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Four Battlegrounds by Paul Scharre

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

v=cUTMhmVh1qs&t=6122s; “Takeaways from OpenAI Five (2019) [AI/ML, Dota Summary,” senrigan.io, April 22, 2019, updated June 25, 2020, https://senrigan.io/blog/takeaways-from-openai-5/. 268OpenAI’s Dota 2 agents: Mike, “OpenAI & DOTA 2: Game Is Hard,” Games by Angelina, updated August 10, 2018, http://www.gamesbyangelina.org/2018/08/openai-dota-2-game-is-hard/; “Open AI Five Benchmark,” streamed on Twitch, 2018, https://www.twitch.tv/videos/293517383?t=2h11m08s; (deleted user), “OpenAI Hex was Within the 200ms Response Time,” r/DotA2, Reddit, 2018, https://www.reddit.com/r/DotA2/comments/94vdpm/openai_hex_was_within_the_200ms_response_time/e3ofipk/; OpenAI et al., Dota 2, 52. 269precisely coordinate their attacks: Mike, “OpenAI & DOTA 2: Game Is Hard.” 269excel in team fights: ProGuides Dota 2 Tips Tricks and Guides, “Dota2: What Can We Learn from OpenAI Five?

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Other researchers have come up with somewhat different rates of progress in the deep learning era, see Dario Amodei and Danny Hernandez, “AI and Compute,” openai.com, May 16, 2018, https://openai.com/blog/ai-and-compute/. 26Moore’s law: Gordon E. Moore, “Cramming More Components onto Integrated Circuits,” Electronics 38, no. 8 (April 19, 1965), https://newsroom.intel.com/wp-content/uploads/sites/11/2018/05/moores-law-electronics.pdf. 26“thousands of GPUs over multiple months”: Open AI et al., Dota 2 with Large Scale Deep Reinforcement Learning (arXiv.org, December 13, 2019), 2, https://arxiv.org/pdf/1912.06680.pdf. 26equivalent to a human playing for 45,000 years: OpenAI, “OpenAI Five Defeats Dota 2 World Champions,” OpenAI blog, April 15, 2019, https://openai.com/blog/openai-five-defeats-dota-2-world-champions/. 2613,000 years of simulated computer time: Ilge Akkaya et al., Solving Rubik’s Cube With a Robot Hand (arXiv.org, October 17, 2019), https://arxiv.org/pdf/1910.07113.pdf. 26spending millions on compute: Ryan Carey, “Interpreting AI Compute Trends,” AI Impacts, n.d., https://aiimpacts.org/interpreting-ai-compute-trends/; Dan H., “How Much Did AlphaGo Zero Cost?”

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., Dota 2, 62. 272overly reliant on their team-fighting skills: Wiggers, “OpenAI’s Dota 2 Bot Defeated 99.4% of Players in Public Matches”; Mike, “OpenAI & DOTA 2: Game Is Hard”; Statt, “OpenAI’s Dota 2 AI Steamrolls World Champion e-Sports Team.” 272poor lineup of characters: “OpenAI Five Benchmark: Results,” OpenAI Blog, August 6, 2018, https://openai.com/blog/openai-five-benchmark-results/. 272AI agents performed poorly and inflexibly: Mike, “OpenAI & DOTA 2: Game Is Hard.” 272certain characters and types of actions off-limits: “OpenAI Five Benchmark,” OpenAI Blog, July 18, 2018, https://openai.com/blog/openai-five-benchmark/; OpenAI et al., Dota 2. 27299.4 percent win average: OpenAI et al., Dota 2. 272perform surgery: OpenAI et al., Dota 2, 7–8, 10–13, 25–29. 272Superhuman precision and speed: Vinyals et al., “Grandmaster Level in StarCraft II”; Pietkäinen, “An Analysis on How Deepmind’s Starcraft 2 AI’s Superhuman Speed Is Probably a Band-Aid.” 272forward-quarter gunshots: Colin “Farva” Price, “Navy F/A-18 Squadron Commander’s Take on AI Repeatedly Beating Real Pilot In Dogfight,” The Drive, August 24, 2020, https://www.thedrive.com/the-war-zone/35947/navy-f-a-18-squadron-commanders-take-on-ai-repeatedly-beating-real-pilot-in-dogfight. 272capture-the-flag computer game: Jaderberg et al., “Human-Level Performance in 3D Multiplayer Games,” 3. 272AlphaStar’s superhuman click rate: In refining their StarCraft II agent, AlphaStar, DeepMind went to great lengths to handicap the AI agent so that it was limited to playing at the rough equivalent to a human level.

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

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

His salary for just the last six months of 2016 was $330,000: OpenAI, form 990, 2016. And in February 2018, Musk left, too: Eduard Gismatullin, “Elon Musk Left OpenAI to Focus on Tesla, SpaceX,” Bloomberg News, February 16, 2019, https://www.bloomberg.com/news/articles/2019-02-17/elon-musk-left-openai-on-disagreements-about-company-pathway. “Excessive automation at Tesla was a mistake”: Elon Musk tweet, April 13, 2018, https://twitter.com/elonmusk/status/984882630947753984?s=19. Altman re-formed the lab as a for-profit company: “OpenAI LP,” OpenAI blog, March 11, 2019, https://openai.com/blog/openai-lp/. an international robotics maker called ABB organized its own contest: Adam Satariano and Cade Metz, “A Warehouse Robot Learns to Sort Out the Tricky Stuff,” New York Times, January 29, 2020, https://www.nytimes.com/2020/01/29/technology/warehouse-robot.html.

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SATYA NADELLA, CEO. AT OPENAI SAM ALTMAN, the president of Silicon Valley start-up incubator Y Combinator who became OpenAI’s CEO. GREG BROCKMAN, the former chief technology officer of fintech start-up Stripe who helped build OpenAI. ELON MUSK, the CEO of electric car maker Tesla and rocket company SpaceX who helped create OpenAI. ILYA SUTSKEVER, the Geoff Hinton protégé who left Google Brain to join OpenAI, the San Francisco AI lab created in response to DeepMind. WOJCIECH ZAREMBA, the former Google and Facebook researcher who was one of OpenAI’s first hires. AT BAIDU ROBIN LI, CEO.

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Musk pushed back, asking how Page could be sure this superintelligence: Ibid. Brockman vowed to build the new lab they all seemed to want: Cade Metz, “Inside OpenAI, Elon Musk’s Wild Plan to Set Artificial Intelligence Free,” Wired, April 27, 2016, https://www.wired.com/2016/04/openai-elon-musk-sam-altman-plan-to-set-artificial-intelligence-free/. nearly $2 million for the first year: OpenAI, form 990, 2016. Musk and Altman painted OpenAI as a counterweight: Steven Levy, “How Elon Musk and Y Combinator Plan to Stop Computers from Taking Over,” “Backchannel,” Wired, December 11, 2015, https://www.wired.com/2015/12/how-elon-musk-and-y-combinator-plan-to-stop-computers-from-taking-over/.

Elon Musk: A Mission to Save the World by Anna Crowley Redding

Albert Einstein, artificial general intelligence, Burning Man, California high-speed rail, Colonization of Mars, El Camino Real, Elon Musk, energy security, Ford Model T, gigafactory, high-speed rail, Hyperloop, Internet Archive, Jeff Bezos, Khan Academy, Kim Stanley Robinson, Kwajalein Atoll, Large Hadron Collider, low earth orbit, Mars Society, Max Levchin, Menlo Park, OpenAI, orbital mechanics / astrodynamics, Peter Thiel, Silicon Valley, Silicon Valley startup, Solyndra, SpaceX Starlink, Stephen Hawking, Steve Jurvetson, TED Talk, Tesla Model S, Wayback Machine

“I met with Obama for one reason”—to talk about the dangers of artificial intelligence.169 In 2015, Elon cofounded a nonprofit called OpenAI to research the development of AI and how can AI be used to benefit humanity instead of … um … to annihilate us. OpenAI is a nonprofit “AI research company, discovering and enacting the path to safe artificial general intelligence.”170 “It’s going to be very tempting to use AI as a weapon. In fact, it will be used as a weapon. The on-ramp to serious AI, the danger is going to be more humans using it against each other, I think, most likely. That will be the danger,”171 he explained in a podcast with Joe Rogan. As of this writing, OpenAI has a team of sixty researchers and engineers working on the project, and they conduct their research without the pressure of having to make money.

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IFLScience, 28 April 2016. www.iflscience.com/technology/elon-musk-unveils-the-ridiculously-big-tesla-gigfactory/. Olsen, Patrick. “Tesla Model 3 Gets CR Recommendation After Braking Update.” Consumer Reports, 30 May 2018. www.consumerreports.org/car-safety/tesla-model-3-gets-cr-recommendation-after-braking-update/. OpenAI. openai.com. Oremus, Will. “Elon Musk Is Not a Comic Book Superhero. He’s Way More Interesting Than That.” Slate, 21 May 2015. slate.com/articles/business/moneybox/2015/05/elon_musk_biography_review_how_did_a_sci_fi_nut_with_a_hero_complex_becoming.html. _____. “Romney Decides That Thriving Electric-Car Start-up Tesla Is a ‘Loser.’”

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Elon Musk, interview by Chris Anderson. 163. Elon Musk, “The Boring Company Information Session.” 164. Vance, p. 43. 165. Strauss, “Elon Musk.” 166. Paine, Do You Trust This Computer? 167. Paine, Do You Trust This Computer? 168. Cellan-Jones, “Stephen Hawking Warns.” 169. Elon Musk, interview by Joe Rogan. 170. OpenAI, openai.com. 171. Elon Musk, interview by Joe Rogan. 172. Paine, Do You Trust This Computer? 173. Elon Musk, interview by Joe Rogan. 174. Neuralink, www.neuralink.com. 175. Elon Musk, interview by Mohammad Abdullah Al Gergawi. 176. Elon Musk, interview by Mohammad Abdullah Al Gergawi. 177.

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

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

These systems are called transformers. Since Google researchers published the first paper on them in 2017, the pace of progress has been staggering. Soon after, OpenAI released GPT-2. (GPT stands for generative pre-trained transformer.) It was, at the time, an enormous model. With 1.5 billion parameters (the number of parameters is a core measure of an AI system’s scale and complexity), GPT-2 was trained on 8 million pages of web text. But it wasn’t until the summer of 2020, when OpenAI released GPT-3, that people started to truly grasp the magnitude of what was happening. With a whopping 175 billion parameters it was, at the time, the largest neural network ever constructed, more than a hundred times larger than its predecessor of just a year earlier.

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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. GO TO NOTE REFERENCE IN TEXT Over the next few years See Martin Ford, Rule of the Robots: How Artificial Intelligence Will Transform Everything (London: Basic Books, 2021), for a developed comparison. GO TO NOTE REFERENCE IN TEXT More realistically, the average American Amy Watson, “Average Reading Time in the U.S. from 2018 to 2021, by Age Group,” Statista, Aug. 3, 2022, www.statista.com/​statistics/​412454/​average-daily-time-reading-us-by-age.

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GO TO NOTE REFERENCE IN TEXT eminent professor of complexity Melanie Mitchell See Melanie Mitchell, Artificial Intelligence: A Guide for Thinking Humans (London: Pelican Books, 2020), and Steven Strogatz, “Melanie Mitchell Takes AI Research Back to Its Roots,” Quanta Magazine, April 19, 2021, www.quantamagazine.org/​melanie-mitchell-takes-ai-research-back-to-its-roots-20210419. GO TO NOTE REFERENCE IN TEXT I think it will be done The Alignment Research Center has already tested GPT-4 for precisely this kind of capability. GPT-4 was, at this stage, “ineffective” at acting autonomously, the research found. “GPT-4 System Card,” OpenAI, March 14, 2023, cdn.openai.com/​papers/​gpt-4-system-card.pdf. Within days of launch people were getting surprisingly close; see, for example, mobile.twitter.com/​jacksonfall/​status/​1636107218859745286. The version of the test here, though, requires far more autonomy than displayed there. GO TO NOTE REFERENCE IN TEXT Chapter 5: The Technology of Life Just as everything from the steam engine Susan Hockfield, The Age of Living Machines: How Biology Will Build the Next Technology Revolution (New York: W.

Elon Musk by Walter Isaacson

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

Musk challenged him to justify how he could legally transform a nonprofit funded by donations into a for-profit that could make millions. Altman tried to show that it was all legitimate, and he insisted that he personally was not a shareholder or cashing in. He also offered Musk shares in the new company, which Musk declined. Instead, Musk unleashed a barrage of attacks on OpenAI and Altman. “OpenAI was created as an open-source (which is why I named it ‘Open’ AI), non-profit company to serve as a counterweight to Google, but now it has become a closed source, maximum-profit company effectively controlled by Microsoft,” he said. “I’m still confused as to how a non-profit to which I donated $100M somehow became a $30B market cap for-profit.

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Musk’s determination to develop artificial intelligence capabilities at his own companies caused a break with OpenAI in 2018. He tried to convince Altman that OpenAI, which he thought was falling behind Google, should be folded into Tesla. The OpenAI team rejected that idea, and Altman stepped in as president of the lab, starting a for-profit arm that was able to raise equity funding. So Musk decided to forge ahead with building a rival AI team to work on Tesla Autopilot. Even as he was struggling with the production hell surges in Nevada and Fremont, he recruited Andrej Karpathy, a specialist in deep learning and computer vision, away from OpenAI. “We realized that Tesla was going to become an AI company and would be competing for the same talent as OpenAI,” Altman says.

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Musk tried to prevent Page and Google from purchasing DeepMind, the company formed by AI pioneer Demis Hassabis. When that failed, he formed a competing lab, a nonprofit called OpenAI, with Sam Altman in 2015. Humans can be pricklier than machines, and Musk eventually split with Altman, left the board of OpenAI, and lured away its high-profile engineer Andrej Karpathy to lead the Autopilot team at Tesla. Altman then formed a for-profit arm of OpenAI, got a $13 billion investment from Microsoft, and recruited Karpathy back. Among the products that OpenAI developed was a bot called ChatGPT that was trained on large internet data sets to answer questions posed by users.

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

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

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. Launched in 2015 with a billion dollars raised by Silicon Valley royalty including the multi billionaires Reid Hoffman and Peter Thiel, the Silicon Valley–based OpenAI is run by a former Google expert on machine learning and staffed with an all-star team of computer scientists cherry-picked from top Big Tech firms.

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But, I’m afraid, there aren’t too many people in Silicon Valley quite as responsible as LinkedIn co-founder Reid Hoffman, a relative paragon of civic virtue, who, during the 2016 American presidential election, promised to donate five million dollars of his own money to a veterans’ charity if Donald Trump publicly disclosed his taxes. In spite of being an investor in OpenAI, Reid Hoffman is skeptical of hubristic Silicon Valley companies that believe they can stand outside history and fix the entire world. Rather than being courageous, he suggests, that’s just myopic. Even juvenile. “It’s great they are being ambitious,” Hoffman told the New Yorker about Sam Altman and some of his Y Combinator projects. “But classically in the Valley, when people try to reinvent an area, it ends badly.”7 Hoffman’s ambivalence about the grandiose promises of OpenAI may be one reason why he is also one of the major investors in the “Fund for Artificial Intelligence and Society” launched by the nonprofit Knight Foundation in early 2017.

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This fund, which also includes the MIT Media Lab and the Berkman Center at Harvard as partners, and, you’ll remember, Betaworks’ John Borthwick as an advisor, is designed—like the Centre for the Study of Existential Risk at Cambridge—to foster a combinatorial network of researchers, ethicists, and technologists focused on studying the impact of AI on society. In contrast with the Deep Mind or OpenAI coalition, this Knight Foundation initiative doesn’t just rely on technologists to make ethical decisions. “My point of view is that it is a massive transformation and does really impact the future of humanity,” Hoffman says about the AI revolution. “But that we can steer it more toward utopia rather than dystopia with intelligence and diligence.”8 John Bracken, a longtime nonprofit executive who runs the Knight Foundation program, told me that Hoffman is a “particularly important” influence on this new fund.

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

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

., “Better language models and their implications,” OpenAI Blog, February 14, 2019, openai.com/blog/better-language-models/. 38. James Vincent, “OpenAI’s latest breakthrough is astonishingly powerful, but still fighting its flaws,” The Verge, July 30, 2020, www.theverge.com/21346343/gpt-3-explainer-openai-examples-errors-agi-potential. 39. Gary Marcus and Ernest Davis, “GPT-3, Bloviator: OpenAI’s language generator has no idea what it’s talking about,” MIT Technology Review, August 22, 2020, www.technologyreview.com/2020/08/22/1007539/gpt3-openai-language-generator-artificial-intelligence-ai-opinion/. 40.

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Microsoft’s 2019 billion-dollar investment in the AI research company OpenAI—which along with Google’s DeepMind is a leader in pushing the frontiers of deep learning—offers a case study in the natural synergy between cloud computing and artificial intelligence. OpenAI will be able to leverage massive computational resources hosted by Microsoft’s Azure service—something that is essential given its focus on building ever larger neural networks. Only cloud computing can deliver compute power on the scale that OpenAI requires for its research. Microsoft, in turn, will gain access to practical innovations that are spawned by OpenAI’s ongoing quest for artificial general intelligence.

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While this is admittedly a far cry from human-level AI, Kurzweil remains confident in his strategy, telling me that “humans use this hierarchical approach” and that ultimately it will be “sufficient for AGI.”36 Yet another path toward artificial general intelligence is being forged by OpenAI, a San Francisco–based research organization that was founded in 2015 with financial backing from, among others, Elon Musk, Peter Thiel and Linked-in co-founder Reid Hoffman. OpenAI was initially set up as a nonprofit entity with a mission to undertake a safe and ethical quest for AGI. The organization was conceived partly in response to Elon Musk’s deep concern about the potential for superhuman machine intelligence to someday pose a genuine threat to humanity. From the onset, OpenAI has attracted some of the field’s top researchers, including Ilya Sutskever, who was part of the team from Geoff Hinton’s University of Toronto Lab that built the neural network that triumphed at the 2012 ImageNet competition.

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

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

I’ve been lucky enough to spend some time talking over beers with Ben and I share this view about the downside of having any corporation or government with as much control over something that will become so powerful. So do many others, including Elon Musk and Reid Hoffman, who helped kick off the OpenAI initiative. OpenAI’s mission is to “build safe AGI and ensure AGI’s benefits are as widely and evenly distributed as possible.” But although these open initiatives are laudable, what hurts many of them is the lack of data and data velocity, which inhibits the learning rate. Core to every one of the major platforms is a product or service that compels you to give them your data for free—from your Google searches, to your Alexa enquiries, to your Instagram pictures.

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The platform then monetizes your data in numerous ways, selling products or services more effectively to you or selling your data to advertisers. All the while the platform is using its tremendous data advantage to make its service better and better. Providing your data seems like a small price to pay for the extraordinary benefit of the service. That in itself becomes the problem with open AI initiatives outside of companies where there is a financial incentive to give away a product or service to get the data to make the product better. It is hard to see any of these open initiatives gaining enough momentum without an extraordinary product or service that is core to their data capture.

System Error by Rob Reich

"Friedman doctrine" OR "shareholder theory", "World Economic Forum" Davos, 2021 United States Capitol attack, A Declaration of the Independence of Cyberspace, Aaron Swartz, AI winter, Airbnb, airport security, Alan Greenspan, Albert Einstein, algorithmic bias, AlphaGo, AltaVista, artificial general intelligence, Automated Insights, autonomous vehicles, basic income, Ben Horowitz, Berlin Wall, Bernie Madoff, Big Tech, bitcoin, Blitzscaling, Cambridge Analytica, Cass Sunstein, clean water, cloud computing, computer vision, contact tracing, contact tracing app, coronavirus, corporate governance, COVID-19, creative destruction, CRISPR, crowdsourcing, data is the new oil, data science, decentralized internet, deep learning, deepfake, DeepMind, deplatforming, digital rights, disinformation, disruptive innovation, Donald Knuth, Donald Trump, driverless car, dual-use technology, Edward Snowden, Elon Musk, en.wikipedia.org, end-to-end encryption, Fairchild Semiconductor, fake news, Fall of the Berlin Wall, Filter Bubble, financial engineering, financial innovation, fulfillment center, future of work, gentrification, Geoffrey Hinton, George Floyd, gig economy, Goodhart's law, GPT-3, Hacker News, hockey-stick growth, income inequality, independent contractor, informal economy, information security, Jaron Lanier, Jeff Bezos, Jim Simons, jimmy wales, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John Perry Barlow, Lean Startup, linear programming, Lyft, Marc Andreessen, Mark Zuckerberg, meta-analysis, minimum wage unemployment, Monkeys Reject Unequal Pay, move fast and break things, Myron Scholes, Network effects, Nick Bostrom, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, NP-complete, Oculus Rift, OpenAI, Panopticon Jeremy Bentham, Parler "social media", pattern recognition, personalized medicine, Peter Thiel, Philippa Foot, premature optimization, profit motive, quantitative hedge fund, race to the bottom, randomized controlled trial, recommendation engine, Renaissance Technologies, Richard Thaler, ride hailing / ride sharing, Ronald Reagan, Sam Altman, Sand Hill Road, scientific management, self-driving car, shareholder value, Sheryl Sandberg, Shoshana Zuboff, side project, Silicon Valley, Snapchat, social distancing, Social Responsibility of Business Is to Increase Its Profits, software is eating the world, spectrum auction, speech recognition, stem cell, Steve Jobs, Steven Levy, strong AI, superintelligent machines, surveillance capitalism, Susan Wojcicki, tech billionaire, tech worker, techlash, technoutopianism, Telecommunications Act of 1996, telemarketer, The Future of Employment, TikTok, Tim Cook: Apple, traveling salesman, Triangle Shirtwaist Factory, trolley problem, Turing test, two-sided market, Uber and Lyft, uber lyft, ultimatum game, union organizing, universal basic income, washing machines reduced drudgery, Watson beat the top human players on Jeopardy!, When a measure becomes a target, winner-take-all economy, Y Combinator, you are the product

What really surprised the AI community, however, was not the model used in GPT-2, the architecture of which was based on simply predicting the next most likely word based on all the previous words in the text. OpenAI’s achievement was that it had scaled the system up to a new level by analyzing text from more than 8 million web pages. The striking thing was the announcement that OpenAI would not release the model, contrary to a trend toward transparency in the research community. “Due to our concerns about malicious applications of the technology,” the OpenAI team wrote, “we are not releasing the trained model. As an experiment in responsible disclosure, we are instead releasing a much smaller model for researchers to experiment with, as well as a technical paper.” OpenAI was created in 2015 as a nonprofit organization funded by wealthy technologists, including Elon Musk, Peter Thiel, Sam Altman, and Reid Hoffman, who were concerned with charting a path toward safe artificial general intelligence.

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It can also craft Harry Potter stories in the style of Ernest Hemingway, invent plausible conversations between famous people in history who never met, summarize movies with emojis, write poetry, and much more. The reason we know about these capabilities is that OpenAI released the GPT-3 model to interested parties, albeit through an application process in which OpenAI controls access. Those granted access began playing with it and posting their findings. OpenAI announced its intention to offer GPT-3 as a revenue-generating commercial product in limited contexts. In the months between announcing GPT-2 and GPT-3, OpenAI found itself needing investment capital and converted from a nonprofit to a for-profit corporation. It promised to adhere to its social mission by pursuing an unusual “capped profit” model, by which investors in the company could get returns up to a specified cap and any additional profits beyond that would be reinvested into OpenAI’s pursuit of safe artificial general intelligence.

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But what seemed a sober precaution was considered by some in the AI world either as running afoul of research norms and rank hypocrisy given the “open” part of OpenAI or as a cheap publicity stunt designed to call attention to the organization. Some AI scientists facetiously said that they, too, had made breakthrough discoveries in the lab but could not share details due to their concerns about bad actors. By late 2019, OpenAI decided to release the full-scale GPT-2 model—with 1.5 billion parameters—as part of a staged release plan. OpenAI’s scientists also reported results from their research partners, shedding more light on the earlier concerns.

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

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

The platform made use of Nvidia’s Compute Unified Device Architecture, or CUDA, which allows programmers to write code to perform highly parallel computations on Nvidia GPUs. For a 2020 retrospective of the stunning increases in the efficiency of training neural networks since AlexNet, see the work of OpenAI’s Danny Hernandez and Tom Brown at https://openai.com/blog/ai-and-efficiency/ and https://cdn.openai.com/papers/ai_and_efficiency.pdf. 21. “Rival.” 22. Jacky Alciné, personal interview, April 19, 2018. 23. See https://twitter.com/jackyalcine/status/615329515909156865 and https://twitter.com/yonatanzunger/status/615355996114804737 for this exchange. 24.

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As a result, these curious agents found their way to the goal in much more vast and complex mazes than the agents without this intrinsic drive. Pathak’s Berkeley group teamed up with a group of researchers from OpenAI, and together they continued to explore this idea of using prediction error as a reward signal. Surprisingly, they found that a dramatic simplification of this architecture—replacing the network designed specifically to predict controllable aspects of the future with one designed to predict random features of the image on screen—worked just as well and in some cases even better.51 The researchers at OpenAI, led by Yuri Burda and Harrison Edwards, worked to refine this idea, which they dubbed random network distillation, or RND.52 It wasn’t long before they began to set their sights on Montezuma’s Revenge.

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., “Synthesizing the Preferred Inputs for Neurons in Neural Networks via Deep Generator Networks.” Goh subsequently joined Olah’s Clarity team at OpenAI. 70. See Mordvintsev, Olah, and Tyka, “Inceptionism,” and Mordvintsev, Olah, and Tyka, “DeepDream.” 71. See Olah, Mordvintsev, and Schubert, “Feature Visualization”; Olah et al., “The Building Blocks of Interpretability”; and Carter et al., “Activation Atlas.” More recent work includes detailed “microscopy” of cornerstone deep-learning models like AlexNet; see, e.g., https://microscope.openai.com/models/alexnet. 72. Chris Olah, personal interview, May 4, 2020. For more, see his “Circuits” collaboration: https://distill.pub/2020/circuits/. 73.

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

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

For more on the work, see also: Ng Wai Foong, “Beginner’s Guide to OpenAI Five at Dota2,” Medium, May 7, 2019, https://medium.com/@ngwaifoong92/beginners-guide-to-openai-five-at-dota2-3b49ee5169b8; Evan Pu, “Understanding OpenAI Five,” Medium, August 12, 2018, https://medium.com/@evanthebouncy/understanding-openai-five-16f8d177a957. OpenAI’s “team spirit” hyper-parameter: Christy Dennison et al., “OpenAI Five,” OpenAI, June 25, 2018, https://openai.com/blog/openai-five. On connecting nothing: T. S. Eliot, The Waste Land (New York: Boni and Liveright, 1922). 4. counterfactuals Foote’s paper: Eunice Foote, “Circumstances Affecting the Heat of the Sun’s Rays,” American Journal of Science and Arts 22, no. 66 (November 1856): 382–83, https://archive.org/stream/mobot31753002152491#page/382/mode/2up.

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AI wins Dota 2: Nick Statt, “OpenAI’s Dota 2 AI Steamrolls World Champion e-Sports Team with Back-to-Back Victories,” The Verge, April 13, 2019, https://www.theverge.com/2019/4/13/18309459/openai-five-dota-2-finals-ai-bot-competition-og-e-sports-the-international-champion. OpenAI Dota 2 research paper: Christopher Berner et al., “Dota 2 with Large Scale Deep Reinforcement Learning,” OpenAI, 2019, https://arxiv.org/abs/1912.06680. For more on the work, see also: Ng Wai Foong, “Beginner’s Guide to OpenAI Five at Dota2,” Medium, May 7, 2019, https://medium.com/@ngwaifoong92/beginners-guide-to-openai-five-at-dota2-3b49ee5169b8; Evan Pu, “Understanding OpenAI Five,” Medium, August 12, 2018, https://medium.com/@evanthebouncy/understanding-openai-five-16f8d177a957.

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Defense of the Ancients, or Dota, is a multiplayer online video game where teams of five players vie to destroy a large structure in the other team’s base (and slaughter one’s enemies in violent battle). It requires complex strategic decisions, long-term planning, and cooperation among players. And it’s a global phenomenon, with international tournaments and talk of adding it as an official Olympic sport. The annual prize money for the top teams reaches a pixel-popping $40 million. In 2019 OpenAI, an AI research organization in San Francisco, built a system that stunned the Dota universe by crushing the best human players at Dota 2. On the surface, it seems that the system could divine causation, generalize from experience, and, with those abstractions, apply causal templates to new circumstances.

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

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

And it’s generally too expensive to train 1,000 robots in parallel. In short, training is hard and slow in the real world, so you generally need a simulated environment at least to bootstrap training. OpenAI gym8 is a toolkit that provides a wide variety of simulated environments (Atari games, board games, 2D and 3D physical simulations, and so on), so you can train agents, compare them, or develop new RL algorithms. Let’s install OpenAI gym. For a minimal OpenAI gym installation, simply use pip: $ pip3 install --upgrade gym Next open up a Python shell or a Jupyter notebook and create your first environment: >>> import gym >>> env = gym.make("CartPole-v0") [2016-10-14 16:03:23,199] Making new env: MsPacman-v0 >>> obs = env.reset() >>> obs array([-0.03799846, -0.03288115, 0.02337094, 0.00720711]) >>> env.render() The make() function creates an environment, in this case a CartPole environment.

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Pac-Man Using Deep Q-Learning min_after_dequeue, RandomShuffleQueue MNIST dataset, MNIST-MNIST model parallelism, Model Parallelism-Model Parallelism model parameters, Gradient Descent, Batch Gradient Descent, Early Stopping, Under the Hood, Quadratic Programming, Creating Your First Graph and Running It in a Session, Construction Phase, Training RNNsdefining, Model-based learning model selection, Model-based learning model zoos, Model Zoos model-based learning, Model-based learning-Model-based learning modelsanalyzing, Analyze the Best Models and Their Errors-Analyze the Best Models and Their Errors evaluating on test set, Evaluate Your System on the Test Set-Evaluate Your System on the Test Set moments, Adam Optimization Momentum optimization, Momentum optimization-Momentum optimization Monte Carlo tree search, Policy Gradients Multi-Layer Perceptrons (MLP), Introduction to Artificial Neural Networks, The Perceptron-Multi-Layer Perceptron and Backpropagation, Neural Network Policiestraining with TF.Learn, Training an MLP with TensorFlow’s High-Level API multiclass classifiers, Multiclass Classification-Multiclass Classification Multidimensional Scaling (MDS), Other Dimensionality Reduction Techniques multilabel classifiers, Multilabel Classification-Multilabel Classification Multinomial Logistic Regression (see Softmax Regression) multinomial(), Neural Network Policies multioutput classifiers, Multioutput Classification-Multioutput Classification MultiRNNCell, Distributing a Deep RNN Across Multiple GPUs multithreaded readers, Multithreaded readers using a Coordinator and a QueueRunner-Multithreaded readers using a Coordinator and a QueueRunner multivariate regression, Frame the Problem N naive Bayes classifiers, Multiclass Classification name scopes, Name Scopes natural language processing (NLP), Recurrent Neural Networks, Natural Language Processing-An Encoder–Decoder Network for Machine Translationencoder-decoder network for machine translation, An Encoder–Decoder Network for Machine Translation-An Encoder–Decoder Network for Machine Translation TensorFlow tutorials, Natural Language Processing, An Encoder–Decoder Network for Machine Translation word embeddings, Word Embeddings-Word Embeddings Nesterov Accelerated Gradient (NAG), Nesterov Accelerated Gradient-Nesterov Accelerated Gradient Nesterov momentum optimization, Nesterov Accelerated Gradient-Nesterov Accelerated Gradient network topology, Fine-Tuning Neural Network Hyperparameters neural network hyperparameters, Fine-Tuning Neural Network Hyperparameters-Activation Functionsactivation functions, Activation Functions neurons per hidden layer, Number of Neurons per Hidden Layer number of hidden layers, Number of Hidden Layers-Number of Hidden Layers neural network policies, Neural Network Policies-Neural Network Policies neuronsbiological, From Biological to Artificial Neurons-Biological Neurons logical computations with, Logical Computations with Neurons neuron_layer(), Construction Phase next_batch(), Execution Phase No Free Lunch theorem, Testing and Validating node edges, Visualizing the Graph and Training Curves Using TensorBoard nonlinear dimensionality reduction (NLDR), LLE(see also Kernel PCA; LLE (Locally Linear Embedding)) nonlinear SVM classification, Nonlinear SVM Classification-Computational Complexitycomputational complexity, Computational Complexity Gaussian RBF kernel, Gaussian RBF Kernel-Gaussian RBF Kernel with polynomial features, Nonlinear SVM Classification-Polynomial Kernel polynomial kernel, Polynomial Kernel-Polynomial Kernel similarity features, adding, Adding Similarity Features-Adding Similarity Features nonparametric models, Regularization Hyperparameters nonresponse bias, Nonrepresentative Training Data nonsaturating activation functions, Nonsaturating Activation Functions-Nonsaturating Activation Functions normal distribution (see Gaussian distribution) Normal Equation, The Normal Equation-Computational Complexity normalization, Feature Scaling normalized exponential, Softmax Regression norms, Select a Performance Measure notations, Select a Performance Measure-Select a Performance Measure NP-Complete problems, The CART Training Algorithm null hypothesis, Regularization Hyperparameters numerical differentiation, Numerical Differentiation NumPy, Create the Workspace NumPy arrays, Handling Text and Categorical Attributes NVidia Compute Capability, Installation nvidia-smi, Managing the GPU RAM n_components, Choosing the Right Number of Dimensions O observation space, Neural Network Policies off-policy algorithm, Temporal Difference Learning and Q-Learning offline learning, Batch learning one-hot encoding, Handling Text and Categorical Attributes one-versus-all (OvA) strategy, Multiclass Classification, Softmax Regression, Exercises one-versus-one (OvO) strategy, Multiclass Classification online learning, Online learning-Online learning online SVMs, Online SVMs-Online SVMs OpenAI Gym, Introduction to OpenAI Gym-Introduction to OpenAI Gym operation_timeout_in_ms, In-Graph Versus Between-Graph Replication Optical Character Recognition (OCR), The Machine Learning Landscape optimal state value, Markov Decision Processes optimizers, Faster Optimizers-Learning Rate SchedulingAdaGrad, AdaGrad-AdaGrad Adam optimization, Faster Optimizers, Adam Optimization-Adam Optimization Gradient Descent (see Gradient Descent optimizer) learning rate scheduling, Learning Rate Scheduling-Learning Rate Scheduling Momentum optimization, Momentum optimization-Momentum optimization Nesterov Accelerated Gradient (NAG), Nesterov Accelerated Gradient-Nesterov Accelerated Gradient RMSProp, RMSProp out-of-bag evaluation, Out-of-Bag Evaluation-Out-of-Bag Evaluation out-of-core learning, Online learning out-of-memory (OOM) errors, Static Unrolling Through Time out-of-sample error, Testing and Validating OutOfRangeError, Reading the training data directly from the graph, Multithreaded readers using a Coordinator and a QueueRunner output gate, LSTM Cell output layer, Multi-Layer Perceptron and Backpropagation OutputProjectionWrapper, Training to Predict Time Series-Training to Predict Time Series output_put_keep_prob, Applying Dropout overcomplete autoencoder, Unsupervised Pretraining Using Stacked Autoencoders overfitting, Overfitting the Training Data-Overfitting the Training Data, Create a Test Set, Soft Margin Classification, Gaussian RBF Kernel, Regularization Hyperparameters, Regression, Number of Neurons per Hidden Layeravoiding through regularization, Avoiding Overfitting Through Regularization-Data Augmentation P p-value, Regularization Hyperparameters PaddingFIFOQueue, PaddingFifoQueue Pandas, Create the Workspace, Download the Datascatter_matrix, Looking for Correlations-Looking for Correlations parallel distributed computing, Distributing TensorFlow Across Devices and Servers-Exercisesdata parallelism, Data Parallelism-TensorFlow implementation in-graph versus between-graph replication, In-Graph Versus Between-Graph Replication-Model Parallelism model parallelism, Model Parallelism-Model Parallelism multiple devices across multiple servers, Multiple Devices Across Multiple Servers-Other convenience functionsasynchronous communication using queues, Asynchronous Communication Using TensorFlow Queues-PaddingFifoQueue loading training data, Loading Data Directly from the Graph-Other convenience functions master and worker services, The Master and Worker Services opening a session, Opening a Session pinning operations across tasks, Pinning Operations Across Tasks sharding variables, Sharding Variables Across Multiple Parameter Servers sharing state across sessions, Sharing State Across Sessions Using Resource Containers-Sharing State Across Sessions Using Resource Containers multiple devices on a single machine, Multiple Devices on a Single Machine-Control Dependenciescontrol dependencies, Control Dependencies installation, Installation-Installation managing the GPU RAM, Managing the GPU RAM-Managing the GPU RAM parallel execution, Parallel Execution-Parallel Execution placing operations on devices, Placing Operations on Devices-Soft placement one neural network per device, One Neural Network per Device-One Neural Network per Device parameter efficiency, Number of Hidden Layers parameter matrix, Softmax Regression parameter server (ps), Multiple Devices Across Multiple Servers parameter space, Gradient Descent parameter vector, Linear Regression, Gradient Descent, Training and Cost Function, Softmax Regression parametric models, Regularization Hyperparameters partial derivative, Batch Gradient Descent partial_fit(), Incremental PCA Pearson's r, Looking for Correlations peephole connections, Peephole Connections penalties (see rewards, in RL) percentiles, Take a Quick Look at the Data Structure Perceptron convergence theorem, The Perceptron Perceptrons, The Perceptron-Multi-Layer Perceptron and Backpropagationversus Logistic Regression, The Perceptron training, The Perceptron-The Perceptron performance measures, Select a Performance Measure-Select a Performance Measureconfusion matrix, Confusion Matrix-Confusion Matrix cross-validation, Measuring Accuracy Using Cross-Validation-Measuring Accuracy Using Cross-Validation precision and recall, Precision and Recall-Precision/Recall Tradeoff ROC (receiver operating characteristic) curve, The ROC Curve-The ROC Curve performance scheduling, Learning Rate Scheduling permutation(), Create a Test Set PG algorithms, Policy Gradients photo-hosting services, Semisupervised learning pinning operations, Pinning Operations Across Tasks pip, Create the Workspace Pipeline constructor, Transformation Pipelines-Select and Train a Model pipelines, Frame the Problem placeholder nodes, Feeding Data to the Training Algorithm placers (see simple placer; dynamic placer) policy, Policy Search policy gradients, Policy Search (see PG algorithms) policy space, Policy Search polynomial features, adding, Nonlinear SVM Classification-Polynomial Kernel polynomial kernel, Polynomial Kernel-Polynomial Kernel, Kernelized SVM Polynomial Regression, Training Models, Polynomial Regression-Polynomial Regressionlearning curves in, Learning Curves-Learning Curves pooling kernel, Pooling Layer pooling layer, Pooling Layer-Pooling Layer power scheduling, Learning Rate Scheduling precision, Confusion Matrix precision and recall, Precision and Recall-Precision/Recall TradeoffF-1 score, Precision and Recall-Precision and Recall precision/recall (PR) curve, The ROC Curve precision/recall tradeoff, Precision/Recall Tradeoff-Precision/Recall Tradeoff predetermined piecewise constant learning rate, Learning Rate Scheduling predict(), Data Cleaning predicted class, Confusion Matrix predictions, Confusion Matrix-Confusion Matrix, Decision Function and Predictions-Decision Function and Predictions, Making Predictions-Estimating Class Probabilities predictors, Supervised learning, Data Cleaning preloading training data, Preload the data into a variable PReLU (parametric leaky ReLU), Nonsaturating Activation Functions preprocessed attributes, Take a Quick Look at the Data Structure pretrained layers reuse, Reusing Pretrained Layers-Pretraining on an Auxiliary Taskauxiliary task, Pretraining on an Auxiliary Task-Pretraining on an Auxiliary Task caching frozen layers, Caching the Frozen Layers freezing lower layers, Freezing the Lower Layers model zoos, Model Zoos other frameworks, Reusing Models from Other Frameworks TensorFlow model, Reusing a TensorFlow Model-Reusing a TensorFlow Model unsupervised pretraining, Unsupervised Pretraining-Unsupervised Pretraining upper layers, Tweaking, Dropping, or Replacing the Upper Layers Pretty Tensor, Up and Running with TensorFlow primal problem, The Dual Problem principal component, Principal Components Principal Component Analysis (PCA), PCA-Randomized PCAexplained variance ratios, Explained Variance Ratio finding principal components, Principal Components-Principal Components for compression, PCA for Compression-Incremental PCA Incremental PCA, Incremental PCA-Randomized PCA Kernel PCA (kPCA), Kernel PCA-Selecting a Kernel and Tuning Hyperparameters projecting down to d dimensions, Projecting Down to d Dimensions Randomized PCA, Randomized PCA Scikit Learn for, Using Scikit-Learn variance, preserving, Preserving the Variance-Preserving the Variance probabilistic autoencoders, Variational Autoencoders probabilities, estimating, Estimating Probabilities-Estimating Probabilities, Estimating Class Probabilities producer functions, Other convenience functions projection, Projection-Projection propositional logic, From Biological to Artificial Neurons pruning, Regularization Hyperparameters, Symbolic Differentiation Pythonisolated environment in, Create the Workspace-Create the Workspace notebooks in, Create the Workspace-Download the Data pickle, Better Evaluation Using Cross-Validation pip, Create the Workspace Q Q-Learning algorithm, Temporal Difference Learning and Q-Learning-Learning to Play Ms.

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actions, Evaluating Actions: The Credit Assignment Problem-Evaluating Actions: The Credit Assignment Problem credit assignment problem, Evaluating Actions: The Credit Assignment Problem-Evaluating Actions: The Credit Assignment Problem discount rate, Evaluating Actions: The Credit Assignment Problem examples of, Learning to Optimize Rewards Markov decision processes, Markov Decision Processes-Markov Decision Processes neural network policies, Neural Network Policies-Neural Network Policies OpenAI gym, Introduction to OpenAI Gym-Introduction to OpenAI Gym PG algorithms, Policy Gradients-Policy Gradients policy search, Policy Search-Policy Search Q-Learning algorithm, Temporal Difference Learning and Q-Learning-Learning to Play Ms. Pac-Man Using Deep Q-Learning rewards, learning to optimize, Learning to Optimize Rewards-Learning to Optimize Rewards Temporal Difference (TD) Learning, Temporal Difference Learning and Q-Learning-Temporal Difference Learning and Q-Learning ReLU (rectified linear units), Modularity-Modularity ReLU activation, ResNet ReLU function, Multi-Layer Perceptron and Backpropagation, Activation Functions, Xavier and He Initialization-Nonsaturating Activation Functions relu(z), Construction Phase render(), Introduction to OpenAI Gym replay memory, Learning to Play Ms.

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

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

‘Human-level performance in 3D multiplayer games with population-based reinforcement learning’, Science 364, no. 6443 (2019): 859–865. 2. Berner, Christopher, Greg Brockman, Brooke Chan, Vicki Cheung, Przemyslaw Debiak, Christy Dennison, David Farhi et al. ‘Dota 2 with large scale deep reinforcement learning’, arXiv preprint arXiv:1912.06680 (2019). 3. See OpenAI, ‘OpenAI Five defeats Dota 2 World Champions’, 15 April 2019, https://openai.com/blog/openai-five-defeats-dota-2-world-champions/. 4. Cohn, Gabe. ‘AI art at Christies sells for $432,500’, The New York Times, 25 October 2018, https://www.nytimes.com/2018/10/25/arts/design/ai-art-sold-christies.html. 5. Elgammal, Ahmed, Bingchen Liu, Mohamed Elhoseiny, and Marian Mazzone.

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Czarnecki, Zhongwen Xu, Hado van Hasselt, Satinder Singh, and David Silver. ‘Discovering Reinforcement Learning Algorithms’, arXiv preprint arXiv:2007.08794 (2020). O’Mara, Margaret, The Code: Silicon Valley and the Remaking of America. New York: Penguin Press, 2019. OpenAI, ‘OpenAI Five defeats Dota 2 World Champions’, 15 April 2019, https://openai.com/blog/openai-five-defeats-dota-2-world-champions/. Ortiz-Catalan, Max, Enzo Mastinu, Paolo Sassu, Oskar Aszmann, Rickard Brånemark. ‘Self-Contained Neuromusculoskeletal Arm Prostheses’, New England Journal of Medicine 382, no. 18 (2020): 1732. Osborn, Kris, ‘Future of war will be “hyperactive battlefields”: US Army General,’ The National Interest, 30 January 2021, https://nationalinterest.org/blog/buzz/future-war-will-be-‘hyperactive-battlefields’-us-army-general-177371.

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But the standout performance came from a team made of one human and one algorithm. Why? Success apparently rested on the combination of unerring tactical skill of the machine—its speed of decision and accuracy of shooting—in harness with the strategic outlook of the human, able to take a longer-range view of the game. Meanwhile, another leading AI research firm, OpenAI, has been using another multiplayer strategy game as its testbed—Dota 2.2 This too is a multiplayer game, pitting two teams against each other, each with five members. The game demands the familiar mix of tactical skill and strategic thinking. Here again, AI has become competitive against world-class human teams, in part on the basis of what one player called its ‘hydraulics’.

The Fourth Industrial Revolution by Klaus Schwab

"World Economic Forum" Davos, 3D printing, additive manufacturing, Airbnb, Amazon Mechanical Turk, Amazon Web Services, Anthropocene, augmented reality, autonomous vehicles, barriers to entry, Baxter: Rethink Robotics, bitcoin, blockchain, Buckminster Fuller, call centre, circular economy, clean water, collaborative consumption, commoditize, conceptual framework, continuous integration, CRISPR, cross-border payments, crowdsourcing, digital divide, digital twin, disintermediation, disruptive innovation, distributed ledger, driverless car, Edward Snowden, Elon Musk, epigenetics, Erik Brynjolfsson, future of work, global value chain, Google Glasses, hype cycle, income inequality, Internet Archive, Internet of things, invention of the steam engine, job automation, job satisfaction, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, life extension, Lyft, Marc Benioff, mass immigration, megacity, meta-analysis, more computing power than Apollo, mutually assured destruction, Narrative Science, Network effects, Nicholas Carr, nuclear taboo, OpenAI, personalized medicine, precariat, precision agriculture, Productivity paradox, race to the bottom, randomized controlled trial, reshoring, RFID, rising living standards, Sam Altman, Second Machine Age, secular stagnation, self-driving car, sharing economy, Silicon Valley, smart cities, smart contracts, social contagion, software as a service, Stephen Hawking, Steve Jobs, Steven Levy, Stuxnet, supercomputer in your pocket, synthetic biology, TaskRabbit, The Future of Employment, The Spirit Level, total factor productivity, transaction costs, Uber and Lyft, uber lyft, Watson beat the top human players on Jeopardy!, Wayback Machine, WikiLeaks, winner-take-all economy, women in the workforce, working-age population, Y Combinator, Zipcar

http://www.nature.com/nature/journal/v489/n7415/full/nature11421.html 60 Stephen Hawking, Stuart Russell, Max Tegmark, Frank Wilczek, “Stephen Hawking: ‘Transcendence looks at the implications of artificial intelligence – but are we taking AI seriously enough?”, The Independent, 2 May 2014. http://www.independent.co.uk/news/science/stephen-hawking-transcendence-looks-at-the-implications-of-artificial-intelligence-but-are-we-taking-9313474.html 61 Greg Brockman, Ilya Sutskever & the OpenAI team, “Introducing OpenAI”, 11 December 2015 https://openai.com/blog/introducing-openai/ 62 Steven Levy, “How Elon Musk and Y Combinator Plan to Stop Computers From Taking Over”, 11 December 2015 https://medium.com/backchannel/how-elon-musk-and-y-combinator-plan-to-stop-computers-from-taking-over-17e0e27dd02a#.qjj55npcj 63 Sara Konrath, Edward O’Brien, and Courtney Hsing.

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As theoretical physicist and author Stephen Hawking and fellow scientists Stuart Russell, Max Tegmark and Frank Wilczek wrote in the newspaper The Independent when considering the implications of artificial intelligence: “Whereas the short-term impact of AI depends on who controls it, the long-term impact depends on whether it can be controlled at all…All of us should ask ourselves what we can do now to improve the chances of reaping the benefits and avoiding the risks”.60 One interesting development in this area is OpenAI, a non-profit AI research company announced in December 2015 with the goal to “advance digital intelligence in the way that is most likely to benefit humanity as a whole, unconstrained by a need to generate financial return”.61 The initiative – chaired by Sam Altman, President of Y Combinator, and Elon Musk, CEO of Tesla Motors - has secured $1 billion in committed funding.

Architects of Intelligence by Martin Ford

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

This is going to be an engineered system, and once we figure out what the key components are, that would be a good time to start thinking about how we modulate and structure them so as to get the best outcomes. Right now, it’s just very ephemeral. MARTIN FORD: There are already a number of think-tank organizations springing up, such as OpenAI. Do you think those are premature in terms of the resources being invested, or do you think it’s a productive thing to start working on? DAPHNE KOLLER: OpenAI does multiple things. A lot of what it does is to create open source AI tools to democratize access to a truly valuable technology. In that respect, I think it’s a great thing. There’s a lot of work being done at those organizations thinking about the other important risks of AI.

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This is where the computer can be more autonomous in the way that it acquires knowledge about the world. Another area of research is in causality, where the computer can not only observe data, like images or videos, but also act on it and see the effect of those actions in order to infer causal relationships in the world. The kinds of things that DeepMind, OpenAI, or Berkeley are doing with virtual agents, for example, are going in the right direction to answer those types of questions, and we’re also doing these kinds of things in Montreal. MARTIN FORD: Are there any particular projects that you would point to as being really at the forefront of deep learning right now?

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YOSHUA BENGIO: There are a number of interesting projects, but the ones that I think are likely in the long run to have a big impact are those that involve virtual worlds in which an agent is trying to solve problems and is trying to learn about their environment. We are working on this at MILA, and there are projects in the same area in progress at DeepMind, OpenAI, Berkeley, Facebook and Google Brain. It’s the new frontier. It’s important to remember, though, that this is not short-term research. We’re not working on a particular application of deep learning, instead we’re looking into the future of how a learning agent makes sense of its environment and how a learning agent can learn to speak or to understand language, in particular what we call grounded language.

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

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

At the time of writing, the state-of-the-art AI for text-based applications are so-called transformers, which include Google’s BERT and OpenAI’s GPT-3 (T. Brown et al. 2020; Devlin et al. 2019; Vaswani et al. 2017). Transformers have also been successfully used for tasks involving audio (Child et al. 2019), images (M. Chen et al. 2020; Dosovitskiy et al. 2021), and video (Wang et al. 2021). The highest-profile AI achievements in real-time strategy games were DeepMind’s AlphaStar defeat of human grandmasters in the game StarCraft II and the OpenAI Five’s defeat of human world champions in Dota 2 (OpenAI et al. 2019; Vinyals et al. 2019). Early successes in image classification (see, e.g., Krizhevsky et al. 2012) are widely seen as having been key for demonstrating the potential of deep learning.

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

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See also the following: speech recognition, Abdel-Hamid et al. (2014); Ravanelli et al. (2019); music, Briot et al. (2020); Choi et al. (2018); Magenta (n.d.); visual art, Gatys et al. (2016); Lecoutre et al. (2017). Building on astonishing progress demonstrated by Ramesh et al. (2021), the ability to create images from text descriptions by combining two AI systems known as VQGAN (Esser et al. 2021) and CLIP (OpenAI 2021b; Radford et al. 2021) caused a Twitter sensation (Miranda 2021). 38. “BERT is now used in every English search, Google says, and it’s deployed across a range of languages, including Spanish, Portuguese, Hindi, Arabic, and German” (Wiggers 2020). BERT is an example of a transformer (see the previous endnote). 39.

We Are Bellingcat: Global Crime, Online Sleuths, and the Bold Future of News by Eliot Higgins

4chan, active measures, Andy Carvin, anti-communist, anti-globalists, barriers to entry, belling the cat, Bellingcat, bitcoin, blockchain, citizen journalism, Columbine, coronavirus, COVID-19, crowdsourcing, cryptocurrency, data science, deepfake, disinformation, Donald Trump, driverless car, Elon Musk, en.wikipedia.org, failed state, fake news, false flag, gamification, George Floyd, Google Earth, hive mind, Julian Assange, Kickstarter, lateral thinking, off-the-grid, OpenAI, pattern recognition, post-truth, rolodex, Seymour Hersh, Silicon Valley, Skype, Tactical Technology Collective, the scientific method, WikiLeaks

Audio deepfakes have already been put to malicious use, with scammers using speech samples of a CEO to replicate his voice digitally, with which they ordered a junior employee to urgently transfer €220,000 into the con artists’ account.30 A research company backed by Elon Musk, OpenAI, created an algorithm that writes coherent text independently, creating the prospect of automated trolls able to do more than just spam; they could engage people in argument, push conspiracy theories and dilute meaningful public discussion. Fearful of misuse, OpenAI decided not release the research.31, 32 While deepfakes are a threat, we can inform ourselves, prepare and respond. To become paranoid about deepfakes would itself have disastrous consequences, leading people to judge all documentation cynically.

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v=cQ54GDm1eL0 28 www.youtube.com/watch?v=sDOo5nDJwgA 29 www.whichfaceisreal.com/ 30 www.wsj.com/articles/fraudsters-use-ai-to-mimic-ceos-voice-in-unusual-cybercrime-case-11567157402 31 amp.theguardian.com/technology/2019/feb/14/elon-musk-backed-ai-writes-convincing-news-fiction?__twitter_impression=true https://openai.com/blog/better-language-models/ 32 www.vice.com/en_us/article/594qx5/there-is-no-tech-solution-to-deepfakes 33 lab.witness.org/projects/synthetic-media-and-deep-fakes/ 34 www.youtube.com/watch?time_continue=1&v=Qh_6cHw50l0 35 amp.axios.com/deepfake-authentication-privacy-5fa05902-41eb-40a7-8850-5450bcad0475.html?

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J. here, here chlorine gas here Christchurch shootings here, here, here, here Christian Science Monitor here citizen journalism, rise of here, here, here climate-change denial here Clinton, Hillary here, here CNN here, here, here, here Columbine massacre here Colvin, Marie here, here confirmation bias here Congo here Counterfactual Community here, here, here, here, here, here, here, here, here, here, here, here Covid-19 pandemic here, here, here Cracked here Crimea, Russian annexation of here, here, here, here criminal justice here CrowdStrike here cyberspace, US domination of here Czuperski, Maks here Dagestan here Daily Mail here Damascus here, here, here, here, here chemical attacks here, here, here, here, here Daraa here Dawes, Kevin here Dawson, Ryan here de Kock, Peter here De Wereld Draait Door here ‘death flights’ here Deep State here Deepfake Detection Challenge here ‘deepfakes’ here, here Democratic National Convention here Denmark here Detroit street gangs here ‘digilantism’ here DigitalGlobe here, here Discord here disinformation here, here, here, here resistance to here and Skripal poisoning here and social media here and Syrian conflict here, here, here see also Counterfactual Community diversity, and online community here Dix, Jacob here DNA profiling here Dobrokhtov, Roman here, here, here, here Donetsk here, here, here, here Douma, see Damascus, chemical attacks Dowler, Milly here doxxing here, here drones here drugs cartels here Duke, David here Dutch Safety Board here, here El Paso shooting here Ellis, Hannah here ‘elves’ here emergency-dispatch calls here Encyclopedia Dramatica here environmental damage here Escher, Federico here ethnic cleansing here European Center for Constitutional and Human Rights (ECCHR) here Europol here Evans, Robert here, here, here, here Extreme Toxicology here fact-checking projects here Faktenfinder here ‘false triangulation’ here Falun Gong here Fancy Bear here, here Far Eastern Military Command Academy here, here Fedotov, Sergey, see Sergeev, Denis 53rd Anti-Aircraft Missile Brigade here, here, here, here, here, here, here, here, here Financial Times here Finland here First Draft News here Firth, Sara here Fisk, Robert here Fitzpatrick, Catherine A. here Floyd, George here Flynn, General Michael here Foley, James here, here Fontanka here Foreign Policy here, here, here Forensic Architecture here, here Forensic Science Centre of Lithuania here Fox News here, here Free Syrian Army here, here, here, here, here Freeman, Lindsay here FSB here, here, here, here Full Fact here Gab here Gaddafi, Muammar here, here, here, here, here, here Galustian, Richard here Gamergate here, here geolocation here, here, here, here, here, here, here GetContact here Ghouta, see Damascus, chemical attacks Global Legal Action Network (GLAN) here GlobalResearch here Google Digital News Initiative here Gorelyh, Ilya here Grant, Hugh here Graph Search here Gray, Freddie here ‘Great Replacement, The’ here Gregory, Sam here Grozev, Christo here, here, here, here, here, here, here GRU here, here, here, here, here, here, here, here Guardian here, here, here, here, here, here, here, here, here, here gun control here Hadi, Abdrabbuh Mansur here Haftar, General Khalifa here Haggard, Andrew here, here, here, here Hague, William here Hama here, here, here, here al-Hamwi, Sami here Hanham, Melissa here Hayden, General Michael here Hebron here Helsingin Sanomat here Henderson, Ian here, here Hersh, Seymour here Heyer, Heather here Hitchens, Peter here Hitler, Adolf here Holocaust here, here Homs here, here, here, here, here, here Houla massacre here, here, here Houthis here Human Rights Center here Human Rights Watch here, here, here, here Hunchly here Hussein, Saddam here Identity Evropa here Ilovaysk, Battle of here IMINT (imagery intelligence) here India here Information Wars here, here, here, here InfoWars here, here, here, here Insider, The here, here, here, here, here, here, here International Criminal Court here, here international criminal law, and technological advances here Internet Research Agency here Interpreter, The here Iran here, here, here Iraq here, here, here, here, here ISIS here, here, here, here, here, here Israel here, here Issacharoff, Dean here ITAR-TASS here Ivannikov, Oleg Vladimirovich here Jabal Shashabo here, here Jabhat al-Nusra here Jespersen, Bjørn here Joint Investigation Team here, here, here, here, here Jones, Alex here, here Jukes, Peter here, here Kahn Sheikhoun sarin attack here, here Kaszeta, Dan here KGB here, here, here Khashoggi, Jamal here, here Al Khatib, Hadi here, here, here Khrushchev, Colonel Evgeny here King, Shaun here Kivimäki, Veli-Pekka (VP) here, here, here Koenig, Alexa here Koettl, Christoph here, here Kommersant here Kovalchuk, Alexander here Krasnodon here Ku Klux Klan here, here, here Kuhotkin, Sergey here Kursk here, here Al-Laham, Mimi here Lane, David here Las Vegas shootings here Lavrov, Sergey here, here, here Lebanon here Leicestershire Police here Lens Young Homsi here Leroy, Aliaume here, here Les Décodeurs here Libya here, here, here, here, here, here, here, here, here, here Al-Saiq Brigade atrocities here Libyan National Army here, here Litvinenko, Alexander here LiveJournal here London Review of Books here Loyga here Luhansk here, here, here Lyons, Josh here, here McClatchy DC Bureau here Macron, Emmanuel here Magnitsky, Sergei here Makarenko, Vladimir here Malaysia Airlines Flight MH17 here, here, here, here, here, here, here, here, here, here, here, here, here, here Malaysia Airlines Flight 370, here Mamontov, Arkady here Martin, Ryan here mass shootings, conspiracy theories here Matrix, The here May, Theresa here Medvedev, Dmitri here Mein Kampf here Middle East Live here Military Medical Academy here Millerovo here, here MintPress News here, here ‘miserabilism’ here, here, here Mishkin, Alexander (‘Alexander Petrov’) here, here, here, here, here Misrata here, here Mnemonic here Moldova here Montenegro coup plot here, here, here Morgan, Daniel here Moussa, Jenan here Mubarak, Hosni here Münster here Murdoch, Rupert here Musk, Elon here al-Musulmani, Ahmad here Myanmar here Mystery Munitions here, here National Center for Media Forensics here NATO here, here, here Navalny, Alexey here Nayda, Vitaly here Nazi affiliations here, here, here, here New York Times here, here, here, here News Provenance Project here New Yorker here News of the World here Newsweek here Newtral here Nimmo, Ben here North Korea here, here NPR here, here Nuremberg trials here Obama, Barack here, here, here, here, here Odnoklassniki here, here Oliphant, Roland here OpenAI here Organisation for the Prohibition of Chemical Weapons (OPCW) here, here, here OSINT (open-source intelligence) here Ostanin, Iggy here, here, here, here Owens, Candace here paedophiles here Pagella Politica here Pakistan here Pandora Intelligence here Panoramio here Paris Match here, here, here, here, here Paris terrorist attacks here Patriot Prayer here Peele, Jordan here Pelosi, Nancy here, here Pepe the Frog here, here Periscope here Peskov, Dmitry here Petrov, Alexander, see Mishkin, Alexander phone-hacking scandal here, here, here, here, here, here Pinochet, General Augusto here Pittsburgh synagogue attack here Postal, Chris here post-traumatic stress disorder (PTSD) here Poway synagogue attack here Press TV here Prison Planet here Professional Pilots Rumour Network here Protocol on Open Source Investigations here ProtonMail here Proud Boys here Putin, Vladimir here, here, here, here, here, here, here, here, here, here, here Radio Free Europe/Radio Liberty here Radio Svoboda here Rapp, Stephen here Reddit here, here ‘red-pilling’ here, here Rees, Gavin here Regular Contributor, The here Reporters’ Lab here Respekt here Reuters here, here reverse image searches here Revolution Man here rhino poaching here Roberts, Zach D. here Romein, Daniel here, here, here, here, here Rosen, Jay here Roshka, Georgy Petrovich here RosPassport database here Rostov Oblast here RTL Nieuws here Russia-1 here Russia Today (RT) here, here, here, here, here, here, here, here, here, here, here Petrov/Boshirov interview here, here, here, here, here Russian databases, leaked here, here Russian Defence Ministry here, here, here, here, here, here, here, here Russian Foreign ministry here Rwanda here St Petersburg here Saleh, Ali Abdullah here, here Saoud, Sari here sarin gas here, here, here, here satellite imagery here, here, here, here Saudi Arabia here, here, here, here, here, here, here Schiphol Airport here Schmitt, Eric here Second Life here Second World War here, here Senezh here Sergeev, Denis (‘Sergey Fedotov’) here shabiha here Shaif, Rawan here Shikhany institute here ‘shitposting’ here, here Simon, Scott here Simonyan, Margarita here Skripal poisoning here, here, here, here, here, here, here, here, here Sky News here Slack here, here Snizhne here, here, here, here, here, here Snopes here, here social media algorithms here archiving here ISIS and here searching here Sofronov, G.

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

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

CHAPTER 11: OUR SKYNET MOMENT 230 The messages were powerful and personal: “We Are the 99 Percent,” tumblr.com, September 14, 2011, http://weare the99percent.tumblr.com/page/231. 231 “AI systems must do what we want them to do”: “An Open Letter: Research Priorities for Robust and Beneficial Artificial Intelligence,” Future of Life Institute, retrieved April 1, 2017, https://futureoflife.org/ai-open-letter/. 231 “unconstrained by a need to generate financial return”: Greg Brockman, Ilya Sutskever, and OpenAI, “Introducing OpenAI,” OpenAI Blog, December 11, 2015, https://blog.openai.com/introduc ing-openai/. 232 best friend of one autistic boy: Judith Newman, “To Siri, with Love,” New York Times, October 17, 2014, https://www.nytimes.com/2014/10/19/fashion/how-apples-siri-became-one-autistic-boys-bff.html. 234 overpopulation on Mars: “Andrew Ng: Why ‘Deep Learning’ Is a Mandate for Humans, Not Just Machines,” Wired, May 2015, retrieved April 1, 2017, https://www.wired.com/brandlab/2015/05/andrew-ng-deep-learning-mandate-humans-not-just-machines/. 235 change how we think and how we feel: Emeran A.

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Recently, a collection of scientific and Silicon Valley luminaries, including Stephen Hawking and Elon Musk, wrote an open letter recommending “expanded research aimed at ensuring that increasingly capable AI systems are robust and beneficial: our AI systems must do what we want them to do.” Groups such as the Future of Life Institute and OpenAI have been formed to study the existential risks of AI, and, as the OpenAI site puts it, “to advance digital intelligence in the way that is most likely to benefit humanity as a whole, unconstrained by a need to generate financial return.” These are noble goals. But they may have come too late. We are already in the thrall of a vast, world-spanning machine that, due to errors in its foundational programming, has developed a disdain for human beings, is working to make them irrelevant, and resists all attempts to bring it back under control.

Robot Rules: Regulating Artificial Intelligence by Jacob Turner

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

, arXiv preprint arXiv:1705.09990 (2017); and Iyad Rahwan, “Society-in-the-Loop: Programming the Algorithmic Social Contract ”, Ethics and Information Technology, Vol. 20, No. 1 (2018), 5–14. See also the work of OpenAI , an NGO which focuses on achieving safe artificial general intelligence: “Homepage”, Website of OpenAI , https://​openai.​com/​, accessed 1 June 2018. The blog of OpenAI and Future of Humanity Institute researcher Paul Christiano also contains many valuable resources and discussions on the topic: https://​ai-alignment.​com/​, accessed 1 June 2018. 4See, for example, the UK Locomotive Act 1865, s.3. 5Toby Walsh, Android Dreams (London: Hurst & Company, 2017), 111.

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We cannot be sure that AI technology will not meet a similar plateau, even after it achieves a form of general intelligence.113 Notwithstanding these limitations, in recent years there have been several significant developments in the capabilities of AI. In January 2017, Google Brain announced that technicians had created AI software which could itself develop further AI software.114 Similar announcements were made around this time by the research group OpenAI,115 MIT,116 the University of California, Berkeley and DeepMind.117 And these are only the ones we know about—companies, governments and even some independent individual AI engineers are likely to be working on processes which go far beyond what those have yet made public. 6 Optimists, Pessimists and Pragmatists Commentators on the future of AI can be grouped into three camps: the optimists, the pessimists and the pragmatists.118 The optimists emphasise the benefits of AI and downplay any dangers.

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See Autonomous weapons Kill Switch L La Commission de Réflexion sur l’Éthique de la Recherche en sciences et technologies du Numérique d’Allistene (CERNA) Laws of Robotics Legal Personality Loomis v Wisconsin Luddites See alsoNeo-Luddites M Machine learning deep learning reinforcement learning supervised learning unsupervised learning Massachusetts Institute of Technology (MIT) Media Lab Mens Rea Microsoft Model Law Monkey Selfie Case. See Naruto v Slater N Narrow AI Natural Law Negligence duty of care reasonable person Neo-Luddites See alsoLuddites No-Fault Accident Compensation O Off Switch. See Kill Switch OpenAI Open Roboethics Institute (ORI) Organisation for Economic Co-operation and Development (OECD) P Paris Climate Agreement Partnership on AI to benefit People and Society Positivism Posthumanism Private Law Product liability EU Product Liability Directive US Restatement (Third) of Torts–Products Liability Professions, The Public International Law Q Qualia R Random Darknet Shopper Rawls, John Red Flag Law Rousseau, Jean Jacques S Safe interruptibility Sandbox Saudi Arabia Self-driving cars.

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

"World Economic Forum" Davos, agricultural Revolution, AI winter, Apollo 11, artificial general intelligence, basic income, bread and circuses, Buckminster Fuller, business cycle, business process, Charles Babbage, Claude Shannon: information theory, clean water, cognitive bias, computer age, CRISPR, crowdsourcing, dark matter, DeepMind, Edward Jenner, Elon Musk, Eratosthenes, estate planning, financial independence, first square of the chessboard, first square of the chessboard / second half of the chessboard, flying shuttle, full employment, Hans Moravec, Hans Rosling, income inequality, invention of agriculture, invention of movable type, invention of the printing press, invention of writing, Isaac Newton, Islamic Golden Age, James Hargreaves, job automation, Johannes Kepler, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, Kevin Kelly, lateral thinking, life extension, Louis Pasteur, low interest rates, low skilled workers, manufacturing employment, Marc Andreessen, Mark Zuckerberg, Marshall McLuhan, Mary Lou Jepsen, Moravec's paradox, Nick Bostrom, On the Revolutions of the Heavenly Spheres, OpenAI, pattern recognition, profit motive, quantum entanglement, radical life extension, Ray Kurzweil, recommendation engine, Rodney Brooks, Sam Altman, self-driving car, seminal paper, Silicon Valley, Skype, spinning jenny, Stephen Hawking, Steve Wozniak, Steven Pinker, strong AI, technological singularity, TED Talk, telepresence, telepresence robot, The Future of Employment, the scientific method, Timothy McVeigh, Turing machine, Turing test, universal basic income, Von Neumann architecture, Wall-E, warehouse robotics, Watson beat the top human players on Jeopardy!, women in the workforce, working poor, Works Progress Administration, Y Combinator

To that end, Elon Musk along with Sam Altman, the president of the start-up incubator Y Combinator, cochair a nonprofit called OpenAI that has as its purpose to help usher in the era of safe and beneficial AI. The initial blog post announcing its formation states, “Because of AI’s surprising history, it’s hard to predict when human-level AI might come within reach. When it does, it’ll be important to have a leading research institution which can prioritize a good outcome for all over its own self-interest.” Collectively, OpenAI’s backers have pledged close to a billion dollars of funding. The approach is to develop, among other things, open-source AI.

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Working to develop the very thing you are worried about might not seem like the best plan, and while the founders acknowledge the risk, they point out that it is better if an AGI is built in an open, collaborative way with much discussion and debate, rather than by a small group with its own agenda. Critics counter that OpenAI may end up giving 99 percent of the formula to anyone who wants it, leaving us all at the mercy of whatever random extremist group or belligerent state happens to figure out the last percent, even if it never would have had the capability to sort the rest of it out on its own. So here we are. We find ourselves racing forward, trying to build something that has the potential to launch us into a perfect world or destroy us all.

The Simulation Hypothesis by Rizwan Virk

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

Given the response times available to AI algorithms, can we expect that AI will learn to play other video games, such as first- person shooters and fighting? Recently, Elon Musk funded OpenAI and announced that it had learned to play DOTA 2, an extremely popular fantasy-themed fighting game. Competitive video gaming, or eSports, is played by professionals and has become a popular spectator sport in the same way sports such as basketball, baseball and football developed in the last century. OpenAI announced that a team of five bots were competitive enough to qualify to play against professional teams! This is an interesting twist, though not entirely unexpected.

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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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See NDEs (near-death experiences) Netscape, 287 Neumann, John von, 100, 260 Neurable, 76 Neurolink, 76 A New Kind of Science (Wolfram, 2002), 266 New York Times, 232 Newton, Isaac, 13, 36, 124–26, 161, 166, 220–21 Niels Bohr Institute, 132 Nintendo Entertainment System (NES), 38–39 nirvana, 203 NLP (Natural Language Processing), 89–92 No Man’s Sky, 46–47, 51, 236 Noack, Marcus, 246 nonhuman earth-based lifeforms, 275 non-player characters (NPCs), 30–31, 39, 82, 280–81 non-player characters (NPCs), graphical, 41–42 non-simulated beings, 114 NPCs (non-player characters), 30–31, 39, 53, 82 NPCs and Turing Test, 115 O OASIS, 56–57, 71 OBEs (out-of-body experiences), 219, 241–42 “object” definition, 70 observation, particle collapse as, 131 Oculus VR, 59–60 OpenAI, 87, 94 optimization, 159–160 optimization techniques, computer graphics, 34, 157 Owhadi, Houman, 254–55 P Pac-Man, 1, 34, 82, 208, 273 parallel lives and future selves, 150–52 parallel universes and simulation hypothesis, 159–160 parallel worlds and Fringe, 152–53 parallel worlds and the multiverse, 148–150 parallel worlds, need for computation, 157–59 Paramahansa Yogananda, 183, 200 particle “local” nature, 127 particles and pixels on screen, 162–64 particle-wave duality, 127–134, 254–55 Pauli, Wolfgang, 121, 125–26 Pauli Exclusion Principle, 126 PCs (player characters), 82 PCs vs.

Being You: A New Science of Consciousness by Anil Seth

AlphaGo, artificial general intelligence, augmented reality, backpropagation, carbon-based life, Claude Shannon: information theory, computer age, computer vision, Computing Machinery and Intelligence, coronavirus, correlation does not imply causation, CRISPR, cryptocurrency, deep learning, deepfake, DeepMind, Drosophila, en.wikipedia.org, Filter Bubble, GPT-3, GPT-4, John Markoff, longitudinal study, Louis Pasteur, mirror neurons, Neil Armstrong, Nick Bostrom, Norbert Wiener, OpenAI, paperclip maximiser, pattern recognition, Paul Graham, Pierre-Simon Laplace, planetary scale, Plato's cave, precautionary principle, Ray Kurzweil, self-driving car, speech recognition, stem cell, systems thinking, technological singularity, TED Talk, telepresence, the scientific method, theory of mind, Thomas Bayes, TikTok, Turing test

GPT-3 has an astonishing 175 billion parameters and was trained on some 45 terabytes of text data. See https://openai.com/blog/openai-api/ and for technical details: https://arxiv.org/abs/2005.14165. it does not understand: Of course this depends on what is meant by ‘understanding’. Some might say that human ‘understanding’ is no different in kind from the sort of ‘understanding’ displayed by GPT-3. The cognitive scientist Gary Marcus argues against this position, and I agree with him. See www.technologyreview.com/2020/08/22/1007539/gpt3-openai-language-generator-artificial-intelligence-ai-opinion/. a five-hundred-word essay: ‘A robot wrote this entire article.

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When the chatbot won, its response was ‘I feel about beating the turing [sic] test in quite convenient way.’ By lowering the bar this far, the test becomes much easier to pass. This was a test of human gullibility, and the humans failed. As AI continues to improve, the Turing test may soon be passed without such artificially low standards. In May 2020, the research lab OpenAI released GPT-3 – a vast artificial neural network trained on examples of natural language drawn from a large swathe of the internet. As well as engaging in chatbot-variety dialogue, GPT-3 can generate substantial passages of text in many different styles when prompted with a few initial words or lines.

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

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

Wall Street Journal (2018): www.wsj.com/articles/should-artificial-intelligence-copy-the-human-brain-1533355265?mod=searchresults&page=1&pos=1. FIGURE 1.2: The exponential growth in computing—300,000-fold—in the largest AI training runs. Source: Adapted from D. Hernandez and D. Amodei, “AI and Compute,” OpenAI (2018): https://blog.openai.com/ai-and-compute/. The number of new deep learning AI algorithms and publications has exploded (Figure 1.1), with exponential growth of machine recognition of patterns from enormous datasets. The 300,000-fold increase in petaflops (computing speed equal to one thousand million million [1015] floating-point operations per second) per day of computing used in AI training further reflects the change since 2012 (Figure 1.2).

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Source: Adapted from J. F. Bonnefon et al., “The Social Dilemma of Autonomous Vehicles,” Science (2016): 352(6293), 1573–1576. The concern for ethical breaches and harm led not only to the formation of the AI Now Institute but to many other efforts across the globe to promote the ethics and safety of AI, including OpenAI, Pervade, Partnership on AI, the Future of Life Institute, the AI for Good Summit, and academic efforts at UC Berkeley, Harvard, the University of Oxford, and Cambridge. Yet, as the AI Now Institute has pointed out, there is no tech company tracking its own adherence to ethical guidelines. That hit home for me when I read a recent Infosys AI healthcare report, “AI for Healthcare: Balancing Efficacy and Ethics.”64 Although the report claimed that the industry as a whole and the organizations in it need “to establish ethical standards and obligations,” it provided no indication of what those standards or obligations were.

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Ng said, “Fearing a rise of killer robots is like worrying about overpopulation on Mars before we populate it,”79 whereas Musk has said that the potential rise of killer robots was one reason we needed to colonize Mars—so that we’ll have a bolt-hole if AI goes rogue and turns on humanity.80 Musk’s deep concerns prompted him and Sam Altman to found a billion-dollar nonprofit institute called OpenAI with the aim of working for safer AI. In addition, he gave $10 million to the Future of Life Institute, in part to construct worst-case scenarios so that they can be anticipated and avoided.81 Max Tegmark, the MIT physicist who directs that institute, convened an international group of AI experts to forecast when we might see artificial general intelligence.

Warnings by Richard A. Clarke

"Hurricane Katrina" Superdome, active measures, Albert Einstein, algorithmic trading, anti-communist, artificial general intelligence, Asilomar, Asilomar Conference on Recombinant DNA, Bear Stearns, behavioural economics, Bernie Madoff, Black Monday: stock market crash in 1987, carbon tax, cognitive bias, collateralized debt obligation, complexity theory, corporate governance, CRISPR, cuban missile crisis, data acquisition, deep learning, DeepMind, discovery of penicillin, double helix, Elon Musk, failed state, financial thriller, fixed income, Flash crash, forensic accounting, friendly AI, Hacker News, Intergovernmental Panel on Climate Change (IPCC), Internet of things, James Watt: steam engine, Jeff Bezos, John Maynard Keynes: Economic Possibilities for our Grandchildren, knowledge worker, Maui Hawaii, megacity, Mikhail Gorbachev, money market fund, mouse model, Nate Silver, new economy, Nicholas Carr, Nick Bostrom, nuclear winter, OpenAI, pattern recognition, personalized medicine, phenotype, Ponzi scheme, Ray Kurzweil, Recombinant DNA, Richard Feynman, Richard Feynman: Challenger O-ring, risk tolerance, Ronald Reagan, Sam Altman, Search for Extraterrestrial Intelligence, self-driving car, Silicon Valley, smart grid, statistical model, Stephen Hawking, Stuxnet, subprime mortgage crisis, tacit knowledge, technological singularity, The Future of Employment, the scientific method, The Signal and the Noise by Nate Silver, Tunguska event, uranium enrichment, Vernor Vinge, WarGames: Global Thermonuclear War, Watson beat the top human players on Jeopardy!, women in the workforce, Y2K

“Machine learning and deep learning algorithms . . . we don’t fully understand today how they work.” The new explainable-AI initiative “will give the human operator more details about how the machine used deep learning to come up with the answer.”20 In 2015, business tycoons Elon Musk and Sam Altman created the OpenAI Institute, a nonprofit company that focuses on researching AI. Musk and Altman believe that by making all of OpenAI’s findings open-source and funding it by private donations, eliminating the need for financial return, they can ensure that AI will be developed for the benefit of all people, not for self-interested or destructive aims. They and others are so convinced of its importance that they have committed a total of $1 billion toward the initiative.

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See also Fukushima Daiichi nuclear disaster Nuclear reactors, 83–84, 85–86 Nuclear weapons, in Pakistan and India, 264–73, 281–82 Nuclear winter theory, 273–78 Obama, Barack, 283 influenza outbreak, 218 Iraq and, 63 Keystone Pipeline and, 254 Madoff fraud and SEC, 118–19 nuclear weapons and, 281 Syria and, 57, 61, 64–74 Ocrant, Michael, 106, 108, 110 O’Dell, Tawni, 121 Office of Strategic Services (OSS), 21 Off-putting personality, 183–84 Offshore drilling, 42 Off-target events, 333–34 Okamura, Yukinobu, 75–82 data of, 78, 79 earthquake geology and safety guidelines, 75–78, 80–81, 90, 93–94, 97 Okhotsk Plate, 80 Onagawa Nuclear Power Plant, 90–91 O’Neill, Eugene, 143 O’Neill, John P., 8 On the Origin of Species (Darwin), 325 OODA loop (observe, orient, decide, act), 371n OPEC (Organization of the Petroleum Exporting Countries), 23–24 OpenAI Institute, 210 OpenSSL, 179 Operation IceBridge, 246 Operation Smiling Buddha, 266 Oppenheimer & Co., 153, 161 Options trading, 103–4 Oregon State University, 240–41, 352 Organic Act of 1910, 124–25 Organizing Committee for the International Conference on Recombinant DNA, 337 Orient No. 2 mine explosion, 127 O-rings, and Challenger disaster, 11–13 Orthogonal thinking, 184, 235 Oswald, Lee Harvey, 99 Our Final Invention (Barrat), 202 Outbreak (movie), 219–20 Outlandishness, 174 Oxford University, 212 Pacific Plateau, 80 Pacific Ring of Fire, 94 Pakistan, 261–73 bin Laden raid, 268–69 Cold Start, 267, 270 Mumbai terrorist attacks of 2008, 261–64 nuclear weapons and India, 264–73, 281–82 partition of, 265–66 Pakistani Army, 266–67 Pakistani Navy, 264, 270 Pakistan War College, 269 Palm Beach Country Club, 101 Panasenko, Sharon, 327 Pandemic disease, 217–36 Panetta, Leon, 64, 200 Panoramic Survey Telescope and Rapid Response System (Pan-STARRS), 310–11, 315 Paris Agreement, 247–50 Path Where No Man Thought, A: Nuclear Winter and the End of the Arms Race (Sagan and Turco), 276–77 Paulson, John, 149 Peabody Prize, 226 Peace Corps, 62 Pearl Harbor: Warning and Decision (Wohlstetter), 19, 21 Pearl Harbor attack, 20–21 Penicillin, 229 Penney, Alexander, 186 People’s Liberation Army Navy, 199 Performance Coal Company, 130, 134, 139 “Permabears,” 236 Persian Gulf War.

The Wires of War: Technology and the Global Struggle for Power by Jacob Helberg

"World Economic Forum" Davos, 2021 United States Capitol attack, A Declaration of the Independence of Cyberspace, active measures, Affordable Care Act / Obamacare, air gap, Airbnb, algorithmic management, augmented reality, autonomous vehicles, Berlin Wall, Bernie Sanders, Big Tech, bike sharing, Black Lives Matter, blockchain, Boris Johnson, Brexit referendum, cable laying ship, call centre, Cambridge Analytica, Cass Sunstein, cloud computing, coronavirus, COVID-19, creative destruction, crisis actor, data is the new oil, data science, decentralized internet, deep learning, deepfake, deglobalization, deindustrialization, Deng Xiaoping, deplatforming, digital nomad, disinformation, don't be evil, Donald Trump, dual-use technology, Edward Snowden, Elon Musk, en.wikipedia.org, end-to-end encryption, fail fast, fake news, Filter Bubble, Francis Fukuyama: the end of history, geopolitical risk, glass ceiling, global pandemic, global supply chain, Google bus, Google Chrome, GPT-3, green new deal, information security, Internet of things, Jeff Bezos, Jeffrey Epstein, John Markoff, John Perry Barlow, knowledge economy, Larry Ellison, lockdown, Loma Prieta earthquake, low earth orbit, low skilled workers, Lyft, manufacturing employment, Marc Andreessen, Mark Zuckerberg, Mary Meeker, Mikhail Gorbachev, military-industrial complex, Mohammed Bouazizi, move fast and break things, Nate Silver, natural language processing, Network effects, new economy, one-China policy, open economy, OpenAI, Parler "social media", Peter Thiel, QAnon, QR code, race to the bottom, Ralph Nader, RAND corporation, reshoring, ride hailing / ride sharing, Ronald Reagan, Russian election interference, Salesforce, Sam Altman, satellite internet, self-driving car, Sheryl Sandberg, side project, Silicon Valley, Silicon Valley ideology, Silicon Valley startup, Skype, smart grid, SoftBank, Solyndra, South China Sea, SpaceX Starlink, Steve Jobs, Steven Levy, Stuxnet, supply-chain attack, Susan Wojcicki, tech worker, techlash, technoutopianism, TikTok, Tim Cook: Apple, trade route, TSMC, Twitter Arab Spring, uber lyft, undersea cable, Unsafe at Any Speed, Valery Gerasimov, vertical integration, Wargames Reagan, Westphalian system, white picket fence, WikiLeaks, Y Combinator, zero-sum game

AI powers self-driving cars and suggests movies we might like on Netflix. The Associated Press has used AI to draft basic articles. IBM’s Watson beat two of Jeopardy!’s greatest contestants and, for good measure, identified genes linked to degenerative illness. In June 2020, the San Francisco company OpenAI’s GPT-3 sent shock waves across the tech industry, proving it possible to algorithmically generate cogent and naturally sounding long-form text on almost any topic. The consulting firm PwC estimates that artificial intelligence will contribute an additional $15.7 trillion to global economic growth by 2030.

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Within twenty-four hours, Microsoft pulled the plug on Tay.43 But these hiccups won’t hold back AI-powered language generation forever. Indeed, natural language processing is only getting more sophisticated, in ways that could be quite frightening. Better language abilities could make it easier for trolls to spread propaganda—and harder for us to identify them. In 2019, OpenAI fed an algorithm the words “Russia has declared war on the United States after Donald Trump accidentally…” The algorithm proceeded to generate the following realistic—and perilous—sentences: Russia has declared war on the United States after Donald Trump accidentally fired a missile in the air. Russia said it had “identified the missile’s trajectory and will take necessary measures to ensure the security of the Russian population and the country’s strategic nuclear forces.”

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., 52, 180, 231 Marriott, xvii–xviii, 152 Mattis, James, 52 Maven, Project, 178–81 May, Theresa, 73 Meleshevich, Kirill, 69 Merkel, Angela, 166, 267 Microsoft, 84, 104, 110, 134, 197, 244 natural language processing and, 140–41 Silicon Valley–Washington relations and, 166, 172, 180, 191 Modi, Narendra, 82–83, 212 Morgenthau, Hans, xiv Moriuchi, Priscilla, 94 Mossadegh, Mohammad, 53 Mubarak, Hosni, 37–39 Mueller, Robert, 66–67 Musk, Elon, 108, 154, 247 Nader, Ralph, xx–xxi Nakasone, Paul, 85, 228 National Aeronautics and Space Administration (NASA), 165, 232, 244 National Defense Authorization Acts, 123, 215, 251 National Defense Education Act, 232–33, 245 National Institute of Manufacturing, 241, 244 National Science Foundation, 23, 164–65, 244, 246 National Security Agency (NSA), xi, xvii, 30, 48, 208, 228 Snowden affair and, 166–68, 172 natural language processing, 80, 140–43, 253 Naughton, John, 116 Navalny, Alexei, 27, 39–40 Netherlands, 93, 110 New Knowledge, 57, 59 New York Times, xii, 28, 35, 47, 53, 57, 71–72, 82, 147, 161, 262 and elections of 2016, 12, 54, 67 Skripal case and, 74–76 New Zealand, 121–22 9/11, 141, 146, 168, 236 Ningsuan Technologies, 111 Nixon, Richard, 48, 208 Nokia, 119, 122, 130, 217–19 North Atlantic Treaty Organization (NATO), 27, 38, 81, 93, 122, 203, 211–12, 216 cyberattacks and, 30, 197–98, 211 Norway, 21, 104n, 214 nuclear weapons, xiv, 39, 141, 180, 208, 229 cyberattacks and, 45–46 deepfakes and, 138–39 Obama, Barack, 4, 7, 11, 35, 39, 49, 63, 100, 166–67, 205, 228, 234 on climate change, 206–7 competitiveness investing and, 236, 238, 242–43 cyberattacks and, 44, 47 deepfakes and, 136, 138–39, 144, 158 Ocasio–Cortez, Alexandria, 239 Office of Personnel Management (OPM), 44–45, 172, 184 Office of Technology Assessment, 174, 210 oil, 10, 26, 45, 99, 157, 238 OpenAI, 132, 141 open radio access networks (RANs), 219 Orwell, George, xiii–xiv, 127, 148 Osnos, Evan, 32, 40 Pacific Deterrence Initiative, 266 Packard, David, 164, 208 Page, Larry, 15, 165 Pakistan, 138–39 China and, 94, 107, 109, 111, 152 Palantir, 6, 180 Pardo, Tamir, 145, 151, 153 Parkland school shooting, 55, 77, 138 Peele, Jordan, 136 Pelosi, Nancy, 93, 138, 173 Pence, Mike, 179 Perry, William, 208 Peskov, Dmitry, 57, 75 Peters, Gary, 241 Pichai, Sundar, 131, 247 Silicon Valley–Washington relations and, 178–79 tech industry congressional hearings and, 159–60 Pincus, Mark, 167 Podesta, John, 12, 48–49, 83–84, 143 Poland, 2, 27, 204 Politkovskaya, Anna, 26–27 Pompeo, Mike, 207, 269 Postel, Jon, 112–13 Poynter Institute, 261–62 Prigozhin, Yevgeniy, 56–57, 80 PRISM, 166–67 Putin, Vladimir, xiii, xxi, 19, 61–62, 69, 74, 76, 80, 86, 133, 135, 145, 172, 222, 229, 233 active measures and, 27–28, 201 cyberattacks and, 47, 201 domestic opposition to, 39–40 and elections of 2016, 49, 58 IRA disinformation and, 56, 58, 60, 63 rise to power of, 26–27 Russian military influence and, 27, 30, 41–42, 203 TV and, 51–52 U.S.

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

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

” — ERIK BRYNJOLFSSON, MIT professor; author, The Second Machine Age and Machine, Platform, Crowd “ Prediction Machines is a must-read for business leaders, policy makers, economists, strategists, and anyone who wants to understand the implications of AI for designing business strategies, decisions, and how AI will have an impact on our society.” — RUSLAN SALAKHUTDINOV, Carnegie Mellon professor; Director of AI Research, Apple “I encounter so many people who feel excited but overwhelmed by AI. This book will ground those feeling lost by giving them a practical framework.” — SHIVON ZILIS, OpenAI Director and Partner, Bloomberg Beta “ The current AI revolution will likely result in abundance, but the process of getting there requires deliberation on tough topics that include increasing unemployment and income disparity. This book presents frameworks that allow decision makers to deeply understand the forces at play

…

The CDL’s dominance in this domain resulted partly from our location in Toronto, where many of the core inventions—in a field called “machine learning”—that drove the recent interest in AI were seeded and nurtured. Experts who were previously based in the computer science department at the University of Toronto today head several of the world’s leading industrial AI teams, including those at Facebook, Apple, and Elon Musk’s Open AI. Being so close to so many applications of AI forced us to focus on how this technology affects business strategy. As we’ll explain, AI is a prediction technology, predictions are inputs to decision making, and economics provides a perfect framework for understanding the trade-offs underlying any decision.

…

Bill Gates advocated for a tax on robots that replace human labor. Sidestepping what would normally be government’s purview, the high-profile startup accelerator Y Combinator is running experiments on providing a basic income for everyone in society.2 Elon Musk organized a group of entrepreneurs and industry leaders to finance Open AI with $1 billion to ensure that no single private-sector company could monopolize the field. Such proposals and actions highlight the complexity of these social issues. As we climb to the pyramid’s top, the choices become strikingly more complex. When thinking about society as a whole, the economics of AI are not so simple anymore.

Ludicrous: The Unvarnished Story of Tesla Motors by Edward Niedermeyer

autonomous vehicles, barriers to entry, Bear Stearns, bitcoin, business climate, call centre, carbon footprint, Clayton Christensen, clean tech, Colonization of Mars, computer vision, crowdsourcing, disruptive innovation, Donald Trump, driverless car, Elon Musk, en.wikipedia.org, facts on the ground, fake it until you make it, family office, financial engineering, Ford Model T, gigafactory, global supply chain, Google Earth, housing crisis, hype cycle, Hyperloop, junk bonds, Kaizen: continuous improvement, Kanban, Kickstarter, Lyft, Marc Andreessen, Menlo Park, minimum viable product, new economy, off grid, off-the-grid, OpenAI, Paul Graham, peak oil, performance metric, Ponzi scheme, ride hailing / ride sharing, risk tolerance, Sand Hill Road, self-driving car, short selling, short squeeze, side project, Silicon Valley, Silicon Valley startup, Skype, smart cities, Solyndra, stealth mode startup, Steve Jobs, Steve Jurvetson, tail risk, technoutopianism, Tesla Model S, too big to fail, Toyota Production System, Uber and Lyft, uber lyft, union organizing, vertical integration, WeWork, work culture , Zipcar

By 2017, however, there had been several unmistakable red flags: claiming that he tweets under the influence of a dangerous cocktail of Ambien and alcohol and admitting he might be bipolar certainly raised eyebrows in some corners. Combined with his ever-growing collection of ambitious ventures—including the Boring Company’s plan to revolutionize tunneling, the Hyperloop tunnel-based transport concept, Neuralink’s “implantable brain-computer interface,” and OpenAI’s effort to promote “friendly artificial intelligence”—it seemed that Musk was beginning to lose himself in an endless quest for more hype. His increasingly erratic behavior burst into the spotlight in 2018, when an escalating series of Twitter conflicts with journalists, analysts, and critics led to a full-scale assault on stock analysts and the media.

…

Edwards, 56 Department of Energy (DOE) loans from, 68–89, 118, 120, 121 as shareholder of Tesla, 82–86, 90 detractors, 102–108 Detroit, Michigan, 2, 4 Detroit Auto Show, 68 disruptive innovator, Tesla as, 195–197 DOE. see Department of Energy doors falcon-wing, 137–141 gull-wing, 136–137 Downey, California, 76 Drori, Ze’ev, 49–50, 65 Dunlay, Jim, 58 E Eberhard, Martin as advocate of Tesla, 67 founding of Tesla by, 21–24, 27–31, 35, 37–40 ouster of, 44–48, 50, 79 EBITDA, 215 Eisner, Michael, 45 Electrek, 97–101 electric vehicles (EVs), 3, 12–14, 24, 77, 202, 207 Energy Independence and Security Act, 67 Enron, 105 environmental issues, 112–113, 119 Esquire, 61 e-tron quattro, 203 EV1, 13, 24, 34 EVs. see electric vehicles F Facebook, 41 Falcon One, 28 falcon-wing doors, 137–141 FCW (Forward Collision Warning), 125 Ferrari, 60, 200–201 Fiat, 11, 34 financial crisis (2008), 75–76, 105 fixed costs, 54 Flextronics, 47 FOIA (Freedom of Information Act), 72, 131 Ford, Henry, 56, 194 Ford Focus, 159 Ford Fusion, 75 Ford Motor Company, 3, 4, 56, 75, 181, 194, 204, 216 Forward Collision Warning (FCW), 125 Founders Edition Roadster, 215 Freedom of Information Act (FOIA), 72, 131 Fremont, California, 53, 206, 218 funding (fundraising), 29, 40, 44–47, 50, 69–71, 85 G Gage, Tom, 27–29 Galileo Galilei, 105 Gao Yaning, 128 Gartner, 175 gas prices, 11, 14 General Motors (GM). see also specific models bankruptcy and bailout of, 2–3, 88 and electric cars, 11–13, 34 Impact concept car, 24 and Lotus, 36, 37, 53 OnStar system, 194 Germany, 203, 204 Ghosn, Carlos, 197–200 Gigafactory, 77, 183–184, 189, 218 GM. see General Motors G170J1-LE1 screens, 228 Goodwill Agreements, 149 Google, 44, 120–124, 171 Graham, Paul, 41 “A Grain of Salt” (blog post), 152–153 Grant, Charley, 100 “green car” companies, 11 GT Advanced Technologies (GTAT), 95–97 gull-wing doors, 136–137 H Harrigan, Mike, 30 Harris Ranch, 115–116, 119 Harvard Business School, 195 herd mentality, 96 Hethel, England, 49 Hoerbiger, 138–140 Holzhausen, Franz von, 137 Honda, 201 “How to Be Silicon Valley” (speech by Paul Graham), 41 Hyperloop, 16, 88, 217 I IDEO, 38 IGBT (insulated-gate bipolar transistor), 49 Impact concept car, 13, 24 imperfection, 55 incumbent companies, 196–197 innovation, 193–210 by Citroën, 193–195 disruptive, 195–197 by Carlos Ghosn, 197–200 by Tesla, 201–210 “Innovation Killers: How Financial Tools Destroy Your Capacity to Do New Things” (Christensen), 196–197 The Innovator’s Dilemma, 197 insulated-gate bipolar transistor (IGBT), 49 internal conflict, 29–32 InvestorsHub, 99 Israel, 4, 12 J Jaguar I-PACE, 202–203 Jivan, Jon, 98 Jonas, Adam, 172 K kaizen, 58, 60 Krafcik, John, 176 L Lambert, Fred, 98–101 Lamborghini, 204 Land Rover, 60 lead-acid batteries, 23–24, 197 Leech, Keith, 146–147, 156 Level 4 autonomous cars, 175–176 Level 5 autonomous cars, 170, 172, 175–176, 178 Lexus, 204 lithium-ion batteries, 22–24, 26, 34 “long tailpipe,” 110 losses, 11 Lotus, 36–37, 38, 43, 44, 49, 59 Lotus Elise, 28, 36, 37, 38, 40, 43 Lotus Evora, 59 “Ludicrous Mode,” 16 Lyons, Dave, 64 M Mac, Ryan, 218 Magna Powertrain, 48–49 Magna Steyr, 202 manufacturing, 180–192 of batteries, 183–184, 188–189 and continuous reiteration of Model 3s, 182–192 Elon Musk on, 180–182 preproduction as, 187–188 Marchionne, Sergio, 11 market saturation, 10 Marks, Michael, 47, 48, 50 Mars, 25 “Master Plan, Part Deux” (blog post), 164 McLaren F1, 25–26, 39 media hype, 88, 90–91, 93–95, 97–102, 130, 211–224 and base version of Model 3, 220–224 Elon Musk as cause of, 217–224 at Semi/Roadster unveiling, 211–215 as stock price stimulant, 215–216 Menlo Park, California, 28, 58 Michelin, 194 Miles, 11 Mobileye, 167–170 mobility technology, 11 Model 3, 8–10, 180–182 base version of, 220–224 production of, 182–192 Model S, 15, 74–75, 81–84, 90, 99, 135–137. see also Whitestar Model T, 56 Model X, 101, 134–145 Model Year 2008, 69 Moggridge, Bill, 38–39 Montana Skeptic, 105–108 Morgan Stanley, 172 Morris, Charles, 43 Motley Fool, 98 Musk, Elon on belief, 21 and branding of Tesla, 16–17 as cause of media hype, 217–224 childhood and personality of, 25–26 clientele knowledge of, 60 “cluelessness” of, 33–35 and culture of Tesla, 60 and Daimler, 68 detractors of, 102–108 and electric cars, 25–28 and Elise-Roadster conversion, 38–39 on financial viability of Tesla, 72–73 and fundraising, 44, 69–71 and loans, 70, 78 on manufacturing, 180–181, 190 on Model 3, 8–9 on Model S, 74 on Model X, 144–145 on obstacles faced by Tesla, 46 offers of, to sell Tesla, 120–121 on price increases, 71 and production process, 142, 165 as public figure, 15 on Series D, 47 and JB Straubel, 26 and stress, 64–67, 77–78 and Superchargers, 109–119 and Tesla cofounders, 29–32, 45, 47–48 on Tesla’s master plan, 21–22, 30–31, 58, 163 at town hall meeting, 70–71 and Whitestar, 51 Musk, Errol, 25 Musk, Justine, 25–26 Musk, Kimball, 65 N National Aeronautics and Space Administration (NASA), 66 National Highway Traffic Safety Administration (NHTSA), 127, 131–132, 149–162 National Transportation Safety Board (NTSB), 132, 167 NDAs. see non-disclosure agreements Neil, Dan, 59 Neuralink, 16, 217 New Mexico, 48, 67 New United Motor Manufacturing, 53 New York Times, 2, 30, 66 NHTSA. see National Highway Traffic Safety Administration Nissan Leaf, 198 Nissan-Renault Alliance, 197–200, 207 Noble M12, 27 nondisclosure agreements (NDAs), 5, 149–151, 152, 155–156 Norway, 12 NTSB (National Transportation Safety Board), 132, 167 NUMMI plant, 76, 81 Nürburgring, 203 NuvoMedia, 23 O Occupy Wall Street, 80–81 Ohno, Taiichi, 57 OnStar, 194 Opel, 36 Opel Speedster, 36 OpenAI, 217 operating profits and losses, 89 P Packet Design, 23 Page, Larry, 44 Paine, Chris, 13, 64, 71, 73–74 Panasonic, 77 Pandora, 41 PayPal, 16, 28 Peak Oil, 11 Pinnacle Research, 25 platforms, 135–136 Porsche, 24, 26, 39, 203–204 Porsche 911, 39 power electronics module (PEM), 49 Powertrain Technology, 58 Prenzler, Christian, 100 preproduction, 187–188 price increases, 71 Prius, 24 profitability, 81–82, 89 Project Better Place, 4–5, 11–12 public, going, 80–81 Q quality, 55, 59–60 Quality Control Systems, 131 R Ranger, 60 Reddit, 97, 99–100 reliability, 143 Renault Kwid, 207 Renault Zoe, 198 Reuters, 66 Revenge of the Electric Car (film), 64 Roadster as Elise conversion, 37–39 launch of, 14–15, 29, 42, 47–51, 59–61 new model of, 211–215 profitability of, 71–72, 81 securing investments for, 44, 45 and Tesla startup, 2–3 robotaxis, 166–167 Rogan, Joe, 219 Rosen, Harold, 26 Rosen Motors, 26 S Saleen, 99–100 San Carlos, California, 28 San Francisco, California, 59 San Jose, California, 75–76 Santa Monica, California, 45 Saudi Arabia, 218–219 Schwarzenegger, Arnold, 45 Scion xB, 27 Seagate, 23 “The Secret Tesla Motors Master Plan” (blog post), 21 Securities and Exchange Commission (SEC), 67, 160, 219–220, 224, 234 Seeking Alpha, 103, 105–107 self-driving cars, 120–133 Semi, 211–215 Senate Finance Committee, 67 Series A funding, 29 Series C funding, 40, 44–45 Series D funding, 46, 47 Series E funding, 50 S 40 model, 84 Shashua, Amnon, 167–170 Silicon Valley, 4, 14, 15, 17, 45, 53, 54, 58 Siry, Darryl, 65, 73 60 Minutes, 66 S 60 model, 84 “skateboard” chassis, 134, 202 Skype, 41 Smart (Tesla car), 68 software startups, 54–55 SolarCity, 110–111, 164 solar power, 109–114 Sorbonne University, 66 South Africa, 25 SpaceX, 15, 16, 25, 28, 39, 66, 78, 100 Spiegel, Mark, 102–103 Stanford University, 4, 26, 27, 28, 121 startups, 41–43, 59, 62, 76 “stealth recalls,” 160–161 stock price, 89, 90, 93, 97, 100, 102–103 StockTwits, 98 Straubel, JB, 26, 28, 48 SunCube, 146–147 Superchargers, 109–119 SYNC, 194 T TACC (Traffic Aware Cruise Control), 125 Tama, 197 Tarpenning, Marc, 21–24, 27, 31, 37, 43, 113 Tea Party movement, 80–81 “Tesla Death Watch” (blog posts), 3 Tesla Energy Group, 68 Tesla Founders Blog, 50 Tesla Motors. see also specific headings and barriers to entry, 35, 56 branding of, 16–17, 18, 59–63, 225–234 and collisions, 127–133 concept of, 34–36 continuous improvement at, 58 culture of, 51–52, 60 detractors of, 102–108 as disruptive innovator, 195–197 EBITDA of, 215 and environmental issues, 112–113, 119 “factory-less” model of, 35–36 innovation by, 201–210 internal conflict at, 29–32 legacy of, 19 Model 3 introduced by, 8–10 personal approach to public relations, xii raising capital for, 44, 69–71, 85 “shaky ground” of, 4, 5 as startup, 2–3 stock price of, 89, 90, 93, 97, 100, 102–103 strategy of, 22 and Supercharger network, 109–119 and whistleblowers, xii Tesla Motors Club (TMC), 95–97 Teslarati, 100 “Tesla stare,” 60 “Tesla Suspension Breakage: It’s Not the Crime, It’s the Coverup” (blog post), 151 Thailand, 48, 218 Think Global, 11, 67 Thrun, Sebastian, 121 TMC (Tesla Motors Club), 95–97 Too Big to Fail, 91 Toyoda, Akio, 76 Toyoda, Sakichi, 57 Toyota, 184, 201. see also specific models auto sales, 11 contract with, 81, 83 electric vehicles of, 159–160 and 2008 financial crisis, 76–77 pragmatism of, 209 safety scandal, 149–151 Toyota Previa, 214 Toyota Production System (TPS), 56–60, 76–77, 142, 183 Toyota Way, 58, 77 TPS. see Toyota Production System Traction Avant, 193–194 trading volume, 89 Traffic Aware Cruise Control (TACC), 125 The Truth About Cars (TTAC) (blog), 1–3 Tse, Bernard, 67 turnarounds, financial, 83–87 Twitter, 41, 98, 104–108, 113, 152, 156, 217–220, 224, 236 tzero, 23–24, 26, 27, 31, 37 V Valor Equity Partners, 47 Vance, Ashlee, 38, 47, 66, 73, 84, 120–121, 137, 227–228 VantagePoint Capital Partners, 66 variable costs, 54 V8 engine, 62 Volkswagen, 11, 171, 203–205 W Wall Street Journal, 2, 18, 100, 129, 132, 168, 187 Waymo, 173–174 Web 2.0, 41 Weintraub, Seth, 97–98, 101 Wharton School of Business, 25 whistleblowers, xii Whitestar, 46–48, 51, 65, 67, 68, 73 Who Killed the Electric Car?

The Loop: How Technology Is Creating a World Without Choices and How to Fight Back by Jacob Ward

2021 United States Capitol attack, 4chan, Abraham Wald, AI winter, Albert Einstein, Albert Michelson, Amazon Mechanical Turk, assortative mating, autonomous vehicles, availability heuristic, barriers to entry, Bayesian statistics, Benoit Mandelbrot, Big Tech, bitcoin, Black Lives Matter, Black Swan, blockchain, Broken windows theory, call centre, Cass Sunstein, cloud computing, contact tracing, coronavirus, COVID-19, crowdsourcing, cuban missile crisis, Daniel Kahneman / Amos Tversky, dark matter, data science, deep learning, Donald Trump, drone strike, endowment effect, George Akerlof, George Floyd, hindsight bias, invisible hand, Isaac Newton, Jeffrey Epstein, license plate recognition, lockdown, longitudinal study, Lyft, mandelbrot fractal, Mark Zuckerberg, meta-analysis, natural language processing, non-fungible token, nudge unit, OpenAI, opioid epidemic / opioid crisis, pattern recognition, QAnon, RAND corporation, Richard Thaler, Robert Shiller, selection bias, self-driving car, seminal paper, shareholder value, smart cities, social contagion, social distancing, Steven Levy, survivorship bias, TikTok, Turing test

By virtue of its mathematical structure, music is, in fact, one of the simplest human domains for AI to simulate. Services like Amper, Google’s Magenta, and Flow of Machines only need a few suggestions from a human as to key, genre, mood, beats per minute, and then almost instantly produce a backing track you could plausibly hear in a movie or behind a rising artist. OpenAI’s Jukebox even includes human singing that to my ear is indistinguishable from the real thing. This uncanny simulation of real music isn’t being done just to make us happier, of course. The larger purpose here is profit. A studio musician who belongs to the American Federation of Musicians charges at least $240 per recording session.

…

Amper offers a complete song to use in an online advertising campaign for only $499. Put aside the aesthetics for a moment. Consider what capitalism will do with this. Imagine just how quickly companies will want to seize on AI to individually tailor never-ending, never-repeating, low-cost entertainment for each of us. As Miles Brundage, a researcher at OpenAI wrote in 2020, “It seems safe to say that the era of almost-exclusively-human-generated and almost-never-individually-customized media will not last much longer.”4 It may not be that any of this is art, in the philosophical sense. It is, of course, all just an imitation, reverse engineered from the echo of an audience’s reaction to past art.

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

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

It’s more speculative, aimed at convincing members to pick a truly new, weird area to examine. Lately the talk has been heavily about artificial intelligence (AI), and the dark magic of writing algorithms that can learn on their own; at least six of Sanghvi’s members have wound up working at Google Brain or the nonprofit OpenAI initiative. Six start-ups that have come out of the Commons were founded by women. That’s an achievement too: Getting more women into critical founder roles means they can deeply influence the trajectory of their firm, and benefit from its success. Sanghvi remembers having to argue over getting a fair share of Facebook’s value.

…

So we could wake up one day, fifteen years from now, to discover that, whoops, someone in Shenzhen has almost accidentally produced a superintelligence. Given that, a phalanx of AI experts has begun to prepare now. “AI is a fundamental risk to the existence of human civilization,” Tesla founder Elon Musk said, and he followed up on his warning by investing in OpenAI, a think tank devoted to planning for “responsible” AI—smart machines that won’t, or can’t, rise up to kill us. If you wanted some comfort, though, consider that of the AI experts I’ve spoken to—the people who, unlike Bostrom and even Musk, build AI all day long—most were considerably less worried about ultraintelligent machines emerging suddenly.

…

“I think there’s real risks of AI that should be thought about,” Martiros agrees. Tons of firms worldwide are all fantasizing about a “general” AI that could think in human terms. “It’d be a trillion-dollar industry, and it’s not implausible. We can’t predict these things.” He’s in favor of groups like OpenAI pondering the hard questions. So for my friends who want to know about superhuman AI? I’d love to have a definite answer, but I can’t offer one. It could be in our lifetimes; it could not. The Association for the Advancement of Artificial Intelligence surveyed 193 of its members, asking them when a Bostrom-like “superintelligence” would emerge.

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

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

[xcviii] In December 2015, Elon Musk and Sam Altman, president of the technology incubator Y Combinator announced the formation of a new company called Open AI. They had recruited a clutch of the top machine learning professionals despite the efforts of Google and Facebook to hang onto them with eye-watering financial offers. There is some uncertainty about whether other companies controlled by Musk and Altman (like Tesla and Solar City) will have privileged access to technologies developed at Open AI, but the thrust of the company is to make advanced AI techniques more widely available in the hope that will de-risk them.[xcix] Because it works, the use of machine learning will continue to grow – fast.

…

[xcii] http://www.bloomberg.com/news/2014-12-23/speech-recognition-better-than-a-human-s-exists-you-just-can-t-use-it-yet.html [xciii] http://www.forbes.com/sites/parmyolson/2014/05/28/microsoft-unveils-near-real-time-language-translation-for-skype/ [xciv] http://www.technologyreview.com/news/544651/baidus-deep-learning-system-rivals-people-at-speech-recognition/#comments [xcv] https://youtu.be/V1eYniJ0Rnk?t=1 [xcvi] http://edge.org/response-detail/26780 [xcvii] http://techcrunch.com/2016/03/19/how-real-businesses-are-using-machine-learning/ [xcviii] http://www.latimes.com/business/technology/la-fi-cutting-edge-ibm-20160422-story.html [xcix] http://www.wired.com/2016/04/openai-elon-musk-sam-altman-plan-to-set-artificial-intelligence-free/ [c] http://www.strategyand.pwc.com/global/home/what-we-think/innovation1000/top-innovators-spenders#/tab-2015 [ci] 2013 data: http://www.ons.gov.uk/ons/rel/rdit1/gross-domestic-expenditure-on-research-and-development/2013/stb-gerd-2013.html [cii] http://insights.venturescanner.com/category/artificial-intelligence-2/ [ciii] http://techcrunch.com/2015/12/25/investing-in-artificial-intelligence/ [civ] http://www.wired.com/2015/11/google-open-sources-its-artificial-intelligence-engine/ [cv] https://www.theguardian.com/technology/2016/apr/13/google-updates-tensorflow-open-source-artificial-intelligence [cvi] http://www.wired.com/2015/12/facebook-open-source-ai-big-sur/ [cvii] The name Parsey McParseFace is a play on a jokey name for a research ship which received a lot of votes in a poll run by the British government in April 2016. http://www.wsj.com/articles/googles-open-source-parsey-mcparseface-helps-machines-understand-english-1463088180 [cviii] Assuming you don't count the Vatican as a proper country. http://www.ibtimes.co.uk/google-project-loon-provide-free-wifi-across-sri-lanka-1513136 [cix] https://setandbma.wordpress.com/2013/02/04/who-coined-the-term-big-data/ [cx] http://www.pcmag.com/encyclopedia/term/37701/amara-s-law [cxi] http://www.lrb.co.uk/v37/n05/john-lanchester/the-robots-are-coming [cxii] Haitz's Law states that the cost per unit of useful light emitted decreases exponentially [cxiii] http://computationalimagination.com/article_cpo_decreasing.php [cxiv] http://www.nytimes.com/2006/06/07/technology/circuits/07essay.html [cxv] . http://arstechnica.com/gadgets/2015/02/intel-forges-ahead-to-10nm-will-move-away-from-silicon-at-7nm/ [cxvi] .

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

"World Economic Forum" Davos, adjacent possible, Airbnb, Albert Einstein, algorithmic bias, algorithmic management, Alvin Toffler, Amazon Web Services, Atul Gawande, augmented reality, automated trading system, basic income, Bayesian statistics, Big Tech, big-box store, Black Lives Matter, business process, call centre, choice architecture, coronavirus, COVID-19, data science, deep learning, deepfake, DeepMind, disinformation, Elon Musk, Erik Brynjolfsson, factory automation, fake news, fault tolerance, Frederick Winslow Taylor, Freestyle chess, future of work, Future Shock, Geoffrey Hinton, George Floyd, gig economy, Google Hangouts, GPT-3, hiring and firing, hustle culture, hype cycle, income inequality, industrial robot, Jeff Bezos, job automation, John Markoff, Kevin Roose, knowledge worker, Kodak vs Instagram, labor-force participation, lockdown, Lyft, mandatory minimum, Marc Andreessen, Mark Zuckerberg, meta-analysis, Narrative Science, new economy, Norbert Wiener, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, off-the-grid, OpenAI, pattern recognition, planetary scale, plutocrats, Productivity paradox, QAnon, recommendation engine, remote working, risk tolerance, robotic process automation, scientific management, Second Machine Age, self-driving car, Shoshana Zuboff, Silicon Valley, Silicon Valley startup, social distancing, Steve Jobs, Stuart Kauffman, surveillance capitalism, tech worker, The Future of Employment, The Wealth of Nations by Adam Smith, TikTok, Travis Kalanick, Uber and Lyft, uber lyft, universal basic income, warehouse robotics, Watson beat the top human players on Jeopardy!, work culture

The results of Google’s initial tests were impressive: after being instructed to build a neural network capable of carrying out a common image-labeling task, Google’s AI was able to build and train a model that was more accurate than the one Google’s own engineers had programmed. And journalists? Forget it. Many of us are eminently automatable, especially those of us whose output tends to be more routine and predictable. In 2020, several publications began experimenting with GPT-3, an advanced AI program developed by the nonprofit research lab OpenAI. The program, which takes a prompt and uses machine learning to complete it, was able to produce long, cogent pieces of writing that amazed human editors with their clarity and style. One publication, The Guardian, used GPT-3 to write an entire op-ed about the future of AI and machine learning, and concluded that “overall, it took less time to edit than many human op-eds.”

…

In 2019, Senators Cory Booker and Ron Wyden, along with Representative Yvette Clarke, introduced something similar with the “Algorithmic Accountability Act,” which would authorize the Federal Trade Commission to audit “highly sensitive automated decision systems,” such as algorithms used for screening job candidates, for evidence of bias or flawed design. Responsible tech companies can also help, by slowing down and considering how their new AI tools could be misused before making them publicly available. In 2019, OpenAI, the nonprofit AI lab, set a good example of responsible deployment when it withheld the full version of its new text generation algorithm, GPT-2. Experts had voiced concerns that GPT-2—which used AI to predict the next words in a sequence and could finish submitted samples of partial texts in an eerily humanlike way—could be used to spread fake news or computer-generated propaganda.

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

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

Computer scientists signed pledges not to use AI for military purposes. Stephen Hawking and Bill Gates made public statements warning of the existential threat posed by AI. Elon Musk and other Silicon Valley entrepreneurs set up a new company, OpenAI, with a one-billion-dollar nest egg and hired Ilya Sutskever, one of Geoffrey Hinton’s former students, to be its first director. Although OpenAI’s stated goal was to ensure that future AI discoveries would be publicly available for all to use, it had another, implicit and more important goal—to prevent private companies from doing evil. For, with AlphaGo’s victory over world Go champion Sedol, a tipping point had been reached.

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

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

Remember, however, that all these tasks are much simpler than the real world: they are fully observable, they involve short time horizons, and they have relatively small state spaces and simple, predictable rules. Relaxing any of these conditions means that the standard methods will fail. Current research, on the other hand, is aimed precisely at going beyond standard methods so that AI systems can operate in larger classes of environments. On the day I wrote the preceding paragraph, for example, OpenAI announced that its team of five AI programs had learned to beat experienced human teams at the game Dota 2. (For the uninitiated, who include me: Dota 2 is an updated version of Defense of the Ancients, a real-time strategy game in the Warcraft family; it is currently the most lucrative and competitive e-sport, with prizes in the millions of dollars.)

…

The DQN system that learns to play a wide variety of video games using deep RL: Volodymyr Mnih et al., “Human-level control through deep reinforcement learning,” Nature 518 (2015): 529–33. 63. Bill Gates’s remarks on Dota 2 AI: Catherine Clifford, “Bill Gates says gamer bots from Elon Musk-backed nonprofit are ‘huge milestone’ in A.I.,” CNBC, June 28, 2018. 64. An account of OpenAI Five’s victory over the human world champions at Dota 2: Kelsey Piper, “AI triumphs against the world’s top pro team in strategy game Dota 2,” Vox, April 13, 2019. 65. A compendium of cases in the literature where misspecification of reward functions led to unexpected behavior: Victoria Krakovna, “Specification gaming examples in AI,” Deep Safety (blog), April 2, 2018. 66.

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., 219, 221, 222 Moore’s law, 34–35 Moravec, Hans, 144 Morgan, Conway Lloyd, 18 Morgenstern, Oskar, 23 Mozi (Mozi), 219 multi-agent cooperation design, 94 Musk, Elon, 153, 164 “Myth of Superhuman AI, The” (Kelly), 148 narrow (tool) artificial intelligence, 46, 47, 136 Nash, John, 30, 195 Nash equilibrium, 30–31, 195–96 National Institutes of Health (NIH), 155 negative altruism, 229–30 NELL (Never-Ending Language Learning) project, 81 nerve nets, 16 NET-VISA, 279–80 Network Enforcement Act (Germany), 108, 109 neural dust, 164–65 Neuralink Corporation, 164 neural lace, 164 neural networks, 288–89 neurons, 15, 16, 19 Never-Ending Language Learning (NELL) project, 81 Newell, Allen, 295 Newton, Isaac, 85–86 New Yorker, The, 88 Ng, Andrew, 151, 152 Norvig, Peter, 2, 62–63 no suicide rule, 287 Nozick, Robert, 223 nuclear industry, 157, 249 nuclear physics, 7–8 Nudge (Thaler & Sunstein), 244 objectives, 11–12, 43, 48–61, 136–42, 165–69. See also goals off-switch game, 196–200 onebillion (software system), 70 One Hundred Year Study on Artificial Intelligence (AI100), 149, 150 OpenAI, 56 operations research, 10, 54, 176 Oracle AI systems, 161–63 orthogonality thesis, 167–68 Ovadya, Aviv, 108 overhypothesis, 85 overly intelligent AI, 132–44 fear and greed, 140–42 gorilla problem, 132–36 intelligence explosions and, 142–44, 208–9 King Midas problem, 136–40 paperclip game, 194–96 Parfit, Derek, 225 Partnership on AI, 180, 250 Pascal, Blaise, 21–22, 40 Passage to India, A (Forster), 254 Pearl, Judea, 54, 275 Perdix (drone), 112 Pinker, Steven, 158, 165–66, 168 Planet (satellite corporation), 75 Politics (Aristotle), 114 Popper, Karl, 221–22 Popular Science, 152 positional goods, 230–31 practical reasoning, 20 pragmatics, 204 preference autonomy principle, 220, 241 preferences.

Power and Progress: Our Thousand-Year Struggle Over Technology and Prosperity by Daron Acemoglu, Simon Johnson

"Friedman doctrine" OR "shareholder theory", "World Economic Forum" Davos, 4chan, agricultural Revolution, AI winter, Airbnb, airline deregulation, algorithmic bias, algorithmic management, Alignment Problem, AlphaGo, An Inconvenient Truth, artificial general intelligence, augmented reality, basic income, Bellingcat, Bernie Sanders, Big Tech, Bletchley Park, blue-collar work, British Empire, carbon footprint, carbon tax, carried interest, centre right, Charles Babbage, ChatGPT, Clayton Christensen, clean water, cloud computing, collapse of Lehman Brothers, collective bargaining, computer age, Computer Lib, Computing Machinery and Intelligence, conceptual framework, contact tracing, Corn Laws, Cornelius Vanderbilt, coronavirus, corporate social responsibility, correlation does not imply causation, cotton gin, COVID-19, creative destruction, declining real wages, deep learning, DeepMind, deindustrialization, Demis Hassabis, Deng Xiaoping, deskilling, discovery of the americas, disinformation, Donald Trump, Douglas Engelbart, Douglas Engelbart, Edward Snowden, Elon Musk, en.wikipedia.org, energy transition, Erik Brynjolfsson, European colonialism, everywhere but in the productivity statistics, factory automation, facts on the ground, fake news, Filter Bubble, financial innovation, Ford Model T, Ford paid five dollars a day, fulfillment center, full employment, future of work, gender pay gap, general purpose technology, Geoffrey Hinton, global supply chain, Gordon Gekko, GPT-3, Grace Hopper, Hacker Ethic, Ida Tarbell, illegal immigration, income inequality, indoor plumbing, industrial robot, interchangeable parts, invisible hand, Isaac Newton, Jacques de Vaucanson, James Watt: steam engine, Jaron Lanier, Jeff Bezos, job automation, Johannes Kepler, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Joseph-Marie Jacquard, Kenneth Arrow, Kevin Roose, Kickstarter, knowledge economy, labor-force participation, land reform, land tenure, Les Trente Glorieuses, low skilled workers, low-wage service sector, M-Pesa, manufacturing employment, Marc Andreessen, Mark Zuckerberg, megacity, mobile money, Mother of all demos, move fast and break things, natural language processing, Neolithic agricultural revolution, Norbert Wiener, NSO Group, offshore financial centre, OpenAI, PageRank, Panopticon Jeremy Bentham, paperclip maximiser, pattern recognition, Paul Graham, Peter Thiel, Productivity paradox, profit maximization, profit motive, QAnon, Ralph Nader, Ray Kurzweil, recommendation engine, ride hailing / ride sharing, Robert Bork, Robert Gordon, Robert Solow, robotic process automation, Ronald Reagan, scientific management, Second Machine Age, self-driving car, seminal paper, shareholder value, Sheryl Sandberg, Shoshana Zuboff, Silicon Valley, social intelligence, Social Responsibility of Business Is to Increase Its Profits, social web, South Sea Bubble, speech recognition, spice trade, statistical model, stem cell, Steve Jobs, Steve Wozniak, strikebreaker, subscription business, Suez canal 1869, Suez crisis 1956, supply-chain management, surveillance capitalism, tacit knowledge, tech billionaire, technoutopianism, Ted Nelson, TED Talk, The Future of Employment, The Rise and Fall of American Growth, The Structural Transformation of the Public Sphere, The Wealth of Nations by Adam Smith, theory of mind, Thomas Malthus, too big to fail, total factor productivity, trade route, transatlantic slave trade, trickle-down economics, Turing machine, Turing test, Twitter Arab Spring, Two Sigma, Tyler Cowen, Tyler Cowen: Great Stagnation, union organizing, universal basic income, Unsafe at Any Speed, Upton Sinclair, upwardly mobile, W. E. B. Du Bois, War on Poverty, WikiLeaks, wikimedia commons, working poor, working-age population

From the Field of AI Dreams People are right to be excited about advances in digital technologies. New machine capabilities can massively expand the things we do and can transform many aspects of our lives for the better. And there have also been tremendous advances. For example, the Generative Pre-trained Transformer 3 (GPT-3), released in 2020 by OpenAI, and ChatGPT released in 2022 by the same company, are natural-language processing systems with remarkable capabilities. Already trained and optimized on massive amounts of text data from the internet, these programs can generate almost human-like articles, including poetry; communicate in typical human language; and, most impressively, turn natural-language instructions into computer code.

…

Verge, July 5. www.the verge.com/2021/7/5/22563751/tesla-elon-musk-full-self-driving-admission-autopilot-crash. Heaven, Will Douglas. 2020. “Artificial General Intelligence: Are We Close, and Does It Even Make Sense to Try?” MIT Technology Review, October 15. www.technologyreview.com/2020/10/15/1010461/artificial-general-intelligence-robots-ai-agi-deepmind-google-openai. Heldring, Leander, James Robinson, and Sebastian Vollmer. 2021a. “The Economic Effects of the English Parliamentary Enclosures.” NBER Working Paper no. 29772. DOI:10.3386/w29772. Heldring, Leander, James Robinson, and Sebastian Vollmer. 2021b. “The Long-Run Impact of the Dissolution of the English Monasteries.”

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“Jobs Lost, Jobs Gained: Workforce Transitions in a Time of Automation,” McKinsey Global Institute, December. https://www.mckinsey.com/~/media/BAB489A30B724BECB5DEDC41E9BB9FAC.ashx. Marantz, Andrew. 2020. Antisocial: Online Extremists, Techno-Utopians and the Hijacking of the American Conversation. New York: Penguin. Marcus, Gary, and Ernest Davis. 2020. “GPT-3, Bloviator: OpenAI’s Language Generator Has No Idea What It’s Talking About.” MIT Technology Review, August 22. Marcus, Steven. 1974 [2015]. Engels, Manchester, and the Working Class. Routledge: London. Marens, Richard. 2011. “We Don’t Need You Anymore: Corporate Social Responsibilities, Executive Class Interests, and Solving Mizruchi and Hirschman’s Paradox.” https://heinonline.org/HOL/Page?

AI 2041 by Kai-Fu Lee, Chen Qiufan

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

For example, the data used to train the AI may be insufficient and inadequately represent race or gender demographics. One company’s recruiting department may find that its AI algorithms are biased against women because the training data didn’t include enough women. Or the data may be biased because it was collected from a biased society. Microsoft’s Tay and OpenAI’s GPT-3 were both known to make inappropriate remarks about minority groups. Recently, research has shown that AI is able to infer sexual orientation with high accuracy based on facial micro-expressions. Such abilities could lead to discrimination. This is similar to what happened to Sahej in “The Golden Elephant,” when his Dalit status was found not directly but by inference.

…

With enough natural data and sufficient processing power, the system can learn on its own to detect arrival and departure times, and a great deal more. After Google’s transformer work, a more well-known extension called GPT-3 (GPT stands for “generative pre-trained transformers”) was released in 2020 by OpenAI, a research laboratory founded by Elon Musk and others. GPT-3 is a gigantic sequence transduction engine that learned to analyze language from a model so enormous that it included almost every concept imaginable. Leveraging one of the most powerful supercomputers in the world, GPT-3 was trained on more than 45 terabytes of text, which would take 500,000 lifetimes for a human to read.

Ubuntu 15.04 Server with systemd: Administration and Reference by Richard Petersen

Amazon Web Services, bash_history, cloud computing, Debian, Firefox, lock screen, Mark Shuttleworth, MITM: man-in-the-middle, OpenAI, operational security, RFC: Request For Comment, SpamAssassin, web application

You can use the service command to start and stop corosync. It separates the core infrastructure from the clustering services. Derived from the OpenAIS project, Corosync provides the underlying cluster infrastructure rather than API. You can find out more about Corosync at: http://www.corosync.org/ Corosync is a plug-in cluster engine with a modular design. Modules, known as service engines, are plugged in to the Corosync engine to make use of Corosync cluster services. Corosync components include Totem communications protocol which is based on the OpenAIS virtual synchrony communications model, a live component replacement (LCR) plugin system, an object database for the service engines and their configuration, a logging system, and inter-process communications (IPC) manager.

…

Service engine modules include configuration for LDAP and corosync/openeais file format, the cluster manager (pacemaker) operates as part of corosync, both fence and fence agents. Corosync is configured by the /etc/corosync.conf configuration file. Currently there are four directives, forming blocks, within which options can be specified. They are the same as those used for OpenAIS. The four directives are totem for the Totem protocol, logging, amf for the AMF service, and event for the event service. See the corosync.conf man page for a complete description of directives and options. Corosync uses its own protocol called Totem to perform multicast communications. Totem configuration is specified in the totem directive of the corosync.conf file as shown here.

…

GFS 2 now works through the Corosync Cluster Engine. You would use Corosync cluster commands for your cluster. GFS2 tools have been placed in the gfs2-utils, package, and the Distributed Lock Manager (DLM) commands in the dlm package. Many former cluster packages and applications have been deprecated with Ubuntu, including cman, rgmanager, openais, heartbeat, luci, and system-config-cluster. Though lower level GFS commands are available in the gfs2-utils package, you are expected to use Corosync and Pacemaker commands to manage your clusters. To run a cluster, you need both a cluster manager and locking mechanism. Pacemaker with the Distributed Lock Manager (dlm) implements cluster management and locking.

Going Dark: The Secret Social Lives of Extremists by Julia Ebner

23andMe, 4chan, Airbnb, anti-communist, anti-globalists, augmented reality, Ayatollah Khomeini, Bellingcat, Big Tech, bitcoin, blockchain, Boris Johnson, Cambridge Analytica, citizen journalism, cognitive dissonance, Comet Ping Pong, crisis actor, crowdsourcing, cryptocurrency, deepfake, disinformation, Donald Trump, Dunning–Kruger effect, Elon Musk, fake news, false flag, feminist movement, game design, gamification, glass ceiling, Google Earth, Greta Thunberg, information security, job satisfaction, Mark Zuckerberg, mass immigration, Menlo Park, Mikhail Gorbachev, Network effects, off grid, OpenAI, Overton Window, pattern recognition, pre–internet, QAnon, RAND corporation, ransomware, rising living standards, self-driving car, Silicon Valley, Skype, Snapchat, social intelligence, Social Justice Warrior, SQL injection, Steve Bannon, Steve Jobs, Transnistria, WikiLeaks, zero day

In coming years, newly released AI tools – so-called ‘deep fakes’ – could further enhance the professionalism of extremist online campaigns. AI can write newspaper articles and books,2 generate pictures of people that don’t exist3 and manipulate faces in real time.4 Such technologies could be used to produce hoax articles, create social bots, change footage and edit speeches. In early 2019, the NGO OpenAI decided against the release of its ‘deep fakes for text tool’ because its researchers feared misuse.5 Even without such sophisticated AI tools, we are already seeing the effects of the tech-savvy extremist campaigns. They have exacerbated political and societal fragmentation and accelerated the populist surge across Europe and the US.

…

., Daniel here Habeck, Robert here HackerOne here hackers and hacking here ‘capture the flag’ operations here, here denial of service operations here ethical hacking here memory-corruption operations here political hacking here ‘qwning’ here SQL injections here techniques here Halle shooting here Hamas here, here Hanks, Tom here Happn here Harris, DeAndre here ‘hashtag stuffing’ here Hate Library here HateAid here, here Hatreon here, here, here Heidegger, Martin here Heise, Thorsten here, here Hensel, Gerald here, here Herzliya International Institute for Counter-Terrorism here Heyer, Heather here, here, here Himmler, Heinrich here Hintsteiner, Edwin here Histiaeus here Hitler, Adolf here, here, here, here, here Mein Kampf here, here Hitler salutes here, here, here, here Hitler Youth here HIV here Hizb ut-Tahrir here, here, here Höcker, Karl-Friedrich here Hofstadter, Richard here Hollywood here Holocaust here Holocaust denial here, here, here, here, here Holy War Hackers Team here Home Office here homophobia here, here, here Hooton Plan here Hoover Dam here Hope Not Hate here, here, here Horgan, John here Horowitz Foundation here Hot or Not here House of Saud here Huda, Noor here human trafficking here, here Hussein, Saddam here, here Hutchins, Marcus here Hyppönen, Mikko here Identity Evropa here, here iFrames here Illuminati here Incels (Involuntary Celibacy) here, here Independent here Inkster, Nigel here Institute for Strategic Dialogue (ISD) here, here, here, here, here, here, here, here Intelius here International Business Times here International Centre for the Study of Radicalisation (ICSR) here International Federation of Journalists here International Holocaust Memorial Day here International Institute for Strategic Studies here Internet Research Agency (IRA) here iPads here iPhones here iProphet here Iranian revolution here Isabella I, Queen of Castile here ISIS here, here, here, here, here, here, here, here, here, here, here, here hackers and here, here, here, here, here Islamophobia here, here, here, here, here, here, here Tommy Robinson and here, here see also Finsbury Mosque attack Israel here, here, here, here, here Israel Defense Forces here, here Jackson, Michael here jahiliyya here Jakarta attacks here Jamaah Ansharud Daulah (JAD) here Japanese anime here Jemaah Islamiyah here Jesus Christ here Jewish numerology here Jews here, here, here, here, here, here, here, here, here see also anti-Semitism; ZOG JFG World here jihadi brides here, here JihadWatch here Jobs, Steve here Johnson, Boris here Jones, Alex here Jones, Ron here Junge Freiheit here Jurgenson, Nathan here JustPasteIt here Kafka, Franz here Kampf der Niebelungen here, here Kapustin, Denis ‘Nikitin’ here Kassam, Raheem here Kellogg’s here Kennedy, John F. here, here Kennedy family here Kessler, Jason here, here Khomeini, Ayataollah here Kim Jong-un here Kohl, Helmut here Köhler, Daniel here Kronen Zeitung here Kronos banking Trojan here Ku Klux Klan here, here Küssel, Gottfried here Lane, David here Le Loop here Le Pen, Marine here LeBretton, Matthew here Lebron, Michael here Lee, Robert E. here Li, Sean here Li family here Libyan Fighting Group here LifeOfWat here Lifton, Robert here Littman, Gisele here live action role play (LARP) here, here, here, here, here, here lobbying here Lokteff, Lana here loneliness here, here, here, here, here, here, here Lorraine, DeAnna here Lügenpresse here McDonald’s here McInnes, Gavin here McMahon, Ed here Macron, Emmanuel here, here, here, here MAGA (Make America Great Again) here ‘mainstream media’ here, here, here ‘Millennium Dawn’ here Manosphere here, here, here March for Life here Maria Theresa statue here, here Marighella, Carlos here Marina Bay Sands Hotel (Singapore) here Marx, Karl here Das Kapital here Masculine Development here Mason, James here MAtR (Men Among the Ruins) here, here Matrix, The here, here, here, here May, Theresa here, here, here Meechan, Mark here Meme Warfare here memes here, here, here, here and terrorist attacks here Men’s Rights Activists (MRA) here Menlo Park here Mercer Family Foundation here Merkel, Angela here, here, here, here MGTOW (Men Going Their Own Way) here, here, here MI6, 158, 164 migration here, here, here, here, here, here, here, here, here see also refugees millenarianism here Millennial Woes here millennials here Minassian, Alek here Mindanao here Minds here, here misogyny here, here, here, here, here see also Incels mixed martial arts (MMA) here, here, here, here Morgan, Nicky here Mounk, Yascha here Movement, The here Mueller, Robert here, here Muhammad, Prophet here, here, here mujahidat here Mulhall, Joe here MuslimCrypt here MuslimTec here, here Mussolini, Benito here Naim, Bahrun here, here Nance, Malcolm here Nasher App here National Action here National Bolshevism here National Democratic Party (NPD) here, here, here, here National Health Service (NHS) here National Policy Institute here, here National Socialism group here National Socialist Movement here National Socialist Underground here NATO DFR Lab here Naturalnews here Nawaz, Maajid here Nazi symbols here, here, here, here, here, here, here see also Hitler salutes; swastikas Nazi women here N-count here Neiwert, David here Nero, Emperor here Netflix here Network Contagion Research Institute here NetzDG legislation here, here Neumann, Peter here New Balance shoes here New York Times here News Corp here Newsnight here Nietzsche, Friedrich here, here Nikolai Alexander, Supreme Commander here, here, here, here, here, here 9/11 attacks here, here ‘nipsters’ here, here No Agenda here Northwest Front (NWF) here, here Nouvelle Droite here, here NPC meme here NSDAP here, here, here Obama, Barack and Michelle here, here, here, here, here Omas gegen Rechts here online harassment, gender and here OpenAI here open-source intelligence (OSINT) here, here Operation Name and Shame here Orbán, Viktor here, here organised crime here Orwell, George here, here Osborne, Darren here, here Oxford Internet Institute here Page, Larry here Panofsky, Aaron here Panorama here Parkland high-school shooting here Patreon here, here, here, here Patriot Peer here, here PayPal here PeopleLookup here Periscope here Peterson, Jordan here Pettibone, Brittany here, here, here Pew Research Center here, here PewDiePie here PewTube here Phillips, Whitney here Photofeeler here Phrack High Council here Pink Floyd here Pipl here Pittsburgh synagogue shooting here Pizzagate here Podesta, John here, here political propaganda here Popper, Karl here populist politicians here pornography here, here Poway synagogue shooting here, here Pozner, Lenny here Presley, Elvis here Prideaux, Sue here Prince Albert Police here Pro Chemnitz here ‘pseudo-conservatives’ here Putin, Vladimir here Q Britannia here QAnon here, here, here, here Quebec mosque shooting here Quilliam Foundation here, here, here Quinn, Zoë here Quran here racist slurs (n-word) here Radio 3Fourteen here Radix Journal here Rafiq, Haras here Ramakrishna, Kumar here RAND Corporation here Rasmussen, Tore here, here, here, here Raymond, Jolynn here Rebel Media here, here, here Reconquista Germanica here, here, here, here, here, here, here Reconquista Internet here Red Pill Women here, here, here, here, here Reddit here, here, here, here, here, here, here, here, here, here redpilling here, here, here, here refugees here, here, here, here, here Relotius, Claas here ‘Remove Kebab’ here Renault here Revolution Chemnitz here Rigby, Lee here Right Wing Terror Center here Right Wing United (RWU) here RMV (Relationship Market Value) here Robertson, Caolan here Robinson, Tommy here, here, here, here, here, here, here, here Rockefeller family here Rodger, Elliot here Roof, Dylann here, here Rosenberg, Alfred here Rothschilds here, here Rowley, Mark here Roy, Donald F. here Royal Family here Russia Today here, here S., Johannes here St Kilda Beach meeting here Salafi Media here Saltman, Erin here Salvini, Matteo here Sampson, Chris here, here Sandy Hook school shooting here Sargon of Akkad, see Benjamin, Carl Schild & Schwert rock festival (Ostritz) here, here, here Schilling, Curt here Schlessinger, Laura C. here Scholz & Friends here SchoolDesk here Schröder, Patrick here Sellner, Martin here, here, here, here, here, here, here, here, here, here Serrano, Francisco here ‘sexual economics’ here SGT Report here Shodan here, here Siege-posting here Sleeping Giants here SMV (Sexual Market Value) here, here, here Social Justice Warriors (SJW) here, here Solahütte here Soros, George here, here Sotloff, Steven here Southern, Lauren here Southfront here Spencer, Richard here, here, here, here, here, here Spiegel TV here spoofing technology here Sputnik here, here SS here, here Stadtwerke Borken here Star Wars here Steinmeier, Frank-Walter here Stewart, Ayla here STFU (Shut the Fuck Up) here Stormfront here, here, here Strache, H.

Blood in the Machine: The Origins of the Rebellion Against Big Tech by Brian Merchant

"World Economic Forum" Davos, Ada Lovelace, algorithmic management, Amazon Mechanical Turk, Any sufficiently advanced technology is indistinguishable from magic, autonomous vehicles, basic income, Bernie Sanders, Big Tech, big-box store, Black Lives Matter, Cambridge Analytica, Charles Babbage, ChatGPT, collective bargaining, colonial rule, commoditize, company town, computer age, computer vision, coronavirus, cotton gin, COVID-19, cryptocurrency, DALL-E, decarbonisation, deskilling, digital rights, Donald Trump, Edward Jenner, Elon Musk, Erik Brynjolfsson, factory automation, flying shuttle, Frederick Winslow Taylor, fulfillment center, full employment, future of work, George Floyd, gig economy, gigafactory, hiring and firing, hockey-stick growth, independent contractor, industrial robot, information asymmetry, Internet Archive, invisible hand, Isaac Newton, James Hargreaves, James Watt: steam engine, Jeff Bezos, Jessica Bruder, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Kevin Roose, Kickstarter, Lyft, Mark Zuckerberg, Marshall McLuhan, means of production, military-industrial complex, move fast and break things, Naomi Klein, New Journalism, On the Economy of Machinery and Manufactures, OpenAI, precariat, profit motive, ride hailing / ride sharing, Sam Bankman-Fried, scientific management, Second Machine Age, self-driving car, sharing economy, Silicon Valley, sovereign wealth fund, spinning jenny, Steve Jobs, Steve Wozniak, super pumped, TaskRabbit, tech billionaire, tech bro, tech worker, techlash, technological determinism, Ted Kaczynski, The Future of Employment, The Wealth of Nations by Adam Smith, Thomas Malthus, Travis Kalanick, Uber and Lyft, uber lyft, union organizing, universal basic income, W. E. B. Du Bois, warehouse automation, warehouse robotics, working poor, workplace surveillance

One notorious study carried out by researchers at the University of Oxford concluded that nearly half of all American jobs are ripe for technological replacement. Critics have taken issue with that forecast, but our largest tech companies would love to see it come true. Robots, the old corporate adage goes, never call in sick. Tech companies like Amazon, Uber, Facebook, OpenAI, and Microsoft have accumulated vast power and influence. In ways large and small, they are already remaking our working lives. We now face a future where work—even for the so-called middle class, even for white-collar workers—is increasingly informal, precarious, and organized by inscrutable and unaccountable technologies.

…

The aim, she says, is to create an anxious, uncertain workforce that has no choice but to be malleable before the algorithm’s demands. “These experiments are now fusing into other parts of the economy,” Dubal says. “These practices are ascendent.” Also ascendent is the use of AI services, which boomed in 2023, promoted by companies like OpenAI. When AI is injected into already precarious work structures, it promises to accelerate insecurity and displacement further still. Factories and automated machinery took workers out of their homes and away from their families. Gig apps run by proprietary algorithms take them away from other people altogether, and impose factory logic onto each individual, who sits at home or in a car, taking orders that must be completed in a rigid and exacting way.

…

Venture capital may be the radical apotheosis of this mode of technological development, capable as it is of funneling enormous sums of money into tech companies that can decide how they would like to build and unleash the products and services that shape society. Take the rise of generative AI. Ambitious start-ups like Midjourney, and well-positioned Silicon Valley companies like OpenAI, are already offering on-demand AI image and prose generation. DALL-E spurred a backlash when it was unveiled in 2022, especially among artists and illustrators, who worry that such generators will take away work and degrade wages. If history is any guide, they’re almost certainly right. DALL-E certainly isn’t as high in quality as a skilled human artist, and likely won’t be for some time, if ever—but as with the skilled cloth workers of the 1800s, that ultimately doesn’t matter.

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

Albert Einstein, AltaVista, Amazon Mechanical Turk, Amazon Web Services, augmented reality, Automated Insights, autonomous vehicles, backpropagation, Big Tech, Cambridge Analytica, Chuck Templeton: OpenTable:, cloud computing, Colossal Cave Adventure, computer age, deep learning, DeepMind, Donald Trump, Elon Musk, fake news, Geoffrey Hinton, information retrieval, Internet of things, Jacques de Vaucanson, Jeff Bezos, lateral thinking, Loebner Prize, machine readable, machine translation, Machine translation of "The spirit is willing, but the flesh is weak." to Russian and back, Mark Zuckerberg, Menlo Park, natural language processing, Neal Stephenson, Neil Armstrong, OpenAI, PageRank, pattern recognition, Ponzi scheme, randomized controlled trial, Ray Kurzweil, Ronald Reagan, Rubik’s Cube, self-driving car, sentiment analysis, Silicon Valley, Skype, Snapchat, speech recognition, statistical model, Steve Jobs, Steve Wozniak, Steven Levy, TechCrunch disrupt, Turing test, Watson beat the top human players on Jeopardy!

Tech company executives praise it for blasting through decades-old problems in conversational AI; they shower experts in the field with salaries that climb into the six figures and higher. Consider the likes of Ilya Sutskever, a computer scientist credited with breakthroughs in image recognition and machine translation. He earned $1.9 million back in 2016—and that was at a nonprofit, the Elon Musk–supported OpenAI. Silver dollars, though, have only belatedly begun to pour from the Valley’s slot machines. For decades, the approach to getting machines to learn from data languished; brief periods of hype were followed by long stretches of frustration. The AI techniques that dominated were ones in which computer scientists wrote rules that told machines what to do and when to do it.

…

See also deep learning neurons, 86–88, 90, 91, 93 New Dimensions in Testimony, 272–74 news providers and voice AI, 214 Nickelodeon, 235 Norse mythology, 64 Nuance Communications, 111–12 O Obama, Barack, 115, 218 Olson, Christi, 207 Olson, Jenny, 196 Olsson, Isabelle, 226 “On Computable Numbers” (Turing), 71 1–800-Flowers, 7, 52 one-shot answers, 200, 204, 206–7, 208, 210–11, 220–21 Onion (satirical publication), 126 ontologies, 31–32 Open Agent Architecture, 21 OpenAI, 85 oracles, AI. See also question answering conventional web search and, 200, 207, 209–10 natural-language understanding and, 204 one-shot answers and, 206–7, 208–9, 211, 220–21 potential costs of, 221 responsibility for content and, 216–20 tech industry disruption and, 211–14 Oren, Dror, 132 Ostendorf, Mari, 158–59 Owens, Ron, 241–42 Owyang, Jeremiah, 280 Oxford University, 98 P Page, Larry, 76 Papert, Seymour, 88–89 parametric synthesis, 112, 113–14 Paro (robotic seal), 192 Parry (chatbot), 75–77, 79 pattern matching, 73, 81, 159–60 Pelczar, Nick, 173–74, 180 Perceptron, 87–89, 90, 100 Perceptrons (Minsky and Papert), 88 personalities of voice AI, 117–39 Alexa, 118, 119, 124 Cortana, 117–18 custom, 135–37 designing, 128–29, 133–34 gender and, 130–32 Google Assistant, 10–11, 118, 119, 124–28 language expertise and, 126–28 naturalness of, 126 race and ethnic identities, 132–33 robot personality development, 137–38 Siri, 118, 119, 123–24 UW’s socialbot and, 152 Phelps, Rick, 237–38 phonemes, 95–96, 114 phrase-based statistical machine translation, 104–5 Picard, Rosalind, 183, 184 Pichai, Sundar, 5, 53, 54, 116 Pistor, Julia, 175 Pitts, Walter, 86–87 Pixar, 126, 171–72 Planet Money (podcast), 214–15 Plimpton, George, 215 Poncho, 128 position zero, 208 post-traumatic stress disorder (PTSD), 244–46 Powesland, Peter, 127 Prasad, Rohit, 42, 43, 44, 158–59 privacy.

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

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

As machine intelligence becomes more powerful, pervasive, and connected, embedding AI in all of our personal and industrial computing devices increases the risk of attacks that can compromise the security infrastructures that protect our resources and communities. Luminaries from the Future of Humanity Institute, OpenAI, Centre for the Study of Existential Risk, and leading universities in the US and UK issued a 100-page policy recommendation paper, “The Malicious Use of Artificial Intelligence,”(35) in which they described the fast-evolving threat landscape, identified key areas of security risk, and made high-level recommendations for preventative action that should be taken immediately.

New Laws of Robotics: Defending Human Expertise in the Age of AI by Frank Pasquale

affirmative action, Affordable Care Act / Obamacare, Airbnb, algorithmic bias, Amazon Mechanical Turk, Anthropocene, augmented reality, Automated Insights, autonomous vehicles, basic income, battle of ideas, Bernie Sanders, Big Tech, Bill Joy: nanobots, bitcoin, blockchain, Brexit referendum, call centre, Cambridge Analytica, carbon tax, citizen journalism, Clayton Christensen, collective bargaining, commoditize, computer vision, conceptual framework, contact tracing, coronavirus, corporate social responsibility, correlation does not imply causation, COVID-19, critical race theory, cryptocurrency, data is the new oil, data science, decarbonisation, deep learning, deepfake, deskilling, digital divide, digital twin, disinformation, disruptive innovation, don't be evil, Donald Trump, Douglas Engelbart, driverless car, effective altruism, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Evgeny Morozov, fake news, Filter Bubble, finite state, Flash crash, future of work, gamification, general purpose technology, Google Chrome, Google Glasses, Great Leap Forward, green new deal, guns versus butter model, Hans Moravec, high net worth, hiring and firing, holacracy, Ian Bogost, independent contractor, informal economy, information asymmetry, information retrieval, interchangeable parts, invisible hand, James Bridle, Jaron Lanier, job automation, John Markoff, Joi Ito, Khan Academy, knowledge economy, late capitalism, lockdown, machine readable, Marc Andreessen, Mark Zuckerberg, means of production, medical malpractice, megaproject, meta-analysis, military-industrial complex, Modern Monetary Theory, Money creation, move fast and break things, mutually assured destruction, natural language processing, new economy, Nicholas Carr, Nick Bostrom, Norbert Wiener, nuclear winter, obamacare, One Laptop per Child (OLPC), open immigration, OpenAI, opioid epidemic / opioid crisis, paperclip maximiser, paradox of thrift, pattern recognition, payday loans, personalized medicine, Peter Singer: altruism, Philip Mirowski, pink-collar, plutocrats, post-truth, pre–internet, profit motive, public intellectual, QR code, quantitative easing, race to the bottom, RAND corporation, Ray Kurzweil, recommendation engine, regulatory arbitrage, Robert Shiller, Rodney Brooks, Ronald Reagan, self-driving car, sentiment analysis, Shoshana Zuboff, Silicon Valley, Singularitarianism, smart cities, smart contracts, software is eating the world, South China Sea, Steve Bannon, Strategic Defense Initiative, surveillance capitalism, Susan Wojcicki, tacit knowledge, TaskRabbit, technological solutionism, technoutopianism, TED Talk, telepresence, telerobotics, The Future of Employment, The Turner Diaries, Therac-25, Thorstein Veblen, too big to fail, Turing test, universal basic income, unorthodox policies, wage slave, Watson beat the top human players on Jeopardy!, working poor, workplace surveillance , Works Progress Administration, zero day

Yet such factors also fit within Solingen’s larger framework, as they underscore Taiwan’s interconnectedness with great powers of the time. In applying this political economy frame to LAWS, the key question is how to ensure not just norms and laws proscribing particularly destructive technology, but also the economic and reputational expense of pursuing them. Not just governments, but also firms, can play a constructive role here. OpenAI’s reluctance in 2019 to release a speech-generating model offers one case in point. AI-driven text generation may not seem like much of a weapon. But once it is combined with automated creation of social media profiles (complete with deepfaked AVIs), bot speech is a perfect tool for authoritarian regimes to use to disrupt organic opinion formation online.

Exponential: How Accelerating Technology Is Leaving Us Behind and What to Do About It by Azeem Azhar

"Friedman doctrine" OR "shareholder theory", "World Economic Forum" Davos, 23andMe, 3D printing, A Declaration of the Independence of Cyberspace, Ada Lovelace, additive manufacturing, air traffic controllers' union, Airbnb, algorithmic management, algorithmic trading, Amazon Mechanical Turk, autonomous vehicles, basic income, Berlin Wall, Bernie Sanders, Big Tech, Bletchley Park, Blitzscaling, Boeing 737 MAX, book value, Boris Johnson, Bretton Woods, carbon footprint, Chris Urmson, Citizen Lab, Clayton Christensen, cloud computing, collective bargaining, computer age, computer vision, contact tracing, contact tracing app, coronavirus, COVID-19, creative destruction, crowdsourcing, cryptocurrency, cuban missile crisis, Daniel Kahneman / Amos Tversky, data science, David Graeber, David Ricardo: comparative advantage, decarbonisation, deep learning, deglobalization, deindustrialization, dematerialisation, Demis Hassabis, Diane Coyle, digital map, digital rights, disinformation, Dissolution of the Soviet Union, Donald Trump, Double Irish / Dutch Sandwich, drone strike, Elon Musk, emotional labour, energy security, Fairchild Semiconductor, fake news, Fall of the Berlin Wall, Firefox, Frederick Winslow Taylor, fulfillment center, future of work, Garrett Hardin, gender pay gap, general purpose technology, Geoffrey Hinton, gig economy, global macro, global pandemic, global supply chain, global value chain, global village, GPT-3, Hans Moravec, happiness index / gross national happiness, hiring and firing, hockey-stick growth, ImageNet competition, income inequality, independent contractor, industrial robot, intangible asset, Jane Jacobs, Jeff Bezos, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John Perry Barlow, Just-in-time delivery, Kickstarter, Kiva Systems, knowledge worker, Kodak vs Instagram, Law of Accelerating Returns, lockdown, low skilled workers, lump of labour, Lyft, manufacturing employment, Marc Benioff, Mark Zuckerberg, megacity, Mitch Kapor, Mustafa Suleyman, Network effects, new economy, NSO Group, Ocado, offshore financial centre, OpenAI, PalmPilot, Panopticon Jeremy Bentham, Peter Thiel, Planet Labs, price anchoring, RAND corporation, ransomware, Ray Kurzweil, remote working, RFC: Request For Comment, Richard Florida, ride hailing / ride sharing, Robert Bork, Ronald Coase, Ronald Reagan, Salesforce, Sam Altman, scientific management, Second Machine Age, self-driving car, Shoshana Zuboff, Silicon Valley, Social Responsibility of Business Is to Increase Its Profits, software as a service, Steve Ballmer, Steve Jobs, Stuxnet, subscription business, synthetic biology, tacit knowledge, TaskRabbit, tech worker, The Death and Life of Great American Cities, The Future of Employment, The Nature of the Firm, Thomas Malthus, TikTok, Tragedy of the Commons, Turing machine, Uber and Lyft, Uber for X, uber lyft, universal basic income, uranium enrichment, vertical integration, warehouse automation, winner-take-all economy, workplace surveillance , Yom Kippur War

This argument is not relevant to my argument, so I don’t consider it here. 19 Azeem Azhar, ‘Beneficial Artificial Intelligence: My Conversation with Stuart Russell’, Exponential View, 22 August 2019 <https://www.exponentialview.co/p/-beneficial-artificial-intelligence> [accessed 16 April 2021]. 20 Dario Amodei and Danny Hernandez, ‘AI and Compute’, OpenAI, 16 May 2018 <https://openai.com/blog/ai-and-compute/> [accessed 12 January 2021]. 21 Charles E. Leiserson et al., ‘There’s Plenty of Room at the Top: What Will Drive Computer Performance after Moore’s Law?’, Science 368(6495), June 2020 <https://doi.org/10.1126/science.aam9744>. 22 Jean-François Bobier et al., ‘A Quantum Advantage in Fighting Climate Change’, BCG Global, 22 January 2020 <https://www.bcg.com/publications/2020/quantum-advantage-fighting-climate-change> [accessed 23 March 2021].

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

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

And those jobs, it expects, will require a very different set of skills.39 The main reason why warehouses still employ large swaths of the population is that order picking remains a largely manual process. Humans still hold the comparative advantage in complex perception and manipulation tasks. But here, too, AI has made many recent breakthroughs possible. At the OpenAI lab in San Francisco, California, set up by Elon Musk, a robotic five-fingered hand called Dactyl bears witness to impressive progress in recent years: “If you give Dactyl an alphabet block and ask it to show you particular letters—let’s say the red O, the orange P and the blue I—it will show them to you and spin, twist and flip the toy in nimble ways.”40 Though this is an easy task for any human, the achievement lies in the fact that AI allows Dactyl to learn new tasks, largely on its own through trial and error.

…

., 208 Morrill Act, 364 mortality gap, 255 mortality rate, 65 mother of invention, 73 motion-picture machine operator, 178 multipurpose robots, 242, 261, 327 Mumford, Lewis, 46 Municipal Corporations Act of 1835, 86 Murnane, Richard, 237, 302 Murray, Charles, 252–53, 281 Musk, Elon, 313 Mutiny Act, 82 Napoléon Bonaparte, 9 Napoleonic War, 130 National Electric Light Association (NELA), 159 National Industrial Recovery Acts of 1933 and 1935, 200 National Labor Relations (“Wagner”) Act of 1935, 200 national minimum wage, introduction of, 211 National Recovery Administration, 178 nation states, rise of, 57 Nazi Labor front, 12 necessity, technological advances emerging from, 76 Neolithic communities, 34 Neolithic revolution, 33, 61 Neural Machine Translation (NMT), 304 neural networks, 303, 305, 314 Newcomen, Thomas, 53, 106, 317 New Deal, 200, 212, 272, 325 Newton, Isaac, 54 New World, discovery of, 19, 80 Nicholas I of Russia, Emperor, 85 Nobel Prize in Economics, 2, 4, 14, 20 Nordhaus, William, 2, 230, 297 Norman Conquest, 44 North, Douglass C., 79 North Africa, 77 nursery cities, 261 Nye, David, 155 Obama, Barack, 238, 277, 290, 322 occupational licensing, 358 occupational statistics, 219 OECD, 243, 321 Offenbach, Jacques, 53 Ogilvie, Sheilagh, 56–57 Old Poor Law, 344 OpenAI, 313 opportunity gap, societal costs of, 351 Osborne, Michael, 315 Otto, Nikolaus, 166 Ottoman Empire, 17, 66 overproduction, crisis of, 266 Owenism, 137 ownership, concept of, 34 Papin, Denis, 52, 86 Pareto improvement, 13 Paris Universal Exposition of 1867, 147 Park Avenue, 1 Paul, Lewis, 101 Pax Romana, 41 Pearl Harbor, attack on, 180 Pennsylvania Railroad, 208 Percy, Hiram, 165 personal computer (PC), 231 Peter the Great, Tsar, 58 Piketty, Thomas, 210, 217, 277, 361 “pink-collar” workforce, 241 plant downsizings, 255 Pliny the Elder, 36, 40 Polanyi’s paradox, 234, 304 polarization, politics of, 272–77; American dream, 280; Blue Wall, 284; civil rights legislation, 280; clientelism, 271; democracy and the middle class, 265–69; “disciplined self” identity, 279; economic inequality, 274, 277; Engels’ pause, 266, 287; feudal order, political participation in, 265; globalization, automation, and populism, 277–85; housing bubble, 282; identity politics, 278; inflation, 294; Labor Party, rise of, 268; labor unions, bargaining power of, 277; laissez-faire regime, 267; legitimacy of democracy, undermining of, 274; liberal democracy, components of, 267; lobbying, corporate spending on, 275; Luddite uprisings, 265; machinery riots, 265, 289; majority-rule voting system, 270; median voter theories, 270; middle class, rise of, 292; New Deal, 272; new Luddites, 286–92; political elites, 288; populist backlash, 293; Progressive Era, reform agenda of, 271; redistributive taxing and spending, 271; Rust Belt, 279, 283, 291; social class, Marx’s theory of, 265; socialism in America, 272; social media, 285; strikes, protection of car companies from, 276; technology types, distinguishing between, 287; unemployment, American social expenditure on, 274; United Auto Workers union, 276; universal white male suffrage, 270; vulnerability to populist revolutions, 264; welfare state, rise of, 272; welfare system, tax-financed, 267; working class, 278, 279 Polhem, Christopher, 149 political elites, 288 poor laws, 344 Pope, Albert A., 165 population curse, 64–67 populism, rise of, 277–85, 365 populist backlash, 293 populist renaissance, 21 populist revolutions, vulnerability to, 264 Port Clinton, Ohio, 250–51 Portuguese caravel ship, 51 power loom, arrival of, 15 prefabrication, 311 Price, Derek, 39 printing press, Gutenberg’s, 17 Procter and Gamble, 199 productivity, populations and, 64 Progressive Era, reform agenda of, 271 property rights: in American culture, 200; concept of, 62, 91; importance of, 20; in preindustrial societies, 33 Protestant Huguenots, 80 Protestant movement, 46 “proto-industrialization,” 68 prototypes: adoption of, 323; Amazon Go store, 312; developed, 261; imperfect, 298, 314; inventions turned into, 73 public clocks, 45 public infrastructure projects, 363 public schooling, 214 purchasing power, 191 Putnam, Robert, 250–51, 272, 276 railroads: arrival of, 108; declining importance of, 170; as enabling technology for revolutions, 85; network, expansion in Britain, 110; revenues (America), 208 Ramey, Valerie, 159, 332 redistributive taxing and spending, 271 Reform Acts of 1832 and 1867, 83 Reich, Robert, 235 relocation, 359–60 Renaissance, 51; as “age of instruments,” 59; beginnings of modern capitalism during, 70; great inventors of, 38; origin of, 51; productivity-enhancing technological improvements of, 54; technological advances of, 51 rent-seeking monarchs, 79 Restrepo, Pascual, 15, 144, 227, 242, 346 retraining, 353–54 Reuther, Walter, 191, 276, 356 Ricardo, David, 4, 116, 206, 345 right-to-work states, 257 robber barons, 208 Robinson, James, 19, 80 robots, 14; automobile assembly, 18; autonomous, 307; creation of new jobs for engineers, 15; flying, 312; human perception and, 318; jobs of machine operators taken over by, 14; middle-income jobs cut out by, 26; multipurpose, 242, 261, 327; of preindustrial times, 74; routine tasks performed by, 229 Rockefeller, John D., 208 Rodrik, Dani, 286–87 Roman alphabet, 47 Roman Empire: fall of, 41; most famous invention of, 38; slavery in, 74 Roosevelt, Franklin D., 157, 179, 211 Rousseau, Jean-Jacques, 62 royal trading monopolies, 80 Rural Electrification Administration, 157 Russell, Bertrand, 33, 78 Rust Belt, 279, 283, 291 Sanders, Bernie, 286 Savery, Thomas, 106, 317 Scheidel, Walter, 211 Schumpeter, Joseph, 73, 294 Schumpeterian growth, absence of, 72 Schumpeterian transformation, 49 scribes, 49, 50 Second Industrial Revolution, 22, 25, 148–73; agriculture, mechanization of, 189; American inequality during, 217; automotive industry, 202; child labor, as opportunity cost to education, 21; elimination of jobs created for machine operators during, 228; greatest virtue of, 155; mechanization following arrival of, 142; new tasks for labor spawned by, 202; skill-biased technological change, 213; skill demand raised by, 209; technological leadership of, 25; tractor use, expansion of, 196; urban-rural wage gap self-employment, 71 serfdom, 41 Shannon, Claude, 302 Sigismund I of Poland, King, 29 Silicon Valley, 257, 359 silk industry, beginnings of, 99 silk-throwing machine, 52 Simon, Herbert, 316, 336 Singer, Isaac, 149 Skill-biased technological change, 213 slavery, 39, 74 smartphone, spread of, 328 Smiles, Samuel, 110 Smith, Adam, 67, 69–70, 83, 228 Smithian growth, Schumpeterian vs., 58, 72 smokestack cities, 263 social class, Marx’s theory of, 265 socialism in America, 272 social media, 285 socioeconomic segregation, 26 Solow, Robert, 4, 180, 206, 325 speech recognition technology, 306 Spence, Michael, 292 spinning jenny, 102 spousal employment, 240 Sprague, Frank J., 152 steam engine: development of, 73; economic virtuosity of, 107; impact of on aggregate growth, 136; universal application of, 249 steel production, changed nature of, 13 Stephenson, George, 109 Stevenson, Betsey, 336 stocking-frame knitting machine, 10, 54, 76 strikes, protection of car companies from, 276 “stylized facts of growth,” 205 subjective well-being, 255 Summers, Lawrence, 261, 349 supercomputers, 290 supply of technology, obstacles to, 77 “symbolic analysts,” 235 task simplification, example of, 311 tax credits, 355–58 taxing and spending, redistributive, 271 tax revenue, 133 technological gap (1500–1700), 51 technology companies, location decisions of, 260 telephone operator, vanishing of, 201 telescope, 59 Tennessee Valley Authority (TVA) Act of 1933, 363 Tesla, Nikola, 152 textile industry, 38, 55, 95 Thirty Years’ War, 58 Thompson, E.

12 Bytes: How We Got Here. Where We Might Go Next by Jeanette Winterson

"Margaret Hamilton" Apollo, "World Economic Forum" Davos, 3D printing, Ada Lovelace, Airbnb, Albert Einstein, Alignment Problem, Amazon Mechanical Turk, Anthropocene, Apollo 11, Apple's 1984 Super Bowl advert, artificial general intelligence, Asilomar, augmented reality, autonomous vehicles, basic income, Big Tech, bitcoin, Bletchley Park, blockchain, Boston Dynamics, call centre, Cambridge Analytica, Capital in the Twenty-First Century by Thomas Piketty, cashless society, Charles Babbage, computer age, Computing Machinery and Intelligence, coronavirus, COVID-19, CRISPR, cryptocurrency, dark matter, Dava Sobel, David Graeber, deep learning, deskilling, digital rights, discovery of DNA, Dominic Cummings, Donald Trump, double helix, driverless car, Elon Musk, fake news, flying shuttle, friendly AI, gender pay gap, global village, Grace Hopper, Gregor Mendel, hive mind, housing crisis, Internet of things, Isaac Newton, Jacquard loom, James Hargreaves, Jeff Bezos, Johannes Kepler, John von Neumann, Joseph-Marie Jacquard, Kickstarter, Large Hadron Collider, life extension, lockdown, lone genius, Mark Zuckerberg, means of production, microdosing, more computing power than Apollo, move fast and break things, natural language processing, Nick Bostrom, Norbert Wiener, off grid, OpenAI, operation paperclip, packet switching, Peter Thiel, pink-collar, Plato's cave, public intellectual, QAnon, QWERTY keyboard, Ray Kurzweil, rewilding, ride hailing / ride sharing, Rutger Bregman, Sam Altman, self-driving car, sharing economy, Sheryl Sandberg, Shoshana Zuboff, Silicon Valley, Skype, Snapchat, SoftBank, SpaceX Starlink, speech recognition, spinning jenny, stem cell, Stephen Hawking, Steve Bannon, Steve Jobs, Steven Levy, Steven Pinker, superintelligent machines, surveillance capitalism, synthetic biology, systems thinking, tech billionaire, tech worker, TED Talk, telepresence, telepresence robot, TikTok, trade route, Turing test, universal basic income, Virgin Galactic, Watson beat the top human players on Jeopardy!, women in the workforce, Y Combinator

If digital social passports become normal, and if such passports can be used to decide who goes where, who does what, gets what, pays what (China is mooting charging systems that offer discounts to exemplary citizens), then how we live changes collectively, as well as individually – and perhaps it will make us less compassionate too. We won’t know what’s in the data of the person turned away or turned down or charged double, and likely we will feel it must be justified – mustn’t it? And we all like to feel superior to others. * * * Elon Musk and Sam Altman (CEO of the start-up funder Y Combinator) launched OpenAI in 2015 as a non-profit organisation promoting more inclusive AI – more benefits for more people – and to explore safe AGI. (We don’t want a Skynet situation.) Musk, who has since left the organisation due to what he calls conflicts of interest, is notably worried about artificial general intelligence – the point where AI becomes an autonomous self-monitoring system.

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

Albert Einstein, anthropic principle, Any sufficiently advanced technology is indistinguishable from magic, Arthur Eddington, Bayesian statistics, behavioural economics, Benoit Mandelbrot, Berlin Wall, bitcoin, Black Swan, conceptual framework, cosmic microwave background, cosmological constant, cosmological principle, CRISPR, cuban missile crisis, dark matter, DeepMind, digital map, discounted cash flows, Donald Trump, Doomsday Clock, double helix, Dr. Strangelove, Eddington experiment, Elon Musk, Geoffrey Hinton, Gerolamo Cardano, Hans Moravec, heat death of the universe, Higgs boson, if you see hoof prints, think horses—not zebras, index fund, Isaac Newton, Jaron Lanier, Jeff Bezos, John Markoff, John von Neumann, Large Hadron Collider, mandelbrot fractal, Mark Zuckerberg, Mars Rover, Neil Armstrong, Nick Bostrom, OpenAI, paperclip maximiser, Peter Thiel, Pierre-Simon Laplace, Plato's cave, probability theory / Blaise Pascal / Pierre de Fermat, RAND corporation, random walk, Richard Feynman, ride hailing / ride sharing, Rodney Brooks, Ronald Reagan, Ronald Reagan: Tear down this wall, Sam Altman, Schrödinger's Cat, Search for Extraterrestrial Intelligence, self-driving car, Silicon Valley, Skype, Stanislav Petrov, Stephen Hawking, strong AI, tech billionaire, Thomas Bayes, Thomas Malthus, time value of money, Turing test

The United States has the Future of Life Institute at MIT, founded by Max Tegmark and Skype cofounder Jaan Tallinn, with a board of advisors including the ubiquitous Elon Musk (who donated $10 million). Silicon Valley has two such think tanks: the Machine Intelligence Research Institute, founded by computer scientist Eliezer Yudkowsky and tech entrepreneurs Brian and Sabine Atkins; and the OpenAI Foundation, founded by Musk, Sam Altman, Peter Thiel, and others. If this zeitgeist has a single axiom, it is that existential risks are different. Bostrom wrote: We cannot necessarily rely on the institutions, moral norms, social attitudes or national security policies that developed from our experience with managing other sorts of risks.

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

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

v=RZ3ahBm3dCk A Mild Dystopia 1. Griffin, Andrew (2017). ‘Facebook’s artificial intelligence robots shut down after they start talking to each other in their own language’, The Independent [online]. Available at: www.independent.co.uk/life-style/facebook-artificial-intelligence-ai-chatbot-new-language-research-openai-google-a7869706.html 2. ‘The 5 Most Infamous Software Bugs in History’, BBVA Open Mind (2015) [online]. Available at: www.bbvaopenmind.com/en/technology/innovation/the-5-most-infamous-software-bugs-in-history 3. Long, Tony (2007). ‘Sept. 26, 1983: The Man Who Saved the World by Doing . . . Nothing’, Wired [online].

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

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

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

Learn Algorithmic Trading by Sebastien Donadio

active measures, algorithmic trading, automated trading system, backtesting, Bayesian statistics, behavioural economics, buy and hold, buy low sell high, cryptocurrency, data science, deep learning, DevOps, en.wikipedia.org, fixed income, Flash crash, Guido van Rossum, latency arbitrage, locking in a profit, market fundamentalism, market microstructure, martingale, natural language processing, OpenAI, p-value, paper trading, performance metric, prediction markets, proprietary trading, quantitative trading / quantitative finance, random walk, risk tolerance, risk-adjusted returns, Sharpe ratio, short selling, sorting algorithm, statistical arbitrage, statistical model, stochastic process, survivorship bias, transaction costs, type inference, WebSocket, zero-sum game

Other Books You May Enjoy If you enjoyed this book, you may be interested in these other books by Packt: Mastering Python for Finance - Second Edition James Ma Weiming ISBN: 9781789346466 Solve linear and nonlinear models representing various financial problems Perform principal component analysis on the DOW index and its components Analyze, predict, and forecast stationary and non-stationary time series processes Create an event-driven backtesting tool and measure your strategies Build a high-frequency algorithmic trading platform with Python Replicate the CBOT VIX index with SPX options for studying VIX-based strategies Perform regression-based and classification-based machine learning tasks for prediction Use TensorFlow and Keras in deep learning neural network architecture Hands-On Machine Learning for Algorithmic Trading Stefan Jansen ISBN: 9781789346411 Implement machine learning techniques to solve investment and trading problems Leverage market, fundamental, and alternative data to research alpha factors Design and fine-tune supervised, unsupervised, and reinforcement learning models Optimize portfolio risk and performance using pandas, NumPy, and scikit-learn Integrate machine learning models into a live trading strategy on Quantopian Evaluate strategies using reliable backtesting methodologies for time series Design and evaluate deep neural networks using Keras, PyTorch, and TensorFlow Work with reinforcement learning for trading strategies in the OpenAI Gym Leave a review - let other readers know what you think Please share your thoughts on this book with others by leaving a review on the site that you bought it from. If you purchased the book from Amazon, please leave us an honest review on this book's Amazon page. This is vital so that other potential readers can see and use your unbiased opinion to make purchasing decisions, we can understand what our customers think about our products, and our authors can see your feedback on the title that they have worked with Packt to create.

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

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

The authors of the original Eroom's Law paper now believe the era of stagnation may be coming to an end thanks to the prevalence and new-found effectiveness of machine learning in the discovery of drugs.23 AI is moving to the front lines of the battle against cancer and a paper in Cell illustrates that ML can use molecular structure to predict the effectiveness of antibacterials (the researchers behind the AI even called the resulting antibacterial ‘halicin’ after HAL, the AI in 2001: A Space Odyssey).24 We need things like this to beat future pandemics. Fusion scientists are optimistic that the application of AI could bring decisive advances in the coming years, and in general the field is now focused on ML approaches to core problems.25 Breakthroughs in natural language processing are coming at pace: the parameters of OpenAI's eye-catching GPT language prediction system grew from hundreds of millions to hundreds of billions in just a few years with some spectacular results, enabling it to write convincing text at length on any subject.26 GPT-3 can take a portion of writing and then continue it with at times shocking plausibility.

The Equality Machine: Harnessing Digital Technology for a Brighter, More Inclusive Future by Orly Lobel

2021 United States Capitol attack, 23andMe, Ada Lovelace, affirmative action, Airbnb, airport security, Albert Einstein, algorithmic bias, Amazon Mechanical Turk, augmented reality, barriers to entry, basic income, Big Tech, bioinformatics, Black Lives Matter, Boston Dynamics, Charles Babbage, choice architecture, computer vision, Computing Machinery and Intelligence, contact tracing, coronavirus, corporate social responsibility, correlation does not imply causation, COVID-19, crowdsourcing, data science, David Attenborough, David Heinemeier Hansson, deep learning, deepfake, digital divide, digital map, Elon Musk, emotional labour, equal pay for equal work, feminist movement, Filter Bubble, game design, gender pay gap, George Floyd, gig economy, glass ceiling, global pandemic, Google Chrome, Grace Hopper, income inequality, index fund, information asymmetry, Internet of things, invisible hand, it's over 9,000, iterative process, job automation, Lao Tzu, large language model, lockdown, machine readable, machine translation, Mark Zuckerberg, market bubble, microaggression, Moneyball by Michael Lewis explains big data, natural language processing, Netflix Prize, Network effects, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, occupational segregation, old-boy network, OpenAI, openstreetmap, paperclip maximiser, pattern recognition, performance metric, personalized medicine, price discrimination, publish or perish, QR code, randomized controlled trial, remote working, risk tolerance, robot derives from the Czech word robota Czech, meaning slave, Ronald Coase, Salesforce, self-driving car, sharing economy, Sheryl Sandberg, Silicon Valley, social distancing, social intelligence, speech recognition, statistical model, stem cell, Stephen Hawking, Steve Jobs, Steve Wozniak, surveillance capitalism, tech worker, TechCrunch disrupt, The Future of Employment, TikTok, Turing test, universal basic income, Wall-E, warehouse automation, women in the workforce, work culture , you are the product

The Equality Machine will take you on a tour of what people can build when aspiring to use the power of AI to make the world a more equal place. Read it and get inspired to join them.” —Gillian Hadfield, director, Schwartz Reisman Institute for Technology and Society, University of Toronto, and senior policy adviser, OpenAI “Lobel offers a contrarian and original view: that technology can be a foundation for equality and inclusion rather than a source of bias and inequality. Read this book to find out why and how.” —Oren Etzioni, CEO, Allen Institute for Artificial Intelligence “With this incisive and engaging book, Lobel invites academics, nonprofit leaders, investors, business leaders, and policymakers to use data to solve the world’s most pressing problems, being neither cavalier nor afraid.”

The Precipice: Existential Risk and the Future of Humanity by Toby Ord

3D printing, agricultural Revolution, Albert Einstein, Alignment Problem, AlphaGo, Anthropocene, artificial general intelligence, Asilomar, Asilomar Conference on Recombinant DNA, availability heuristic, biodiversity loss, Columbian Exchange, computer vision, cosmological constant, CRISPR, cuban missile crisis, decarbonisation, deep learning, DeepMind, defense in depth, delayed gratification, Demis Hassabis, demographic transition, Doomsday Clock, Dr. Strangelove, Drosophila, effective altruism, Elon Musk, Ernest Rutherford, global pandemic, Goodhart's law, Hans Moravec, Herman Kahn, Higgs boson, Intergovernmental Panel on Climate Change (IPCC), Isaac Newton, James Watt: steam engine, Large Hadron Collider, launch on warning, Mark Zuckerberg, Mars Society, mass immigration, meta-analysis, Mikhail Gorbachev, mutually assured destruction, Nash equilibrium, Nick Bostrom, Norbert Wiener, nuclear winter, ocean acidification, OpenAI, p-value, Peter Singer: altruism, planetary scale, power law, public intellectual, race to the bottom, RAND corporation, Recombinant DNA, Ronald Reagan, self-driving car, seminal paper, social discount rate, Stanislav Petrov, Stephen Hawking, Steven Pinker, Stewart Brand, supervolcano, survivorship bias, synthetic biology, tacit knowledge, the scientific method, Tragedy of the Commons, uranium enrichment, William MacAskill

The chief issues facing early environmentalists were pollution, biodiversity loss, extinction and resource scarcity. But they didn’t call themselves “extinctionists” or “pollutionists.” They found their identity not in the problems they were fighting, but in the positive value they were fighting to protect. 71 At the time of writing, DeepMind and OpenAI are the most prominent examples. They are in need of great researchers in AI safety, and also great software engineers—especially those who take existential risk seriously. 72 Organizations focused on reducing existential risk include: The Future of Humanity Institute (FHI) The Centre for the Study of Existential Risk (CSER) The Future of Life Institute (FLI) The Global Catastrophic Risk Institute (GCRI) The Berkeley Existential Risk Initiative (BERI) The Open Philanthropy Project (OpenPhil) The Nuclear Threat Initiative (NTI) The Bulletin of the Atomic Scientists The Global Challenges Foundation The Law and Governance of Existential Risk group (LGER) Alliance to Feed the Earth in Disasters (ALLFED) The high-impact careers site 80,000 Hours maintains an up-to-date job board, including such positions: 80000hours.org/job-board and explanations of the kinds of careers that can really help: 80000hours.org/career-reviews 73 In keeping with this, I have signed over the entire advance and royalties from this book to charities helping protect the longterm future of humanity. 74 Eig (2014).

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

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

They’re using billions of dollars in cash and dizzying stockpiles of data to gobble up available AI talent. They’re also working to construct the “power grids” for the AI age: privately controlled computing networks that distribute machine learning across the economy, with the corporate giants acting as “utilities.” It’s a worrisome phenomenon for those who value an open AI ecosystem and also poses a potential stumbling block to China’s rise as an AI superpower. But bringing AI’s power to bear on the broader economy can’t be done by private companies alone—it requires an accommodating policy environment and can be accelerated by direct government support. As you recall, soon after Ke Jie’s loss to AlphaGo, the Chinese central government released a sweeping blueprint for Chinese leadership in AI.

…

But broadly speaking, if one of these companies makes a unique breakthrough—a trade secret that could generate massive profits for that company alone—it will do its best to keep a lid on it and will try to extract maximum value before the word gets out. A groundbreaking discovery occurring within one of these closed systems poses the greatest threat to the world’s open AI ecosystem. It also threatens to stymie China in its goal of becoming a global leader in AI. The way things stand today, China already has the edge in entrepreneurship, data, and government support, and it’s rapidly catching up to the United States in expertise. If the technological status quo holds for the coming years, an array of Chinese AI startups will begin fanning out across different industries.

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

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

When we talk, are we just producing new words based on the last few words we said, based on some probability distribution we’ve come to learn based on all the other utterances we’ve ever heard? It’s not just that. We do, after all, choose our words to make some reference to the world around us. We’re not just riffing on things we’ve already said. And yet, modern-day Markov chains can produce something remarkably like human language. An algorithm like Open AI’s GPT-3 is the spiritual descendant of Shannon’s text machine, only much bigger. The input, instead of being three letters, is a chunk of text hundreds of words long, but the principle is the same: given the passage of text most recently produced, what is the probability that the next word is “the,” or “geometry,” or “graupel”?

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See also trees numerology, 276, 278 Oblique Strategies, 172–73 O’Connor, Sandra Day, 365, 371, 405 octane, 316–17 odd numbers, 224–25 Odlyzko, Andrew, 312 Oireachtas, 349 Oldbury, Derek, 97 Old Sarum, 350–51 Olympia Academy (dinner club), 83 “Once in a Lifetime” (Talking Heads), 175, 224n On-Line Encyclopedia of Integer Sequences, 235, 283n, 318n “On the Motion of Small Particles Suspended in a Stationary Liquid, as Required by the Molecular Kinetic Theory of Heat” (Einstein), 82 Open AI, 95 opinion polling, 70–74 Orange County, Virginia, 363 organizational charts, 106, 107 Orlin, Ben, 19 Oscar, King of Sweden, 39 Ostwald, Wilhelm, 59, 83 Ottman, Tad, 347, 393 outliers, 205, 389, 401, 402 Oven of Akhnai, 420–21 overfitting, 174 Pacioli, Luca Bartolomeo de, 115, 277 PageRank, 290–92, 330 Painlevé, Paul, 80–81 Pakistan, 226 palindromes, 30, 31–32 Panama, 304 Pancatuccio, Paulo, 132–33 pandemics.

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

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

For years, AI programs have composed news stories about sports and finance for real news organizations like the Associated Press. The constrained nature of much reporting on those topics has made them easier to adapt to AI. AI is now being used to write more general stories. Modern text-creation systems like Open AI’s GPT-3 can be fed facts and write true stories, but they can just as easily be fed untruths and write fake news. It doesn’t take much imagination to see how AI will degrade political discourse. Already, AI-driven personas can write personalized letters to newspapers and elected officials, leave intelligible comments on news sites and message boards, and intelligently debate politics on social media.

Bold: How to Go Big, Create Wealth and Impact the World by Peter H. Diamandis, Steven Kotler

3D printing, additive manufacturing, adjacent possible, Airbnb, Amazon Mechanical Turk, Amazon Web Services, Apollo 11, augmented reality, autonomous vehicles, Boston Dynamics, Charles Lindbergh, cloud computing, company town, creative destruction, crowdsourcing, Daniel Kahneman / Amos Tversky, data science, deal flow, deep learning, dematerialisation, deskilling, disruptive innovation, driverless car, Elon Musk, en.wikipedia.org, Exxon Valdez, fail fast, Fairchild Semiconductor, fear of failure, Firefox, Galaxy Zoo, Geoffrey Hinton, Google Glasses, Google Hangouts, gravity well, hype cycle, ImageNet competition, industrial robot, information security, Internet of things, Jeff Bezos, John Harrison: Longitude, John Markoff, Jono Bacon, Just-in-time delivery, Kickstarter, Kodak vs Instagram, Law of Accelerating Returns, Lean Startup, life extension, loss aversion, Louis Pasteur, low earth orbit, Mahatma Gandhi, Marc Andreessen, Mark Zuckerberg, Mars Rover, meta-analysis, microbiome, minimum viable product, move fast and break things, Narrative Science, Netflix Prize, Network effects, Oculus Rift, OpenAI, optical character recognition, packet switching, PageRank, pattern recognition, performance metric, Peter H. Diamandis: Planetary Resources, Peter Thiel, pre–internet, Ray Kurzweil, recommendation engine, Richard Feynman, ride hailing / ride sharing, risk tolerance, rolodex, Scaled Composites, self-driving car, sentiment analysis, shareholder value, Sheryl Sandberg, Silicon Valley, Silicon Valley startup, skunkworks, Skype, smart grid, SpaceShipOne, stem cell, Stephen Hawking, Steve Jobs, Steven Levy, Stewart Brand, Stuart Kauffman, superconnector, Susan Wojcicki, synthetic biology, technoutopianism, TED Talk, telepresence, telepresence robot, Turing test, urban renewal, Virgin Galactic, Wayback Machine, web application, X Prize, Y Combinator, zero-sum game

“They provided so much support and guidance,” he explains, “that we were able to build our entire Watson-powered prototype in two weeks.” One of this book’s core goals is to point out those pivotal moments when a technology becomes ready for entrepreneurial prime time. Watson in the cloud, tied to an openly available API, is the beginning of one such moment, the potential for a Mosaic-like interface explosion, opening AI to all sorts of new businesses and heralding its transition from deceptive to disruptive growth. Attention, exponential entrepreneurs: What are you waiting for? And everything we’ve just covered is here today. “Soon,” says Ray Kurzweil,40 “we will give an AI permission to listen to every phone conversation you have.

The Raging 2020s: Companies, Countries, People - and the Fight for Our Future by Alec Ross

"Friedman doctrine" OR "shareholder theory", "World Economic Forum" Davos, Affordable Care Act / Obamacare, air gap, air traffic controllers' union, Airbnb, Albert Einstein, An Inconvenient Truth, autonomous vehicles, barriers to entry, benefit corporation, Bernie Sanders, Big Tech, big-box store, British Empire, call centre, capital controls, clean water, collective bargaining, computer vision, coronavirus, corporate governance, corporate raider, COVID-19, deep learning, Deng Xiaoping, Didi Chuxing, disinformation, Dissolution of the Soviet Union, Donald Trump, Double Irish / Dutch Sandwich, drone strike, dumpster diving, employer provided health coverage, Francis Fukuyama: the end of history, future of work, general purpose technology, gig economy, Gini coefficient, global supply chain, Goldman Sachs: Vampire Squid, Gordon Gekko, greed is good, high-speed rail, hiring and firing, income inequality, independent contractor, information security, intangible asset, invisible hand, Jeff Bezos, knowledge worker, late capitalism, low skilled workers, Lyft, Marc Andreessen, Marc Benioff, mass immigration, megacity, military-industrial complex, minimum wage unemployment, mittelstand, mortgage tax deduction, natural language processing, Oculus Rift, off-the-grid, offshore financial centre, open economy, OpenAI, Parag Khanna, Paris climate accords, profit motive, race to the bottom, RAND corporation, ride hailing / ride sharing, Robert Bork, rolodex, Ronald Reagan, Salesforce, self-driving car, shareholder value, side hustle, side project, Silicon Valley, smart cities, Social Responsibility of Business Is to Increase Its Profits, sovereign wealth fund, sparse data, special economic zone, Steven Levy, stock buybacks, strikebreaker, TaskRabbit, tech bro, tech worker, transcontinental railway, transfer pricing, Travis Kalanick, trickle-down economics, Uber and Lyft, uber lyft, union organizing, Upton Sinclair, vertical integration, working poor

It played by the rules and avoided the mistakes that hobbled other US tech companies in their pursuit of Chinese customers. But it occupied a strategic industry, and its success would have encroached on the technological ambitions of the Chinese government. China’s public-private capital apparatus kicked into gear, and the foreign rival was run out of town. After beating out Uber, Didi Chuxing opened AI research labs in Beijing and Silicon Valley. In July 2020, the company announced it would partner with the Chinese central bank to test a new digital currency. It was a familiar story that has played out with countless Western technology companies: Western company enters China; company cuts promising deals with local partners; company slowly loses market share to a homegrown rival; company retreats from China; homegrown rival entrenches its dominant position.

The Science of Hate: How Prejudice Becomes Hate and What We Can Do to Stop It by Matthew Williams

3D printing, 4chan, affirmative action, agricultural Revolution, algorithmic bias, Black Lives Matter, Brexit referendum, Cambridge Analytica, citizen journalism, cognitive dissonance, coronavirus, COVID-19, dark matter, data science, deep learning, deindustrialization, desegregation, disinformation, Donald Trump, European colonialism, fake news, Ferguson, Missouri, Filter Bubble, gamification, George Floyd, global pandemic, illegal immigration, immigration reform, impulse control, income inequality, longitudinal study, low skilled workers, Mark Zuckerberg, meta-analysis, microaggression, Milgram experiment, Oklahoma City bombing, OpenAI, Overton Window, power law, selection bias, Snapchat, statistical model, The Turner Diaries, theory of mind, TikTok, twin studies, white flight

But this task is made more challenging when there are opposing forces dedicated to using the world’s most powerful communications network to accomplish their extreme goals. Notes 1. J. Weizenbaum, ‘ELIZA – a Computer Program for the Study of Natural Language Communication between Man and Machine’, Communications of the ACM 9 (1966), 36–45. 2. ‘Microsoft Opens AI Framework to Other Firms’, China Daily, 22 August 2019. 3. G. King, J. Pan and M. E. Roberts, ‘How the Chinese Government Fabricates Social Media Posts for Strategic Distraction, Not Engaged Argument’, American Political Science Review 111 (2017), 484–501. 4. N. Newman et al., ‘Reuters Institute Digital News Report 2020’, Oxford: Reuters Institute, 2020. 5.

UNIX® Network Programming, Volume 1: The Sockets Networking API, 3rd Edition by W. Richard Stevens, Bill Fenner, Andrew M. Rudoff

Dennis Ritchie, exponential backoff, failed state, fudge factor, global macro, history of Unix, information retrieval, OpenAI, OSI model, p-value, RFC: Request For Comment, Richard Stallman, UUNET, web application

The only information returned will be for datagram sockets. 316 Name and Address Conversions Chapter 11 The members of the hints structure that can be set by the caller are: • • • • ai_flags (zero or more AI_XXX values OR’ed together) ai_family (an AF_xxx value) ai_socktype (a SOCK_xxx value) ai_protocol The possible values for the ai_flags member and their meanings are: AI_PASSIVE The caller will use the socket for a passive open. AI_CANONNAME Tells the function to return the canonical name of the host. AI_NUMERICHOST Prevents any kind of name-to-address mapping; the hostname argument must be an address string. AI_NUMERICSERV Prevents any kind of name-to-service mapping; the service argument must be a decimal port number string.