163, 191, 196, 199; unemployment and, 5, 8– 14, 18, 20, 26, 29, 35, 44, 46, 51, 60, 65– 67, 108–109, 131–132, 136–138, 159– 166, 169, 171–174, 177–180, 183–184, 188, 191–192, 197–198; voters and, 18, 25, 29, 61, 64, 163, 184, 188–191, 197n3, 199; welfare and, 2, 6–30, 33, 36– 39, 48, 51–58, 68–69, 105, 108–109, 115, 138, 140, 188, 191, 193, 197, 201, 203 Rogers, Will, 108 Rothschild, Michael, 19, 25, 41 Rothstein, Bo, 52 Rueda, David, 162 Sample Survey of Income and Expenditure (EVS), 134–135, 137 SAP government, 182–183 savings: credit markets and, 114, 116–117, 133, 157; health savings plans and, 7, 17, 229 33, 96; private markets and, 96–97; wealth and, 1, 7–8, 17, 20–21, 29, 33–34, 36, 46–47, 51, 66, 96–97, 114, 116–117, 133, 136, 141, 160, 180, 190, 193 savings and loans (S&Ls), 116–117 Scottish Mutual, 55 Scottish Presbyterian Widows Fund, 44–46, 49, 83, 193 Scottish Reformation, 44 segmentation: choice and, 8; concept of, 6; credit markets and, 40, 159, 192; health insurance and, 70; historical perspective on, 50, 52–53, 58–59, 61, 63, 66–67; inequality and, 59, 61, 188–189, 196; information and, 2, 5–8, 11–18, 58–59, 66–67, 70, 89, 94, 159, 162, 165, 177, 180, 188–189, 192, 196; information levels and, 2, 5; integration and, 2, 5; interest rates and, 52, 58, 70; labor markets and, 14, 50, 67, 159, 162, 165, 177, 180, 182, 188, 192, 198; opting out and, 8; private markets and, 2, 5, 8, 11, 13–14, 18, 40, 53, 58–59, 63, 67, 70, 89, 94, 165, 180, 196; regulation and, 2, 70, 89, 94; risk and, 2, 6, 11, 13–14, 16, 18, 40, 50, 52–53, 58–59, 61, 63, 66–67, 70, 89, 94, 159, 162, 165, 177, 180, 188– 189, 192–193, 196, 198; state programs and, 11, 18, 50, 52–53, 159, 188; theoretical model and, 16, 18, 40; unemployment insurance and, 177–183; welfare and, 8, 18, 52–53, 188 self-insurance, 11–12, 20–22, 50n2, 51, 57, 60, 67, 73, 93, 190 self-interest, 19, 29, 52, 191 Shapley decomposition, 169, 170, 170 sickness pay, 44, 48 Single Family Loan Level Dataset, 121 social capital, 51–52, 203 social insurance: future politics of, 199–201; historical perspective on, 44, 51–52, 54, 56–60, 65, 67; information and, 2–13, 189–190, 193, 198; labor markets and, 159–160, 163, 177; private markets and, 70, 96; theoretical model and, 15, 19, 21n4, 30, 35, 37, 39 social media, 80–81 social networks, 11–12, 14, 18, 25, 66–67, 164, 183–184, 196 Social Security, 47, 67 https://doi.org/10.1017/9781009151405.009 Published online by Cambridge University Press 230 Index solidarity: COVID-19 pandemic and, 61; cross-class, 8, 14, 18, 203; emergence of, 53–58; fragmentation of, 58–67; information revolution and, 58–67, 71, 201; mutual aid societies (MASs) and, 46, 53–58; reciprocity and, 203; uncertainty and, 66, 160, 196; unemployment insurance and, 183, 192; welfare and, 8, 18, 40n21, 201, 203 Spain, 90, 102, 147 Stiglitz, Joseph, 19, 25 Stolle, Dietlind, 52 Study Watch, 62 subsidies: credit markets and, 109, 116, 118, 131n14, 138, 139, 144; historical perspective on, 54, 61, 67; homeownership, 131, 138–139, 197; labor markets and, 182, 185; pay-as-yougo (PAYG) systems and, 18, 67; private markets and, 94, 192; risk and, 1, 4, 11, 17–18, 23, 25, 28, 30, 54, 61, 67, 109, 116, 118, 185, 192, 197, 199; tax, 4, 37, 54, 199 supplementary health insurance, 88–94 Swaan, Abram de, 50–51 Sweden, 11, 38, 66, 90, 102; “Alliance for Sweden” campaign, 184; Bildt and, 11, 177; credit markets and, 107, 147; Democrats, 182–183; Ghent system and, 177, 179–180, 182, 184, 198; Job Pact and, 182; Law on Employment Protection and, 180; Left Party, 182; politics of private markets and, 180; Reinfeldt and, 92, 177; SAP government and, 182–183; unemployment insurance funds (UIFs) and, 180–184; unions and, 182 Swedish Confederation of Professional Associations (SACO), 179–180, 182 Swedish Confederation of Professional Employees (TCO), 180, 182 symmetric information, 20, 25–29, 39, 55, 82n17 taxes: coercive, 12; credit markets and, 114– 115, 139, 144; credits, 9, 195, 199; deductions, 50, 92, 115, 199; flat-rate, 37, 114–115, 132, 144–146; historical perspective on, 47, 50, 54–56, 60, 63, 66; inequality and, 19, 60, 100, 188–189; labor markets and, 159, 177, 180, 181; mutual aid societies (MASs) and, 47; paying for social protection by, 4, 8, 15, 19, 25, 31, 198–200; pensions and, 19, 31; power to, 54, 191; preference formation and, 35–37; price nondiscrimination and, 39; private markets and, 89, 92, 100; public system and, 9, 15, 19, 25, 31, 37, 39, 54, 60, 195, 200; regulation and, 19, 50, 63, 115, 195, 199; subsidies and, 4, 37, 54, 199; transfers and, 8, 114–115, 144, 188–191, 200; voters and, 25, 31, 188–191 time-inconsistency: adverse selection and, 30, 34; asymmetric information and, 56, 190, 199; elderly and, 7, 16–18, 30–35, 47, 56, 89, 96, 193; historical perspective on, 45, 47–48, 54, 56; intergenerational bargains and, 47, 191, 193; market feasibility and, 16–18, 30–35; mutual aid societies (MASs) and, 6–7, 16, 45, 47–48, 54, 56, 199; overlapping generations models and, 32; pay-as-you-go (PAYG) systems and, 16, 31–35, 47, 56, 96, 191, 193; persistence of, 7; private markets and, 71, 89, 96–99; redistribution and, 30; risk and, 7, 30–35, 45, 47, 54, 56, 89, 96, 191, 199; theoretical model and, 30– 35; voters and, 32, 191, 193, 199; younger generation and, 6–7, 16–18, 30–35, 47–48, 56, 96, 190, 194 top-up plans, 9, 36, 89, 179–182, 195 trackers, 3–4, 29, 76, 79–81, 100, 191 transfers: credit markets and, 109, 114–115, 144; democracy and, 16, 30, 67, 190; funded systems and, 7, 16, 47, 64, 96; intergenerational, 6–7, 13, 55, 56, 190; mutual aid societies (MASs), 6, 48, 57– 58; pay-as-you-go (PAYG) systems, 16, 47, 55, 191; poor people and, 7–8, 55, 115, 200; poverty and, 13; private markets and, 80n15, 81, 96; redistribution and, 16, 30, 64, 109, 144, 188–189, 200; taxes and, 8, 114–115, 144, 188–191, 200; theoretical model and, 16, 20, 30, 47–48, 55, 56–57, 64–65, 67; younger generation and, 6–7, 13, 16, 30, 47–48, 56, 67, 96, 190 uncertainty: democracy and, 8; incomplete information and, 8, 66–67; industrialization and, 189; labor markets and, 160, 163n2; preferences and, 16, 26, https://doi.org/10.1017/9781009151405.009 Published online by Cambridge University Press Index 66, 199; private markets and, 101; public system and, 8, 15–16, 30, 61, 67; risk and, 8, 13, 16, 26, 30, 36, 56, 61, 66–67, 160, 163, 191, 196, 199; solidarity and, 66, 160, 196; voters and, 31, 61, 101, 163, 199; welfare and, 8, 13, 36, 56, 189, 191 underwriting: actuarial science and, 49; artificial intelligence (AI) and, 81–82; COVID-19 pandemic and, 74, 77; current practices of, 73–76; Department of Motor Vehicles and, 75; diagnostics and, 10, 27, 49, 62, 81, 83–88, 94, 100, 193; digitalization and, 76–79; electronic health records (EHRs) and, 76–79; health insurance and, 17, 92– 94, 100; innovations in, 76–82; laboratories and, 81, 83, 87; Lexis Nexis Risk Classifier and, 76; life insurance and, 71, 73–82, 87–88, 100–101; Medical Information Bureau (MIB) and, 72n4, 75, 78–79; mortgages, 120–121, 207–208; prescription databases and, 75, 77; trackers and, 3–4, 29, 76, 79–81, 100, 191; unemployment insurance funds (UIFs) and, 180 unemployment: benefits during, 14, 65, 109, 131–133, 136n24, 137–138, 169–172, 173, 182–184, 185, 198, 200; credit markets and, 108–109, 131–138; disability and, 44, 139, 197; education and, 11, 60, 66, 159, 161–162, 165, 174, 179, 183–184, 192, 197n3, 198; Germany and, 14, 65, 165, 168–173, 185–186, 198; high levels of, 180, 182, 184; historical perspective on, 44, 46, 51, 55, 60, 65–67; homeownership and, 134– 137; information and, 8–14; insurance for, 4, 11, 14, 34, 35, 46, 55, 65–67, 159– 160, 163, 165, 177–184, 192, 198; lost income and, 109, 188; occupational unemployment rates (OURs), 174n0; private markets and, 4; reform and, 14, 29, 65, 67, 131–137, 165, 177–182, 198; risk and, 5, 8–11, 13–14, 18, 20, 26, 29, 35, 44, 46, 51, 60, 65–67, 108–109, 131– 132, 136–138, 159–166, 169, 171–174, 177–180, 183–184, 188, 191–192, 197– 198; theoretical model and, 16, 18, 20, 25, 26n10, 29–30, 35; United States and, 198 231 unemployment insurance funds (UIFs), 11, 14, 66, 177–184, 192, 198–199 unemployment protection, 46, 159, 164, 197n3 unions: fall of, 12, 188; historical perspective on, 58, 66; Job Pact and, 182; labor markets and, 159, 161, 164, 174, 177–184, 200; rise of, 12; Sweden and, 182; unemployment insurance funds (UIFs) and, 11, 14, 66, 177–184, 192, 198–199 UnitedHealth, 80 United Kingdom, 80, 90, 93, 147 United States: 401(k) plans, 33, 64; Bush and, 17; Clinton and, 116; credit markets and, 106–107, 109, 117, 121, 124, 131, 139–140; employer-based coverage, 58; Fair Housing Act and, 12; Fannie Mae, 65, 109, 116–117, 121; financial crisis of, 14; fraternal societies and, 47, 52; Freddie Mac, 65, 109, 116–117, 119–130, 140n25, 197; Great Depression, 30, 46, 117, 189; guaranty associations and, 33; healthcare costs in, 29n13, 62; health savings accounts (HSAs), 17, 195; individual retirement accounts (IRAs), 193; information revolution and, 58–60; labor markets and, 66; Medicaid, 8, 10, 60, 68, 96–99, 133; Medicare, 2, 7, 9, 17, 59–60, 96–99, 193; mutual aid societies (MASs) and, 44, 46, 49, 55; Obama and, 76, 81, 90; private markets and, 8, 18, 44, 51, 70, 74, 77–84, 89n23, 90, 91–99, 102–103, 195; Republican Party and, 94; self-insurance and, 11; Social Security, 47, 67; as stingy welfare state, 197; unemployment and, 198 universal public system, 18, 30, 91 University of Edinburgh, 45 urbanization, 6, 30, 51, 189 US Genetic Information Nondiscrimination Act (GINA), 38, 63, 93, 94 Verily Life Sciences, 62, 81 Vitality Health, 79–80 voluntary private health insurance (VPHI), 63, 89–93 voters: Comparative Study of Electoral Systems (CSES), 176; labor markets and, 163–164, 184; median, 25, 32, 64; https://doi.org/10.1017/9781009151405.009 Published online by Cambridge University Press 232 Index middle-class, 1; pay-as-you-go (PAYG) systems and, 193; private markets and, 101; reform and, 18, 29; risk and, 18, 25, 29, 61, 64, 163, 184, 188–191, 197n3, 199; self-interested, 29; taxes and, 25, 31, 188–191; time inconsistency and, 32, 191, 193, 199; uncertainty and, 31, 61, 101, 163, 199 wage protection, 159 Wallace, Robert, 45 wealth: credit markets and, 108, 110, 111n2, 133, 140; discretionary income and, 100, 105, 108–115, 118, 138, 140, 142, 196; historical perspective on, 56; mobility and, 49, 66, 68, 189, 191–192, 200; private markets and, 70, 97, 100; public system and, 15; savings and, 1, 7–8, 17, 20–21, 29, 33–36, 46–47, 51, 66, 96– 97, 114–117, 133, 136, 141, 160, 180, 190, 193; self-insurance and, 11–12, 20–22, 50n2, 51, 57, 60, 67, 73, 93, 190, 192 Webster, Alexander, 45 welfare: Bismarckian, 52–53, 58, 67, 191, 199–201; credit markets and, 105, 108– 115, 131–138, 140; democracy and, 8; destitution and, 45, 67; discretionary income and, 110–111; elderly and, 4, 7–8, 13–14, 18, 33, 53–54, 58, 105, 188, 193, 199; Golden Age of, 54; historical perspective on, 44–58, 68–69; homeownership and, 131–138; information and, 2–14; loans and, 110–111, 113–115, 131–138; middle class and, 6, 8, 13, 15, 54, 68–69, 193– 195, 199; mutual aid societies (MASs) and, 6, 8, 10, 12–13, 15–16, 25, 48, 51– 52, 54, 56; partisanship and, 12; pay-asyou-go (PAYG) systems and, 16, 18, 33, 48, 53, 193; preferences and, 2, 9, 12, 18, 21, 30, 37, 39, 68, 203; private markets and, 19, 37–38, 70; public system and, 19; redistribution and, 6, 12, 16, 18, 21, 36, 38, 53, 56, 58, 68, 115, 188, 191, 197, 203; regulation and, 37–38; risk and, 2, 6–30, 33, 36–39, 48, 51–58, 68–69, 105, 108–109, 115, 138, 140, 188, 191, 193, 197, 201, 203; role of, 188; segmentation and, 8, 18, 52–53, 188; solidarity and, 8, 18, 40n21, 201, 203; theoretical model and, 15–25, 30–33, 36–40; uncertainty and, 8, 13, 36, 56, 189, 191 Westcott, Edward Noyes, 108 Wiedemann, Andreas, 109 Wienk, Ron, 116n7 Willen, Paul, 120–121 World Health Organization (WHO), 86, 93 World War II era, 4, 30, 36, 51, 189 younger generation: deductibles and, 17; health and, 4, 6–7, 13, 17–18, 30–31, 48, 56, 67, 86, 92, 96, 101, 193–195; health savings plans and, 7, 17, 33, 96; market feasibility and, 16–18, 30–35; pay-asyou-go (PAYG) systems and, 16, 18, 31, 33, 47–48, 56, 64, 67, 96, 193; private markets and, 84–86, 92, 96–97, 101; redistribution and, 16, 30, 64; support of elderly by, 4; time-inconsistency and, 6–7, 16–18, 30–35, 47–48, 56, 96, 190, 193; transfers and, 6–7, 13, 16, 30, 47–48, 56, 67, 96, 190 https://doi.org/10.1017/9781009151405.009 Published online by Cambridge University Press CAMBRIDGE STUDIES IN COMPARATIVE POLITICS Other Books in the Series (continued from page ii) Laia Balcells, Rivalry and Revenge: The Politics of Violence during Civil War Lisa Baldez, Why Women Protest?

The Dark Cloud: How the Digital World Is Costing the Earth
by Guillaume Pitron
Published 14 Jun 2023

Thus, some scientists are considering the hypothesis of a superhuman, or even strong, AI that alone could undertake such a mission.52 It would be the ultimate phase of the ‘sustainable digital’ order discussed at the start of this book: ‘green IT’ in its purest form. Entrepreneurs have already declared their ambitions. One of them is Demis Hassabis, chief founder of the UK company DeepMind, whose mission, he says, is twofold: ‘Step one, solve intelligence. Step two, use it to solve everything else’ — that ‘everything else’ includes climate change.53 A report published in 2018 by the consulting firm PricewaterhouseCoopers (PwC) puts it plainly: ‘It’s time to put AI to work for the planet.’54 Overconfidence?

The Ages of Globalization
by Jeffrey D. Sachs
Published 2 Jun 2020

Starting from no information whatsoever other than the rules of chess, the AI system plays against itself in millions of chess games and uses the results to update the neural-network weights in order to learn chess-playing skills. Remarkably, in just four hours of self-play, an advanced computer AI system developed by the company DeepMind learned all of the skills needed to handily defeat the world’s best human chess players as well as the previous AI world-champion chess player!3 A few hours of blank-slate learning bested 600 years of learning of chess play by all of the chess experts in history. Technological Advances and the End of Poverty In 2006, I published a book titled The End of Poverty in which I suggested that the end of extreme poverty was within the reach of our generation, indeed by 2025, if we made increased global efforts to help the poor.4 I had in mind special efforts to bolster health, education, and infrastructure for the world’s poorest people, notably in sub-Saharan African and South Asia, home to most of the world’s extreme poverty.

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After the Jeopardy championship, Watson went on to the field of medicine, working with doctors to hone expert diagnostic systems. More recently, we have seen stunning breakthroughs in deep neural networks, that is neural networks with hundreds of layers of artificial neurons. In 2016, an AI system, AlphaGo from the company Deep Mind, took on the world’s eighteen-time world Go champion, Lee Sedol. Go is a board game of such sophistication and subtlety that it was widely believed that machines would be unable to compete with human experts for years or decades to come. Sedol, like Kasparov before him, believed that he would triumph easily over AlphaGo.

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

In contrast to narrow AI, which cleverly performs a specific limited task, such as machine translation or auto navigation, strong AI refers to “thinking machines” that might perform any intellectual task that a human being could. Characteristics of a strong AI would include the ability to reason, make judgments, plan, learn, communicate, and unify these skills toward achieving common goals across a variety of domains, and commercial interest is growing. In 2014, Google purchased DeepMind Technologies for more than $500 million in order to strengthen its already strong capabilities in deep learning AI. In the same vein, Facebook created a new internal division specifically focused on advanced AI. Optimists believe that the arrival of AGI may bring with it a period of unprecedented abundance in human history, eradicating war, curing all disease, radically extending human life, and ending poverty.

…

Anderson Cancer Center: “IBM Watson Hard at Work,” Memorial Sloan Kettering Cancer Center, Feb. 8, 2013; Larry Greenemeier, “Will IBM’s Watson Usher in a New Era of Cognitive Computing,” Scientific American, Nov. 13, 2013. 14 Ray Kurzweil has popularized: Ray Kurzweil, The Singularity Is Near: When Humans Transcend Biology (New York: Penguin Books, 2006), 7. 15 In 2014, Google purchased: Catherine Shu, “Google Acquires Artificial Intelligence Startup DeepMind,” TechCrunch, Jan. 26, 2014. 16 “Whereas the short-term impact”: Stephen Hawking et al., “Stephen Hawking: ‘Transcendence Looks at the Implications of Artificial Intelligence—but Are We Taking AI Seriously Enough?,’ ” Independent, May 1, 2014. 17 Tens of millions of dollars: Reed Albergotti, “Zuckerberg, Musk Invest in Artificial Intelligence Company,” Wall Street Journal, March 21, 2014. 18 In April 2013: “Brain Research Through Advancing Innovative Neurotechnologies,” Aug. 25, 2014, http://​www.​nih.​gov/​science/​brain/; Susan Young Rojahn, “The BRAIN Project Will Develop New Technologies to Understand the Brain,” MIT Technology Review, April 8, 2013. 19 Though such a machine: Priya Ganapati, “Cognitive Computing Project Aims to Reverse-Engineer the Mind,” Wired, Feb. 6, 2009; Vincent James, “Chinese Supercomputer Retains ‘World’s Fastest’ Title, Beating US and Japanese Competition,” Independent, Nov. 19, 2013. 20 As far-fetched as the idea: Ray Kurzweil, How to Create a Mind: The Secret of Human Thought Revealed (New York: Penguin Books, 2013); Michio Kaku, The Future of the Mind: The Scientific Quest to Understand, Enhance, and Empower the Mind (New York: Doubleday, 2014). 21 Though many have dismissed: Joseph Brean, “Build a Better Brain,” National Post, March 31, 2012; Cade Metz, “IBM Dreams Impossible Dream,” Wired, Aug. 9, 2013. 22 Under laboratory conditions: Kaku, Future of the Mind, 80–103, 108–9, 175–77. 23 The chip has an unprecedented: Peter Clarke, “IBM Seeks Customers for Neural Network Breakthrough,” Electronics360, Aug. 7, 2014. 328 “a major step”: Paul A.

Upscale: What It Takes to Scale a Startup. By the People Who've Done It.
by James Silver
Published 15 Nov 2018

If your company genuinely uses emerging technology, then make the most of that with investors Tan White acknowledges that - at the time of writing - it’s possible that, post-Brexit, there may be less capital available to startups in the UK. However, the UK’s growing global reputation in key areas such as artificial intelligence/machine learning - in the wake of acquisitions by US tech giants of UK companies like Evi (Amazon), DeepMind (Google), SwiftKey (Microsoft), Magic Pony (Twitter) and Vocal IQ (Apple) - means capital will continue to flow here, and startups can capitalise on that. ‘It may be that as a founder you need to be strategic about how you’re going to stay current,’ she says. ‘I don’t mean every startup should describe themselves as “a machine learning company” when it’s not fundamental to their business, because all investors will just see through that.

Know Thyself
by Stephen M Fleming
Published 27 Apr 2021

Computational neuroscientists have shown that exactly this kind of progression—from computing local features to representing more global properties—can be found in the ventral visual stream of human and monkey brains.7 Scaled-up versions of this kind of architecture can be very powerful indeed. By combining artificial neural networks with reinforcement learning, the London-based technology company DeepMind has trained algorithms to solve a wide range of board and video games, all without being instructed about the rules in advance. In March 2016, its flagship algorithm, AlphaGo, beat Lee Sedol, the world champion at the board game Go and one of the greatest players of all time. In Go, players take turns placing their stones on intersections of a nineteen-by-nineteen grid, with the objective of encircling or capturing the other player’s stones.

The Art of Statistics: How to Learn From Data
by David Spiegelhalter
Published 2 Sep 2019

Notable successes include speech recognition systems built into phones, tablets and computers; programs such as Google Translate which know little grammar but have learned to translate text from an immense published archive; and computer vision software that uses past images to ‘learn’ to identify, say, faces in photographs or other cars in the view of self-driving vehicles. There has also been spectacular progress in systems playing games, such as the DeepMind software learning the rules of computer games and becoming an expert player, beating world-champions at chess and Go, while IBM’s Watson has beaten competing humans in general knowledge quizzes. These systems did not begin by trying to encode human expertise and knowledge. They started with a vast number of examples, and learned through trial and error rather like a naïve child, even by playing themselves at games.

The Fourth Age: Smart Robots, Conscious Computers, and the Future of Humanity
by Byron Reese
Published 23 Apr 2018

For example, it could ingest huge amounts of Internet traffic, effectively seeing what everyone is typing and looking at. It could read everyone’s emails. With voice recognition, it could listen not only to every phone call, but also to conversations everywhere near a microphone. Cameras already blanket the world, and face recognition is coming into its own. Researchers at Oxford University and Google DeepMind have made great strides forward in lip reading, which could be combined with the cameras. The result of all of this? A machine that would be effectively both omnipresent and omniscient. There is nothing surprising about an agency of some sort being able to gather all that data. That’s old news.

The Art of Statistics: Learning From Data
by David Spiegelhalter
Published 14 Oct 2019

Notable successes include speech recognition systems built into phones, tablets and computers; programs such as Google Translate which know little grammar but have learned to translate text from an immense published archive; and computer vision software that uses past images to ‘learn’ to identify, say, faces in photographs or other cars in the view of self-driving vehicles. There has also been spectacular progress in systems playing games, such as the DeepMind software learning the rules of computer games and becoming an expert player, beating world-champions at chess and Go, while IBM’s Watson has beaten competing humans in general knowledge quizzes. These systems did not begin by trying to encode human expertise and knowledge. They started with a vast number of examples, and learned through trial and error rather like a naïve child, even by playing themselves at games.

On the Edge: The Art of Risking Everything
by Nate Silver
Published 12 Aug 2024

I’d urge you to at least accept the mildest version of doomerism, this simple, one-sentence statement on AI risk—“Mitigating the risk of extinction from AI should be a global priority alongside other societal-scale risks such as pandemics and nuclear war”—which was signed by the CEOs of the three most highly-regarded AI companies (Altman’s OpenAI, Anthropic, and Google DeepMind) in 2023 along with many of the world’s foremost experts on AI. To dismiss these concerns with the eye-rolling treatment that people in the Village sometimes do is ignorant. Ignorant of the scientific consensus, ignorant of the parameters of the debate, ignorant and profoundly incurious about mankind’s urge, with no clear exceptions so far in human history, to push technological development to the edge.

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What about the Sentinelese, an indigenous group of roughly one hundred people living on North Sentinel Island in the Bay of Bengal, who have remained hostile to outsiders and largely undisturbed by modern technology? Nope, the Machine Gods will hunt them down. Billionaires in outer space? They won’t make it either. Yudkowsky referenced a conversation between Elon Musk and Demis Hassabis, the cofounder of Google DeepMind. In Yudkowsky’s stylized version of the dialog, Musk expressed his concern about AI risk by suggesting it was “important to become a multiplanetary species—you know, like set up a Mars colony. And Demis said, ‘They’ll follow you.’ ” Before I unpack how Yudkowsky came to this grim conclusion, I should say that he’d slightly mellowed on his certainty of p(doom) by the time I caught up with him again at the Manifest conference in September 2023.

Being You: A New Science of Consciousness
by Anil Seth
Published 29 Aug 2021

In other words, for functionalists, simulation means instantiation – it means coming into being, in reality. How reasonable is this? For some things, simulation certainly counts as instantiation. A computer that plays Go, such as the world-beating AlphaGo Zero from the British artificial intelligence company DeepMind, is actually playing Go. But there are many situations where this is not the case. Think about weather forecasting. Computer simulations of weather systems, however detailed they may be, do not get wet or windy. Is consciousness more like Go or more like the weather? Don’t expect an answer – there isn’t one, at least not yet.

Decoding the World: A Roadmap for the Questioner
by Po Bronson
Published 14 Jul 2020

Tony sits next to me on the white couch. We are alone. “You really think AI is the biggest threat to humanity we have ever seen?” I ask. “Yes. We are summoning the demon,” Tony replies. “AI is learning at rates humanity hasn’t seen before. The more we feed it the more it is capable of. Until it literally takes over. I mean, Google’s DeepMind already has admin-level access to the Google data center servers to manage power levels.” “I don’t get it. How does it take over?” “Well, first you have to imagine how we lose control of it. How we can’t rein it in.” “How does that happen?” “So we’ve told the AI to go learn. Go learn about all the people on the system, for instance.

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

These will continue to invest in new tech because it is their power alley. We’ll see an ebb and flow like we have with Microsoft over the last couple of decades, but players like Apple, Google and Facebook still have plenty of growth left in them. 2. Artificial Intelligence Start-ups. These are the players building the architecture of the world moving forward. Google DeepMind, Facebook’s Wit.ai, MetaMind, Sentient Technologies, The Grid, Enlitic, x.ai, to name just a few. Don’t forget the machine intelligence players either though—self-driving car companies, healthcare diagnosis and sensor networks, IBM’s Watson and others. We haven’t even seen the start of this industry but, without doubt, it is going to be like the dot-com, the social media boom or the PC boom all over again, just bigger. 3.

Because Internet: Understanding the New Rules of Language
by Gretchen McCulloch
Published 22 Jul 2019

people who read a lot of fiction: Julie Sedivy. April 27, 2017. “Why Doesn’t Ancient Fiction Talk About Feelings?” Nautilus. nautil.us/issue/47/consciousness/why-doesnt-ancient-fiction-talk-about-feelings. Chapter 6. How Conversations Change In one video: (No author cited.) July 12, 2017. “Google’s DeepMind AI Just Taught Itself to Walk.” Tech Insider YouTube channel. www.youtube.com/watch?v=gn4nRCC9TwQ. In another, a metallic: (No author cited.) (No date cited.) “How to Teach a Robot to Walk.” Smithsonian Channel. www.smithsonianmag.com/videos/category/innovation/how-to-teach-a-robot-to-walk/.

Warnings
by Richard A. Clarke
Published 10 Apr 2017

Yes, we asked Yudkowsky how he convinced the gatekeepers to let him out, but he’s keeping that to himself. 14. Eliezer Yudkowsky, “Lonely Dissent,” Less Wrong, Dec. 28, 2007, http://lesswrong.com/lw/mb/lonely_dissent (accessed Oct. 8, 2016). 15. David Gilbert, “From Deep Mind to Watson: Why You Should Stop Worrying and Love AI,” International Business Times, Mar. 18, 2016, www.ibtimes.com/deepmind-watson-why-you-should-learn-stop-worrying-love-ai-2339231 (accessed Oct. 8, 2016), quoting Harriet Green, general manager of Watson’s Internet of Things Unit. 16. Ray Kurzweil, “Don’t Fear Artificial Intelligence,” www.kurzweilai.net/dont-fear-artificial-intelligence-by-ray-kurzweil (accessed Oct. 8, 2016). 17.

The Missing Billionaires: A Guide to Better Financial Decisions
by Victor Haghani and James White
Published 27 Aug 2023

I'm not superstitious, but who knows, maybe things would have turned out differently if we'd carried on playing. One thing's for sure, having fun in the workplace is an underrated and essential ingredient in successful organizations. Note a. At the time of writing, our friends Richard Dewey and Jeff Rosenbluth have been working with researchers at Google DeepMind to develop a computer program to play Liar's Poker at a level at or above the best human players of the game. We wish them luck and hope their efforts will introduce more people to this most excellent game of skill and luck. Cheat Sheet Getting risk right is very important. It's nearly impossible to go broke by picking bad investments if you get the sizing right, but it's easy to go bust by taking too much risk, even if you choose great investments!

The One Device: The Secret History of the iPhone
by Brian Merchant
Published 19 Jun 2017

When Gruber says knowledge, I think he means a firm, robust grasp on how the world works and how to reason. Today, researchers are less interested in developing AI’s ability to reason and more intent on having them do more and more complex machine learning, which is not unlike automated data mining. You might have heard the term deep learning. Projects like Google’s DeepMind neural network work essentially by hoovering up as much data as possible, then getting better and better at simulating desired outcomes. By processing immense amounts of data about, say, Van Gogh’s paintings, a system like this can be instructed to create a Van Gogh painting—and it will spit out a painting that looks kinda-sorta like a Van Gogh.

The Book of Why: The New Science of Cause and Effect
by Judea Pearl and Dana Mackenzie
Published 1 Mar 2018

When finished training a new network, the programmer has no idea what computations it is performing or why they work. If the network fails, she has no idea how to fix it. Perhaps the prototypical example is AlphaGo, a convolutional neural-network-based program that plays the ancient Asian game of Go, developed by DeepMind, a subsidiary of Google. Among human games of perfect information, Go had always been considered the toughest nut for AI. Though computers conquered humans in chess in 1997, they were not considered a match even for the lowest-level professional Go players as recently as 2015. The Go community thought that computers were still a decade or more away from giving humans a real battle.

Evil Geniuses: The Unmaking of America: A Recent History
by Kurt Andersen
Published 14 Sep 2020

The debate among technologists tends to focus on when they’ll manage to create artificial general intelligence, machines able to figure out any problem and carry out any cognitive task that a person can. People at Facebook and Google and Stanford and elsewhere say they’ll do it by the mid-2020s, that they’ll then have machines “better than human level at all of the primary human senses” and “general cognition” (Zuckerberg), true “human-level A.I.” (the head of Google’s DeepMind). The state of the art right now is “narrow AI” or “weak AI,” software that can merely beat human champions at Jeopardy or predict the shapes of cellular proteins or drive cars. But most jobs are fairly “narrow” and don’t require a lot of high-level creative problem-solving. I used to hire freelance transcribers and translators, but in the last few years I’ve replaced them with software that does the work a little roughly but well enough to serve my needs.

Spies, Lies, and Algorithms: The History and Future of American Intelligence
by Amy B. Zegart
Published 6 Nov 2021

Center for Strategic and International Studies (CSIS), “Maintaining the Intelligence Edge: Reimagining and Reinventing Intelligence through Innovation,” A Report of the CSIS Technology and Intelligence Task Force, January 2021, https://www.csis.org/analysis/maintaining-intelligence-edge-reimagining-and-reinventing-intelligence-through-innovation (accessed January 17, 2021), 13–14. 124. CSIS, 13–14. 125. Pedro A. Ortega, Vishal Maini, and the Deepmind Safety Team, “Building Safe Artificial Intelligence: Specification, Robustness, and Assurance,” Medium, September 27, 2018, https://medium.com/@deepmindsafetyresearch/building-safe-artificial-intelligence-52f5f75058f1. 126. U.S. Census Bureau, Statistical Abstract of the United States: 2012 (Washington, D.C.: Government Printing Office, 2012), 506.

The Technology Trap: Capital, Labor, and Power in the Age of Automation
by Carl Benedikt Frey
Published 17 Jun 2019

But ironically, at any other task, Kasparov would have won. The only thing Deep Blue could do was evaluate two hundred million board positions per second. It was designed for one specific purpose. AlphaGo, on the other hand, relies on neural networks, which can be used to perform a seemingly endless number of tasks. Using neural networks, DeepMind has already achieved superhuman performance at some fifty Atari video games, including Video Pinball, Space Invaders, and Ms. Pac-Man.7 Of course, a programmer provided the instruction to maximize the game score, but an algorithm learned the best game strategies by itself over thousands of trials.

…

When the rules of a task are unknown, we can apply statistics and inductive reasoning to let the machine learn by itself. Outside of the technology sector, AI is still in the experimental stage. Yet the frontiers of AI research are steadily advancing, which in turn has expanded the potential set of tasks that computers can perform. The victory of Deep Mind’s AlphaGo over the world’s best professional Go player, Lee Sedol, in 2016 is probably the best-known example. With the defeat of Sedol, humans lost their competitive edge in the last of the classical board games, two decades after being superseded in chess. As we all know, in a six-game match played in 1996, the chess master Garry Kasparov prevailed against IBM’s Deep Blue by three wins but lost in a historic rematch a year later.

…

See mass production American Telephone and Telegraph Company (AT&T), 315 annus mirabilis of 1769, 97, 148 anti-Amazon law, 290 Antikythera mechanism, 39 Appius Claudius, 37 Archimedes, 30, 39 Aristotle, 1, 39 Arkwright, Richard, 94, 101 artificial intelligence (AI), 5, 36, 301–41, 228, 342; Alexa (Amazon), 306; AlphaGo (Deep Mind), 301, 302; Amara’s Law, 323–25; artificial neural networks, 304; autonomous robots, 307; autonomous vehicles, 308, 310, 340; big data, 303; Chinese companies, 313; Dactyl, 313; data, as the new oil, 304; Deep Blue (IBM), 301, 302; deep learning, 304; -driven unemployment, 356; Google Translate, 304; Gripper, 313; internet traffic, worldwide, 303; JD. com, 313; Kiva Systems, 311; machine social intelligence, 317; Microsoft, 306; misconception, 311; multipurpose robots, 327; Neural Machine Translation, 304; neural networks, 303, 305, 314; pattern recognition, 319; phrase-based machine translation, 304; Siri (Apple), 306; speech recognition technology, 306; Turing test, 317; virtual agents, 306; voice assistant, 306; warehouse automation, 314 artisan craftsmen, 8; in domestic system, 118, 131; emigration of, 83; factory job, transition to, 124; fates of, 17; full-time, 34; middle-income, 11, 16, 24, 135; replacement of, 9, 16, 218 Ashton, T.

Shape: The Hidden Geometry of Information, Biology, Strategy, Democracy, and Everything Else
by Jordan Ellenberg
Published 14 May 2021

Would people still give their lives to chess if they knew a perfect game always ended in a tie, that there was no winning by magnificence, only losing by screwing up? Or would it feel empty? Lee Se-dol, one of the best Go players alive, quit the game after losing a match to AlphaGo, a machine player developed by the AI firm DeepMind. “Even if I become the number one,” he said, “there is an entity that cannot be defeated.” And Go isn’t even solved! Compared to the redwood that’s chess, Go is—well, if there were a tree somewhat bigger than a googol redwoods it would be that tree. Read chess and Go forums and you’ll see a lot of people grappling with the same anxieties Lee expressed.

…

M., 56, 58 cranial capacity, 319 Cremer, Gerhard, 306–7 Cremona transformation, 212–13 Crowe, Russell, 330n crow-fly geometry, 303 Crucifixion (Corpus Hypercubus) (Dali), 183 cryptography, 129–33, 133–37 cubic fit model, 242–43, 254, 259 curriculum standards, 30n curved surfaces, 305–10 curve fitting, 260–61, 263 cylinders, 308 dactyl, 236 Daily News, 244–45 Dalí, Salvador, 183, 325n Dante Alighieri, 11 Dantzig, Tobias, 40 data visualization, 77 da Vinci, Leonardo, 277, 278 Da Vinci Code, The (Brown), 278 Davis, Jefferson, 134 Davis v. Bandemer, 365, 385 death projections, 254–55, 259 decision-making, 173–77 Declaration of Independence, 13n decomposition, 294, 297–98 deduction, 12, 17–18, 24–25 deep learning, 177–86 Deep Mind, 141 DeFord, Daryl, 392, 399n democracy and democratic norms, 348, 350–51, 406–7, 409. See also election polling; gerrymandering Democratic Party. See redistricting demographics, 225–26, 286, 334, 371n, 387 De Quincey, Thomas, 4, 6–7 derivatives, 167, 186n Descartes, René, 211–12 “Detection of Defective Members of Large Populations, The” (Dorfman), 227–28 determinism, 65, 87, 113, 127 Dhar, Deepak, 398 Diaconis, Persi, 325, 330–31, 330n “Diet Code,” 278 Die Wahrscheinlichkeitsansteckung (Eggenberger), 299 differences Difference Engine, 253–54 differential equations, 230–31, 234–36, 239–41, 260, 263, 332 and square root calculation, 249–54 difficulty of math, 26–28, 200–202, 203, 203–4, 419–20.

The Unwinding: An Inner History of the New America
by George Packer
Published 4 Mar 2014

If there was one breakthrough technology, it was likely to be artificial intelligence. As computers became capable of improving themselves, they would eventually outsmart human beings, with unpredictable results—a scenario known as the singularity. Whether it would be for better or worse, it would be extremely important. Founders Fund invested in a British AI company called DeepMind Technologies, and the Thiel Foundation gave a quarter million dollars a year to the Singularity Institute, a think tank in Silicon Valley. AI could solve problems that human beings couldn’t even imagine solving. The singularity was so weird and hard to visualize that it was under the radar, completely unregulated, and that was where Thiel liked to focus.

Rentier Capitalism: Who Owns the Economy, and Who Pays for It?
by Brett Christophers
Published 17 Nov 2020

Just as companies often make strategic corporate acquisitions in order to stifle competition in product markets, so they can turn to acquisitions when they fear competition in the labour market. And digital platform operators have done exactly that: In recent years, tech companies have rushed to hire programmers who specialize in machine learning. A common way of acquiring such talent is to purchase machine learning startups: Google bought DeepMind, Microsoft bought Maluuba, Apple bought Lattice Data. In contrast, the tech companies could have tried to hire workers directly by luring them from the incumbent employers using promises of high compensation. It seems likely that the share of the gains accruing to workers (as opposed to investors and the few at the top of these start-ups) from open competition would have been greater than under an acquisition strategy.65 This approach was thus another means of wage suppression.

Lonely Planet Iceland (Travel Guide)
by Lonely Planet , Carolyn Bain and Alexis Averbuck
Published 31 Mar 2015

Windswept farmsteads lie frozen in time, and boulder-strewn hills, crowned with flattened granite, roll skyward. Near the beginning of the track, the farm at Hvammur produced a whole line of prominent Icelanders, including Snorri Sturluson of Prose Edda fame. It was settled in around 895 by Auður the Deep-Minded, the wife of the Irish king Olaf Godfraidh, who has a bit part in Laxdæla Saga. Árni Magnússon, who rescued most of the Icelandic sagas from a fire in Copenhagen in 1728, was also raised at Hvammur. You can spend the night at recently renovated Vogur Country Lodge (%894 4396; www.vogur.org; s/d/q Ikr19,200/22,800/28,500; Wc) or remote, lovely Nýp (%896 1930; www.nyp.is; Skarðsströnd; d incl breakfast Ikr15,500; W).

Lonely Planet Iceland
by Lonely Planet

Windswept farmsteads lie frozen in time, and boulder-strewn hills, crowned with flattened granite, roll skyward. Keep a lookout for white-tailed eagles. Near the beginning of the track, the farm at Hvammur produced a whole line of prominent Icelanders, including Snorri Sturluson of Prose Edda fame. It was settled in around 895 by Auður the Deep-Minded, the wife of the Irish king Olaf Godfraidh, who has a bit part in Laxdæla Saga. Árni Magnússon, who rescued most of the Icelandic sagas from a fire in Copenhagen in 1728, was also raised at Hvammur. You can spend the night at well-renovated Vogur Country Lodge ( GOOGLE MAP ; %435 0002; www.vogur.org; Rte 590, Fellsströnd; d/q from kr22,500/26,600; hrestaurant 6-9pm) or remote, lovely Guesthouse Nýp ( GOOGLE MAP ; %896 1930; www.nyp.is; Rte 590, Skarðsströnd; d without bathroom incl breakfast kr16,600).

In Our Own Image: Savior or Destroyer? The History and Future of Artificial Intelligence
by George Zarkadakis
Published 7 Mar 2016

Google has a similar aspiration: it wants to use AI technology to understand context and meaning, and thus provide better search resources, video recognition, speech recognition and translation, increased security, and smarter services when it comes to Google’s social networks and e-commerce platforms. When Google spent half a billion dollars to acquire the British company Deep Mind, it was in fact hedging a bet that Artificial Intelligence will define the second machine age. In this chapter I shall explore what all this means. How close are we to truly intelligent machines – complete with self-awareness? What will the repercussions be for our economy and society as thinking machines begin to replace us in the workplace?

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Dick. 1989: Tim Berners-Lee invents the World Wide Web. 1990: Seiji Ogawa presents the first fMRI machine. 1993: Rodney Brooks and others start the MIT Cog Project, an attempt to build a humanoid robot child in five years. 1997: Deep Blue defeats Garry Kasparov at chess. 2000: Cynthia Breazeal at MIT describes Kismet, a robot with a face that simulates expressions. 2004: DARPA launches the Grand Challenge for autonomous vehicles. 2009: Google builds the self-driving car. 2011: IBM’s Watson wins the TV game show Jeopardy!. 2014: Google buys UK company Deep Mind for $650 million. 2014: Eugene Goostman, a computer program that simulates a thirteen-year-old boy, passes the Turing Test. 2014: Estimated number of robots in the world reaches 8.6 million.1 2015: Estimated number of PCs in the world reaches two billion.2 NOTES Introduction 1PCs (‘Personal computers’) started becoming widely available in the early 1980s: IBM 5150 in 1981, Commodore PET in 1983.

Peopleware: Productive Projects and Teams
by Tom Demarco and Timothy Lister
Published 2 Jan 1987

Just as important as the loss of effective time is the accompanying frustration. The worker who tries and tries to get into flow and is interrupted each time is not a happy person. He gets tantalizingly close to involvement only to be bounced back into awareness of his surroundings. Instead of the deep mindfulness that he craves, he is continually channeled into the promiscuous changing of direction that the modern office tries to force upon him. Put yourself in the position of the participant who filled out her Coding War Games time sheet with the entries shown in Table 10–2. Table 10–2. Segment of a CWG Time Sheet A few days like that and anybody is ready to look for a new job.

Life as a Passenger: How Driverless Cars Will Change the World
by David Kerrigan
Published 18 Jun 2017

Automation “Live out of your imagination, not your history” Stephen Covey The role of machines in our world is fundamentally changing as their abilities evolve. After decades of anticipation, they are finally starting to learn on their own. They’ve learned to understand what we say (e.g. Amazon Alexa/Apple Siri/Google Assistant), identify people in photos (e.g. Facebook) and defeat world champions at games as complex as Go (Deep Mind). Now they’re learning to drive, with all that entails outside a closed environment in the real world. After much academic debate and endless fictional scenarios, driverless cars are the first example of in-your-face intelligent, disruptive technology that will impact us all daily. This debate is surely only the first of many we face in coming years.

The Startup Wife
by Tahmima Anam
Published 2 Jun 2021

I stumble back into the boardroom, where Larry is grilling Jules on the MAU-WAU-DAU of the platform. “Ah, Asha,” Jules says. “Gerard was just asking how you set up the framework for the community side.” I run through the technical points with Gerard, who is at pains to inform me that he started his career as a programmer. “I was employee number eighteen at Deep Mind,” he says. I talk about how Ren and I have instrumented the platform so that you can see exactly what people are doing, how long they’re spending with us, how many posts and photos they’re sharing. “Our minutes per session are going up every month.” “How do you deal with people who break the rules?”

The Doomsday Calculation: How an Equation That Predicts the Future Is Transforming Everything We Know About Life and the Universe
by William Poundstone
Published 3 Jun 2019

“I agree with Elon Musk and some others on this and don’t understand why some people are not concerned.” Gates supplied a blurb for Bostrom’s Superintelligence. But Oren Etzioni, head of Microsoft cofounder Paul Allen’s Allen Institute for Artificial Intelligence, has dismissed Bostrom’s ideas as a “Frankenstein complex.” In 2014 Google paid more than $500 million for the British AI start-up Deep Mind. Corporate parent Alphabet is establishing well-funded AI centers across the globe. “I don’t buy into the killer robot [theory],” Google director of research Peter Norvig told CNBC. Another Google researcher, the psychologist and computer scientist Geoffrey Hinton, said, “I am in the camp that it is hopeless.”

Radical Markets: Uprooting Capitalism and Democracy for a Just Society
by Eric Posner and E. Weyl
Published 14 May 2018

Antitrust authorities, who are accustomed to worrying about competition within existing, well-defined, and easily measurable markets, have allowed most mergers between dominant tech firms and younger potential disrupters to proceed. Google was allowed to buy mapping start-up Waze and artificial intelligence powerhouse Deep Mind; Facebook to buy Instagram and WhatsApp; and Microsoft to buy Skype and LinkedIn. While such acquisitions doubtless help accelerate a path to market for start-up products and provide badly needed financing, they also have a dark side. Economist Luís Cabral has named these mergers “Standing on the Shoulders of Dwarfs”: they may crush the possibility of new firms emerging to challenge the business model of existing industry leaders, instead co-opting them to cement the dominance of those leaders.57 To prevent this dampening of innovation and competition, antitrust authorities must learn to think more like entrepreneurs and venture capitalists, seeing possibilities beyond existing market structures to the potential markets and technologies of the future, even if these are highly uncertain.

Revolution Française: Emmanuel Macron and the Quest to Reinvent a Nation
by Sophie Pedder
Published 20 Jun 2018

Serge Weinberg, a financier who sat on the commission, was among those who whispered Macron’s name to David de Rothschild, who recruited him the following year to join Rothschild & Cie. The Attali Commission served at once as an incubator of policy ideas and an invaluable address book. ‘It was obvious that he was bright, very cultured, had a deep mind, and that he would go far,’ said Jacques Delpla, an economist at the Toulouse School of Economics, who first met Macron when they sat together on the commission: ‘But it didn’t cross my mind that he would go into politics. I saw him more as a future director of the Treasury.’28 Mathieu Laine, a liberal intellectual and friend of Macron’s who met him after he joined Rothschild’s, had the same impression.

What's Left?: How Liberals Lost Their Way
by Nick Cohen
Published 15 Jul 2015

She informed the reader that: The move from a structuralist account in which capital is understood to structure social relations in relatively homologous ways to a view of hegemony in which power relations are subject to repetition, convergence, and rearticulation brought the question of temporality into the thinking of structure, and marked a shift from a form of Althusserian theory that takes structural totalities as theoretical objects to one in which the insights into the contingent possibility of structure inaugurate a renewed conception of hegemony as bound up with the contingent sites and strategies of the rearticulation of power. To ask what Butler means is to miss the point, said Dutton. ‘This sentence beats readers into submission and instructs them that they are in the presence of a great and deep mind. Actual communication has nothing to do with it.’ The response of the theorists was instructive. Instead of accepting that they were going badly wrong, they produced books in defence of bad writing. The authors of Critical Terms for Literary Study turned on opponents who claimed their ‘artificially difficult style,’ hid the truth that the theorists had ‘nothing to say’.

Fifty Degrees Below
by Kim Stanley Robinson
Published 25 Oct 2005

Still, he had to laugh; listening to Spencer was like seeing himself in a funhouse mirror, hearing one of his theories being parodied by an expert mimic. The wild glee in Spencer’s blue eyes suggested there was some truth to this interpretation. He would have to be more careful in what he said. But the facts of the situation remained, and could not be ignored. His unconscious mind, his deep mind, was at that very moment humming happily through all its parcellations. It was a total response. Deep inside lay an ancient ability to throw things at things, waiting patiently for its moment of redeployment. “That was good,” he said as he got up to leave. “Google Acheulian hand axes,” Spencer said.