artificial general intelligence

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pages: 303 words: 67,891

Advances in Artificial General Intelligence: Concepts, Architectures and Algorithms: Proceedings of the Agi Workshop 2006 by Ben Goertzel, Pei Wang

AI winter, artificial general intelligence, bioinformatics, brain emulation, combinatorial explosion, complexity theory, computer vision, conceptual framework, correlation coefficient, epigenetics, friendly AI, G4S, information retrieval, Isaac Newton, John Conway, Loebner Prize, Menlo Park, natural language processing, Occam's razor, p-value, pattern recognition, performance metric, Ray Kurzweil, Rodney Brooks, semantic web, statistical model, strong AI, theory of mind, traveling salesman, Turing machine, Turing test, Von Neumann architecture, Y2K

Adams, Eric Baum, Pei Wang, Steve Grand, Ben Goertzel and Phil Goetz 283 Author Index 295 Advances in Artificial General Intelligence: Concepts, Architectures and Algorithms B. Goertzel and P. Wang (Eds.) IOS Press, 2007 © 2007 The authors and IOS Press. All rights reserved. 1 Introduction: Aspects of Artificial General Intelligence Pei WANG and Ben GOERTZEL Introduction This book contains materials that come out of the Artificial General Intelligence Research Institute (AGIRI) Workshop, held in May 20-21, 2006 at Washington DC. The theme of the workshop is “Transitioning from Narrow AI to Artificial General Intelligence.” In this introductory chapter, we will clarify the notion of “Artificial General Intelligence”, briefly survey the past and present situation of the field, analyze and refute some common objections and doubts regarding this area of research, and discuss what we believe needs to be addressed by the field as a whole in the near future.

The next major step in this direction was the May 2006 AGIRI Workshop, of which this volume is essentially a proceedings. The term AGI, artificial general intelligence, was introduced as a modern successor to the earlier strong AI. Artificial General Intelligence What is artificial general intelligence? The AGIRI website lists several features, describing machines • • • • with human-level, and even superhuman, intelligence. that generalize their knowledge across different domains. that reflect on themselves. and that create fundamental innovations and insights. Even strong AI wouldn’t push for this much, and this general, an intelligence. Can there be such an artificial general intelligence? I think there can be, but that it can’t be done with a brain in a vat, with humans providing input and utilizing computational output.

New York: Basic Books, 1958. [4] Goertzel, Ben and Cassio Pennachin (2006). The Novamente Design for Artificial General Intelligence. In Artificial General Intelligence, Springer-Verlag. [5] Goertzel, Ben (2006). Patterns, Hypergraphs and General Intelligence. Proceedings of International Joint Conference on Neural Networks, IJCNN 2006, Vancouver CA, to appear. [6] Goertzel, Ben, C. Pennachin, A. Senna, T. Maia, G. Lamacie. (2003) “Novamente: an integrative architecture for Artificial General Intelligence.” Proceedings of IJCAI 2003 Workshop on Cognitive Modeling of Agents. Acapulco, Mexico, 2003. [7] Goertzel, Ben, C. Pennachin, A. Senna, M. Looks. (2004) “The Novamente Artificial General Intelligence Architecture.” Proceedings of AAAI Symposium on Achieving Intelligence Through Integrated Systems And Research.


pages: 477 words: 75,408

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

3D printing, additive manufacturing, agricultural Revolution, AI winter, Airbnb, artificial general intelligence, augmented reality, autonomous vehicles, banking crisis, basic income, Baxter: Rethink Robotics, Berlin Wall, Bernie Sanders, bitcoin, blockchain, call centre, Chris Urmson, congestion charging, credit crunch, David Ricardo: comparative advantage, Douglas Engelbart, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Flynn Effect, full employment, future of work, gender pay gap, gig economy, Google Glasses, Google X / Alphabet X, 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, knowledge worker, lifelogging, lump of labour, Lyft, Marc Andreessen, Mark Zuckerberg, Martin Wolf, McJob, means of production, Milgram experiment, Narrative Science, natural language processing, new economy, Occupy movement, Oculus Rift, 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, software is eating the world, speech recognition, Stephen Hawking, Steve Jobs, TaskRabbit, technological singularity, The Future of Employment, Thomas Malthus, transaction costs, Tyler Cowen: Great Stagnation, Uber for X, uber lyft, universal basic income, Vernor Vinge, working-age population, Y Combinator, young professional

There is no reason to suppose that humans have attained anywhere near the maximum possible level of intelligence, and it seems highly probable that we will eventually create machines that are more intelligent than us in all respects – assuming we don't blow ourselves up first. We don't yet know whether those machines will be conscious, let alone whether they will be more conscious than us – if that is even a meaningful question. Artificial General Intelligence (AGI) and Superintelligence As we noted in chapter 1, the term for a machine which equals or exceeds human intelligence in all respects is artificial general intelligence, or AGI. The day when the first such machine is built will be a momentous one, as the arrival of superintelligence will not be far beyond it. The likelihood of an intelligence explosion is commonly referred to as the technological singularity. This could be an astonishingly positive development for humankind, or a disastrously negative one.

[lxviii] At the beginning of this chapter we noted that intelligence is not a single, unitary skill or process. The fact that Watson is an amalgam – some would say a kludge – of numerous different techniques does not in itself mark it out as different and perpetually inferior to human intelligence. It is nowhere near an artificial general intelligence which is human-level or beyond in all respects. It is not conscious. It does not even know that it won the Jeopardy match. But it may prove to be an early step in the direction of artificial general intelligence. In January 2016, an AI system called AlphaGo developed by Google's DeepMind beat Fan Hui, the European champion of Go, a board game. This was hailed as a major step forward: the game of chess has more possible moves (3580) than there are atoms in the visible universe, but Go has even more – 250150.

Chapter 6.6 adopted Kevin Kelly’s term Protopia for a successful transition, and suggested that the blockchain might turn out to be the mechanism to administer society’s collectively owned assets, notably its artificial intelligence. 7.2 – The two singularities In my previous book, “Surviving AI”, I wrote at length about the challenge and the opportunity presented by the technological singularity, the moment when (and if) we create an artificial general intelligence which continues to improve its cognitive performance and becomes a superintelligence. Ensuring that we survive that event is, I believe, the single most important task facing the next generation or two of humans – along with making sure we don’t blow ourselves up with nuclear weapons, or unleash a pathogen which kills everyone. If we secure the good outcome to the technological singularity, the future of humanity is glorious almost beyond imagination. As DeepMind co-founder Demis Hassabis likes to say, humanity’s plan for the future should consist of two steps: first, solve artificial general intelligence, and second, use that to solve everything else. “Everything else” includes poverty, illness, war and even death itself.


pages: 144 words: 43,356

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

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

Whether intelligence resides in the machine or in the software is analogous to the question of whether it resides in the neurons in your brain or in the electrochemical signals that they transmit and receive. Fortunately we don’t need to answer that question here. ANI and AGI We do need to discriminate between two very different types of artificial intelligence: artificial narrow intelligence (ANI) and artificial general intelligence (AGI (4)), which are also known as weak AI and strong AI, and as ordinary AI and full AI. The easiest way to do this is to say that artificial general intelligence, or AGI, is an AI which can carry out any cognitive function that a human can. We have long had computers which can add up much better than any human, and computers which can play chess better than the best human chess grandmaster. However, no computer can yet beat humans at every intellectual endeavour.

History doesn’t repeat itself, and even though it sometimes rhymes, the rhyme is often irregular and impossible to forecast, although it seems natural in hindsight. As we saw in the introduction to this book, nobody suggested thirty years ago that we would have powerful AIs in our pockets in the form of telephones, even though now that it has happened it seems a natural and logical development. PART TWO: AGI Artificial General Intelligence CHAPTER 4 CAN WE BUILD AN AGI? 4.1 – Is it possible in principle? The three biggest questions about artificial general intelligence (AGI) are: Can we build one? If so, when? Will it be safe? The first of these questions is the closest to having an answer, and that answer is “probably, as long as we don’t go extinct first”. The reason for this is that we already have proof that it is possible for a general intelligence to be developed using very common materials.

TABLE OF CONTENTS TITLE PAGE INTRODUCTION: SURVIVING AI PART ONE: ANI (ARTIFICIAL NARROW INTELLIGENCE) CHAPTER 1 CHAPTER 2 CHAPTER 3 PART TWO: AGI (ARTIFICIAL GENERAL INTELLIGENCE) CHAPTER 4 CHAPTER 5 PART THREE: ASI (ARTIFICIAL SUPERINTELLIGENCE) CHAPTER 6 CHAPTER 7 PART FOUR: FAI (FRIENDLY ARTIFICIAL INTELLIGENCE) CHAPTER 8 CHAPTER 9 ACKNOWLEDGEMENTS ENDNOTES COMMENTS ON SURVIVING AI A sober and easy-to-read review of the risks and opportunities that humanity will face from AI. Jaan Tallinn, co-founder Skype, co-founder Centre for the Study of Existential Risk (CSER), co-founder Future of Life Institute (FLI) Understanding AI – its promise and its dangers – is emerging as one of the great challenges of coming decades and this is an invaluable guide to anyone who’s interested, confused, excited or scared.


pages: 340 words: 97,723

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

Ada Lovelace, AI winter, Airbnb, airport security, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, artificial general intelligence, Asilomar, autonomous vehicles, Bayesian statistics, Bernie Sanders, bioinformatics, blockchain, Bretton Woods, business intelligence, Cass Sunstein, Claude Shannon: information theory, cloud computing, cognitive bias, complexity theory, computer vision, crowdsourcing, cryptocurrency, Daniel Kahneman / Amos Tversky, Deng Xiaoping, distributed ledger, don't be evil, Donald Trump, Elon Musk, Filter Bubble, Flynn Effect, gig economy, Google Glasses, Grace Hopper, Gödel, Escher, Bach, Inbox Zero, Internet of things, Jacques de Vaucanson, Jeff Bezos, Joan Didion, job automation, John von Neumann, knowledge worker, Lyft, Mark Zuckerberg, Menlo Park, move fast and break things, move fast and break things, natural language processing, New Urbanism, one-China policy, optical character recognition, packet switching, pattern recognition, personalized medicine, RAND corporation, Ray Kurzweil, ride hailing / ride sharing, Rodney Brooks, Rubik’s Cube, Sand Hill Road, Second Machine Age, self-driving car, SETI@home, side project, Silicon Valley, Silicon Valley startup, skunkworks, Skype, smart cities, South China Sea, sovereign wealth fund, speech recognition, Stephen Hawking, strong AI, superintelligent machines, technological singularity, The Coming Technological Singularity, theory of mind, Tim Cook: Apple, trade route, Turing machine, Turing test, uber lyft, Von Neumann architecture, Watson beat the top human players on Jeopardy!, zero day

As it evolves, AI is helping us mature into better humans. With the G-MAFIA, federal government, and GAIA taking active roles in the transition from artificial narrow intelligence to artificial general intelligence, we feel comfortably nudged. 2049: The Rolling Stones Are Dead (But They’re Making New Music) By the 2030s, researchers working within the G-MAFIA published an exciting paper, both because of what it revealed about AI and because of how the work was completed. Working from the same set of standards and supported with ample funds (and patience) by the federal government, researchers collaborated on advancing AI. As a result, the first system to reach artificial general intelligence was developed. The system had passed the Contributing Team Member Test. It took a long time for the AI community to accept that the Turing test, and others of its ilk, was the wrong barometer to gauge machine intelligence.

We will also take a deep dive into the unique situations faced by America’s Big Nine members and by Baidu, Alibaba, and Tencent in China. In Part II, you’ll see detailed, plausible futures over the next 50 years as AI advances. The three scenarios you’ll read range from optimistic to pragmatic and catastrophic, and they will reveal both opportunity and risk as we advance from artificial narrow intelligence to artificial general intelligence to artificial superintelligence. These scenarios are intense—they are the result of data-driven models, and they will give you a visceral glimpse at how AI might evolve and how our lives will change as a result. In Part III, I will offer tactical and strategic solutions to all the problems identified in the scenarios along with a concrete plan to reboot the present. Part III is intended to jolt us into action, so there are specific recommendations for our governments, the leaders of the Big Nine, and even for you.

They started making wild, bold predictions about AI, saying that within ten years—meaning by 1967—computers would • beat all the top-ranked grandmasters to become the world’s chess champion, • discover and prove an important new mathematical theorem, and • write the kind of music that even the harshest critics would still value.26 Meantime, Minsky made predictions about a generally intelligent machine that could do much more than take dictation, play chess, or write music. He argued that within his lifetime, machines would achieve artificial general intelligence—that is, computers would be capable of complex thought, language expression, and making choices.27 The Dartmouth workshop researchers wrote papers and books. They sat for television, radio, newspaper, and magazine interviews. But the science was difficult to explain, and so oftentimes explanations were garbled and quotes were taken out of context. Wild predictions aside, the public’s expectations for AI became more and more fantastical, in part because the story was misreported.


pages: 590 words: 152,595

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

active measures, Air France Flight 447, algorithmic trading, artificial general intelligence, augmented reality, automated trading system, autonomous vehicles, basic income, brain emulation, Brian Krebs, cognitive bias, computer vision, cuban missile crisis, dark matter, DARPA: Urban Challenge, DevOps, drone strike, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, facts on the ground, fault tolerance, Flash crash, Freestyle chess, friendly fire, IFF: identification friend or foe, ImageNet competition, Internet of things, Johann Wolfgang von Goethe, John Markoff, Kevin Kelly, Loebner Prize, loose coupling, Mark Zuckerberg, moral hazard, mutually assured destruction, Nate Silver, pattern recognition, Rodney Brooks, Rubik’s Cube, self-driving car, sensor fusion, South China Sea, speech recognition, Stanislav Petrov, Stephen Hawking, Steve Ballmer, Steve Wozniak, Stuxnet, superintelligent machines, Tesla Model S, The Signal and the Noise by Nate Silver, theory of mind, Turing test, universal basic income, Valery Gerasimov, Wall-E, William Langewiesche, Y2K, zero day

Patti Domm, “False Rumor of Explosion at White House Causes Stocks to Briefly Plunge; AP Confirms Its Twitter Feed Was Hacked,” April 23, 2013, http://www.cnbc.com/id/100646197. 185 deep neural networks to understand text: Xiang Zhang and Yann LeCun, “Text Understanding from Scratch,” April 4, 2016, https://arxiv.org/pdf/1502.01710v5.pdf. 185 Associated Press Twitter account was hacked: Domm, “False Rumor of Explosion at White House Causes Stocks to Briefly Plunge; AP Confirms Its Twitter Feed Was Hacked.” 186 design deep neural networks that aren’t vulnerable: “Deep neural networks are easily fooled.” 186 “counterintuitive, weird” vulnerability: Jeff Clune, interview, September 28, 2016. 186 “[T]he sheer magnitude, millions or billions”: JASON, “Perspectives on Research in Artificial Intelligence and Artificial General Intelligence Relevant to DoD,” 28–29. 186 “the very nature of [deep neural networks]”: Ibid, 28. 186 “As deep learning gets even more powerful”: Jeff Clune, interview, September 28, 2016. 186 “super complicated and big and weird”: Ibid. 187 “sobering message . . . tragic extremely quickly”: Ibid. 187 “[I]t is not clear that the existing AI paradigm”: JASON, “Perspectives on Research in Artificial Intelligence and Artificial General Intelligence Relevant to DoD,” Ibid, 27. 188 “nonintuitive characteristics”: Szegedy et al., “Intriguing Properties of Neural Networks.” 188 we don’t really understand how it happens: For a readable explanation of this broader problem, see David Berreby, “Artificial Intelligence Is Already Weirdly Inhuman,” Nautilus, August 6, 2015, http://nautil.us/issue/27/dark-matter/artificial-intelligence-is-already-weirdly-inhuman. 12 Failing Deadly: The Risk of Autonomous Weapons 189 “I think that we’re being overly optimistic”: John Borrie, interview, April 12, 2016. 189 “If you’re going to turn these things loose”: John Hawley, interview, December 5, 2016. 189 “[E]ven with our improved knowledge”: Perrow, Normal Accidents, 354. 191 “robo-cannon rampage”: Noah Shachtman, “Inside the Robo-Cannon Rampage (Updated),” WIRED, October 19, 2007, https://www.wired.com/2007/10/inside-the-robo/. 191 bad luck, not deliberate targeting: “ ‘Robotic Rampage’ Unlikely Reason for Deaths,” New Scientist, accessed June 12, 2017, https://www.newscientist.com/article/dn12812-robotic-rampage-unlikely-reason-for-deaths/. 191 35 mm rounds into a neighboring gun position: “Robot Cannon Kills 9, Wounds 14,” WIRED, accessed June 12, 2017, https://www.wired.com/2007/10/robot-cannon-ki/. 191 “The machine doesn’t know it’s making a mistake”: John Hawley, interview, December 5, 2016. 193 “incidents of mass lethality”: John Borrie, interview, April 12, 2016. 193 “If you put someone else”: John Hawley, interview, December 5, 2016. 194 “I don’t have a lot of good answers for that”: Peter Galluch, interview, July 15, 2016. 13 Bot vs.

., 165–66, 171–72 simulated threat test, 167–69 testing and training, 176, 177 and USS Vincennes incident, 169–70 Aegis Training and Readiness Center, 163 aerial bombing raids, 275–76, 278, 341–42 Afghanistan War (2001– ), 2–4 distinguishing soldiers from civilians, 253 drones in, 14, 25, 209 electromagnetic environment, 15 goatherder incident, 290–92 moral decisions in, 271 runaway gun incident, 191 AGI, see advanced artificial intelligence; artificial general intelligence AGM-88 high-speed antiradiation missile, 141 AI (artificial intelligence), 5–6, 86–87; see also advanced artificial intelligence; artificial general intelligence AI FOOM, 233 AIM-120 Advanced Medium-Range Air-to-Air Missile, 41 Air Force, U.S. cultural resistance to robotic weapons, 61 future of robotic aircraft, 23–25 Global Hawk drone, 17 nuclear weapons security lapse, 174 remotely piloted aircraft, 16 X-47 drone, 60–61 Air France Flight 447 crash, 158–59 Alexander, Keith, 216, 217 algorithms life-and-death decisions by, 287–90 for stock trading, see automated stock trading Ali Al Salem Air Base (Kuwait), 138–39 Alphabet, 125 AlphaGo, 81–82, 125–27, 150, 242 AlphaGo Zero, 127 AlphaZero, 410 al-Qaeda, 22, 253 “always/never” dilemma, 175 Amazon, 205 AMRAAM (Advanced Medium-Range Air-to-Air Missile), 41, 43 Anderson, Kenneth, 255, 269–70, 286, 295 anthropocentric bias, 236, 237, 241, 278 anthropomorphizing of machines, 278 Anti-Ballistic Missile (ABM) Treaty (1972), 301 antipersonnel autonomous weapons, 71, 355–56, 403n antipersonnel mines, 268, 342; see also land mines anti-radiation missiles, 139, 141, 144 anti-ship missiles, 62, 302 Anti-submarine warfare Continuous Trail Unmanned Vessel (ACTUV), 78–79 anti-vehicle mines, 342 Apollo 13 disaster, 153–54 appropriate human involvement, 347–48, 358 appropriate human judgment, 91, 347, 358 approval of autonomous weapons, see authorization of autonomous weapons Argo amphibious ground combat robot, 114 Arkhipov, Vasili, 311, 318 Arkin, Ron, 280–85, 295, 346 armed drones, see drones Arms and Influence (Schelling), 305, 341 arms control, 331–45 antipersonnel weapons, 355–56 ban of fully autonomous weapons, 352–55 debates over restriction/banning of autonomous weapons, 266–69 general principles on human judgment’s role in war, 357–59 inherent problems with, 284, 346–53 legal status of treaties, 340 limited vs. complete bans, 342–43 motivations for, 345 preemptive bans, 343–44 “rules of the road” for autonomous weapons, 356–57 successful/unsuccessful treaties, 332–44, 333t–339t types of weapons bans, 332f unnecessary suffering standards, 257–58 verification regimes, 344–45 arms race, 7–8, 117–19 Armstrong, Stuart, 238, 240–42 Army, U.S.

cultural resistance to robotic weapons, 61 Gray Eagle drone, 17 overcoming resistance to killing, 279 Patriot Vigilance Project, 171–72 Shadow drone, 209 ARPA (Advanced Research Projects Agency), 76–77 Article 36, 118 artificial general intelligence (AGI); See also advanced artificial intelligence and context, 238–39 defined, 231 destructive potential, 232–33, 244–45 ethical issues, 98–99 in literature and film, 233–36 narrow AI vs., 98–99, 231 timetable for creation of, 232, 247 as unattainable, 242 artificial intelligence (AI), 5–6, 86–87; see also advanced artificial intelligence; artificial general intelligence “Artificial Intelligence, War, and Crisis Stability” (Horowitz), 302, 312 Artificial Intelligence for Humans, Volume 3 (Heaton), 132 artificial superintelligence (ASI), 233 Art of War, The (Sun Tzu), 229 Asaro, Peter, 265, 285, 287–90 Asimov, Isaac, 26–27, 134 Assad, Bashar al-, 7, 331 Association for the Advancement of Artificial Intelligence (AAAI), 243 Atari, 124, 127, 247–48 Atlas ICBM, 307 atomic bombs, see nuclear weapons ATR (automatic target recognition), 76, 84–88 attack decision to, 269–70 defined, 269–70 human judgment and, 358 atypical events, 146, 176–78 Australia, 342–43 authorization of autonomous weapons, 89–101 DoD policy, 89–90 ethical questions, 90–93 and future of lethal autonomy, 96–99 information technology and revolution in warfare, 93–96 past as guide to future, 99–101 Auto-GCAS (automatic ground collision avoidance system), 28 automated machines, 31f, 32–33 automated (algorithmic) stock trading, 200–201, 203–4, 206, 210, 244, 387n automated systems, 31 automated weapons first “smart” weapons, 38–40 precision-guided munitions, 39–41 automatic machines, 31f automatic systems, 30–31, 110 automatic target recognition (ATR), 76, 84–88 automatic weapons, 37–38 Gatling gun as predecessor to, 35–36 machine guns, 37–38 runaway gun, 190–91 automation (generally) Aegis vs.


pages: 574 words: 164,509

Superintelligence: Paths, Dangers, Strategies by Nick Bostrom

agricultural Revolution, AI winter, Albert Einstein, algorithmic trading, anthropic principle, anti-communist, artificial general intelligence, autonomous vehicles, barriers to entry, Bayesian statistics, bioinformatics, brain emulation, cloud computing, combinatorial explosion, computer vision, cosmological constant, dark matter, DARPA: Urban Challenge, data acquisition, delayed gratification, demographic transition, different worldview, Donald Knuth, Douglas Hofstadter, Drosophila, Elon Musk, en.wikipedia.org, endogenous growth, epigenetics, fear of failure, Flash crash, Flynn Effect, friendly AI, Gödel, Escher, Bach, income inequality, industrial robot, informal economy, information retrieval, interchangeable parts, iterative process, job automation, John Markoff, John von Neumann, knowledge worker, longitudinal study, Menlo Park, meta analysis, meta-analysis, mutually assured destruction, Nash equilibrium, Netflix Prize, new economy, Norbert Wiener, NP-complete, nuclear winter, optical character recognition, pattern recognition, performance metric, phenotype, prediction markets, price stability, principal–agent problem, race to the bottom, random walk, Ray Kurzweil, recommendation engine, reversible computing, social graph, speech recognition, Stanislav Petrov, statistical model, stem cell, Stephen Hawking, strong AI, superintelligent machines, supervolcano, technological singularity, technoutopianism, The Coming Technological Singularity, The Nature of the Firm, Thomas Kuhn: the structure of scientific revolutions, transaction costs, Turing machine, Vernor Vinge, Watson beat the top human players on Jeopardy!, World Values Survey, zero-sum game

The definition of HLMI used by Nilsson is “AI able to perform around 80% of jobs as well or better than humans perform” (Kruel 2012). 81. The table shows the results of four different polls as well as the combined results. The first two were polls taken at academic conferences: PT-AI, participants of the conference Philosophy and Theory of AI in Thessaloniki 2011 (respondents were asked in November 2012), with a response rate of 43 out of 88; and AGI, participants of the conferences Artificial General Intelligence and Impacts and Risks of Artificial General Intelligence, both in Oxford, December 2012 (response rate: 72/111). The EETN poll sampled the members of the Greek Association for Artificial Intelligence, a professional organization of published researchers in the field, in April 2013 (response rate: 26/250). The TOP100 poll elicited the opinions among the 100 top authors in artificial intelligence as measured by a citation index, in May 2013 (response rate: 29/100). 82.

(The different lines in the plot correspond to different data sets, which yield slightly different estimates.6) Great expectations Machines matching humans in general intelligence—that is, possessing common sense and an effective ability to learn, reason, and plan to meet complex information-processing challenges across a wide range of natural and abstract domains—have been expected since the invention of computers in the 1940s. At that time, the advent of such machines was often placed some twenty years into the future.7 Since then, the expected arrival date has been receding at a rate of one year per year; so that today, futurists who concern themselves with the possibility of artificial general intelligence still often believe that intelligent machines are a couple of decades away.8 Two decades is a sweet spot for prognosticators of radical change: near enough to be attention-grabbing and relevant, yet far enough to make it possible to suppose that a string of breakthroughs, currently only vaguely imaginable, might by then have occurred. Contrast this with shorter timescales: most technologies that will have a big impact on the world in five or ten years from now are already in limited use, while technologies that will reshape the world in less than fifteen years probably exist as laboratory prototypes.

A more relevant distinction for our purposes is that between systems that have a narrow range of cognitive capability (whether they be called “AI” or not) and systems that have more generally applicable problem-solving capacities. Essentially all the systems currently in use are of the former type: narrow. However, many of them contain components that might also play a role in future artificial general intelligence or be of service in its development—components such as classifiers, search algorithms, planners, solvers, and representational frameworks. One high-stakes and extremely competitive environment in which AI systems operate today is the global financial market. Automated stock-trading systems are widely used by major investing houses. While some of these are simply ways of automating the execution of particular buy or sell orders issued by a human fund manager, others pursue complicated trading strategies that adapt to changing market conditions.


pages: 586 words: 186,548

Architects of Intelligence by Martin Ford

3D printing, agricultural Revolution, AI winter, Apple II, artificial general intelligence, Asilomar, augmented reality, autonomous vehicles, barriers to entry, basic income, Baxter: Rethink Robotics, Bayesian statistics, bitcoin, business intelligence, business process, call centre, cloud computing, cognitive bias, Colonization of Mars, computer vision, correlation does not imply causation, crowdsourcing, DARPA: Urban Challenge, deskilling, disruptive innovation, Donald Trump, Douglas Hofstadter, Elon Musk, Erik Brynjolfsson, Ernest Rutherford, Fellow of the Royal Society, Flash crash, future of work, gig economy, Google X / Alphabet X, Gödel, Escher, Bach, Hans Rosling, ImageNet competition, income inequality, industrial robot, information retrieval, job automation, John von Neumann, Law of Accelerating Returns, life extension, Loebner Prize, Mark Zuckerberg, Mars Rover, means of production, Mitch Kapor, natural language processing, new economy, optical character recognition, pattern recognition, phenotype, Productivity paradox, Ray Kurzweil, recommendation engine, Robert Gordon, Rodney Brooks, Sam Altman, self-driving car, sensor fusion, sentiment analysis, Silicon Valley, smart cities, social intelligence, speech recognition, statistical model, stealth mode startup, stem cell, Stephen Hawking, Steve Jobs, Steve Wozniak, Steven Pinker, strong AI, superintelligent machines, Ted Kaczynski, 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, zero-sum game, Zipcar

If an AI doesn’t have this approach as a fallback, then it’s going to fall through the cracks in some situations and fail to drive safely. That’s not good enough in the real world, of course. MARTIN FORD: You’ve noted the limitations in current narrow or specialized AI technology. Let’s talk about the prospects for AGI, which promises to someday solve these problems. Can you explain exactly what Artificial General Intelligence is? What does AGI really mean, and what are the main hurdles we need to overcome before we can achieve AGI? STUART J. RUSSELL: Artificial General Intelligence is a recently coined term, and it really is just a reminder of our real goals in AI—a general-purpose intelligence much like our own. In that sense, AGI is actually what we’ve always called artificial intelligence. We’re just not finished yet, and we have not created AGI yet. The goal of AI has always been to create general-purpose intelligent machines.

Manyika offers a unique perspective as an experienced AI and robotics researcher who has lately turned his efforts toward understanding the impact of these technologies on organizations and workplaces. The McKinsey Global Institute is a leader in conducting research into this area, and this conversation includes many important insights into the nature of the unfolding workplace disruption. The second question I directed at everyone concerns the path toward human-level AI, or what is typically called Artificial General Intelligence (AGI). From the very beginning, AGI has been the holy grail of the field of artificial intelligence. I wanted to know what each person thought about the prospect for a true thinking machine, the hurdles that would need to be surmounted and the timeframe for when it might be achieved. Everyone had important insights, but I found three conversations to be especially interesting: Demis Hassabis discussed efforts underway at DeepMind, which is the largest and best funded initiative geared specifically toward AGI.

We can imagine systems that can learn by themselves without the need for huge volumes of labeled training data. However, it is also one of the most difficult challenges facing the field. A breakthrough that allowed machines to efficiently learn in a truly unsupervised way would likely be considered one of the biggest events in AI so far, and an important waypoint on the road to human-level AI. ARTIFICIAL GENERAL INTELLIGENCE (AGI) refers to a true thinking machine. AGI is typically considered to be more or less synonymous with the terms HUMAN-LEVEL AI or STRONG AI. You’ve likely seen several examples of AGI—but they have all been in the realm of science fiction. HAL from 2001 A Space Odyssey, the Enterprise’s main computer (or Mr. Data) from Star Trek, C3PO from Star Wars and Agent Smith from The Matrix are all examples of AGI.


pages: 294 words: 81,292

Our Final Invention: Artificial Intelligence and the End of the Human Era by James Barrat

AI winter, AltaVista, Amazon Web Services, artificial general intelligence, Asilomar, Automated Insights, Bayesian statistics, Bernie Madoff, Bill Joy: nanobots, brain emulation, cellular automata, Chuck Templeton: OpenTable:, cloud computing, cognitive bias, commoditize, computer vision, cuban missile crisis, Daniel Kahneman / Amos Tversky, Danny Hillis, data acquisition, don't be evil, drone strike, Extropian, finite state, Flash crash, friendly AI, friendly fire, Google Glasses, Google X / Alphabet X, Isaac Newton, Jaron Lanier, John Markoff, John von Neumann, Kevin Kelly, Law of Accelerating Returns, life extension, Loebner Prize, lone genius, mutually assured destruction, natural language processing, Nicholas Carr, optical character recognition, PageRank, pattern recognition, Peter Thiel, prisoner's dilemma, Ray Kurzweil, Rodney Brooks, Search for Extraterrestrial Intelligence, self-driving car, semantic web, Silicon Valley, Singularitarianism, Skype, smart grid, speech recognition, statistical model, stealth mode startup, stem cell, Stephen Hawking, Steve Jobs, Steve Wozniak, strong AI, Stuxnet, superintelligent machines, technological singularity, The Coming Technological Singularity, Thomas Bayes, traveling salesman, Turing machine, Turing test, Vernor Vinge, Watson beat the top human players on Jeopardy!, zero day

He acknowledges hazards but devotes his energy to advocating for the likelihood of a long snag-free journey down the digital birth canal. My informal survey of about two hundred computer scientists at a recent AGI conference confirmed what I’d expected. The annual AGI Conferences, organized by Goertzel, are three-day meet-ups for people actively working on artificial general intelligence, or like me who are just deeply interested. They present papers, demo software, and compete for bragging rights. I attended one generously hosted by Google at their headquarters in Mountain View, California, often called the Googleplex. I asked the attendees when artificial general intelligence would be achieved, and gave them just four choices—by 2030, by 2050, by 2100, or not at all? The breakdown was this: 42 percent anticipated AGI would be achieved by 2030; 25 percent by 2050; 20 percent by 2100; 10 percent by 2100, and 2 percent never.

Andrew Rubin, Google’s Senior Vice President of Mobile: Fried, Ina, “Android Chief Says Your Phone Should Not Be Your Assistant,” All Things D, October 19, 2011, http://allthingsd.com/20111019/android-chief-says-your-phone-should-not-be-your-assistant/ (accessed November 13, 2011). It may be that we need a scientific breakthrough: Goertzel, Ben, “Editor’s Blog Report on the Fourth Conference on Artificial General Intelligence,” H+ Magazine, September 1, 2011, http://hplusmagazine.com/2011/09/01/report-on-the-fourth-conference-on-artificial-general-intelligence/ (accessed November 22, 2011). LIDA scores like a human: Biever, Celeste, “Bot shows signs of consciousness,” New Scientist, April 1, 2011, http://www.newscientist.com/article/mg21028063.400-bot-shows-signs-of-consciousness.html (accessed June 1, 2011). committing the Holocaust: Goertzel, Ben, “The Machine Intelligence Research Institute’s Scary Idea (and Why I Don’t Buy It),” The Multiverse According to Ben (blog), October 29, 2010, http://multiverseaccordingtoben. blogspot.com/2010/10/singularity-institutes-scary-idea-and.html (accessed June 1, 2011).

Aboujaoude, Elias accidents AI and, see risks of artificial intelligence nuclear power plant Adaptive AI affinity analysis agent-based financial modeling “Age of Robots, The” (Moravec) Age of Spiritual Machines, The: When Computers Exceed Human Intelligence (Kurzweil) AGI, see artificial general intelligence AI, see artificial intelligence AI-Box Experiment airplane disasters Alexander, Hugh Alexander, Keith Allen, Paul Allen, Robbie Allen, Woody AM (Automatic Mathematician) Amazon Anissimov, Michael anthropomorphism apoptotic systems Apple iPad iPhone Siri Arecibo message Aristotle artificial general intelligence (AGI; human-level AI): body needed for definition of emerging from financial markets first-mover advantage in jump to ASI from; see also intelligence explosion by mind-uploading by reverse engineering human brain time and funds required to develop Turing test for artificial intelligence (AI): black box tools in definition of drives in, see drives as dual use technology emotional qualities in as entertainment examples of explosive, see intelligence explosion friendly, see Friendly AI funding for jump to AGI from Joy on risks of, see risks of artificial intelligence Singularity and, see Singularity tight coupling in utility function of virtual environments for artificial neural networks (ANNs) artificial superintelligence (ASI) anthropomorphizing gradualist view of dealing with jump from AGI to; see also intelligence explosion morality of nanotechnology and runaway Artilect War, The (de Garis) ASI, see artificial superintelligence Asilomar Guidelines ASIMO Asimov, Isaac: Three Laws of Robotics of Zeroth Law of Association for the Advancement of Artificial Intelligence (AAAI) asteroids Atkins, Brian and Sabine Automated Insights availability bias Banks, David L.


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

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

Just as people sitting around at the dawn of mankind, speculating on where the creation of a language would lead us would be unlikely to think about the finer points of Twitter hashtags, so it is impossible to imagine how a superior intellect will view – and no doubt fundamentally alter – the world. The Difference between Narrow and Wide A lifetime of sci-fi movies and books have ingrained in us the expectation that there will be some Singularity-style ‘tipping point’ at which Artificial General Intelligence will take place. Devices will get gradually smarter and smarter until, somewhere in a secret research lab deep in Silicon Valley, a message pops up on Mark Zuckerberg or Sergey Brin’s computer monitor, saying that AGI has been achieved. Like Ernest Hemingway once wrote about bankruptcy, Artificial General Intelligence will take place ‘gradually, then suddenly’. This is the narrative played out in films like James Cameron’s seminal Terminator 2: Judgment Day. In that movie we, the audience, are informed that the supercomputer Skynet becomes ‘self-aware’ at exactly 2.14 a.m.

It is reminiscent of a magician who is praised not for his ability to perform genuine magic, but rather for his use of sleight-of-hand and misdirection to create an impressive illusion. ‘Unfortunately, the chatbots of today can only resort to trickery to hopefully fool a human into thinking they are sentient,’ one recent entrant in the Loebner Prize told me. ‘And it is highly unlikely without a yet-undiscovered novel approach to simulating an AI that any chatbot technology employed today could ever fool an experienced chatbot creator into believing they possess [artificial] general intelligence.’ Turing wasn’t particularly concerned with the metaphysical question of whether a machine can actually think. In his famous 1950 essay, ‘Computing Machinery and Intelligence’, he described it as ‘too meaningless to deserve discussion’. Instead he was interested in getting machines to perform activities that would be considered intelligent if they were carried out by a human. It is this idea that the MIT psychoanalyst and computer researcher Sherry Turkle talks about when she says that we should take computers at ‘interface value’.

In some cases, AI has indeed demonstrated a superior ability for invention, particularly when dealing with the kind of genetic algorithms described in chapter six. Manipulating human leaders could meanwhile refer to the handing-over of important tasks to the AI assistants that will come to run our lives, while the development of AI weapons has been a goal since virtually the field’s earliest days. What he and Musk were specifically pointing towards was something called Artificial General Intelligence, or AGI. So far, all of the applications of Artificial Intelligence described in this book have come under the broad umbrella heading of ‘Narrow AI’ or ‘Weak AI’. This has nothing to do with how robust the technology is. As we saw in the early chapters, today’s deep learning neural networks are orders of magnitude less brittle than the symbol-crunching Artificial Intelligence that made up Good Old-Fashioned AI.


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

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

When Computers Can Think 1. When Computers Can Think 1. Back Cover 2. Copyright 3. Acknowledgements 4. Overview 2. Part I: Could Computers Ever Think? 1. People Thinking About Computers 1. The Question 2. Vitalism 3. Science vs. vitalism 4. The vital mind 5. Computers cannot think now 6. Diminishing returns 7. AI in the background 8. Robots leave factories 9. Intelligent tasks 10. Artificial General Intelligence (AGI) 11. Existence proof 12. Simulating neurons, feathers 13. Moore's law 14. Definition of intelligence 15. Turing Test 16. Robotic vs cognitive intelligence 17. Development of intelligence 18. Four year old child 19. Recursive self-improvement 20. Busy Child 21. AI foom 2. Computers Thinking About People 1. The question 2. The bright future 3. Man and machine 4. Rapture of the geeks 5.

But as they begin to be programmed to learn about the world and solve general problems this becomes a much looser constraint than the way a business application is programmed to mindlessly implement business rules. AI programs often surprise their developers with what they can (and cannot) do. Kasparov stated that Deep Blue had produced some very creative chess moves even though it used a relatively simple brute force strategy. Certainly Deep Blue was a much better chess player than its creators. Artificial General Intelligence (AGI) It is certainly the case that computers are becoming ever more intelligent and capable of addressing a widening variety of difficult problems. This book argues that it is only a matter of time before they achieve general, human level intelligence. This would mean that they could reason not only about the tasks at hand but also about the world in general, including their own thoughts.

At some point computers will have basic human level-intelligence for every-day tasks but will not yet be intelligent enough to program themselves by themselves. These machines will be very intelligent in some ways, yet quite limited in others. It is unclear how long this intermediate period will last, it could be months or many decades. Such machines are often referred to as being an Artificial General Intelligence, or AGI. General meaning general purpose, not restricted in the normal way that programs are. Artificial intelligence techniques such as genetic algorithms are already being used to help create artificial intelligence software as is discussed in part II. This process is likely to continue, with better tools producing better machines that produce better tools. It seems likely that the slow shift will be ongoing from human researchers being the main drivers of innovation to the machines being the main drivers.


pages: 48 words: 12,437

Smarter Than Us: The Rise of Machine Intelligence by Stuart Armstrong

artificial general intelligence, brain emulation, effective altruism, Flash crash, friendly AI, shareholder value, Turing test

See, for instance, Bill Hibbard, “Super-Intelligent Machines,” ACM SIGGRAPH Computer Graphics 35, no. 1 (2001): 13–15, http://www.siggraph.org/publications/newsletter/issues/v35/v35n1.pdf; Ben Goertzel and Joel Pitt, “Nine Ways to Bias Open-Source AGI Toward Friendliness,” Journal of Evolution and Technology 22, no. 1 (2012): 116–131, http://jetpress.org/v22/goertzel-pitt.htm. 4. Ben Goertzel, “CogPrime: An Integrative Architecture for Embodied Artificial General Intelligence,” OpenCog Foundation, October 2, 2012, accessed December 31, 2012, http://wiki.opencog.org/w/CogPrime_Overview. Chapter 10 A Summary There are no convincing reasons to assume computers will remain unable to accomplish anything that humans can. Once computers achieve something at a human level, they typically achieve it at a much higher level soon thereafter. An AI need only be superhuman in one of a few select domains for it to become incredibly powerful (or empower its controllers).

See MIRI’s work on the fragility of values and FHI’s work on the problem of containing oracles: Luke Muehlhauser and Louie Helm, “The Singularity and Machine Ethics,” in Singularity Hypotheses: A Scientific and Philosophical Assessment, ed. Amnon Eden et al., The Frontiers Collection (Berlin: Springer, 2012); Stuart Armstrong, Anders Sandberg, and Nick Bostrom, “Thinking Inside the Box: Controlling and Using an Oracle AI,” Minds and Machines 22, no. 4 (2012): 299–324, doi:10.1007/s11023-012-9282-2. 2. Stephen M. Omohundro, “The Basic AI Drives,” in Artificial General Intelligence 2008: Proceedings of the First AGI Conference, Frontiers in Artificial Intelligence and Applications 171 (Amsterdam: IOS, 2008), 483–492. 3. Roman V. Yampolskiy, “Leakproofing the Singularity: Artificial Intelligence Confinement Problem,” Journal of Consciousness Studies 2012, nos. 1–2 (2012): 194–214, http://www.ingentaconnect.com/content/imp/jcs/2012/00000019/F0020001/art00014. 4.

Journal of Consciousness Studies 17, nos. 9–10 (2010): 7–65. http://www.ingentaconnect.com/content/imp/jcs/2010/00000017/f0020009/art00001. Eden, Amnon, Johnny Søraker, James H. Moor, and Eric Steinhart, eds. Singularity Hypotheses: A Scientific and Philosophical Assessment. The Frontiers Collection. Berlin: Springer, 2012. Goertzel, Ben. “CogPrime: An Integrative Architecture for Embodied Artificial General Intelligence.” OpenCog Foundation. October 2, 2012. Accessed December 31, 2012. http://wiki.opencog.org/w/CogPrime_Overview. Goertzel, Ben, and Joel Pitt. “Nine Ways to Bias Open-Source AGI Toward Friendliness.” Journal of Evolution and Technology 22, no. 1 (2012): 116–131. http://jetpress.org/v22/goertzel-pitt.htm. Hanson, Robin. “Economics of the Singularity.” IEEE Spectrum 45, no. 6 (2008): 45–50. doi:10.1109/MSPEC.2008.4531461. ———.


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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, artificial general intelligence, Asilomar, autonomous vehicles, basic income, Benoit Mandelbrot, Bill Joy: nanobots, Buckminster Fuller, cellular automata, Claude Shannon: information theory, Daniel Kahneman / Amos Tversky, Danny Hillis, David Graeber, easy for humans, difficult for computers, Elon Musk, Eratosthenes, Ernest Rutherford, finite state, friendly AI, future of work, Geoffrey West, Santa Fe Institute, gig economy, income inequality, industrial robot, information retrieval, invention of writing, James Watt: steam engine, Johannes Kepler, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, Kevin Kelly, Kickstarter, Laplace demon, Loebner Prize, market fundamentalism, Marshall McLuhan, Menlo Park, Norbert Wiener, optical character recognition, pattern recognition, personalized medicine, Picturephone, profit maximization, profit motive, RAND corporation, random walk, Ray Kurzweil, 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, technological singularity, technoutopianism, telemarketer, telerobotics, the scientific method, theory of mind, Turing machine, Turing test, universal basic income, Upton Sinclair, Von Neumann architecture, Whole Earth Catalog, Y2K, zero-sum game

Chapter 3 THE PURPOSE PUT INTO THE MACHINE STUART RUSSELL Stuart Russell is a professor of computer science and Smith-Zadeh Professor in Engineering at UC Berkeley. He is the co-author (with Peter Norvig) of Artificial Intelligence: A Modern Approach. Computer scientist Stuart Russell, along with Elon Musk, Stephen Hawking, Max Tegmark, and numerous others, has insisted that attention be paid to the potential dangers in creating an intelligence on the superhuman (or even the human) level—an AGI, or artificial general intelligence, whose programmed purposes may not necessarily align with our own. His early work was on understanding the notion of “bounded optimality” as a formal definition of intelligence that you can work on. He developed the technique of rational metareasoning, “which is, roughly speaking, that you do the computations that you expect to improve the quality of your ultimate decision as quickly as possible.”

Chapter 8 LET’S ASPIRE TO MORE THAN MAKING OURSELVES OBSOLETE MAX TEGMARK Max Tegmark is an MIT physicist and AI researcher, president of the Future of Life Institute, scientific director of the Foundational Questions Institute, and the author of Our Mathematical Universe and Life 3.0: Being Human in the Age of Artificial Intelligence. I was introduced to Max Tegmark some years ago by his MIT colleague Alan Guth, the father of inflation theory. A distinguished theoretical physicist and cosmologist himself, Max’s principal concern nowadays is the looming existential risk posed by the creation of an AGI (artificial general intelligence—that is, one that matches human intelligence). Four years ago, Max co-founded, with Jaan Tallinn and others, the Future of Life Institute (FLI), which bills itself as “an outreach organization working to ensure that tomorrow’s most powerful technologies are beneficial for humanity.” While on a book tour in London, he was in the midst of planning for FLI, and he admits to being driven to tears in a tube station after a trip to the London Science Museum, with its exhibitions spanning the gamut of humanity’s technological achievements.

But from my perspective as a physicist, intelligence is simply a certain kind of information processing performed by elementary particles moving around, and there’s no law of physics that says one can’t build machines more intelligent in every way than we are, and able to seed cosmic life. This suggests that we’ve seen just the tip of the intelligence iceberg; there’s an amazing potential to unlock the full intelligence latent in nature and use it to help humanity flourish—or flounder. Others, including some of the authors in this volume, dismiss the building of an AGI (artificial general intelligence—an entity able to accomplish any cognitive task at least as well as humans) not because they consider it physically impossible but because they deem it too difficult for humans to pull off in less than a century. Among professional AI researchers, both types of dismissal have become minority views because of recent breakthroughs. There is a strong expectation that AGI will be achieved within a century, and the median forecast is only decades away.


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The Transhumanist Reader by Max More, Natasha Vita-More

23andMe, Any sufficiently advanced technology is indistinguishable from magic, artificial general intelligence, augmented reality, Bill Joy: nanobots, bioinformatics, brain emulation, Buckminster Fuller, cellular automata, clean water, cloud computing, cognitive bias, cognitive dissonance, combinatorial explosion, conceptual framework, Conway's Game of Life, cosmological principle, data acquisition, discovery of DNA, Douglas Engelbart, Drosophila, en.wikipedia.org, endogenous growth, experimental subject, Extropian, fault tolerance, Flynn Effect, Francis Fukuyama: the end of history, Frank Gehry, friendly AI, game design, germ theory of disease, hypertext link, impulse control, index fund, John von Neumann, joint-stock company, Kevin Kelly, Law of Accelerating Returns, life extension, lifelogging, Louis Pasteur, Menlo Park, meta analysis, meta-analysis, moral hazard, Network effects, Norbert Wiener, pattern recognition, Pepto Bismol, phenotype, positional goods, prediction markets, presumed consent, Ray Kurzweil, reversible computing, RFID, Ronald Reagan, scientific worldview, silicon-based life, Singularitarianism, social intelligence, stem cell, stochastic process, superintelligent machines, supply-chain management, supply-chain management software, technological singularity, Ted Nelson, telepresence, telepresence robot, telerobotics, the built environment, The Coming Technological Singularity, the scientific method, The Wisdom of Crowds, transaction costs, Turing machine, Turing test, Upton Sinclair, Vernor Vinge, Von Neumann architecture, Whole Earth Review, women in the workforce, zero-sum game

Copyright © 2007 Andy Clark. 12 Artificial General Intelligence and the Future of Humanity Ben Goertzel What will be the next huge leap in humanity’s progress? We cannot know for sure, but I am ­reasonably confident that it will involve the radical extension of technology into the domain of thought. Ray Kurzweil (2000, 2005) has eloquently summarized the arguments in favor of this ­position. We have created tools to carry out much of the practical work previously done by human bodies. Next we will create tools to carry out the work currently done by human minds. We will create powerful robots and artificially intelligent software programs – not merely “narrow AI” programs carrying out specific tasks, but AGIs, artificial general intelligences capable of coping with unpredictable situations in intelligent and creative ways.

Freitas, Raymond Kurzweil, Marvin Minsky, Max More, Christine Peterson, Michael D. Shapiro, Lee Silver, Gregory Stock, Natasha Vita-More, Roy Walford, and Michael West. See http://www.extropy.org/summitkeynotes.htm. Statement for Extropy Institute Vital Progress Summit February 18, 2004. Index accelerating change adaptability aesthetics ageless AGI, see artificial general intelligence aging alchemy alterity anti-aging Aristotle Armstrong, Rachel artifact artificial general intelligence artificial intelligence artificial life Ascott, Roy atheism atom atomic augmentation authoritarian autonomous self (agent) autonomy avatar Bacon, Francis Bailey, Ronald Bainbridge, William Beloff, Laura Berger, Ted Benford, Gregory Beyond Therapy: Biotechnology and the Pursuit of Happiness bias bioart bioconservative biocultural capital bioethics biofeedback biopolitics biotechnology Blackford, Russell Blue Brain body alternative body biological body biopolitic computer interaction and body cyborg body modification morphological freedom posthuman body prosthetic body regenerated simulated transformative transhuman body wearable, see Hybronaut Bostrom, Nick brain–computer interface brain–machine interface (BMI) brain preservation Brin, David Broderick, Damien Caplan, Arthur Chalmers, David Chislenko, Alexander “Sasha,” Church, George Clark, Andy Clarke, Arthur C.

Nanorobotics Nanorobotics Revolution by the 2020s Conclusions 7 Life Expansion Media Living Matter Degeneration/Regeneration Transmutation Dialectics of Desirability and Viability Cybernetics Human-machine Interfaces and the Prosthetic Body Life Expansion 8 The Hybronaut Affair Techno-Organic Environment The Umwelt Bubble Network and the Hybronaut The Appendix-tail Conclusion 9 Transavatars Avatars and Simulation Avatar Censuses Secondary and Posthumous Avatars Conclusion 10 Alternative Biologies Biology as Technology The Rise of Machines Complexity The Science of Complexity Synthetic Biology – Complex Embodied Technology Top-Down Synthetic Biology Bottom-Up Synthetic Biology Protocells Artificial Biology From Proposition to Reality Future Venice Artificial Biology and Human Enhancement Part III Human Enhancement: The Cognitive Sphere 11 Re-Inventing Ourselves I. Introduction: Where the Rubber Meets the Road II. What’s in an Interface? III. New Systemic Wholes IV. Incorporation Versus Use V. Extended Cognition VI. Profound Embodiment VII. Enhancement or Subjugation? VIII. Conclusions 12 Artificial General Intelligence and the Future of Humanity The Top Priority for Mankind AGI and the Transformation of Individual and Collective Experience AGI and the Global Brain What is a Mind that We Might Build One? Why So Little Work on AGI? Why the “AGI Sputnik” Will Change Things Dramatically and Launch a New Phase of the Intelligence Explosion The Risks and Rewards of Advanced AGI 13 Intelligent Information Filters and Enhanced Reality Preface Text Translation and Its Consequences Enhanced Multimedia Structure of Enhanced Reality Historical Observations Truth vs.


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

agricultural Revolution, AI winter, artificial general intelligence, basic income, Buckminster Fuller, business cycle, business process, Claude Shannon: information theory, clean water, cognitive bias, computer age, crowdsourcing, dark matter, Elon Musk, Eratosthenes, estate planning, financial independence, first square of the chessboard, first square of the chessboard / second half of the chessboard, full employment, 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 skilled workers, manufacturing employment, Marc Andreessen, Mark Zuckerberg, Marshall McLuhan, Mary Lou Jepsen, Moravec's paradox, On the Revolutions of the Heavenly Spheres, pattern recognition, profit motive, Ray Kurzweil, recommendation engine, Rodney Brooks, Sam Altman, self-driving car, Silicon Valley, Skype, spinning jenny, Stephen Hawking, Steve Wozniak, Steven Pinker, strong AI, technological singularity, telepresence, telepresence robot, The Future of Employment, the scientific method, Turing machine, Turing test, universal basic income, Von Neumann architecture, Wall-E, Watson beat the top human players on Jeopardy!, women in the workforce, working poor, Works Progress Administration, Y Combinator

The First Age: Language and Fire 2. The Second Age: Agriculture and Cities 3. The Third Age: Writing and Wheels 4. The Fourth Age: Robots and AI 5. Three Big Questions PART TWO: NARROW AI AND ROBOTS THE STORY OF JOHN HENRY 6. Narrow AI 7. Robots 8. Technical Challenges 9. Will Robots Take All Our Jobs? 10. Are There Robot-Proof Jobs? 11. The Big Questions 12. The Use of Robots in War PART THREE: ARTIFICIAL GENERAL INTELLIGENCE THE STORY OF THE SORCERER’S APPRENTICE 13. The Human Brain 14. AGI 15. Should We Build an AGI? PART FOUR: COMPUTER CONSCIOUSNESS THE STORY OF JOHN FRUM 16. Sentience 17. Free Will 18. Consciousness 19. Can Computers Become Conscious? 20. Can Computers Be Implanted in Human Brains? 21. Humanity, Redefined? PART FIVE: THE ROAD FROM HERE THE STORY OF JEAN-LUC PICARD 22. The Invention of Progress 23.

By 2004, the number of transistors manufactured surpassed the number of grains of rice that were grown across the planet. Just six years later, in 2010, you could buy that same 125,000 transistors that cost a million dollars in 1960 for the same price as you would pay for a single grain of rice. Technology is relentless: It gets better and cheaper, never stopping. And it is on this fact that many computer scientists base their claims on the future capabilities of computers, such as artificial general intelligence and machine consciousness, the topics we will discuss for the rest of the book. Just how deeply have we embedded computers into the fabric of our lives? No one knows how many billions of computers are in operation around the world. It is believed that computers use roughly 10 percent of all of the electricity produced. They are so much a part of our lives that we literally may not be able to live without them, certainly not at our present standard of living.

But—and this is really important—there are two completely different things people mean today when they talk about artificial intelligence. There is “narrow AI” and there is “general AI.” The kind of AI we have today is narrow AI, also known as weak AI. It is the only kind of AI we know how to build, and it is incredibly useful. Narrow AI is the ability for a computer to solve a specific kind of problem or perform a specific task. The other kind of AI is referred to by three different names: general AI, strong AI, or artificial general intelligence (AGI). Although the terms are interchangeable, I will use AGI from this point forward to refer to an artificial intelligence as smart and versatile as you or me. A Roomba vacuum cleaner, Siri, and a self-driving car are powered by narrow AI. A hypothetical robot that can unload the dishwasher would be powered by narrow AI. But if you wanted a robot MacGyver, that would require AGI, because MacGyver has to respond to situations that he has not previously considered.


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Rise of the Robots: Technology and the Threat of a Jobless Future by Martin Ford

"Robert Solow", 3D printing, additive manufacturing, Affordable Care Act / Obamacare, AI winter, algorithmic trading, Amazon Mechanical Turk, artificial general intelligence, assortative mating, autonomous vehicles, banking crisis, basic income, Baxter: Rethink Robotics, Bernie Madoff, Bill Joy: nanobots, business cycle, call centre, Capital in the Twenty-First Century by Thomas Piketty, Chris Urmson, Clayton Christensen, clean water, cloud computing, collateralized debt obligation, commoditize, computer age, creative destruction, debt deflation, deskilling, disruptive innovation, diversified portfolio, Erik Brynjolfsson, factory automation, financial innovation, Flash crash, Fractional reserve banking, Freestyle chess, full employment, Goldman Sachs: Vampire Squid, Gunnar Myrdal, High speed trading, income inequality, indoor plumbing, industrial robot, informal economy, iterative process, Jaron Lanier, job automation, John Markoff, John Maynard Keynes: technological unemployment, John von Neumann, Kenneth Arrow, Khan Academy, knowledge worker, labor-force participation, liquidity trap, low skilled workers, low-wage service sector, Lyft, manufacturing employment, Marc Andreessen, McJob, moral hazard, Narrative Science, Network effects, new economy, Nicholas Carr, Norbert Wiener, obamacare, optical character recognition, passive income, Paul Samuelson, performance metric, Peter Thiel, plutocrats, Plutocrats, post scarcity, precision agriculture, price mechanism, Ray Kurzweil, rent control, rent-seeking, reshoring, RFID, Richard Feynman, Rodney Brooks, Sam Peltzman, secular stagnation, self-driving car, Silicon Valley, Silicon Valley startup, single-payer health, software is eating the world, sovereign wealth fund, speech recognition, Spread Networks laid a new fibre optics cable between New York and Chicago, stealth mode startup, stem cell, Stephen Hawking, Steve Jobs, Steven Levy, Steven Pinker, strong AI, Stuxnet, technological singularity, telepresence, telepresence robot, The Bell Curve by Richard Herrnstein and Charles Murray, The Coming Technological Singularity, The Future of Employment, Thomas L Friedman, too big to fail, Tyler Cowen: Great Stagnation, uber lyft, union organizing, Vernor Vinge, very high income, Watson beat the top human players on Jeopardy!, women in the workforce

See artificial intelligence (AI) “AI winters,” 231 Alaska, annual dividend, 268 algorithms acceleration in development of, 71 automated trading, 56, 113–115 increasing efficiency of, 64 machine learning, 89, 93, 100–101, 107–115, 130–131 threat to jobs, xv, 85–86 alien invasion parable, 194–196, 240 “All Can Be Lost: The Risk of Putting Our Knowledge in the Hands of Machines” (Carr), 254 all-payer ceiling, 168–169 all-payer rates, 167–169 Amazon.com, 16–17, 76, 89 artificial intelligence and, 231 cloud computing and, 104–105, 107 delivery model, 190, 190n “Mechanical Turk” service, 125n AMD (Advanced Micro Devices), 70n American Airlines, 179 American Hospital Association, 168 American Motors, 76 Andreesen, Marc, 107 Android, 6, 21, 79, 121 Apple, Inc., 17, 20, 51, 92, 106–107, 279 Apple Watch, 160 apps, difficulty in monetizing, 79 Arai, Noriko, 127–128 Aramco, 68 Ariely, Dan, 47n Arrow, Kenneth, 162, 169 art, machines creating, 111–113 Artificial General Intelligence (AGI), 231–233 dark side of, 238–241 the Singularity and, 233–238 artificial intelligence (AI), xiv arms race and, 232, 239–240 in medicine, 147–153 narrow, 229–230 offshoring and, 118–119 warnings concerning dangers of, 229 See also Artificial General Intelligence (AGI); automation; information technology Artificial Intelligence Laboratory (Stanford University), 6 artificial neural networks, 90–92. See also deep learning The Atlantic (magazine), 71, 237, 254, 273 AT&T, 135, 159, 166 Audi, 184 Australian agriculture, x–xi, 24–25 Australian Centre for Field Robotics (ACFR), 24–25 AutoDesk, 234 automated invention machines, 110 automated trading algorithms, 56, 113–115 automation alien invasion parable, 194–196, 240 anti-automation view, 253–257 cars and (see autonomous cars) effect on Chinese manufacturing, 3, 10–11, 225–226 effect on prices, 215–216 health care jobs and, 172–173 information technology and, 52 job-market polarization and, 50–51 low-wage jobs and, 26–27 offshoring as precursor to, 115, 118–119 predictions of effect of, 30–34 reshoring and, 10 retail sector and, 16–20 risk of, 256 service sector and, 12–20 solutions to rise of, 273–278 (see also basic income guarantee) as threat to workers with varying education and skill levels, xiv–xv, 59 of total US employment, 223 Triple Revolution report, 30–31 white-collar, 85–86, 105–106, 126–128 See also robotics; robots automotive industry, 3, 76, 193–194 autonomous cars, xiii, 94, 176, 181–191 as shared resource, 186–190 Autor, David, 50 Average Is Over (Cowen), 123, 126n aviation, 66–67, 179, 256 AVT, Inc., 18 Ayres, Ian, 125 Babbage, Charles, 79 Baker, Stephen, 96n, 102n Barra, Hugo, 121 Barrat, James, 231, 238–239 basic income guarantee, 31n, 257–261 approaches to, 261–262 downsides and risks of, 268–271 economic argument for, 264–267 economic risk taking and, 267–268 incentives and, 261–264 paying for, 271–273 Baxter (robot), 5–6, 7, 10 BD Focal Point GS Imaging System, 153 Beaudry, Paul, 127 Beijing Genomics Institute, 236n Bell Labs, 159 Berg, Andrew G., 214–215 Bernanke, Ben, 37 big data, xv, 25n, 86–96 collection of, 86–87 correlation vs. cause and, 88–89, 102 deep learning and, 92–93 health care and, 159–160 knowledge-based jobs and, 93–96 machine learning and, 89–92 The Big Switch (Carr), 72 Bilger, Burkhard, 186 “BinCam,” 125n “Bitter Pill” (Brill), 160 Blinder, Alan, 117–118, 119 Blockbuster, 16, 19 Bloomberg, 113–114 Bluestone, Barry, 220 Borders, 16 Boston Consulting Group, 9 Boston Globe (newspaper), 149 Boston Red Sox, 83 Boston University, 141 Bowley, Arthur, 38 Bowley’s Law, 38–39, 41 box-moving robot, 1–2, 5–6 brain, reverse engineering of human, 237 breast cancer screening, 152 Brill, Steven, 160, 163 Brin, Sergey, 186, 188, 189, 236 Brint, Steven, 251 Brooks, Rodney, 5 Brown, Jerry, 134 Brynjolfsson, Erik, 60, 122, 254 Bureau of Labor Statistics, 13, 16, 38n, 158, 222–223, 281 Bush, George W., 116 business interest lobbying, economic policy and, 57–58 “Busy child scenario,” (Barrat) 238–239 Calico, 236 California Institute of Technology, 133 Canada, 41, 58, 167n, 251 “Can Nanotechnology Create Utopia?”

The extraordinary power of today’s computers combined with advances in specific areas of AI research, as well as in our understanding of the human brain, are generating a great deal of optimism. James Barrat, the author of a recent book on the implications of advanced AI, conducted an informal survey of about two hundred researchers in human-level, rather than merely narrow, artificial intelligence. Within the field, this is referred to as Artificial General Intelligence (AGI). Barrat asked the computer scientists to select from four different predictions for when AGI would be achieved. The results: 42 percent believed a thinking machine would arrive by 2030, 25 percent said by 2050, and 20 percent thought it would happen by 2100. Only 2 percent believed it would never happen. Remarkably, a number of respondents wrote comments on their surveys suggesting that Barrat should have included an even earlier option—perhaps 2020.2 Some experts in the field worry that another expectations bubble might be building.

AI is becoming indispensable to militaries, intelligence agencies, and the surveillance apparatus in authoritarian states.* Indeed, an all-out AI arms race might well be looming in the near future. The real question, I think, is not whether the field as a whole is in any real danger of another AI winter but, rather, whether progress remains limited to narrow AI or ultimately expands to Artificial General Intelligence as well. If AI researchers do eventually manage to make the leap to AGI, there is little reason to believe that the result will be a machine that simply matches human-level intelligence. Once AGI is achieved, Moore’s Law alone would likely soon produce a computer that exceeded human intellectual capability. A thinking machine would, of course, continue to enjoy all the advantages that computers currently have, including the ability to calculate and access information at speeds that would be incomprehensible for us.


pages: 345 words: 104,404

Pandora's Brain by Calum Chace

AI winter, Any sufficiently advanced technology is indistinguishable from magic, artificial general intelligence, brain emulation, Extropian, friendly AI, hive mind, lateral thinking, mega-rich, Ray Kurzweil, self-driving car, Silicon Valley, Singularitarianism, Skype, speech recognition, stealth mode startup, Stephen Hawking, strong AI, technological singularity, theory of mind, Turing test, Wall-E

‘What I am going to tell you may sound melodramatic, but please hear me out because I mean every word of it.’ He paused, and looked at Matt and Leo in turn. ‘We are engaged in a race, gentlemen. A race for the survival of our species. Humanity is sleepwalking towards an apocalypse.’ Matt and Leo were listening attentively, but Ivan held up his hand anyway, as if to forestall interruptions. ‘The great majority of our fellow human beings have no clue that the first artificial general intelligence – human-level AI, a conscious machine – will almost certainly be created in the first half of this century. Of the few who do realise where the technology is heading, most are Californian dreamers who think nothing can go wrong: they love technology and they love computers, and they cannot conceive that an intelligent computer will not be their friend.’ ‘I’ve read some of their stuff,’ Matt agreed.

I probably don’t know everything that the military is up to with regard to machine intelligence, but I think I know about their most advanced projects. Their resources are formidable. We’re supervised by the Strategic Technology Office, and I have a high level of clearance. The organisation I run was no mean outfit before I teamed up with Norman and his pals, so I like to think that if the US Army does turn out to be the first institution to build an artificial general intelligence, there will be a well-informed and well-connected civilian organisation standing shoulder-to-shoulder with them and making sure they don’t go off in all sorts of unhealthy directions. ‘Norman and his colleagues have been incredibly helpful. Not only with money, but with contacts, technologies, advice, and of course intelligence. Which brings us to our friend Ivan, and to you, Matt.’

‘But on the other hand, going public too late could be much worse,’ Matt said. ‘If the idea is effectively sprung on people just before it becomes a reality, the panic you are worried about could be enormously damaging.’ Leo was nodding as Matt spoke. ‘If we withhold some of the story and then it gets out, people will be suspicious about what else is being hidden. If it leaks out that the US Army is close to creating the first artificial general intelligence, and has been less than truthful about it, a lot of people will get very concerned. But to be honest I’m more concerned about the more immediate problems. For instance, is it realistic to insist that Matt never speaks to anyone outside this room about his experience – not now and not for the rest of his life? And what about Ivan’s people on the boat and elsewhere? How many of them know more than you’re proposing to disclose?’


pages: 307 words: 88,180

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

AI winter, Airbnb, Albert Einstein, algorithmic trading, artificial general intelligence, autonomous vehicles, barriers to entry, basic income, business cycle, cloud computing, commoditize, computer vision, corporate social responsibility, creative destruction, crony capitalism, Deng Xiaoping, deskilling, Donald Trump, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, full employment, future of work, gig economy, Google Chrome, happiness index / gross national happiness, if you build it, they will come, ImageNet competition, 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, mandatory minimum, Mark Zuckerberg, Menlo Park, minimum viable product, natural language processing, new economy, 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, special economic zone, speech recognition, Stephen Hawking, Steve Jobs, strong AI, The Future of Employment, Travis Kalanick, Uber and Lyft, uber lyft, universal basic income, urban planning, Y Combinator

Bringing them to market requires no major new breakthroughs in AI research, just the nuts-and-bolts work of everyday implementation: gathering data, tweaking formulas, iterating algorithms in experiments and different combinations, prototyping products, and experimenting with business models. But the age of implementation has done more than make these practical products possible. It has also set ablaze the popular imagination when it comes to AI. It has fed a belief that we’re on the verge of achieving what some consider the Holy Grail of AI research, artificial general intelligence (AGI)—thinking machines with the ability to perform any intellectual task that a human can—and much more. Some predict that with the dawn of AGI, machines that can improve themselves will trigger runaway growth in computer intelligence. Often called “the singularity,” or artificial superintelligence, this future involves computers whose ability to understand and manipulate the world dwarfs our own, comparable to the intelligence gap between human beings and, say, insects.

These breakthroughs would need to remove key constraints on the “narrow AI” programs that we run today and empower them with a wide array of new abilities: multidomain learning; domain-independent learning; natural-language understanding; commonsense reasoning, planning, and learning from a small number of examples. Taking the next step to emotionally intelligent robots may require self-awareness, humor, love, empathy, and appreciation for beauty. These are the key hurdles that separate what AI does today—spotting correlations in data and making predictions—and artificial general intelligence. Any one of these new abilities may require multiple huge breakthroughs; AGI implies solving all of them. The mistake of many AGI forecasts is to simply take the rapid rate of advance from the past decade and extrapolate it outward or launch it exponentially upward in an unstoppable snowballing of computer intelligence. Deep learning represents a major leveling up in machine learning, a movement onto a new plateau with a variety of real-world uses: the age of implementation.

I cannot guarantee that scientists definitely will not make the breakthroughs that would bring about AGI and then superintelligence. In fact, I believe we should expect continual improvements to the existing state of the art. But I believe we are still many decades, if not centuries, away from the real thing. There is also a real possibility that AGI is something humans will never achieve. Artificial general intelligence would be a major turning point in the relationship between humans and machines—what many predict would be the most significant single event in the history of the human race. It’s a milestone that I believe we should not cross unless we have first definitively solved all problems of control and safety. But given the relatively slow rate of progress on fundamental scientific breakthroughs, I and other AI experts, among them Andrew Ng and Rodney Brooks, believe AGI remains farther away than often imagined.


pages: 315 words: 89,861

The Simulation Hypothesis by Rizwan Virk

3D printing, Albert Einstein, Apple II, artificial general intelligence, augmented reality, Benoit Mandelbrot, bioinformatics, butterfly effect, 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, Pierre-Simon Laplace, Ralph Waldo Emerson, Ray Kurzweil, Richard Feynman, Schrödinger's Cat, Search for Extraterrestrial Intelligence, Silicon Valley, Stephen Hawking, Steve Jobs, Steve Wozniak, technological singularity, Turing test, Vernor Vinge, Zeno's paradox

Dick, and his beliefs on the simulation hypothesis. Finally, I’d like to thank Ellen McDonough for her endless patience with me as I droned on and on about the simulation hypothesis, and for her never-ending support through thick and thin! Index A The Adjustment Bureau, 8, 79 The Adjustment Team (Dick), 8–9 AFK - away from keyboard, 209–10 AGI (Artificial Generalized Intelligence), 90–91, 96–99 AGI (Artificial Generalized Intelligence) and social media, 104–5 AI (artificial intelligence) as element of Great Simulation, 280–81 ethics and uses, 97–100 gods, angels and the simulation hypothesis, 226–28 and NPCs, 82–84 super-intelligence, 100–101 and virtual reality and simulated consciousness, 16–18 AI (artificial intelligence), history of AI and games, 85–86 DeepMind, AlphaGo and video games, 86–88 digital psychiatrist, 88–89 NLP, AI and quest to pass the Turing Test, 89–92 Turing Test, 84–85 Al-Akhirah, 221–23 Al-Dunya, 221–23 Alexa, 88, 90 aliens, 275–76 allegory of the cave, 270–71 Almheiri, Ahmed, 260 AlphaGo, 86–88 Altered Carbon (Morgan, 2002), 103–4 analog, 161 ancestor simulation, 108–9, 114–15 Anderson, Kevin J., 97 Andreessen, Marc, 287 angels, 225–26 AR (augmented reality), 62–64 AR glasses, 62 arcade-type mechanics, 34 “Are You Living in a Simulation?”

Index A The Adjustment Bureau, 8, 79 The Adjustment Team (Dick), 8–9 AFK - away from keyboard, 209–10 AGI (Artificial Generalized Intelligence), 90–91, 96–99 AGI (Artificial Generalized Intelligence) and social media, 104–5 AI (artificial intelligence) as element of Great Simulation, 280–81 ethics and uses, 97–100 gods, angels and the simulation hypothesis, 226–28 and NPCs, 82–84 super-intelligence, 100–101 and virtual reality and simulated consciousness, 16–18 AI (artificial intelligence), history of AI and games, 85–86 DeepMind, AlphaGo and video games, 86–88 digital psychiatrist, 88–89 NLP, AI and quest to pass the Turing Test, 89–92 Turing Test, 84–85 Al-Akhirah, 221–23 Al-Dunya, 221–23 Alexa, 88, 90 aliens, 275–76 allegory of the cave, 270–71 Almheiri, Ahmed, 260 AlphaGo, 86–88 Altered Carbon (Morgan, 2002), 103–4 analog, 161 ancestor simulation, 108–9, 114–15 Anderson, Kevin J., 97 Andreessen, Marc, 287 angels, 225–26 AR (augmented reality), 62–64 AR glasses, 62 arcade-type mechanics, 34 “Are You Living in a Simulation?” (Bostrom, 2003), 109 artificial consciousness, portrayals of, 95–97 Artificial Generalized Intelligence (AGI), 90–91, 96–99 artificial intelligence (AI). See AGI (Artificial Generalized Intelligence); AI (artificial intelligence); AI (artificial intelligence), history of Aserinsky, Eugene, 189 Ashely-Farrand, 206 Asimov, Isaac, 99 assembly language, 33 Asteroids, 36–37 Atari, 2, 4, 32, 38 atom, 167–68 atomic clocks, 170 augmented images, photorealistic, 63–64 augmented reality (AR), 62–64 Avatar, 58, 64 avatars, 44–45, 46f, 49, 273–74 B bag of karma, 117, 208 basic game loop, 31 BASIC programming language, 33 Beane, Silas, 255 Bhagavad Gita, 204–5 big game world, 30 “big TOE” (Theory of Everything), 156–57 biological materials, 3D printers, 71–72 bitmap, 163–64 black holes, 178–79 Blackthorn, 55 Blade Runner, 9, 77–78, 94 Blade Runner 2049, 65 Bohr, Niels, 13, 122, 124–25, 131, 167 Book of the Dead/Bardo Thol, 192 Boolean logic gates, 258 Born, Max, 131, 167 Bostrom, Nick, 5, 24–26, 105, 114–15, 220–21, 247, 281 Bostrom’s Simulation Argument, 110–11 Bostrom’s Simulation Argument, statistical basis for, 111–14 Brahman, 191 branching, 159 Breakout, 87 A Brief History of Time (Hawking), 10 Brinkley, Dannion, 229–231, 241 Buddha, 1, 183, 249 Buddhism, 14–15 Buddhist Dream Yoga, 191–94 Bushnell, Nolan, 34 butterfly effect, 18–19 Byte, 33 C c (speed of light), 174 C# programming language, 33, 171–73 CAD (computer-aided design), 287 Cameron, James, 64, 96–97 Campbell, Thomas, 156–57, 173–76, 250, 254–55 Capra, Fritjof, 203–4 Carmack, John, 59–60 central processing units (CPUs), 137 CGI (computer-generated imagery) techniques, 63–66 Chalmers, David, 246–47 chaos theory, 18–19 chat-bot, 31, 88, 98, 118 checksums, 256 Chess, 104 chess-playing computer, 86f Choose Your Own Adventure, 83 Christianity, 15–16 Christianity and Judaism, 223–25 Clarke, Arthur C., 96 classical physics, 29, 125, 161, 166, 283–84, 288 classical vs. relativistic physics, 122–24 Cline, Ernest, 56 clock-speed and quantized time, computer simulations, 171–73 Close Encounters of the Third Kind, 232, 276 cloud of probabilities, 127 collective dream, 187–88 Colossal Cave Adventure, 27–29, 32, 34 Colossal Cave Adventure, map of, 29f computation, 18–19 computation, and other sciences, 287 computation, evidence of, 256–57, 267–68 overview, 246–47 computation in nature, evidence of, 263–66 computational irreducibility, 18, 79, 266 computer simulations clock-speed and quantized time, 171–73 . see also ancestor simulation; Great Simulation; Simulation Argument; simulation hypothesis; Simulation Point computer-generated imagery (CGI) techniques, 64–66 “Computing Machinery and Intelligence” (Turing, 1950), 85 conditional rendering, evidence of, 253–55 conflict resolution, 173 conscious players, people as, 114–15 consciousness, 148 as digital informaion, 17–18 as information and computation, 82 consciousness, defined, 115–16 consciousness, digital vs. spiritual, 116–18 consciousness and metaphysical experiments, 249–250 consciousness as information, 104–5 consciousness transference, 198–99 Constraints on the Universe as a Numerical Simulation (Beane, Davoudi and Savage), 255 Copenhagen interpretation, 131 Cosmos, 251 CPUs (central processing units), 137 . see also GPUs/CPUs Creative Labs, 62 Crichton, Michael, 71–72 Crick, Francis, 116 Crowther, Will, 27 Curry, Adam, 76 D Dalai Lama, 207 Data, Star Trek: The Next Generation, 95–96, 115 Davoudi, Zohreh, 255 deathmatch mode, 43–44 Deep Blue, 86 DeepMind, 86–88, 94, 98 déjà vu, 240–41 delayed-choice double slit experiment, 145f delayed-choice experiment, 143–46 delayed-measurement experiment, 146 DELTA t (T), 174 Department of Defense (DOD), 232 Descartes, René, 11 DeWitt, Bryce, 149 dharma, 191 Dick, Leslie “Tessa” B., 8–9 Dick, Philip K., 274, 289 and alternate realities, 8–9 computer simulations and variables, 19 and implanted memories, 77–78 life as computer-generated simulation, 78–79 Metz Sci-Fi Convention, 1977, 2 question of reality vs. fiction, 71–72 simulated worlds, 80 speculative technologies, 53 digital consciousness, 116–18 digital film resolution, 65 digital immortality, 82, 105 digital psychiatrist, 88–89, 161 directed graph, 153–55 Discrete World, 165–66 Do Androids Dream of Electric Sheep, 9 Donkey Kong, 1 Doom, 43–44, 43f, 59–60, 137–38 DOTA 2, 87, 94 dot-matrix printers (2D), 69–71 double slit experiment, 128–29, 129f downloadable consciousness, 54, 101–4, 198, 207, 281 downloadable consciousness and seventh yoga, 197–99 Dr.

After the initial news about Google Duplex, though, there was widespread concern that robo-calls could now sound authentic and that this might lead to a whole new wave of spam phone calls! Google quickly backtracked and decided that it would always have autonomous agents making phone calls “self-identify” as an agent. An AI that can pass the Turing Test and do other things that humans can do has been dubbed “Artificial Generalized Intelligence,” or AGI. Thus far, most AI applications have focused on specific tasks—reading handwriting, predicting certain patterns from numbers, helping a human with solving limited tasks, etc. While developments in NLP technology have made incredible strides in the past few decades, many experts still believe that we are probably within a decade of being able to create artificially intelligent characters (or NPCs) that can pass the Turing Test, within games or in the real world.


Falter: Has the Human Game Begun to Play Itself Out? by Bill McKibben

23andMe, Affordable Care Act / Obamacare, Airbnb, American Legislative Exchange Council, Anne Wojcicki, artificial general intelligence, Bernie Sanders, Bill Joy: nanobots, Burning Man, call centre, carbon footprint, Charles Lindbergh, clean water, Colonization of Mars, computer vision, David Attenborough, Donald Trump, double helix, Edward Snowden, Elon Musk, ending welfare as we know it, energy transition, Flynn Effect, Google Earth, Hyperloop, impulse control, income inequality, Intergovernmental Panel on Climate Change (IPCC), Jane Jacobs, Jaron Lanier, Jeff Bezos, job automation, life extension, light touch regulation, Mark Zuckerberg, mass immigration, megacity, Menlo Park, moral hazard, Naomi Klein, Nelson Mandela, obamacare, off grid, oil shale / tar sands, pattern recognition, Peter Thiel, plutocrats, Plutocrats, profit motive, Ralph Waldo Emerson, Ray Kurzweil, Robert Mercer, Ronald Reagan, Sam Altman, self-driving car, Silicon Valley, Silicon Valley startup, smart meter, Snapchat, stem cell, Stephen Hawking, Steve Jobs, Steve Wozniak, Steven Pinker, strong AI, supervolcano, technoutopianism, The Wealth of Nations by Adam Smith, traffic fines, Travis Kalanick, urban sprawl, Watson beat the top human players on Jeopardy!, Y Combinator, Y2K, yield curve

When the fully self-driving car finally arrives in your driveway, that will be weak AI to the max: thousands of sensors deployed to perform a specific task better than you can do it. You’ll be able to drink IPAs for hours at your local tavern, and the self-driving car will take you home—and it may well be able to recommend precisely which IPAs you’d like best. But it won’t be able to carry on an interesting discussion about whether this is the best course for your life. That next step up is artificial general intelligence, sometimes referred to as “strong AI.” That’s a computer “as smart as a human across the board, a machine that can perform any intellectual task a human being can,” in Urban’s description. This kind of intelligence would require “the ability to reason, plan, solve problems, think abstractly, comprehend complex ideas, learn quickly, and learn from experience.”9 Five years ago a pair of researchers asked hundreds of AI experts at a series of conferences when we’d reach this milestone—more precisely, it asked them to name a “median optimistic year,” when there was a 10 percent chance we’d get there; a median realistic year, a 50 percent chance; and a “pessimistic” year, in which there was a 90 percent chance.

“I keep sounding the alarm bell,” he continued, “but until people see robots going down the street killing people, they don’t know how to react, it seems so ethereal.”19 All the big brains were talking the same way. Hawking wrote that success in AI would be “the biggest event in human history,” but it might “also be the last, unless we learn to avoid the risks.”20 And here’s Michael Vassar, president of the Machine Intelligence Research Institute: “I definitely think people should try to develop Artificial General Intelligence with all due care. In this case all due care means much more scrupulous caution than would be necessary for dealing with Ebola or plutonium.”21 Why are people so scared? Let the Swedish philosopher Nick Bostrom explain. He’s hardly a Luddite. Indeed, he gave a speech in 1999 to a California convention of “transhumanists” that may mark the rhetorical high water of the entire techno-utopian movement.

The approaching epochal moment when computers will be as smart as humans becomes just a meaningless way station. As the science writer Tim Urban points out, an AI “wouldn’t see human-level intelligence as some important milestone—it’s only a relevant marker from our point of view—and wouldn’t have any reason to stop at our level. And given the advantages over us that even human-intelligence-equivalent artificial general intelligence (AGI) would have, it’s pretty obvious that it would only hit human intelligence for a brief instant before racing onwards to the realm of superior-to-human intelligence.”5 After all, AGI’s got better components. Already today’s microprocessors run about ten million times the speed of our brains, whose internal communications “are horribly outmatched by a computer’s ability to communicate optically at the speed of light,” Urban observes.


pages: 331 words: 47,993

Artificial You: AI and the Future of Your Mind by Susan Schneider

artificial general intelligence, brain emulation, Elon Musk, Extropian, hive mind, life extension, megastructure, pattern recognition, Ray Kurzweil, Search for Extraterrestrial Intelligence, silicon-based life, Stephen Hawking, superintelligent machines, technological singularity, The Coming Technological Singularity, theory of mind, Turing machine, Turing test, Whole Earth Review, wikimedia commons

The development of AI is driven by market forces and the defense industry—billions of dollars are now pouring into constructing smart household assistants, robot supersoldiers, and supercomputers that mimic the workings of the human brain. Indeed, the Japanese government has launched an initiative to have androids take care of the nation’s elderly, in anticipation of a labor shortage. Given the current rapid-fire pace of its development, AI may advance to artificial general intelligence (AGI) within the next several decades. AGI is intelligence that, like human intelligence, can combine insights from different topic areas and display flexibility and common sense. Indeed, AI is already projected to outmode many human professions within the next decades. According to a recent survey, for instance, the most-cited AI researchers expect AI to “carry out most human professions at least as well as a typical human” within a 50 percent probability by 2050, and within a 90 percent probability by 2070.1 I’ve mentioned that many observers have warned of the rise of superintelligent AI: synthetic intelligences that outthink the smartest humans in every domain, including common sense reasoning and social skills.

“Response to Susan Schneider’s ‘The Philosophy of “Her,’ ” H+ Magazine, March 26, http://hplusmagazine.com/2014/03/26/response-to-susan-schneiders-the-philosophy-of-her/. Zimmer, Carl. 2010. “Sizing Up Consciousness By Its Bits,” New York Times, September 20. INDEX Page numbers in italics indicate illustrations. Aaronson, Scott, 64 ACT test, 50–57, 60, 65, 67 Active SETI, 105–9 afterlife of the brain, 8, 145 AGI (artificial general intelligence), 9, 43 AI (artificial intelligence), 1–15, 148–50 alien/extraterrestrial, 5, 98–119 (See also alien/extraterrestrial AI) consciousness issues, 2–6 (See also consciousness) development of, 9–10 implications, importance of thinking through, 2–3, 10 Jetsons fallacy and, 12–13 merging humans with AI, 6–8, 72–81 (See also merging humans with AI) mind design, concept of, 1 postbiological, 99 singularity, approach of, 11–12 software, mind viewed as, 7–8, 120–47 (See also software, mind viewed as) transhumanism, 13–15 (See also transhumanism) uncertainties and unknowns regarding, 15 AI consciousness, problem of, 3–6, 16–32, 148–49 alien intelligences, postbiological, 5 alien/extraterrestrial AI, 110–11 biological naturalism, arguments against, 18–22, 34, 158n4 capability of machines for consciousness, 17–18 Chinese Room thought experiment and, 19–22, 20, 34, 148 control problem, 4–5 ethical treatment of conscious/potentially conscious AIs, 39, 67–69, 149 isomorph thought experiment and, 26–31, 57, 158nn13–14, 159nn10–11 “problem of other minds” and, 158n3 slavery and, 4, 39 techno-optimism, arguments for, 18, 23–26, 31, 34 value placed on humans by, 5 “Wait and See Approach” to, 33–34, 45 AI slavery, 4, 39 Alcor, 121, 145 alien/extraterrestrial AI, 5, 98–119 BISAs (biologically inspired superintelligent aliens), 113–19 consciousness, 110–11 control problem and, 104–5 postbiological cosmos approach, 99–104 SETI (Search for Extraterrestrial Intelligence), 101, 105–9, 106 software theory, 119 superintelligent AI minds, encountering, 109–19 Alzheimer’s disease, 44, 58 Amazon, 131 Arrival (film), 107 artificial general intelligence (AGI), 9, 43 artificial intelligence.

Aaronson, Scott, 64 ACT test, 50–57, 60, 65, 67 Active SETI, 105–9 afterlife of the brain, 8, 145 AGI (artificial general intelligence), 9, 43 AI (artificial intelligence), 1–15, 148–50 alien/extraterrestrial, 5, 98–119 (See also alien/extraterrestrial AI) consciousness issues, 2–6 (See also consciousness) development of, 9–10 implications, importance of thinking through, 2–3, 10 Jetsons fallacy and, 12–13 merging humans with AI, 6–8, 72–81 (See also merging humans with AI) mind design, concept of, 1 postbiological, 99 singularity, approach of, 11–12 software, mind viewed as, 7–8, 120–47 (See also software, mind viewed as) transhumanism, 13–15 (See also transhumanism) uncertainties and unknowns regarding, 15 AI consciousness, problem of, 3–6, 16–32, 148–49 alien intelligences, postbiological, 5 alien/extraterrestrial AI, 110–11 biological naturalism, arguments against, 18–22, 34, 158n4 capability of machines for consciousness, 17–18 Chinese Room thought experiment and, 19–22, 20, 34, 148 control problem, 4–5 ethical treatment of conscious/potentially conscious AIs, 39, 67–69, 149 isomorph thought experiment and, 26–31, 57, 158nn13–14, 159nn10–11 “problem of other minds” and, 158n3 slavery and, 4, 39 techno-optimism, arguments for, 18, 23–26, 31, 34 value placed on humans by, 5 “Wait and See Approach” to, 33–34, 45 AI slavery, 4, 39 Alcor, 121, 145 alien/extraterrestrial AI, 5, 98–119 BISAs (biologically inspired superintelligent aliens), 113–19 consciousness, 110–11 control problem and, 104–5 postbiological cosmos approach, 99–104 SETI (Search for Extraterrestrial Intelligence), 101, 105–9, 106 software theory, 119 superintelligent AI minds, encountering, 109–19 Alzheimer’s disease, 44, 58 Amazon, 131 Arrival (film), 107 artificial general intelligence (AGI), 9, 43 artificial intelligence. See AI asbestos, 66 Asimov, Isaac, “Robot Dreams,” 57 astronauts and conscious AI, 41–43, 42, 103 Battlestar Galactica (TV show), 99 Bello, Paul, 159n1 Berger, Theodore, 44 Bess, Michael, 12 Big Think, 126 biological naturalism, 18–22, 34, 158n4 biologically inspired superintelligent aliens (BISAs), 113–19 Black Box Problem, 46 black holes, 10 Blade Runner (film), 17, 57 Block, Ned, 159n1, 162n11 “The Mind as the Software of the Brain,” 134 body.


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

4chan, Affordable Care Act / Obamacare, Airbnb, Alvin Roth, Amazon Mechanical Turk, Amazon Web Services, artificial general intelligence, augmented reality, autonomous vehicles, barriers to entry, basic income, Bernie Madoff, Bernie Sanders, Bill Joy: nanobots, bitcoin, blockchain, Bretton Woods, Brewster Kahle, British Empire, business process, call centre, Capital in the Twenty-First Century by Thomas Piketty, Captain Sullenberger Hudson, Chuck Templeton: OpenTable:, Clayton Christensen, clean water, cloud computing, cognitive dissonance, collateralized debt obligation, commoditize, computer vision, corporate governance, corporate raider, creative destruction, crowdsourcing, Danny Hillis, data acquisition, deskilling, DevOps, Donald Davies, Donald Trump, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Filter Bubble, Firefox, Flash crash, full employment, future of work, George Akerlof, gig economy, glass ceiling, Google Glasses, Gordon Gekko, gravity well, greed is good, Guido van Rossum, High speed trading, hiring and firing, Home mortgage interest deduction, Hyperloop, income inequality, 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 Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Kevin Kelly, Khan Academy, Kickstarter, knowledge worker, Kodak vs Instagram, Lao Tzu, Larry Wall, Lean Startup, Leonard Kleinrock, Lyft, Marc Andreessen, Mark Zuckerberg, market fundamentalism, Marshall McLuhan, McMansion, microbiome, microservices, minimum viable product, mortgage tax deduction, move fast and break things, move fast and break things, Network effects, new economy, Nicholas Carr, obamacare, Oculus Rift, packet switching, PageRank, pattern recognition, Paul Buchheit, peer-to-peer, peer-to-peer model, Ponzi scheme, 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, 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, strong AI, TaskRabbit, telepresence, the built environment, 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, transaction costs, transcontinental railway, transportation-network company, Travis Kalanick, trickle-down economics, Uber and Lyft, Uber for X, uber lyft, ubercab, universal basic income, US Airways Flight 1549, VA Linux, 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

Rather than spelling out every procedure, a base program such as an image recognizer or categorizer is built, and then trained by feeding it large amounts of data labeled by humans until it can recognize patterns in the data on its own. We teach the program what success looks like, and it learns to copy us. This leads to the fear that these programs will become increasingly independent of their creators. Artificial general intelligence (also sometimes referred to as “strong AI”) is still the stuff of science fiction. It is the product of a hypothetical future in which an artificial intelligence isn’t just trained to be smart about a specific task, but to learn entirely on its own, and can effectively apply its intelligence to any problem that comes its way. The fear is that an artificial general intelligence will develop its own goals and, because of its ability to learn on its own at superhuman speeds, will improve itself at a rate that soon leaves humans far behind. The dire prospect is that such a superhuman AI would have no use for humans, or at best might keep us in the way that we keep pets or domesticated animals.

No one even knows what such an intelligence might look like, but people like Nick Bostrom, Stephen Hawking, and Elon Musk postulate that once it exists, it will rapidly outstrip humanity, with unpredictable consequences. Bostrom calls this hypothetical next step in strong AI “artificial superintelligence.” Deep learning pioneers Demis Hassabis and Yann LeCun are skeptical. They believe we’re still a long way from artificial general intelligence. Andrew Ng, formerly the head of AI research for Chinese search giant Baidu, compared worrying about hostile AI of this kind to worrying about overpopulation on Mars. Even if we never achieve artificial general intelligence or artificial superintelligence, though, I believe that there is a third form of AI, which I call hybrid artificial intelligence, in which much of the near-term risk resides. When we imagine an artificial intelligence, we assume it will have an individual self, an individual consciousness, just like us.

The highly publicized victory of AlphaGo over Lee Sedol, one of the top-ranked human Go players, represented a milestone for AI, because of the difficulty of the game and the impossibility of using brute-force analysis of every possible move. But DeepMind cofounder Demis Hassabis wrote, “We’re still a long way from a machine that can learn to flexibly perform the full range of intellectual tasks a human can—the hallmark of true artificial general intelligence.” Yann LeCun also blasted those who oversold the significance of AlphaGo’s victory, writing, “most of human and animal learning is unsupervised learning. If intelligence was a cake, unsupervised learning would be the cake, supervised learning would be the icing on the cake, and reinforcement learning would be the cherry on the cake. We know how to make the icing and the cherry, but we don’t know how to make the cake.


pages: 385 words: 111,113

Augmented: Life in the Smart Lane by Brett King

23andMe, 3D printing, additive manufacturing, Affordable Care Act / Obamacare, agricultural Revolution, Airbnb, Albert Einstein, Amazon Web Services, Any sufficiently advanced technology is indistinguishable from magic, Apple II, artificial general intelligence, asset allocation, augmented reality, autonomous vehicles, barriers to entry, bitcoin, blockchain, business intelligence, business process, call centre, chief data officer, Chris Urmson, Clayton Christensen, clean water, congestion charging, crowdsourcing, cryptocurrency, deskilling, different worldview, disruptive innovation, distributed generation, distributed ledger, double helix, drone strike, Elon Musk, Erik Brynjolfsson, Fellow of the Royal Society, fiat currency, financial exclusion, Flash crash, Flynn Effect, future of work, gig economy, Google Glasses, Google X / Alphabet X, Hans Lippershey, Hyperloop, income inequality, industrial robot, information asymmetry, Internet of things, invention of movable type, invention of the printing press, invention of the telephone, invention of the wheel, James Dyson, Jeff Bezos, job automation, job-hopping, John Markoff, John von Neumann, Kevin Kelly, Kickstarter, Kodak vs Instagram, Leonard Kleinrock, lifelogging, low earth orbit, low skilled workers, Lyft, M-Pesa, Mark Zuckerberg, Marshall McLuhan, megacity, Metcalfe’s law, Minecraft, mobile money, money market fund, more computing power than Apollo, Network effects, new economy, obamacare, Occupy movement, Oculus Rift, off grid, packet switching, pattern recognition, peer-to-peer, Ray Kurzweil, RFID, ride hailing / ride sharing, Robert Metcalfe, Satoshi Nakamoto, Second Machine Age, selective serotonin reuptake inhibitor (SSRI), self-driving car, sharing economy, Shoshana Zuboff, Silicon Valley, Silicon Valley startup, Skype, smart cities, smart grid, smart transportation, Snapchat, social graph, software as a service, speech recognition, statistical model, stem cell, Stephen Hawking, Steve Jobs, Steve Wozniak, strong AI, TaskRabbit, technological singularity, telemarketer, telepresence, telepresence robot, Tesla Model S, The Future of Employment, Tim Cook: Apple, trade route, Travis Kalanick, Turing complete, Turing test, uber lyft, undersea cable, urban sprawl, V2 rocket, Watson beat the top human players on Jeopardy!, white picket fence, WikiLeaks

Neural networks or algorithms that can make human equivalent decisions for very specific functions, and perform better than humans on a benchmark basis. This does not prohibit the intelligence from having machine learning or cognition capabilities so that it can learn new tasks or process new information outside of its initial programming. In fact, many machine intelligences already have this capability. Examples include: Google self-driving car, IBM Watson, high-frequency trading (HFT) algorithms, facial recognition software • Artificial General Intelligence—a human-equivalent machine intelligence that not only passes the Turing Test and responds as a human would but can also make human equivalent decisions. It will likely also process non-logic or informational cues such as emotion, tone of voice, facial expression and nuances that currently a living intelligence could (can your dog tell if you are angry or sad?). Essentially, such an AI would be capable of successfully performing any intellectual task that a human being could

Amazingly, the right robots might enable her to have the best of both worlds… What if Maria could stay in Manila and still work with patients in the United States? Imagine Maria in a call centre or even working from home. She is at her computer monitoring ten robot companions in an assisted care facility in Los Angeles. Each patient has a personal dedicated companion robot sitting by his or her bedside, running standard artificial general intelligence (AGI) software in a semi-autonomous mode. In this mode, the personal robot will be able to carry on conversations, answer basic questions and help the patient get assistance or entertainment. Cameras and sensors in the robot will be able to read the patient’s blood pressure, wakefulness, heart rate, emotional state, etc. At any time, Maria can extend her telepresence into the robot and thereby see through the eyes of the robot and make use of the data from the robot’s sensors.

He has cameras on his eyes and on his chest, which allow him to recognize people’s faces, not only that, but recognize their gender, their age, whether they are happy or sad, and that makes him very exciting for places like hotels for example, where you need to appreciate the customers in front of you and react accordingly.” Jong Lee, CEO of Hanson Robotics Hanson Robotics is combining EQ in the form of an advanced artificial general intelligence and the most human robots on the planet. If you like gambling, you might soon be at a table, money burning a hole in your pocket, and meet Eva, who is being tested to be a beautiful baccarat dealer for casinos in Macau, China. Eva will be able to stand at the dealer’s position and deal the cards from a real deck and interact with the players. Eva can deal the cards from the shoe using her advanced robotics arms while using her advanced AGI, cameras and sensors to appear as human as any dealer.


pages: 419 words: 109,241

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

3D printing, agricultural Revolution, AI winter, Airbnb, Albert Einstein, algorithmic trading, artificial general intelligence, autonomous vehicles, basic income, Bertrand Russell: In Praise of Idleness, blue-collar work, British Empire, Capital in the Twenty-First Century by Thomas Piketty, cloud computing, computer age, computer vision, computerized trading, creative destruction, David Graeber, David Ricardo: comparative advantage, demographic transition, deskilling, disruptive innovation, Donald Trump, Douglas Hofstadter, drone strike, Edward Glaeser, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, financial innovation, future of work, gig economy, Gini coefficient, Google Glasses, Gödel, Escher, Bach, income inequality, income per capita, industrial robot, interchangeable parts, invisible hand, Isaac Newton, Jacques de Vaucanson, James Hargreaves, job automation, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, Joi Ito, Joseph Schumpeter, Kenneth Arrow, Khan Academy, Kickstarter, low skilled workers, lump of labour, Marc Andreessen, Mark Zuckerberg, means of production, Metcalfe’s law, natural language processing, Network effects, Occupy movement, offshore financial centre, Paul Samuelson, Peter Thiel, pink-collar, precariat, purchasing power parity, Ray Kurzweil, ride hailing / ride sharing, road to serfdom, Robert Gordon, Sam Altman, Second Machine Age, self-driving car, shareholder value, sharing economy, Silicon Valley, Snapchat, social intelligence, software is eating the world, sovereign wealth fund, spinning jenny, Stephen Hawking, Steve Jobs, strong AI, telemarketer, The Future of Employment, The Rise and Fall of American Growth, the scientific method, The Wealth of Nations by Adam Smith, Thorstein Veblen, Travis Kalanick, Turing test, Tyler Cowen: Great Stagnation, universal basic income, upwardly mobile, Watson beat the top human players on Jeopardy!, We are the 99%, wealth creators, working poor, working-age population, Y Combinator

abandonment ability bias Acemoglu, Daron adaptive learning systems admissions policies, conditional basic income and affective capabilities affective computing Age of Labor ALM hypothesis and optimism and overview of before and during twentieth century in twenty-first century Agesilaus AGI. See artificial general intelligence agoge agriculture Airbnb airborne fulfillment centers Alaska Permanent Fund Alexa algorithms alienation al-Khwarizmi, Abdallah Muhammad ibn Musa ALM (Autor-Levy-Murnane) hypothesis AlphaGo AlphaGo Zero AlphaZero Altman, Sam Amara, Roy Amazon artificial intelligence and changing-pie effect and competition and concerns about driverless vehicles and market share of network effects and profit and Andreessen, Marc ANI. See artificial narrow intelligence antitrust legislation apathy apotheosis Apple Archilochus Arendt, Hannah Ariely, Dan aristocracy Aristotle Arrow, Kenneth artificial general intelligence (AGI) artificial intelligence (AI) automata and bottom-up vs. top-down economics and fallacy of first wave of general history of priority shift and second wave of artificial narrow intelligence (ANI) artificial neural networks artisan class assembly lines AT&T Atari video games Atkinson, Anthony ATMs (automatic teller machines) automata automation, number of jobs at risk of automation anxiety automation risk autonomous vehicles Autor, David bandwagon effect bank tellers basic income conditional overview of universal Becker, Gary Bell, Daniel Berlin, Isaiah Beveridge, William Beveridge Report bigger-pie effect Big State capital-sharing and conditional basic income and income-sharing and labor-supporting meaning creation and overview of taxation and welfare state vs.

Consider the final words of On the Origin of Species: “There is grandeur in this view of life, with its several powers, having been originally breathed into a few forms or into one; and that, whilst this planet has gone cycling on according to the fixed law of gravity, from so simple a beginning endless forms most beautiful and most wonderful have been, and are being, evolved.”19 This is not the writing of a metaphysical grinch. Darwin’s view of life without a creator has a “grandeur” to it, and is articulated with an almost religious sense of awe. One day we may feel that way about our unhuman machines as well. ARTIFICIAL GENERAL INTELLIGENCE The ancient Greek poet Archilochus once wrote: “The fox knows many things, but the hedgehog knows one big thing.” Isaiah Berlin, who found this mysterious line in the surviving scraps of Archilochus’s poetry, famously used it as a metaphor to distinguish between two types of human being: people who know a little about a lot (the foxes) and people who know a lot about a little (the hedgehogs).20 In our setting, we can repurpose that metaphor to think about human beings and machines.

At the moment, machines are prototypical hedgehogs, each of them designed to be very strong at some extremely specific, narrowly defined task—think of Deep Blue and chess, or AlphaGo and go—but hopeless at performing a range of different tasks. Human beings, on the other hand, are proud foxes, who might now find themselves thrashed by machines at certain undertakings, but can still outperform them at a wide spread of others. For many AI researchers, the intellectual holy grail is to build machines that are foxes rather than hedgehogs. In their terminology, they want to build an “artificial general intelligence” (AGI), with wide-ranging capabilities, rather than an “artificial narrow intelligence” (ANI), which can only handle very particular assignments.21 That is what interests futurists like Ray Kurzweil and Nick Bostrom. But there has been little success in that effort, and critics often put forward the elusiveness of AGI as a further reason for being skeptical about the capabilities of machines.


pages: 181 words: 52,147

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

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

Equally excitingly, with a simple interface and the guidance of computers, melomics will enable anybody to compose pleasing music. Ultimately, powerful computational systems—Siris on steroids—will reason creatively to solve problems in mathematics and physics that have bedeviled humans. These systems will synthesize inputs to arrive at something resembling original works or to solve unstructured problems without benefit of specific rules or guidance. Such broader reasoning ability is known as artificial general intelligence (A.G.I.), or hard A.I. One step beyond this is artificial superintelligence, the stuff out of science fiction that is still so far away—and crazy—that I don’t even want think about it. This is when the computers become smarter than us. I would rather stay focused on today’s A.I., the narrow and practical stuff that is going to change our lives. The fact is that, no matter what the experts say, no one really knows how A.I. will evolve in the long term.

Robots will soon become sure-footed; and a robot will, rather than merely open a door, succeed in opening it while holding a bag of groceries and ensuring that the dog doesn’t escape. When I buy Rosie, I may have to show her around the house, but she’ll quickly learn what I need, where my washer and dryer are located, and how to navigate around and clean the bathroom. And I expect that she will be as witty and lovable as she was on TV. No, she won’t have the artificial general intelligence that will make her seem human, but she will be able to have fun conversations with us. In fact, a very limited version of Rosie can be found at hospitals around the country. Her name is Tug, and she is produced by Aethon Inc. of Pittsburgh. Tug performs the most essential duties of today’s hospital orderly, such as delivering medications and equipment to different floors. Tug costs considerably less than the orderly position she replaces.

They encouraged Alex and me to redraft the book, crystallize our thoughts, and produce something that I hope has the potential to help you make a difference in our world. INDEX Abbeel, Pieter, 85–86 Accelerating returns, law of, 12–13 Addison Lee, 8–9 Africa health care in, 75, 116 technology in, 14, 23, 72n, 181, 186–187, 189 AIC Chile, 181 Airliner, supersonic, 7 Anderson, Chris, 115 Anger in society, 3–4 Argus retinal prosthesis, 167–168 Artificial general intelligence (A.G.I.), 40 Artificial intelligence (A.I.), 7, 12, 13. See also specific topics benefits of, 43 fostering autonomy vs. dependence, 44–46 hard, 40 how it will affect our lives and take our jobs, 40–43 and the labor market, 96–97 in medical field, 73–76 narrow/soft, 38 Preparing for the Future of Artificial Intelligence, 45–46 risks of, 45 Artificial intelligence (A.I.) assistants, 43, 46, 85.


pages: 688 words: 147,571

Robot Rules: Regulating Artificial Intelligence by Jacob Turner

Ada Lovelace, Affordable Care Act / Obamacare, AI winter, algorithmic trading, artificial general intelligence, Asilomar, Asilomar Conference on Recombinant DNA, autonomous vehicles, Basel III, bitcoin, blockchain, brain emulation, Clapham omnibus, cognitive dissonance, corporate governance, corporate social responsibility, correlation does not imply causation, crowdsourcing, distributed ledger, don't be evil, Donald Trump, easy for humans, difficult for computers, effective altruism, Elon Musk, financial exclusion, financial innovation, friendly fire, future of work, hive mind, Internet of things, iterative process, job automation, John Markoff, John von Neumann, Loebner Prize, medical malpractice, Nate Silver, natural language processing, nudge unit, obamacare, off grid, pattern recognition, Peace of Westphalia, race to the bottom, Ray Kurzweil, 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, Turing test, Vernor Vinge

Hart, Punishment and Responsibility: Essays in the Philosophy of Law (Oxford: Clarendon Press, 1978). 137Robert Lowe and Tom Ziemke, “Exploring the Relationship of Reward and Punishment in Reinforcement Learning: Evolving Action Meta-Learning Functions in Goal Navigation” (ADPRL), 2013 IEEE Symposium, pp. 140–147 (IEEE, 2013). 138Stephen M. Omohundro, “The Basic AI Drives”, in Proceedings of the First Conference on Artificial General Intelligence, 2008. 139Stuart Russell, “Should We Fear Supersmart Robots?”, Scientific American, Vol. 314 (June 2016), 58–59. 140Nate Soares and Benja Fallenstein, “Aligning Superintelligence with Human Interests: A Technical Research Agenda”, in The Technological Singularity (Berlin and Heidelberg: Springer, 2017), 103–125. See also Stephen M. Omohundro, “The Basic AI Drives”, in Proceedings of the First Conference on Artificial General Intelligence, 2008. 141Ibid. 142Nick Bostrom, Superintelligence : Paths, Dangers, Strategies (Oxford: Oxford University Press, 2014), Chapter 9. 143See John von Neumann and Oskar Morgenstern, Theory of Games and Economic Behavior (Princeton, NJ: Princeton University Press, 1944). 144Nate Soares and Benja Fallenstein, “Toward Idealized Decision Theory”, Technical Report 2014–7 (Berkeley, CA: Machine Intelligence Research Institute, 2014), https://​arxiv.​org/​abs/​1507.​01986, accessed 1 June 2018. 145See, for example, Thomas Harris, The Silence of the Lambs (London: St.

As Wallach and Allen comment: “pessimists tend to get weeded out of the profession”, Wendell Wallach and Colin Allen, Moral Machines: Teaching Robots Right from Wrong (Oxford: Oxford University Press, 2009), 68. For instance, Margaret Boden was one of the most well-known proponents of the sceptical view, although in her latest work, Margaret Boden, AI: Its nature and Future (Oxford: Oxford University Press, 2016), 119 et seq she acknowledges the potential for “real” artificial intelligence, but maintains that “…no one knows for sure, whether [technology described as Artificial General Intelligence] could really be intelligent”. 20See further Chapter 3 at s. 2.1.2. 21As to AI systems developing the capacity to self-improve, see further FN 114 below and more generally Chapter 2 at s. 3.2. 22Our prediction for the process of narrow AI gradually coming closer to general AI is similar to evolution. Homo sapiens did not appear overnight as if by magic. Instead, we developed iteratively through a series of gradual upgrades to our hardware (bodies) and software (minds) on the basis of trial and error experiments, otherwise known as natural selection. 23Jerry Kaplan, Artificial Intelligence: What Everyone Needs to Know (New York: Oxford University Press, 2016), 1. 24Peter Stone et al., “Defining AI”, in “Artificial Intelligence and Life in 2030”.

See also Daniel Kahneman, Thinking, Fast and Slow (London: Penguin, 2011). 59See more general discussion in Chapter 8 at s. 5.4.2. 60See Laurent Orseau and Stuart Armstrong, “Safely Interruptible Agents”, 28 October 2016, http://​intelligence.​org/​files/​Interruptibility​.​pdf, accessed 1 June 2018; El Mahdi El Mhamdi, Rachid Guerraoui, Hadrien Hendrikx, and Alexandre Maure, “Dynamic Safe Interruptibility for Decentralized Multi-Agent Reinforcement Learning”, EPFL Working Paper (2017) No. EPFL-WORKING-229332. 61Dylan Hadfield-Menell, Anca Dragan, Pieter Abbeel, and Stuart Russell, “The Off-Switch Game”, arXiv preprint arXiv:1611.08219 (2016), 1. 62See, for example, Stephen Omohundro, “The Basic AI Drives”, in Proceedings of the First Conference on Artificial General Intelligence (2008). 63Ibid. 64Arguably, an excess of confidence in the “rightness” of an ultimate goal—particularly where that goal is not of a nature that is observable in the natural world—can lead to undesirable consequences in human actions, as well as those of AI. For instance, it might be said that belief-based fundamentalists, whether on the basis of religion, animal rights , nationalism, etc., suffer from an excess of confidence.


pages: 513 words: 152,381

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

3D printing, agricultural Revolution, Albert Einstein, artificial general intelligence, Asilomar, Asilomar Conference on Recombinant DNA, availability heuristic, Columbian Exchange, computer vision, cosmological constant, cuban missile crisis, decarbonisation, defense in depth, delayed gratification, demographic transition, Doomsday Clock, Drosophila, effective altruism, Elon Musk, Ernest Rutherford, global pandemic, Intergovernmental Panel on Climate Change (IPCC), Isaac Newton, James Watt: steam engine, Mark Zuckerberg, mass immigration, meta analysis, meta-analysis, Mikhail Gorbachev, mutually assured destruction, Nash equilibrium, Norbert Wiener, nuclear winter, p-value, Peter Singer: altruism, planetary scale, race to the bottom, RAND corporation, Ronald Reagan, self-driving car, Stanislav Petrov, Stephen Hawking, Steven Pinker, Stewart Brand, supervolcano, survivorship bias, the scientific method, uranium enrichment

The world’s best Go players had long thought that their play was close to perfection, so were shocked to find themselves beaten so decisively.80 As the reigning world champion, Ke Jie, put it: “After humanity spent thousands of years improving our tactics, computers tell us that humans are completely wrong… I would go as far as to say not a single human has touched the edge of the truth of Go.”81 It is this generality that is the most impressive feature of cutting edge AI, and which has rekindled the ambitions of matching and exceeding every aspect of human intelligence. This goal is sometimes known as artificial general intelligence (AGI), to distinguish it from the narrow approaches that had come to dominate. While the timeless games of chess and Go best exhibit the brilliance that deep learning can attain, its breadth was revealed through the Atari video games of the 1970s. In 2015, researchers designed an algorithm that could learn to play dozens of extremely different Atari games at levels far exceeding human ability.82 Unlike systems for chess or Go, which start with a symbolic representation of the board, the Atari-playing systems learned and mastered these games directly from the score and the raw pixels on the screen.

But if enough humans wanted to, we could select any of a dizzying variety of possible futures. The same is not true for chimpanzees. Or blackbirds. Or any other of Earth’s species. As we saw in Chapter 1, our unique position in the world is a direct result of our unique mental abilities. Unmatched intelligence led to unmatched power and thus control of our destiny. What would happen if sometime this century researchers created an artificial general intelligence surpassing human abilities in almost every domain? In this act of creation, we would cede our status as the most intelligent entities on Earth. So without a very good plan to keep control, we should also expect to cede our status as the most powerful species, and the one that controls its own destiny.88 On its own, this might not be too much cause for concern. For there are many ways we might hope to retain control.

We need to better understand the existential risks—how likely they are, their mechanisms, and the best ways to reduce them. While there has been substantial research into nuclear war, climate change and biosecurity, very little of this has looked at the most extreme events in each area, those that pose a threat to humanity itself.64 Similarly, we need much more technical research into how to align artificial general intelligence with the values of humanity. We also need more research on how to address major risk factors, such as war between the great powers, and on major security factors too. For example, on the best kinds of institutions for international coordination or for representing future generations. Or on the best approaches to resilience, increasing our chance of recovery from non-fatal catastrophes.


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50 Future Ideas You Really Need to Know by Richard Watson

23andMe, 3D printing, access to a mobile phone, Albert Einstein, artificial general intelligence, augmented reality, autonomous vehicles, BRICs, Buckminster Fuller, call centre, clean water, cloud computing, collaborative consumption, computer age, computer vision, crowdsourcing, dark matter, dematerialisation, digital Maoism, digital map, Elon Musk, energy security, failed state, future of work, Geoffrey West, Santa Fe Institute, germ theory of disease, global pandemic, happiness index / gross national happiness, hive mind, hydrogen economy, Internet of things, Jaron Lanier, life extension, Mark Shuttleworth, Marshall McLuhan, megacity, natural language processing, Network effects, new economy, oil shale / tar sands, pattern recognition, peak oil, personalized medicine, phenotype, precision agriculture, profit maximization, RAND corporation, Ray Kurzweil, RFID, Richard Florida, Search for Extraterrestrial Intelligence, self-driving car, semantic web, Skype, smart cities, smart meter, smart transportation, statistical model, stem cell, Stephen Hawking, Steve Jobs, Steven Pinker, Stewart Brand, strong AI, Stuxnet, supervolcano, telepresence, The Wisdom of Crowds, Thomas Malthus, Turing test, urban decay, Vernor Vinge, Watson beat the top human players on Jeopardy!, web application, women in the workforce, working-age population, young professional

timeline 1662 Dodo becomes extinct 1966 Last Arabian ostrich 1989 Golden toads extinct 1998 Poll of 400 scientists reveals that 70 percent believe mass extinction is happening 2000 Last Pyrenean Ibex dies (it’s cloned back in 2009 but later dies) 2005 Extinct Laotian Rock Rat rediscovered 2006 Freshwater dolphin declared dead 2035 50 percent of European amphibians extinct 46 The Singularity Moore’s Law (named after Gordon Moore) says that computers double their processing ability every 18 months or so. But imagine if this rate of exponential growth was itself exponential. That’s one potential consequence of what future tech-heads call the “Singularity,” where computers will be able to create AGIs (artificial general intelligences) more intelligent than human beings. Proponents of the Singularity, most notably the inventor and futurist Ray Kurzweil, say that if computers continue to advance at their current rate, the singularity is a mere 20–30 years away—perhaps sooner if useful quantum computers are developed. Intel is already reinventing the humble transistor by harnessing photons and quantum properties to increase processing power, and Kurzweil has set up the so-called Singularity University, backed by Google and NASA, to educate the next generation in making the Singularity possible.

Moreover, an intellect would not need to be humanlike to be reckoned with. In many ways it could be worse to deal with if it were not, because it’s entirely possible that such an intellect could not be reasoned with using human logic or emotion. the condensed idea Machines much smarter than people timeline 2011 Voice declines significantly as a human-to-human communication medium 2040 AGI (artificial general intelligence) exists 2045 The distinction between virtual and real life becomes almost meaningless 2050 Full virtual-reality immersion 2060 The first human brain enters a machine body 2070 Computer viruses become the main threat to human existence 2080 Scientists acknowledge that immortality exists for those that want it 2095 Human-robot hybrids (brains in boxes) take off to explore distant galaxies 47 Me or we?

Gus Bally, Arcade Inc. 1994 “I will believe in the 500-channel world only when I see it.” Sumner Redstone, chairman, Viacom and CBS 2002 “There is no doubt that Saddam Hussein has weapons of mass destruction.” Dick Cheney Glossary 3D printer A way to produce 3D objects from digital instructions and layered materials dispersed or sprayed on via a printer. Affective computing Machines and systems that recognize or simulate human effects or emotions. AGI Artificial general intelligence, a term usually used to describe strong AI (the opposite of narrow or weak AI). It is machine intelligence that is equivalent to, or exceeds, human intelligence and it’s usually regarded as the long-term goal of AI research and development. Ambient intelligence Electronic or artificial environments that recognize the presence of other machines or people and respond to their needs. Artificial photosynthesis The artificial replication of natural photosynthesis to create or store solar fuels.


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Singularity Rising: Surviving and Thriving in a Smarter, Richer, and More Dangerous World by James D. Miller

23andMe, affirmative action, Albert Einstein, artificial general intelligence, Asperger Syndrome, barriers to entry, brain emulation, cloud computing, cognitive bias, correlation does not imply causation, crowdsourcing, Daniel Kahneman / Amos Tversky, David Brooks, David Ricardo: comparative advantage, Deng Xiaoping, en.wikipedia.org, feminist movement, Flynn Effect, friendly AI, hive mind, impulse control, indoor plumbing, invention of agriculture, Isaac Newton, John von Neumann, knowledge worker, Long Term Capital Management, low skilled workers, Netflix Prize, neurotypical, Norman Macrae, pattern recognition, Peter Thiel, phenotype, placebo effect, prisoner's dilemma, profit maximization, Ray Kurzweil, recommendation engine, reversible computing, Richard Feynman, Rodney Brooks, Silicon Valley, Singularitarianism, Skype, statistical model, Stephen Hawking, Steve Jobs, supervolcano, technological singularity, The Coming Technological Singularity, the scientific method, Thomas Malthus, transaction costs, Turing test, twin studies, Vernor Vinge, Von Neumann architecture

This “crowdsourcing,” which occurs when a problem is thrown open to anyone, helps a company by allowing them to draw on the talents of strangers, while only paying the strangers if they help the firm. This kind of crowdsourcing works only if, as with a video recommendation system, there is an easy and objective way of measuring progress toward the crowdsourced goal. 13.Potential Improvement All the Way Up to Superhuman Artificial General Intelligence—A recommendation AI could slowly morph into a content creator. At first, the AI might make small changes to content, such as improving sound quality, zooming in on the interesting bits of the video, or running in slow motion the part of a certain cat video in which a kitten falls into a bowl of milk. Later, the AI might make more significant alterations by, for example, developing a mathematical model of what people consider cute in kittens, and then changing kittens’ appearances to make them cuter.

THE SINGULARITY INSTITUTE Michael Vassar, a director and former president of the Singularity Institute for Artificial Intelligence, has told me that he would like an endowment of about $50 million to fund a serious program to create a seed AI that will undergo an intelligence explosion and create a friendly artificial intelligence, although he said that a $10 million endowment would be enough to mount a serious effort. Even with the money, Vassar admitted, the Institute would succeed only if it attracted extremely competent programmers because the programming team would be working under the disadvantage of trying to make an AI that’s mathematically certain to yield a friendly ultra-intelligence, whereas other organizations trying to build artificial general intelligence might not let concerns about friendliness slow them down. The Institute’s annual budget is currently around $500,000 per year. Even if Eliezer and the Singularity Institute have no realistic chance of creating a friendly AI, they still easily justify their institute’s existence. As Michael Anissimov, media director for the Institute, once told me in a personal conversation, at the very least the Institute has reduced the chance of humanity’s destruction by repeatedly telling artificial-intelligence programmers about the threat of unfriendly AI.

Robots have the potential to be another huge source of demand for computing hardware. And, of course, robots would necessarily have at least limited artificial intelligence. I doubt much time would elapse between the creation of Rosie, the robot maid on the 1960s TV show The Jetsons, and a Singularity. Similarly, I believe we would have a Singularity thrust upon us very quickly after someone creates an AI like HAL from the movie 2001: A Space Odyssey. Any artificial general intelligence such as HAL could almost certainly become much smarter and more capable just by running on faster or more numerous computers. Consequently (and perhaps tragically), I strongly suspect that: HAL + Continued Exponential Growth in Computing Power = Not-Too-Distant Singularity Brain implants that can raise the general intelligence of a healthy person would be a strong sign that mankind is near a Singularity.


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

"Robert Solow", 3D printing, Amazon Web Services, anti-globalists, artificial general intelligence, augmented reality, autonomous vehicles, basic income, Bretton Woods, business process, cashless society, charter city, cloud computing, complexity theory, computer age, computer vision, corporate social responsibility, crowdsourcing, Deng Xiaoping, Donald Trump, Douglas Engelbart, Edward Snowden, Elon Musk, en.wikipedia.org, equal pay for equal work, everywhere but in the productivity statistics, fault tolerance, Gini coefficient, global supply chain, Google Glasses, Grace Hopper, industrial robot, Internet of things, Jeff Bezos, job automation, John Markoff, John von Neumann, knowledge worker, Mars Rover, Minecraft, Mother of all demos, NP-complete, Oculus Rift, pattern recognition, place-making, Richard Feynman, Robert Gordon, Ronald Reagan, Second Machine Age, self-driving car, side project, Silicon Valley, Skype, Snapchat, special economic zone, speech recognition, Stephen Hawking, Steve Ballmer, Steve Jobs, telepresence, telerobotics, The Rise and Fall of American Growth, Tim Cook: Apple, trade liberalization, two-sided market, universal basic income, Wall-E, Watson beat the top human players on Jeopardy!, young professional, zero-sum game

At astonishing rates, data is being gathered and made available thanks to the exponential growth of cameras and sensors in our everyday life. AI needs data to learn. The cloud has made tremendous computing power available to everyone, and complex algorithms can now be written to discern insights and intelligence from the mountains of data. But far from Baymax or Brenner, AI today is some ways away from becoming what’s known as artificial general intelligence (AGI), the point at which a computer matches or even surpasses human intellectual capabilities. Like human intelligence, artificial intelligence can be categorized by layer. The bottom layer is simple pattern recognition. The middle layer is perception, sensing more and more complex scenes. It’s estimated that 99 percent of human perception is through speech and vision. Finally, the highest level of intelligence is cognition—deep understanding of human language.

Ultimately, the state of the art is when computers learn to learn—when computers generate their own programs. Like humans, computers will go beyond mimicking what people do and will invent new, better solutions to problems. Deep neural networks and transfer learning are leading to breakthroughs today, but AI is like a ladder and we are just on the first step of that ladder. At the top of the ladder is artificial general intelligence and complete machine understanding of human language. It’s when a computer exhibits intelligence that is equal to or indistinguishable from a human. One of our top AI researchers decided to try an experiment to demonstrate how a computer can learn to learn. A highly esteemed computer scientist and medical doctor, Eric Horvitz, runs our Redmond research lab and has long been fascinated with machines that perceive, learn, and reason.

., 20 Alien and Sedition Acts, 188 Allen, Colin, 209 Allen, Paul, 4, 21, 28, 64, 69, 87, 127 Alphago, 199 ALS, 10–11 Altair, 87 Althoff, Judson, 82 Amar, Akhil Reed, 186 Amazon, 47, 51, 54, 59, 85, 122, 125, 200, 228 Amazon Fire, 125 Amazon Web Service (AWS), 45–46, 52, 54, 58 ambient intelligence, 228–39 ambition, 76–78, 80, 90 American Dream, 238 American Revolution, 185–86 Amiss, Dennis, 37 Anderson, Brad, 58, 82 Android. 59, 66, 70–72, 123, 125, 132–33, 222 antitrust case, 130 AOL, 174 Apple Computer, 15, 45, 51, 66, 69–70, 72, 128, 132, 174, 177–78, 189 partnership with, 121–25 apprenticeship, 227 artificial general intelligence (AGI), 150, 153–54 artificial intelligence (AI), 11, 13, 50, 52, 59, 76, 88, 110, 139–42, 149–59, 161, 164, 166–67, 186, 212, 223, 239 ethics and, 195–210 Artificial Intelligence and Life in 2030 (Stanford report), 208 Asia, 86, 219 Asimov, Isaac, 202–3 astronauts, 146, 148 asynchronous transfer model (ATM), 30 AT&T, 174 Atari 2600, 146 at-scale services, 53, 61 auction-based pricing, 47, 50 Australia, 38–39, 149, 228, 230 autism, 149 Autodesk, 127–28 automation, 208, 214, 226, 231–32, 236 automobile, 127, 153, 230 driverless, 209, 226, 228 aviation, 210 Azure, 58–61, 85, 125, 137 backdoors, 177–78 Bahl, Kunal, 33 Baig, Abbas Ali, 36 Bain Capital, 220 Baldwin, Richard, 236 Ballmer, Steve, 3–4, 12, 14, 29, 46–48, 51–55, 64, 67, 72, 91, 94, 122 Banga, Ajay Singh, 20 Baraboo project, 145 Baraka, Chris, 97 BASIC, 87, 143 Batelle, John, 234 Bates, Tony, 64 Bayesian estimators, 54 Baymax (robot), 150 Beauchamp, Tom, 179 Belgium, 215 Best Buy, 87, 127 Bezos, Jeff, 54 bias, 113–15 Bicycle Corporation of America, 232 Big Data, 13, 58, 70, 150–51, 183–84 Big Hero 6 (film), 150 Bill & Melinda Gates Foundation, 46, 74 Bill of Rights, 190 Bing, 47–54, 57, 59, 61, 125, 134 Birla Institute of Technology, 21 Bishop, Christopher, 199 black-hat groups, 170 Blacks @ Microsoft (BAM), 116–17.


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

3D printing, Airbnb, algorithmic trading, Amazon Web Services, anti-work, artificial general intelligence, autonomous vehicles, basic income, business cycle, cloud computing, collective bargaining, correlation does not imply causation, creative destruction, data is the new oil, David Graeber, David Ricardo: comparative advantage, deindustrialization, deskilling, disintermediation, Donald Trump, Erik Brynjolfsson, feminist movement, Frederick Winslow Taylor, future of work, gig economy, global supply chain, income inequality, informal economy, Internet of things, Jarndyce and Jarndyce, Jarndyce and Jarndyce, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, Joseph Schumpeter, knowledge economy, Loebner Prize, low skilled workers, Lyft, Mark Zuckerberg, means of production, moral panic, Network effects, new economy, off grid, pattern recognition, post-work, Ronald Coase, Second Machine Age, self-driving car, sharing economy, Steve Jobs, strong AI, technoutopianism, The Chicago School, The Future of Employment, the market place, The Nature of the Firm, The Wealth of Nations by Adam Smith, Thorstein Veblen, Turing test, Uber for X, uber lyft, universal basic income, wealth creators, working poor

Proceedings of the European Conference on Artificial Intelligence. 122 S. Colton Cook, M., Colton, S., & Gow, J. (2016). The ANGELINA Videogame Design System, Parts I and II. IEEE Transactions on Computational Intelligence and AI in Games, 9(2/3), 1–13. Gallie, W. B. (1955). Essentially Contested Concepts. Proceedings of the Aristotelian Society, 56, 167–198. Goertzel, B. (2014). Artificial General Intelligence: Concept, State of the Art, and Future Prospects. Journal of Artificial General Intelligence, 5(1), 1–26. Hurst, D. (2018, February 6). Japan Lays Groundwork for Boom in Robot Carers. The Guardian. Price, R. (2019, April 12). Uber Says Its Future is Riding on the Success of Self-­ driving Cars, but Warns Investors That There’s a Lot That Can Go Wrong. Business Insider. Shead, S. (2016, April 4). There’s a Worldwide Shortage of the Board Game Go after Google’s Computer Beat the World Champ.

Such speculation has been fuelled by books like Nick Bostrom’s on SuperIntelligence (Bostrom 2014), where he is clear that his philosophical enquiry is entirely speculative, except in one respect: that AI superintelligence can and probably will occur in lightning fast time, 12 Possibilities and Limitations for AI: What Can’t Machines Do? 113 for example overnight. While this is a brilliant and much-used science fiction meme, it is, unfortunately, bad fictional science. No AI researcher I know has the first clue about how we could achieve overnight superintelligence, and as far as I know, no-one has a reasonable road-map for so-called Artificial General Intelligence, with metrics for partial progress remaining controversial and problematic (Goertzel 2014). It is worth debunking a couple of Bostrom’s ideas on how such rapid superintelligence could be achieved. One is incremental automated AI engineering, that is an AI system writing a slightly more intelligent AI system, which itself writes an even more intelligent system, and so on. This is basically the wet dream of undergraduates embarking on an AI course.


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Rationality: From AI to Zombies by Eliezer Yudkowsky

Albert Einstein, Alfred Russel Wallace, anthropic principle, anti-pattern, anti-work, Arthur Eddington, artificial general intelligence, availability heuristic, Bayesian statistics, Berlin Wall, Build a better mousetrap, Cass Sunstein, cellular automata, cognitive bias, cognitive dissonance, correlation does not imply causation, cosmological constant, creative destruction, Daniel Kahneman / Amos Tversky, dematerialisation, different worldview, discovery of DNA, Douglas Hofstadter, Drosophila, effective altruism, experimental subject, Extropian, friendly AI, fundamental attribution error, Gödel, Escher, Bach, hindsight bias, index card, index fund, Isaac Newton, John Conway, John von Neumann, Long Term Capital Management, Louis Pasteur, mental accounting, meta analysis, meta-analysis, money market fund, Nash equilibrium, Necker cube, NP-complete, P = NP, pattern recognition, Paul Graham, Peter Thiel, Pierre-Simon Laplace, placebo effect, planetary scale, prediction markets, random walk, Ray Kurzweil, reversible computing, Richard Feynman, risk tolerance, Rubik’s Cube, Saturday Night Live, Schrödinger's Cat, scientific mainstream, scientific worldview, sensible shoes, Silicon Valley, Silicon Valley startup, Singularitarianism, Solar eclipse in 1919, speech recognition, statistical model, Steven Pinker, strong AI, technological singularity, The Bell Curve by Richard Herrnstein and Charles Murray, the map is not the territory, the scientific method, Turing complete, Turing machine, ultimatum game, X Prize, Y Combinator, zero-sum game

These essays serve the secondary purpose of explaining the author’s general approach to philosophy and the science of rationality, which is strongly informed by his work in AI. Rebuilding Intelligence Yudkowsky is a decision theorist and mathematician who works on foundational issues in Artificial General Intelligence (AGI), the theoretical study of domain-general problem-solving systems. Yudkowsky’s work in AI has been a major driving force behind his exploration of the psychology of human rationality, as he noted in his very first blog post on Overcoming Bias, The Martial Art of Rationality: Such understanding as I have of rationality, I acquired in the course of wrestling with the challenge of Artificial General Intelligence (an endeavor which, to actually succeed, would require sufficient mastery of rationality to build a complete working rationalist out of toothpicks and rubber bands). In most ways the AI problem is enormously more demanding than the personal art of rationality, but in some ways it is actually easier.

Even on an intuitive level, complexity is often worth thinking about—you have to judge the complexity of a hypothesis and decide if it’s “too complicated” given the supporting evidence, or look at a design and try to make it simpler. But concepts are not useful or useless of themselves. Only usages are correct or incorrect. In the step Marcello was trying to take in the dance, he was trying to explain something for free, get something for nothing. It is an extremely common misstep, at least in my field. You can join a discussion on Artificial General Intelligence and watch people doing the same thing, left and right, over and over again—constantly skipping over things they don’t understand, without realizing that’s what they’re doing. In an eyeblink it happens: putting a non-controlling causal node behind something mysterious, a causal node that feels like an explanation but isn’t. The mistake takes place below the level of words. It requires no special character flaw; it is how human beings think by default, how they have thought since the ancient times.

I have often used this edict with groups I have led—particularly when they face a very tough problem, which is when group members are most apt to propose solutions immediately. While I have no objective criterion on which to judge the quality of the problem solving of the groups, Maier’s edict appears to foster better solutions to problems. This is so true it’s not even funny. And it gets worse and worse the tougher the problem becomes. Take Artificial Intelligence, for example. A surprising number of people I meet seem to know exactly how to build an Artificial General Intelligence, without, say, knowing how to build an optical character recognizer or a collaborative filtering system (much easier problems). And as for building an AI with a positive impact on the world—a Friendly AI, loosely speaking—why, that problem is so incredibly difficult that an actual majority resolve the whole issue within fifteen seconds. Give me a break. This problem is by no means unique to AI.


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The Age of Em: Work, Love and Life When Robots Rule the Earth by Robin Hanson

8-hour work day, artificial general intelligence, augmented reality, Berlin Wall, bitcoin, blockchain, brain emulation, business cycle, business process, Clayton Christensen, cloud computing, correlation does not imply causation, creative destruction, demographic transition, Erik Brynjolfsson, Ethereum, ethereum blockchain, experimental subject, fault tolerance, financial intermediation, Flynn Effect, hindsight bias, information asymmetry, job automation, job satisfaction, John Markoff, Just-in-time delivery, lone genius, Machinery of Freedom by David Friedman, market design, meta analysis, meta-analysis, Nash equilibrium, new economy, prediction markets, rent control, rent-seeking, reversible computing, risk tolerance, Silicon Valley, smart contracts, statistical model, stem cell, Thomas Malthus, trade route, Turing test, Vernor Vinge

Obviously, the first 30 years of such forecasts were quite wrong. However, researchers who don’t go out of their way to publish predictions, but are instead asked for forecasts in a survey, tend to give durations roughly 10 years longer than researchers who do make public predictions (Armstrong and Sotala 2012; Grace 2014). Shorter durations are given by researchers in the small AI subfield of “artificial general intelligence,” which is more ambitious in trying to write software that is good at a great many tasks at once. A recent survey of the 100 most cited living AI researchers got 29 responses, who gave a median forecast of 37 years until there is a 50% chance of human level AI (Müller and Bostrom 2014). Incidentally, none of those 29 thought that brain emulation “might contribute the most” to human level AI.

The size, location, and specializations of clans, firms, and cities are also distorted in the direction of making such things easier to defend, and better able to launch successful attacks. For example, if it is hard to protect cities against nuclear attacks, cities will be smaller and spread further apart. However, to the extent that there are em enclaves well protected against attack, those probably look more like the scenario described in this book. In a second variation, we might create artificial general intelligence that is similar to ems, except that it is made via a shallower analysis of higher-level human brain processes, instead of via directly emulating lower-level brain processes as in a classic em. Such variations on ems probably are not greatly redesigned at the highest levels of organization, and thus are relatively human in behavior and style. The main ways these differ from ems is that they probably do not remember being human, they might not run as easily on parallel computer hardware, and they might require a lot less computer hardware.

“Democracy and the Variability of Economic Performance.” Economics and Politics 14(3): 225–257. Alston, Julian, Matthew Andersen, Jennifer James, and Philip Pardey. 2011. “The Economic Returns to U.S. Public Agricultural Research.” American Journal of Agricultural Economics 93(5): 1257–1277. Alstott, Jeff. 2013. “Will We Hit a Wall? Forecasting Bottlenecks to Whole Brain Emulation Development.” Journal of Artificial General Intelligence 4(3): 153–163. Alvanchi, Amin, SangHyun Lee, and Simaan AbouRizk. 2012. “Dynamics of Working Hours in Construction.” Journal of Construction Engineering and Management 138(1): 66–77. Alwin, Duane, and Jon Krosnick. 1991. “Aging, Cohorts, and the Stability of Sociopolitical Orientations Over the Life Span.” American Journal of Sociology 97(1): 169–195. Anderson, David. 1999. “The Aggregate Burden of Crime.”


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

Airbnb, Amazon Web Services, artificial general intelligence, autonomous vehicles, business intelligence, business process, call centre, chief data officer, computer vision, conceptual framework, en.wikipedia.org, future of work, industrial robot, Internet of things, iterative process, Jeff Bezos, job automation, Marc Andreessen, natural language processing, new economy, pattern recognition, performance metric, price discrimination, randomized controlled trial, recommendation engine, self-driving car, sentiment analysis, Silicon Valley, skunkworks, software is eating the world, source of truth, speech recognition, statistical model, strong AI, technological singularity

Though computers trounce humans at large-scale computational tasks, their expertise is narrow, and machine capability lags behind human intelligence in other areas. The rest of this chapter will help you to understand the state of artificial intelligence today. AI vs. AGI Artificial intelligence, also known as AI, has been misused in pop culture to describe almost any kind of computerized analysis or automation. To avoid confusion, technical experts in the field of AI prefer to use the term Artificial General Intelligence (AGI) to refer to machines with human-level or higher intelligence, capable of abstracting concepts from limited experience and transferring knowledge between domains. AGI is also called “Strong AI” to differentiate from “Weak AI” or “Narrow AI," which refers to systems designed for one specific task and whose capabilities are not easily transferable to other systems. We go into more detail about the distinction between AI and AGI in our Machine Intelligence Continuum in Chapter 2.

Because we are Systems That Master, humans have no trouble with this. A System That Masters is an intelligent agent capable of constructing abstract concepts and strategic plans from sparse data. By creating modular, conceptual representations of the world around us, we are able to transfer knowledge from one domain to another, a key feature of general intelligence. As we discussed earlier, no modern AI system is an AGI, or artificial general intelligence. While humans are Systems That Master, current AI programs are not. Systems That Evolve This final category refers to systems that exhibit superhuman intelligence and capabilities, such as the ability to dynamically change their own design and architecture to adapt to changing conditions in their environment. As humans, we’re limited in our intelligence by our biological brains, also known as “wetware."


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

agricultural Revolution, AI winter, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, algorithmic trading, artificial general intelligence, augmented reality, autonomous vehicles, basic income, bitcoin, blockchain, clean water, cognitive dissonance, Colonization of Mars, complexity theory, computer age, computer vision, constrained optimization, corporate personhood, cosmological principle, cryptocurrency, cuban missile crisis, Danny Hillis, dark matter, discrete time, Douglas Engelbart, Elon Musk, Emanuel Derman, endowment effect, epigenetics, Ernest Rutherford, experimental economics, Flash crash, friendly AI, functional fixedness, global pandemic, Google Glasses, hive mind, income inequality, information trail, Internet of things, invention of writing, iterative process, Jaron Lanier, job automation, Johannes Kepler, John Markoff, John von Neumann, Kevin Kelly, knowledge worker, loose coupling, microbiome, Moneyball by Michael Lewis explains big data, natural language processing, Network effects, Norbert Wiener, pattern recognition, Peter Singer: altruism, phenotype, planetary scale, Ray Kurzweil, recommendation engine, Republic of Letters, RFID, Richard Thaler, Rory Sutherland, Satyajit Das, Search for Extraterrestrial Intelligence, self-driving car, sharing economy, Silicon Valley, Skype, smart contracts, social intelligence, speech recognition, statistical model, stem cell, Stephen Hawking, Steve Jobs, Steven Pinker, Stewart Brand, strong AI, Stuxnet, superintelligent machines, supervolcano, the scientific method, The Wisdom of Crowds, theory of mind, Thorstein Veblen, too big to fail, Turing machine, Turing test, Von Neumann architecture, Watson beat the top human players on Jeopardy!, Y2K

One doesn’t need to be a superintelligent AI to realize that running unprepared toward the biggest event in human history would be just plain stupid. “TURING+” QUESTIONS TOMASO POGGIO Eugene McDermott Professor, Department of Brain and Cognitive Sciences, and director, Center for Brains, Minds, and Machines, MIT Recent months have seen an increasingly public debate forming around the risks of artificial intelligence—in particular, AGI (artificial general intelligence). AI has been called by some (including the physicist Stephen Hawking) the top existential risk to humankind, and such recent films as Her and Transcendence have reinforced the message. Thoughtful comments by experts in the field—Rod Brooks and Oren Etzioni among them—have done little to settle the debate. I argue here that research on how we think and on how to make machines that think is good for society.

My suspicion is that replicating the effectiveness of this evolved intelligence in an artificial agent will require amounts of computation not that much lower than evolution has required, which would far outstrip our abilities for many decades, even given exponential growth in computational efficiency per Moore’s Law—and that’s even if we understood how to correctly employ that computation. I assign a probability of about 1 percent for artificial general intelligence (AGI) arising in the next ten years, and about 10 percent over the next thirty years. (This essentially reflects a probability that my analysis is wrong, times a probability more representative of AI experts, who—albeit with lots of variation—tend to assign somewhat higher numbers.) On the other hand, I assign a rather high probability that, if AGI is created (and especially if it arises relatively quickly), it will be—in a word—insane.

ZIYAD MARAR Global publishing director, SAGE; author, Intimacy: Understanding the Subtle Power of Human Connection There’s something old-fashioned about visions of the future. The majority of predictions, like three-day weeks, personal jet packs, and the paperless office, tell us more about the times in which they were proposed than about contemporary experience. When people point to the future, we’d do well to run an eye back up the arm to see who’s doing the pointing. The possibility of artificial general intelligence has long invited such crystal-ball gazing, whether utopian or dystopian in tone. Yet speculations on this theme have reached such a pitch and intensity in the last few months alone (enough to trigger an Edge Question, no less) that this may reveal something about ourselves and our culture today. We’ve known for some time that machines can outthink humans in a narrow sense. The question is whether they do so in any way that could or should ever resemble the baggier mode of human thought.


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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, Andrew Wiles, artificial general intelligence, Asilomar, Asilomar Conference on Recombinant DNA, augmented reality, autonomous vehicles, basic income, blockchain, brain emulation, Cass Sunstein, Claude Shannon: information theory, complexity theory, computer vision, connected car, crowdsourcing, Daniel Kahneman / Amos Tversky, delayed gratification, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Ernest Rutherford, Flash crash, full employment, future of work, Gerolamo Cardano, 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, Mark Zuckerberg, Nash equilibrium, Norbert Wiener, NP-complete, openstreetmap, P = NP, Pareto efficiency, Paul Samuelson, Pierre-Simon Laplace, positional goods, probability theory / Blaise Pascal / Pierre de Fermat, profit maximization, RAND corporation, random walk, Ray Kurzweil, recommendation engine, RFID, Richard Thaler, ride hailing / ride sharing, Robert Shiller, Robert Shiller, 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, Thales of Miletus, The Future of Employment, Thomas Bayes, Thorstein Veblen, transport as a service, Turing machine, Turing test, universal basic income, uranium enrichment, Von Neumann architecture, Wall-E, Watson beat the top human players on Jeopardy!, web application, zero-sum game

From the standpoint of modern AI, the laws fail to acknowledge any element of probability and risk: the legality of robot actions that expose a human to some probability of harm—however infinitesimal—is therefore unclear. 9. The notion of instrumental goals is due to Stephen Omohundro, “The nature of self-improving artificial intelligence” (unpublished manuscript, 2008). See also Stephen Omohundro, “The basic AI drives,” in Artificial General Intelligence 2008: Proceedings of the First AGI Conference, ed. Pei Wang, Ben Goertzel, and Stan Franklin (IOS Press, 2008). 10. The objective of Johnny Depp’s character, Will Caster, seems to be to solve the problem of physical reincarnation so that he can be reunited with his wife, Evelyn. This just goes to show that the nature of the overarching objective doesn’t matter—the instrumental goals are all the same. 11.

Letting rats push the button: James Olds, “Self-stimulation of the brain; its use to study local effects of hunger, sex, and drugs,” Science 127 (1958): 315–24. 24. Letting humans push the button: Robert Heath, “Electrical self-stimulation of the brain in man,” American Journal of Psychiatry 120 (1963): 571–77. 25. A first mathematical treatment of wireheading, showing how it occurs in reinforcement learning agents: Mark Ring and Laurent Orseau, “Delusion, survival, and intelligent agents,” in Artificial General Intelligence: 4th International Conference, ed. Jürgen Schmidhuber, Kristinn Thórisson, and Moshe Looks (Springer, 2011). One possible solution to the wireheading problem: Tom Everitt and Marcus Hutter, “Avoiding wireheading with value reinforcement learning,” arXiv:1605.03143 (2016). 26. How it might be possible for an intelligence explosion to occur safely: Benja Fallenstein and Nate Soares, “Vingean reflection: Reliable reasoning for self-improving agents,” technical report 2015-2, Machine Intelligence Research Institute, 2015. 27.

How it might be possible for an intelligence explosion to occur safely: Benja Fallenstein and Nate Soares, “Vingean reflection: Reliable reasoning for self-improving agents,” technical report 2015-2, Machine Intelligence Research Institute, 2015. 27. The difficulty agents face in reasoning about themselves and their successors: Benja Fallenstein and Nate Soares, “Problems of self-reference in self-improving space-time embedded intelligence,” in Artificial General Intelligence: 7th International Conference, ed. Ben Goertzel, Laurent Orseau, and Javier Snaider (Springer, 2014). 28. Showing why an agent might pursue an objective different from its true objective if its computational abilities are limited: Jonathan Sorg, Satinder Singh, and Richard Lewis, “Internal rewards mitigate agent boundedness,” in Proceedings of the 27th International Conference on Machine Learning, ed. Johannes Fürnkranz and Thorsten Joachims (2010), icml.cc/Conferences/2010/papers/icml2010proceedings.zip.


Demystifying Smart Cities by Anders Lisdorf

3D printing, artificial general intelligence, autonomous vehicles, bitcoin, business intelligence, business process, chief data officer, clean water, cloud computing, computer vision, continuous integration, crowdsourcing, data is the new oil, digital twin, distributed ledger, don't be evil, Elon Musk, en.wikipedia.org, facts on the ground, Google Glasses, income inequality, Infrastructure as a Service, Internet of things, Masdar, microservices, Minecraft, platform as a service, ransomware, RFID, ride hailing / ride sharing, risk tolerance, self-driving car, smart cities, smart meter, software as a service, speech recognition, Stephen Hawking, Steve Jobs, Steve Wozniak, Stuxnet, Thomas Bayes, Turing test, urban sprawl, zero-sum game

AI has been generalized to all tasks where a computer can perform indistinguishably from a human. Furthermore, we do not expect AI to be merely indistinguishable from humans; we typically want it to also be superior to humans, whether in precision, scope, time, or some other parameter. We typically want AI to be better than us. Another thing to keep in mind is a distinction between Artificial General Intelligence (AGI), as measured by the Turing test, and Artificial Narrow Intelligence (ANI), which is an application of humanlike intelligence in a particular area for a particular purpose. In our context, we will not go further into AGI and the philosophical implications of this but focus on ANI since this has many contemporary applications. The promise and threat of AI When we think about what AI can do for us, we can think about it in the same way as steam power in the industrial revolution.

They will be the intelligence inside the artificial intelligence if no one else steps in. In many ways, this is already the case. A more peaceful case in point is online dating: a programmer has essentially decided who should find love and who shouldn’t through the matching algorithm and the input used. Inside the AI is the programmer making decisions no one ever agreed they should. Artificial General Intelligence is as elusive as ever – no matter how many resources we throw at AI and no matter how impressive it can be at simple games. Life will throw us the same problems as it always has, and at the end of the day, the intelligence will be human anyway. Artificial Intelligence meets the real world Another important constraint for AI is ecological – not in the sense of the tech ecosystem consisting of different vendors, projects, and organizations.


pages: 533

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

3D printing, additive manufacturing, affirmative action, agricultural Revolution, Airbnb, airport security, Andrew Keen, artificial general intelligence, augmented reality, automated trading system, autonomous vehicles, basic income, Bertrand Russell: In Praise of Idleness, bitcoin, blockchain, brain emulation, British Empire, business process, Capital in the Twenty-First Century by Thomas Piketty, cashless society, Cass Sunstein, cellular automata, cloud computing, computer age, computer vision, continuation of politics by other means, correlation does not imply causation, crowdsourcing, cryptocurrency, digital map, distributed ledger, Donald Trump, easy for humans, difficult for computers, Edward Snowden, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Ethereum, ethereum blockchain, Filter Bubble, future of work, Google bus, Google X / Alphabet X, Googley, industrial robot, informal economy, intangible asset, Internet of things, invention of the printing press, invention of writing, Isaac Newton, Jaron Lanier, John Markoff, Joseph Schumpeter, Kevin Kelly, knowledge economy, lifelogging, Metcalfe’s law, mittelstand, more computing power than Apollo, move fast and break things, move fast and break things, natural language processing, Network effects, new economy, night-watchman state, Oculus Rift, Panopticon Jeremy Bentham, pattern recognition, payday loans, price discrimination, price mechanism, RAND corporation, ransomware, Ray Kurzweil, Richard Stallman, ride hailing / ride sharing, road to serfdom, Robert Mercer, Satoshi Nakamoto, Second Machine Age, selection bias, self-driving car, sexual politics, sharing economy, Silicon Valley, Silicon Valley startup, Skype, smart cities, Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia, smart contracts, Snapchat, speech recognition, Steve Jobs, Steve Wozniak, Steven Levy, technological singularity, the built environment, The Structural Transformation of the Public Sphere, The Wisdom of Crowds, Thomas L Friedman, universal basic income, urban planning, Watson beat the top human players on Jeopardy!, working-age population

AI has spawned a multiplicity of sub-fields, each applying ­different methods to a wide range of problems. There is a spectrum of approach, for instance, between those who seek to recreate the neural engineering of the human brain, just as ‘early designs for flying machines included flapping wings’, and those who employ entirely new techniques tailored for artificial computation.24 Some researchers seek the holy grail of an artificial general intelligence like the human mind, endowed with consciousness, creativity, common sense, and the ability to ‘think’ abstractly across different environments. One way to achieve this goal might be whole-brain emulation, currently being pursued in the Blue Brain project in Switzerland. This involves trying to map, simulate, and replicate the activity of the (more than) 80 billion neurons and tens of trillions of synapses in the human brain, together with the workings of the central nervous system.25 Whole-brain emulation remains a remote prospect but is not thought to be technically impossible.26 As Murray Shanahan argues, our own brains are proof that it’s physically possible to assemble ‘billions of ultra-low-power, nano-scale components into a device capable of human-level intelligence’.27 Most contemporary AI research, however, is not concerned with artificial general intelligence or whole-brain emulation.

This involves trying to map, simulate, and replicate the activity of the (more than) 80 billion neurons and tens of trillions of synapses in the human brain, together with the workings of the central nervous system.25 Whole-brain emulation remains a remote prospect but is not thought to be technically impossible.26 As Murray Shanahan argues, our own brains are proof that it’s physically possible to assemble ‘billions of ultra-low-power, nano-scale components into a device capable of human-level intelligence’.27 Most contemporary AI research, however, is not concerned with artificial general intelligence or whole-brain emulation. Rather, it is geared toward creating machines capable of performing specific, often quite narrow, tasks with an extraordinary degree of efficacy. AlphaGo, Deep Blue, and Watson did not possess ‘minds’ like those of a human being. Deep Blue, whose only function was to play chess, used ‘brute number-crunching force’ to process hundreds of millions of positions each second, generating every possible move for up to twenty or so moves.28 OUP CORRECTED PROOF – FINAL, 26/05/18, SPi РЕЛИЗ ПОДГОТОВИЛА ГРУППА "What's News" VK.COM/WSNWS 34 FUTURE POLITICS It’s tempting to get hung up on the distinction between machines that have a narrow field of cognitive capacity and those able to ‘think’ or solve problems more generally.

Such systems, to operate stably, would need three characteristics. First, they would have to be self-directing in the sense of being sufficiently coded to discharge their functions without any further intervention by human beings. This would either mean being engineered to deal with every possible situation or capable of ‘learning’ how to deal with new situations on the job. (This kind of self-direction does not, however, require artificial general intelligence or even a sense of morality. Aeroplane autopilot systems have a high degree of self-direction but no moral or cognitive capacity, and yet we trust their ability to keep us safe in the sky. Like an aeroplane, which is neither a moral agent nor even conscious of its own existence, a system could exert power without being aware that it is doing so.)48 Second, such systems would have to be self-sustaining, in the sense of being able to survive for a decent period without assistance from humans.


pages: 424 words: 114,905

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

23andMe, Affordable Care Act / Obamacare, AI winter, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, artificial general intelligence, augmented reality, autonomous vehicles, bioinformatics, blockchain, cloud computing, cognitive bias, Colonization of Mars, computer age, computer vision, conceptual framework, creative destruction, crowdsourcing, Daniel Kahneman / Amos Tversky, dark matter, David Brooks, digital twin, Elon Musk, en.wikipedia.org, epigenetics, Erik Brynjolfsson, fault tolerance, George Santayana, Google Glasses, ImageNet competition, Jeff Bezos, job automation, job satisfaction, Joi Ito, Mark Zuckerberg, medical residency, meta analysis, meta-analysis, microbiome, natural language processing, new economy, Nicholas Carr, nudge unit, pattern recognition, performance metric, personalized medicine, phenotype, placebo effect, randomized controlled trial, recommendation engine, Rubik’s Cube, Sam Altman, self-driving car, Silicon Valley, speech recognition, Stephen Hawking, text mining, the scientific method, Tim Cook: Apple, War on Poverty, Watson beat the top human players on Jeopardy!, working-age population

Whether and when we will ever build autonomous agents with superintelligence that operate like sentient life, designing and building new iterations of themselves, that can accomplish any goal at least as well as humans is unclear. We have clearly been inoculated with the idea, however, after exposure to cumulative doses of Skynet in The Terminator, HAL 9000 in 2001: A Space Odyssey, and Agent Smith in The Matrix. These extremely popular films portrayed sentient machines with artificial general intelligence, and many sci-fi movies have proven to be prescient, so fears about AI shouldn’t come as much of a surprise.73 We’ve heard doom projections from high-profile figures like Stephen Hawking (“the development of full AI could spell the end of the human race”), Elon Musk (“with AI we are summoning the demon”), Henry Kissinger (“could cause a rupture in history and unravel the way civilization works”), Bill Gates (“potentially more dangerous than a nuclear catastrophe”), and others.

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. The consensus, albeit with a fair amount of variability, was by the year 2055. Similarly, a report by researchers at the Future of Humanity Institute at Oxford and Yale Universities from a large survey of machine learning experts concluded, “There is a 50 percent chance of AI outperforming humans in all tasks in 45 years and automating all human jobs in 120 years.”82 Of interest, the Future of Humanity Institute’s director Nick Bostrom is the author of Superintelligence and the subject of an in-depth profile in the New Yorker as the proponent of AI as the “Doomsday Invention.”83 Tegmark points to the low probability of that occurring: “Superintelligence arguably falls into the same category as a massive asteroid strike such as the one that wiped out the dinosaurs.”84 Regardless of what the future holds, today’s AI is narrow.

Similarly, a report by researchers at the Future of Humanity Institute at Oxford and Yale Universities from a large survey of machine learning experts concluded, “There is a 50 percent chance of AI outperforming humans in all tasks in 45 years and automating all human jobs in 120 years.”82 Of interest, the Future of Humanity Institute’s director Nick Bostrom is the author of Superintelligence and the subject of an in-depth profile in the New Yorker as the proponent of AI as the “Doomsday Invention.”83 Tegmark points to the low probability of that occurring: “Superintelligence arguably falls into the same category as a massive asteroid strike such as the one that wiped out the dinosaurs.”84 Regardless of what the future holds, today’s AI is narrow. Although one can imagine an artificial general intelligence that will treat humans as pets or kill us all, it’s a reach to claim that the moment is upon us: we’re Life 2.0 now, as Tegmark classifies us, such that we, as humans, can redesign our software, learning complex new skills but quite limited with respect to modulating our biological hardware. Whether Life 3.0 will come along, with beings that can design both their hardware and software, remains to be seen.


pages: 281 words: 71,242

World Without Mind: The Existential Threat of Big Tech by Franklin Foer

artificial general intelligence, back-to-the-land, Berlin Wall, big data - Walmart - Pop Tarts, big-box store, Buckminster Fuller, citizen journalism, Colonization of Mars, computer age, creative destruction, crowdsourcing, data is the new oil, don't be evil, Donald Trump, Double Irish / Dutch Sandwich, Douglas Engelbart, Edward Snowden, Electric Kool-Aid Acid Test, Elon Musk, Fall of the Berlin Wall, Filter Bubble, global village, Google Glasses, Haight Ashbury, hive mind, income inequality, intangible asset, Jeff Bezos, job automation, John Markoff, Kevin Kelly, knowledge economy, Law of Accelerating Returns, Marc Andreessen, Mark Zuckerberg, Marshall McLuhan, means of production, move fast and break things, move fast and break things, new economy, New Journalism, Norbert Wiener, offshore financial centre, PageRank, Peace of Westphalia, Peter Thiel, planetary scale, Ray Kurzweil, self-driving car, Silicon Valley, Singularitarianism, software is eating the world, Steve Jobs, Steven Levy, Stewart Brand, strong AI, supply-chain management, the medium is the message, the scientific method, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, Thomas L Friedman, Thorstein Veblen, Upton Sinclair, Vernor Vinge, Whole Earth Catalog, yellow journalism

There’s a school of incrementalists, who cherish everything that has been accomplished to date—victories like the PageRank algorithm or the software that allows ATMs to read the scrawled writing on checks. This school holds out little to no hope that computers will ever acquire anything approximating human consciousness. Then there are the revolutionaries who gravitate toward Kurzweil and the singularitarian view. They aim to build computers with either “artificial general intelligence” or “strong AI.” For most of Google’s history, it trained its efforts on incremental improvements. During that earlier era, the company was run by Eric Schmidt—an older, experienced manager, whom Google’s investors forced Page and Brin to accept as their “adult” supervisor. That’s not to say that Schmidt was timid. Those years witnessed Google’s plot to upload every book on the planet and the creation of products that are now commonplace utilities, like Gmail, Google Docs, and Google Maps.

He assigned him the task of teaching computers to read—the sort of exponential breakthrough that would hasten the arrival of the superintelligence that Kurzweil celebrates. “This is the culmination of literally 50 years of my focus on artificial intelligence,” Kurzweil said upon signing up with Google. When you listen to Page talk to his employees, he returns time and again to the metaphor of the moonshot. The company has an Apollolike program for reaching artificial general intelligence: a project called Google Brain, a moniker with creepy implications. (“The Google policy on a lot of things is to get right up to the creepy line and not cross it,” Eric Schmidt has quipped.) Google has spearheaded the revival of a concept first explored in the sixties, one that has failed until recently: neural networks, which involve computing modeled on the workings of the human brain.


pages: 472 words: 117,093

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

"Robert Solow", 3D printing, additive manufacturing, AI winter, Airbnb, airline deregulation, airport security, Albert Einstein, Amazon Mechanical Turk, Amazon Web Services, artificial general intelligence, augmented reality, autonomous vehicles, backtesting, barriers to entry, bitcoin, blockchain, British Empire, business cycle, business process, carbon footprint, Cass Sunstein, centralized clearinghouse, Chris Urmson, cloud computing, cognitive bias, commoditize, complexity theory, computer age, creative destruction, crony capitalism, crowdsourcing, cryptocurrency, Daniel Kahneman / Amos Tversky, Dean Kamen, discovery of DNA, disintermediation, disruptive innovation, distributed ledger, double helix, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Ethereum, ethereum blockchain, everywhere but in the productivity statistics, family office, fiat currency, financial innovation, George Akerlof, global supply chain, Hernando de Soto, hive mind, information asymmetry, Internet of things, inventory management, iterative process, Jean Tirole, Jeff Bezos, jimmy wales, John Markoff, joint-stock company, Joseph Schumpeter, Kickstarter, law of one price, longitudinal study, Lyft, Machine translation of "The spirit is willing, but the flesh is weak." to Russian and back, Marc Andreessen, Mark Zuckerberg, meta analysis, meta-analysis, Mitch Kapor, moral hazard, multi-sided market, Myron Scholes, natural language processing, Network effects, new economy, Norbert Wiener, Oculus Rift, PageRank, pattern recognition, peer-to-peer lending, performance metric, plutocrats, Plutocrats, precision agriculture, prediction markets, pre–internet, price stability, principal–agent problem, Ray Kurzweil, Renaissance Technologies, Richard Stallman, ride hailing / ride sharing, risk tolerance, Ronald Coase, Satoshi Nakamoto, Second Machine Age, self-driving car, sharing economy, Silicon Valley, Skype, slashdot, smart contracts, Snapchat, speech recognition, statistical model, Steve Ballmer, Steve Jobs, Steven Pinker, supply-chain management, TaskRabbit, Ted Nelson, The Market for Lemons, The Nature of the Firm, Thomas Davenport, Thomas L Friedman, too big to fail, transaction costs, transportation-network company, traveling salesman, Travis Kalanick, two-sided market, Uber and Lyft, Uber for X, uber lyft, ubercab, Watson beat the top human players on Jeopardy!, winner-take-all economy, yield management, zero day

For the great majority of humans, our common sense does an admirable job of carrying us through the world’s barrage of complexity and inconsistency, even though, as discussed in the previous chapter, it’s biased and buggy. We have not yet designed symbolic digital systems that understand how the world actually works as well as our own biological System 1 does. Our systems are increasingly effective at “narrow” artificial intelligence, for particular domains like Go or image recognition, but we are far from achieving what Shane Legg, a cofounder of Deep-Mind, has dubbed artificial general intelligence (AGI), which can apply intelligence to a variety of unanticipated types of problems. Polanyi’s Pervasive Paradox Davis and Marcus describe what is perhaps the most instrumental barrier to building such systems: “In doing commonsense reasoning, people . . . are drawing on . . . reasoning processes largely unavailable to introspection.” In other words, the cognitive work that we humans do to navigate so easily through so many thickets of rules is an ongoing demonstration of Polanyi’s Paradox, the strange phenomenon that we know more than we can tell.

Acton, Brian, 140 additive manufacturing, 107; See also 3D printing Adore Me, 62 adults, language learning by, 68–69 advertising content platforms and, 139 data-driven decision making for, 48, 50–51 Facebook and, 8–9 radio airplay as, 148 advertising agencies, 48 advertising revenue Android as means of increasing, 166 Craigslist’s effect on, 139 free apps and, 162 print media and, 130, 132, 139 African Americans identifying gifted students, 40 and search engine bias, 51–52 aggregators, 139–40 AGI (artificial general intelligence), 71 agriculture automated milking systems, 101 drones and, 99–100 “food computers,” 272 machine learning and, 79–80 robotics and, 101–2 Airbnb future of, 319–20 hotel experience vs., 222–23 lack of assets owned by, 6–7 limits to effects on hotel industry, 221–23 network effects, 193 as O2O platform, 186 peer reviews, 209–10 rapid growth of, 9 as two-sided network, 214 value proposition compared to Uber, 222 Airline Deregulation Act, 181n airlines, revenue management by, 181–82 air travel, virtualization in, 89 Akerlof, George, 207, 210 albums, recorded music, 145 algorithms; See also data-driven decision making bias in systems, 51–53 and Cambrian Explosion of robotics, 95–96 comparing human decisions to, 56 O2O platforms and, 193 Quantopian and, 267–70 superiority to System 1 reasoning, 38–41 “algo traders,” 268; See also automated investing Alibaba, 6–8 Alipay, 174 AlphaGo, 4–6, 14, 74, 80 Alter, Lloyd, 90 Amazon automatic price changes, 47 bar code reader app, 162 data-driven product recommendations, 47 development of Web Services, 142–43 Mechanical Turk, 260 as stack, 295 warehouse robotics, 103 Amazon EC2, 143 Amazon Go, 90–91 Amazon S3, 143 Amazon Web Services (AWS), 75, 142–43 American Airlines (AA), 182 amino acid creation, 271–72 analog copies, digital copies vs., 136 “Anatomy of a Large-Scale Hypertextual Web Search Engine, The” (Page and Brin), 233 Anderson, Chris, 98–100 Anderson, Tim, 94 Andreessen, Marc on crowdfunding, 262–63 and Netscape, 34 as self-described “solutionist,” 297 on Teespring, 263–64 Android Blackberry vs., 168 contribution to Google revenue/profits, 204 iOS vs., 166–67 Angry Birds, 159–61 anonymity, digital currency and, 279–80 Antikythera mechanism, 66 APIs (application programming interfaces), 79 apophenia, 44n apparel, 186–88 Apple; See also iPhone acquiring innovation by acquiring companies, 265 and industrywide smartphone profits, 204 leveraging of platforms by, 331 Postmates and, 173, 185 profitability (2015), 204 revenue from paid apps, 164 “Rip, Mix, Burn” slogan, 144n as stack, 295 application programming interfaces (APIs), 79 AppNexus, 139 apps; See also platforms for banking, 89–90 demand curve and, 157–61 iPhone, 151–53 App Store, 158 Apter, Zach, 183 Aral, Sinan, 33 Archilochus, 60–61 architecture, computer-designed, 118 Aristophanes, 200 Arnaout, Ramy, 253 Arthur, Brian, 47–48 artificial general intelligence (AGI), 71 artificial hands, 272–75 artificial intelligence; See also machine learning current state of, 74–76 defined, 67 early attempts, 67–74 implications for future, 329–30 rule-based, 69–72 statistical pattern recognition and, 72–74 Art of Thinking Clearly, The (Dobelli), 43 arts, digital creativity in, 117–18 Ashenfelter, Orley, 38–39 ASICs (application-specific integrated circuits), 287 assets and incentives, 316 leveraging with O2O platforms, 196–97 replacement by platforms, 6–10 asymmetries of information, 206–10 asymptoting, 96 Atkeson, Andrew, 21 ATMs, 89 AT&T, 96, 130 August (smart door lock), 163 Austin, Texas, 223 Australia, 100 Authorize.Net, 171 Autodesk, 114–16, 119, 120 automated investing, 266–70 automation, effect on employment/wages, 332–33 automobiles, See cars Autor, David, 72, 101 background checks, 208, 209 back-office work, 82–83 BackRub, 233 Baidu, 192 Bakos, Yannis, 147n Bakunin, Mikhail, 278 Ballmer, Steve, 151–52 bandwagon effect, 217 banking, virtualization and, 89–90, 92 Bank of England, 280n bank tellers, 92 Barksdale, Jim, 145–46 barriers to entry, 96, 220 Bass, Carl, 106–7, 119–20 B2B (business-to-business) services, 188–90 Beastmode 2.0 Royale Chukkah, 290 Behance, 261 behavioral economics, 35, 43 Bell, Kristen, 261, 262 Benioff, Mark, 84–85 Benjamin, Robert, 311 Benson, Buster, 43–44 Berlin, Isiah, 60n Berners-Lee, Tim, 33, 34n, 138, 233 Bernstein, Michael, 260 Bertsimas, Dimitris, 39 Bezos, Jeff, 132, 142 bias of Airbnb hosts, 209–10 in algorithmic systems, 51–53 digital design’s freedom from, 116 management’s need to acknowledge, 323–24 and second-machine-age companies, 325 big data and Cambrian Explosion of robotics, 95 and credit scores, 46 and machine learning, 75–76 biology, computational, 116–17 Bird, Andrew, 121 Bitcoin, 279–88 China’s dominance of mining, 306–7 failure mode of, 317 fluctuation of value, 288 ledger for, 280–87 as model for larger economy, 296–97 recent troubles with, 305–7 and solutionism, 297 “Bitcoin: A Peer-to-Peer Electronic Cash System” (Nakamoto), 279 BlaBlaCar, 190–91, 197, 208 BlackBerry, 168, 203 Blitstein, Ryan, 117 blockchain as challenge to stacks, 298 and contracts, 291–95 development and deployment, 283–87 failure of, 317 and solutionism, 297 value as ledger beyond Bitcoin, 288–91 Blockchain Revolution (Tapscott and Tapscott), 298 Bloomberg Markets, 267 BMO Capital Markets, 204n Bobadilla-Suarez, Sebastian, 58n–59n Bock, Laszlo, 56–58 bonds, 131, 134 bonuses, credit card, 216 Bordeaux wines, 38–39 Boudreau, Kevin, 252–54 Bowie, David, 131, 134, 148 Bowie bonds, 131, 134 brand building, 210–11 Brat, Ilan, 12 Bredeche, Jean, 267 Brin, Sergey, 233 Broward County, Florida, 40 Brown, Joshua, 81–82 Brusson, Nicolas, 190 Burr, Donald, 177 Bush, Vannevar, 33 business conference venues, 189 Business Insider, 179 business processes, robotics and, 88–89 business process reengineering, 32–35 business travelers, lodging needs of, 222–23 Busque, Leah, 265 Buterin, Vitalik, 304–5 Byrne, Patrick, 290 Cairncross, Francis, 137 California, 208; See also specific cities Calo, Ryan, 52 Cambrian Explosion, 94–98 Cameron, Oliver, 324 Camp, Garrett, 200 capacity, perishing inventory and, 181 Card, David, 40 Care.com, 261 cars automated race car design, 114–16 autonomous, 17, 81–82 decline in ownership of, 197 cash, Bitcoin as equivalent to, 279 Casio QV-10 digital camera, 131 Caves, Richard, 23 Caviar, 186 CDs (compact discs), 145 cell phones, 129–30, 134–35; See also iPhone; smartphones Census Bureau, US, 42 central bankers, 305 centrally planned economies, 235–37 Chabris, Chris, 3 Chambers, Ephraim, 246 Champy, James, 32, 34–35, 37, 59 Chandler, Alfred, 309n Chase, 162 Chase Paymentech, 171 check-deposit app, 162 children, language learning by, 67–69 China Alibaba in, 7–8 concentration of Bitcoin wealth in, 306–7 and failure mode of Bitcoin, 317 mobile O2O platforms, 191–92 online payment service problems, 172 robotics in restaurants, 93 Shanghai Tower design, 118 Xiaomi, 203 Chipotle, 185 Choudary, Sangeet, 148 Christensen, Clay, 22, 264 Churchill, Winston, 301 Civil Aeronautics Board, US, 181n Civis Analytics, 50–51 Clash of Clans, 218 classified advertising revenue, 130, 132, 139 ClassPass, 205, 210 and economics of perishing inventory, 180–81 future of, 319–20 and problems with Unlimited offerings, 178–80, 184 and revenue management, 181–84 user experience, 211 ClassPass Unlimited, 178–79 Clear Channel, 135 clinical prediction, 41 Clinton, Hillary, 51 clothing, 186–88 cloud computing AI research, 75 APIs and, 79 Cambrian Explosion of robotics, 96–97 platform business, 195–96 coaches, 122–23, 334 Coase, Ronald, 309–13 cognitive biases, 43–46; See also bias Cohen, Steven, 270 Coles, John, 273–74 Collison, John, 171 Collison, Patrick, 171–74 Colton, Simon, 117 Columbia Record Club, 131 commoditization, 220–21 common sense, 54–55, 71, 81 companies continued dominance of, 311–12 continued relevance of, 301–27 DAO as alternative to, 301–5 decreasing life spans of, 330 economics of, 309–12 future of, 319–26 leading past the standard partnership, 323–26 management’s importance in, 320–23 markets vs., 310–11 as response to inherent incompleteness of contracts, 314–17 solutionism’s alternatives to, 297–99 TCE and, 312–15 and technologies of disruption, 307–9 Compass Fund, 267 complements (complementary goods) defined, 156 effect on supply/demand curves, 157–60 free, perfect, instant, 160–63 as key to successful platforms, 169 and open platforms, 164 platforms and, 151–68 and revenue management, 183–84 Stripe and, 173 complexity theory, 237 Composite Fund (D.

., 166–67 Angry Birds, 159–61 anonymity, digital currency and, 279–80 Antikythera mechanism, 66 APIs (application programming interfaces), 79 apophenia, 44n apparel, 186–88 Apple; See also iPhone acquiring innovation by acquiring companies, 265 and industrywide smartphone profits, 204 leveraging of platforms by, 331 Postmates and, 173, 185 profitability (2015), 204 revenue from paid apps, 164 “Rip, Mix, Burn” slogan, 144n as stack, 295 application programming interfaces (APIs), 79 AppNexus, 139 apps; See also platforms for banking, 89–90 demand curve and, 157–61 iPhone, 151–53 App Store, 158 Apter, Zach, 183 Aral, Sinan, 33 Archilochus, 60–61 architecture, computer-designed, 118 Aristophanes, 200 Arnaout, Ramy, 253 Arthur, Brian, 47–48 artificial general intelligence (AGI), 71 artificial hands, 272–75 artificial intelligence; See also machine learning current state of, 74–76 defined, 67 early attempts, 67–74 implications for future, 329–30 rule-based, 69–72 statistical pattern recognition and, 72–74 Art of Thinking Clearly, The (Dobelli), 43 arts, digital creativity in, 117–18 Ashenfelter, Orley, 38–39 ASICs (application-specific integrated circuits), 287 assets and incentives, 316 leveraging with O2O platforms, 196–97 replacement by platforms, 6–10 asymmetries of information, 206–10 asymptoting, 96 Atkeson, Andrew, 21 ATMs, 89 AT&T, 96, 130 August (smart door lock), 163 Austin, Texas, 223 Australia, 100 Authorize.Net, 171 Autodesk, 114–16, 119, 120 automated investing, 266–70 automation, effect on employment/wages, 332–33 automobiles, See cars Autor, David, 72, 101 background checks, 208, 209 back-office work, 82–83 BackRub, 233 Baidu, 192 Bakos, Yannis, 147n Bakunin, Mikhail, 278 Ballmer, Steve, 151–52 bandwagon effect, 217 banking, virtualization and, 89–90, 92 Bank of England, 280n bank tellers, 92 Barksdale, Jim, 145–46 barriers to entry, 96, 220 Bass, Carl, 106–7, 119–20 B2B (business-to-business) services, 188–90 Beastmode 2.0 Royale Chukkah, 290 Behance, 261 behavioral economics, 35, 43 Bell, Kristen, 261, 262 Benioff, Mark, 84–85 Benjamin, Robert, 311 Benson, Buster, 43–44 Berlin, Isiah, 60n Berners-Lee, Tim, 33, 34n, 138, 233 Bernstein, Michael, 260 Bertsimas, Dimitris, 39 Bezos, Jeff, 132, 142 bias of Airbnb hosts, 209–10 in algorithmic systems, 51–53 digital design’s freedom from, 116 management’s need to acknowledge, 323–24 and second-machine-age companies, 325 big data and Cambrian Explosion of robotics, 95 and credit scores, 46 and machine learning, 75–76 biology, computational, 116–17 Bird, Andrew, 121 Bitcoin, 279–88 China’s dominance of mining, 306–7 failure mode of, 317 fluctuation of value, 288 ledger for, 280–87 as model for larger economy, 296–97 recent troubles with, 305–7 and solutionism, 297 “Bitcoin: A Peer-to-Peer Electronic Cash System” (Nakamoto), 279 BlaBlaCar, 190–91, 197, 208 BlackBerry, 168, 203 Blitstein, Ryan, 117 blockchain as challenge to stacks, 298 and contracts, 291–95 development and deployment, 283–87 failure of, 317 and solutionism, 297 value as ledger beyond Bitcoin, 288–91 Blockchain Revolution (Tapscott and Tapscott), 298 Bloomberg Markets, 267 BMO Capital Markets, 204n Bobadilla-Suarez, Sebastian, 58n–59n Bock, Laszlo, 56–58 bonds, 131, 134 bonuses, credit card, 216 Bordeaux wines, 38–39 Boudreau, Kevin, 252–54 Bowie, David, 131, 134, 148 Bowie bonds, 131, 134 brand building, 210–11 Brat, Ilan, 12 Bredeche, Jean, 267 Brin, Sergey, 233 Broward County, Florida, 40 Brown, Joshua, 81–82 Brusson, Nicolas, 190 Burr, Donald, 177 Bush, Vannevar, 33 business conference venues, 189 Business Insider, 179 business processes, robotics and, 88–89 business process reengineering, 32–35 business travelers, lodging needs of, 222–23 Busque, Leah, 265 Buterin, Vitalik, 304–5 Byrne, Patrick, 290 Cairncross, Francis, 137 California, 208; See also specific cities Calo, Ryan, 52 Cambrian Explosion, 94–98 Cameron, Oliver, 324 Camp, Garrett, 200 capacity, perishing inventory and, 181 Card, David, 40 Care.com, 261 cars automated race car design, 114–16 autonomous, 17, 81–82 decline in ownership of, 197 cash, Bitcoin as equivalent to, 279 Casio QV-10 digital camera, 131 Caves, Richard, 23 Caviar, 186 CDs (compact discs), 145 cell phones, 129–30, 134–35; See also iPhone; smartphones Census Bureau, US, 42 central bankers, 305 centrally planned economies, 235–37 Chabris, Chris, 3 Chambers, Ephraim, 246 Champy, James, 32, 34–35, 37, 59 Chandler, Alfred, 309n Chase, 162 Chase Paymentech, 171 check-deposit app, 162 children, language learning by, 67–69 China Alibaba in, 7–8 concentration of Bitcoin wealth in, 306–7 and failure mode of Bitcoin, 317 mobile O2O platforms, 191–92 online payment service problems, 172 robotics in restaurants, 93 Shanghai Tower design, 118 Xiaomi, 203 Chipotle, 185 Choudary, Sangeet, 148 Christensen, Clay, 22, 264 Churchill, Winston, 301 Civil Aeronautics Board, US, 181n Civis Analytics, 50–51 Clash of Clans, 218 classified advertising revenue, 130, 132, 139 ClassPass, 205, 210 and economics of perishing inventory, 180–81 future of, 319–20 and problems with Unlimited offerings, 178–80, 184 and revenue management, 181–84 user experience, 211 ClassPass Unlimited, 178–79 Clear Channel, 135 clinical prediction, 41 Clinton, Hillary, 51 clothing, 186–88 cloud computing AI research, 75 APIs and, 79 Cambrian Explosion of robotics, 96–97 platform business, 195–96 coaches, 122–23, 334 Coase, Ronald, 309–13 cognitive biases, 43–46; See also bias Cohen, Steven, 270 Coles, John, 273–74 Collison, John, 171 Collison, Patrick, 171–74 Colton, Simon, 117 Columbia Record Club, 131 commoditization, 220–21 common sense, 54–55, 71, 81 companies continued dominance of, 311–12 continued relevance of, 301–27 DAO as alternative to, 301–5 decreasing life spans of, 330 economics of, 309–12 future of, 319–26 leading past the standard partnership, 323–26 management’s importance in, 320–23 markets vs., 310–11 as response to inherent incompleteness of contracts, 314–17 solutionism’s alternatives to, 297–99 TCE and, 312–15 and technologies of disruption, 307–9 Compass Fund, 267 complements (complementary goods) defined, 156 effect on supply/demand curves, 157–60 free, perfect, instant, 160–63 as key to successful platforms, 169 and open platforms, 164 platforms and, 151–68 and revenue management, 183–84 Stripe and, 173 complexity theory, 237 Composite Fund (D.


pages: 238 words: 77,730

Final Jeopardy: Man vs. Machine and the Quest to Know Everything by Stephen Baker

23andMe, AI winter, Albert Einstein, artificial general intelligence, business process, call centre, clean water, commoditize, computer age, Frank Gehry, information retrieval, Iridium satellite, Isaac Newton, job automation, pattern recognition, Ray Kurzweil, Silicon Valley, Silicon Valley startup, statistical model, theory of mind, thinkpad, Turing test, Vernor Vinge, Wall-E, Watson beat the top human players on Jeopardy!

In August 2010, hundreds of computer scientists, cognitive psychologists, futurists, and curious technophiles descended on San Francisco’s Hyatt hotel, on the Embarcadero, for the two-day Singularity Summit. For most of these people, programming machines to catalogue knowledge and answer questions, whether manually or by machine, was a bit pedestrian. They weren’t looking for advances in technology that already existed. Instead, they were focused on a bolder challenge, the development of deep and broad machine intelligence known as Artificial General Intelligence. This, they believed, would lead to the next step of human evolution. The heart of the Singularity argument, as explained by the technologists Vernor Vinge and Ray Kurzweil, the leading evangelists of the concept, lies in the power of exponential growth. As Samuel Butler noted, machines evolve far faster than humans. But information technology, which Butler only glimpsed, races ahead at an even faster rate.

A diminutive thirty-four-year-old British neuroscientist, Hassabis told the crowd that technology wasn’t the only thing growing exponentially. Research papers on the brain were also doubling every year. Some fifty thousand academic papers on neuroscience had been published in 2008 alone. “If you looked at neuroscience in 2005, or before that, you’re way out of date now,” he said. But which areas of brain research would lead to the development of Artificial General Intelligence? Hassabis had followed an unusual path toward AI research. At thirteen, he was the highest ranked chess player of his age on earth. But computers were already making inroads in chess. So why dedicate his brain, which he had every reason to believe was exceptional, to a field that machines would soon conquer? (From the perspective of futurists, chess was an early sighting of the Singularity.)


pages: 492 words: 141,544

Red Moon by Kim Stanley Robinson

artificial general intelligence, basic income, blockchain, Brownian motion, correlation does not imply causation, cryptocurrency, Deng Xiaoping, gig economy, Hyperloop, illegal immigration, income inequality, invisible hand, low earth orbit, Magellanic Cloud, megacity, precariat, Schrödinger's Cat, seigniorage, strong AI, Turing machine, universal basic income, zero-sum game

It needed to incorporate the symbolic logic of earlier AI attempts, and the various programs that instructed an AI to pursue “child’s play,” meaning randomly created activities and improvements. There also had to be encouragements in the form of actually programmed prompts to help machine learning occur mechanically, to make algorithms create more algorithms. All this was hard; and even if he managed to do some of it, at best he would still be left with nothing more than an advanced search engine. Artificial general intelligence was just a phrase, not a reality. Nothing even close to consciousness would be achieved; a mouse had more consciousness than an AI by a factor that was essentially all to nothing, so a kind of infinity. But despite its limitations, this particular combination of programs might still find more than he or it knew it was looking for. And the outside possibility of a rapid assemblage of stronger cognitive powers was always there.

He had had to weave those particular taps into the system as potentialities only, and Little Eyeball would have to turn them on and make its way through them back into the Great Firewall and elsewhere. But the AI would still be operating, and he had left precise instructions for this contingency. Precise at first, anyway, then completely general: do the best you can! Help all good causes! It would be a test to see just how general its intelligence was. Artificial general intelligence: these names were so presumptuous, such hopeful bits of hype. As if calling something new by an old name would give it those old qualities. People did that a lot. It was a fund-raiser’s ontology. But on the other hand, attempts had to be made. So his little system would stay powered, hopefully, and even if restricted to a single device in Chengdu, it would at least not be destroyed.

Propose improvements to current situation. Use Monte Carlo tree search to evaluate potential outcomes. Initiate direct insertion of improvements into current codes and laws. Announce these improvements after insertions completed. Press them by way of persuasive design methodology as outlined in captology and exploitationware studies. Flood the seams between system and lifeworld (Habermas). Always remember: an artificial general intelligence is not like human intelligence. AI operates by way of a set of algorithms, without consciousness. Its volition is as algorithmic as the rest of its operations, and is based on programmed axioms. Its sphere of action is sharply circumscribed. What it can do is extend its reach where it can. It can follow instructions. It can be widely comprehensive. It can work fast. CHAPTER FIFTEEN mozhe shitou guo he Crossing the River by Feeling the Stones (Deng) The far side of the moon quickly revealed itself to be a very rough landscape.


pages: 677 words: 206,548

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

23andMe, 3D printing, active measures, additive manufacturing, Affordable Care Act / Obamacare, Airbnb, airport security, Albert Einstein, algorithmic trading, artificial general intelligence, Asilomar, Asilomar Conference on Recombinant DNA, augmented reality, autonomous vehicles, Baxter: Rethink Robotics, Bill Joy: nanobots, bitcoin, Black Swan, blockchain, borderless world, Brian Krebs, business process, butterfly effect, call centre, Charles Lindbergh, Chelsea Manning, cloud computing, cognitive dissonance, computer vision, connected car, corporate governance, crowdsourcing, cryptocurrency, data acquisition, data is the new oil, Dean Kamen, disintermediation, don't be evil, double helix, Downton Abbey, drone strike, Edward Snowden, Elon Musk, Erik Brynjolfsson, Filter Bubble, Firefox, Flash crash, future of work, game design, global pandemic, Google Chrome, Google Earth, Google Glasses, Gordon Gekko, high net worth, High speed trading, hive mind, Howard Rheingold, hypertext link, illegal immigration, impulse control, industrial robot, Intergovernmental Panel on Climate Change (IPCC), Internet of things, Jaron Lanier, Jeff Bezos, job automation, John Harrison: Longitude, John Markoff, Joi Ito, Jony Ive, Julian Assange, Kevin Kelly, Khan Academy, Kickstarter, knowledge worker, Kuwabatake Sanjuro: assassination market, Law of Accelerating Returns, Lean Startup, license plate recognition, lifelogging, litecoin, low earth orbit, M-Pesa, Mark Zuckerberg, Marshall McLuhan, Menlo Park, Metcalfe’s law, MITM: man-in-the-middle, mobile money, more computing power than Apollo, move fast and break things, move fast and break things, Nate Silver, national security letter, natural language processing, obamacare, Occupy movement, Oculus Rift, off grid, offshore financial centre, optical character recognition, Parag Khanna, pattern recognition, peer-to-peer, personalized medicine, Peter H. Diamandis: Planetary Resources, Peter Thiel, pre–internet, RAND corporation, ransomware, Ray Kurzweil, refrigerator car, RFID, ride hailing / ride sharing, Rodney Brooks, Ross Ulbricht, Satoshi Nakamoto, Second Machine Age, security theater, self-driving car, shareholder value, Silicon Valley, Silicon Valley startup, Skype, smart cities, smart grid, smart meter, Snapchat, social graph, software as a service, speech recognition, stealth mode startup, Stephen Hawking, Steve Jobs, Steve Wozniak, strong AI, Stuxnet, supply-chain management, technological singularity, telepresence, telepresence robot, Tesla Model S, The Future of Employment, The Wisdom of Crowds, Tim Cook: Apple, trade route, uranium enrichment, Wall-E, Watson beat the top human players on Jeopardy!, Wave and Pay, We are Anonymous. We are Legion, web application, Westphalian system, WikiLeaks, Y Combinator, zero day

“Don Watson” might even engage in murder for hire by geo-locating human targets and hacking into objects connected to the Internet of Things surrounding victims, such as cars, elevators, and robots, in order to cause accidents resulting in the death of its prey. While such activities would be at the extreme level of what a narrow AI might accomplish, they would be easy for the next generation of computing: artificial general intelligence. Man’s Last Invention: Artificial General Intelligence By the time Skynet became self-aware, it had spread into millions of computer servers all across the planet. Ordinary computers in office buildings, dorm rooms, everywhere. It was software, in cyberspace. There was no system core. It could not be shut down. JOHN CONNOR, TERMINATOR 3: RISE OF THE MACHINES Ray Kurzweil has popularized the idea of the technological singularity: that moment in time in which nonhuman intelligence exceeds human intelligence for the first time in history—a shift so profound that it’s often been referred to as our “final invention.”

It’s Written All Over Your Face On Your Best Behavior Augmenting Reality The Rise of Homo virtualis CHAPTER 15: RISE OF THE MACHINES: WHEN CYBER CRIME GOES 3-D We, Robot The Military-Industrial (Robotic) Complex A Robot in Every Home and Office Humans Need Not Apply Robot Rights, Law, Ethics, and Privacy Danger, Will Robinson Hacking Robots Game of Drones Robots Behaving Badly Attack of the Drones The Future of Robotics and Autonomous Machines Printing Crime: When Gutenberg Meets Gotti CHAPTER 16: NEXT-GENERATION SECURITY THREATS: WHY CYBER WAS ONLY THE BEGINNING Nearly Intelligent Talk to My Agent Black-Box Algorithms and the Fallacy of Math Neutrality Al-gorithm Capone and His AI Crime Bots When Watson Turns to a Life of Crime Man’s Last Invention: Artificial General Intelligence The AI-pocalypse How to Build a Brain Tapping Into Genius: Brain-Computer Interface Mind Reading, Brain Warrants, and Neuro-hackers Biology Is Information Technology Bio-computers and DNA Hard Drives Jurassic Park for Reals Invasion of the Bio-snatchers: Genetic Privacy, Bioethics, and DNA Stalkers Bio-cartels and New Opiates for the Masses Hacking the Software of Life: Bio-crime and Bioterrorism The Final Frontier: Space, Nano, and Quantum PART THREE SURVIVING PROGRESS CHAPTER 17: SURVIVING PROGRESS Killer Apps: Bad Software and Its Consequences Software Damages Reducing Data Pollution and Reclaiming Privacy Kill the Password Encryption by Default Taking a Byte out of Cyber Crime: Education Is Essential The Human Factor: The Forgotten Weak Link Bringing Human-Centered Design to Security Mother (Nature) Knows Best: Building an Immune System for the Internet Policing the Twenty-First Century Practicing Safe Techs: The Need for Good Cyber Hygiene The Cyber CDC: The World Health Organization for a Connected Planet CHAPTER 18: THE WAY FORWARD Ghosts in the Machine Building Resilience: Automating Defenses and Scaling for Good Reinventing Government: Jump-Starting Innovation Meaningful Public-Private Partnership We the People Gaming the System Eye on the Prize: Incentive Competitions for Global Security Getting Serious: A Manhattan Project for Cyber Final Thoughts Appendix: Everything’s Connected, Everyone’s Vulnerable: Here’s What You Can Do About It Acknowledgments Notes PROLOGUE The Irrational Optimist: How I Got This Way My entrée into the world of high-tech crime began innocuously in 1995 while working as a twenty-eight-year-old investigator and sergeant at the LAPD’s famed Parker Center police headquarters.

• Heavier-than-air flying machines are impossible (Lord Kelvin, British mathematician, physicist, and president of the Royal Society, 1895). • This “telephone” has too many shortcomings to be seriously considered as a means of communication. The device is inherently of no value to us (internal memo at Western Union, 1878). Somehow, the impossible always seems to become the possible. In the world of artificial intelligence, that next phase of development is called artificial general intelligence (AGI), or strong AI. In contrast to narrow AI, which cleverly performs a specific limited task, such as machine translation or auto navigation, strong AI refers to “thinking machines” that might perform any intellectual task that a human being could. Characteristics of a strong AI would include the ability to reason, make judgments, plan, learn, communicate, and unify these skills toward achieving common goals across a variety of domains, and commercial interest is growing.


pages: 360 words: 100,991

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

3D printing, AI winter, artificial general intelligence, Asperger Syndrome, augmented reality, Berlin Wall, brain emulation, Buckminster Fuller, call centre, cognitive bias, cognitive dissonance, computer age, computer vision, crowdsourcing, Elon Musk, en.wikipedia.org, epigenetics, friendly AI, ghettoisation, industrial robot, Internet of things, invention of writing, Jacques de Vaucanson, job automation, John von Neumann, Kevin Kelly, Law of Accelerating Returns, Loebner Prize, Menlo Park, meta analysis, meta-analysis, Metcalfe’s law, neurotypical, Oculus Rift, old age dependency ratio, pattern recognition, RAND corporation, Ray Kurzweil, Rodney Brooks, self-driving car, Skype, social intelligence, software as a service, Stephen Hawking, Steven Pinker, superintelligent machines, technological singularity, telepresence, telepresence robot, The Future of Employment, the scientific method, theory of mind, Turing test, twin studies, undersea cable, Vernor Vinge, Watson beat the top human players on Jeopardy!, Whole Earth Review, working-age population, zero day

This condescending robot is quite ready to dismiss all of our intellectual accomplishments, but the human emotional experience remains a treasure beyond its grasp. Over the following decades, many authors would explore the idea of machines trying to comprehend and manipulate human feelings, as if they foresaw emotion all too soon becoming the one remaining bastion of humanity. In Arthur C. Clarke’s 2001, the artificial general intelligence known as HAL 9000 is capable of reading and to some degree expressing emotions.3 In fact, within the film version of 2001 by Stanley Kubrick, HAL is probably (and intentionally) the most emotive of all the characters. Much film analysis is been written about HAL going insane, but in many respects the computer was merely applying pure logic to the matter of its personal survival and fulfilling its mission.

Some recent efforts by organizations such as DARPA seek to ensure that AIs can “explain” their reasoning, but it remains questionable whether such an approach will be successful.10 Then there’s the argument about the difficulty or impossibility of developing a human-equivalent AI. This is a common assumption and a common error. Many people conflate the terms human intelligence, human-equivalent AI, and human-level machine intelligence with one another, when each must by definition be distinctly different. A truly human, artificially generated intelligence may never exist outside of a biologically-based substrate. Or if it ever does, it won’t be for a very long time. Likewise for human-equivalent AI. AI that thinks exactly as humans do will be extremely difficult to attain and therefore will probably take a significant amount of time to realize. However, in seeking to build an intelligent machine, why should we try to emulate humans at all?


pages: 350 words: 98,077

Artificial Intelligence: A Guide for Thinking Humans by Melanie Mitchell

Ada Lovelace, AI winter, Amazon Mechanical Turk, Apple's 1984 Super Bowl advert, artificial general intelligence, autonomous vehicles, Bernie Sanders, Claude Shannon: information theory, cognitive dissonance, computer age, computer vision, dark matter, Douglas Hofstadter, Elon Musk, en.wikipedia.org, Gödel, Escher, Bach, I think there is a world market for maybe five computers, ImageNet competition, Jaron Lanier, job automation, John Markoff, John von Neumann, Kevin Kelly, Kickstarter, license plate recognition, Mark Zuckerberg, natural language processing, Norbert Wiener, ought to be enough for anybody, pattern recognition, performance metric, RAND corporation, Ray Kurzweil, recommendation engine, ride hailing / ride sharing, Rodney Brooks, self-driving car, sentiment analysis, Silicon Valley, Singularitarianism, Skype, speech recognition, Stephen Hawking, Steve Jobs, Steve Wozniak, Steven Pinker, strong AI, superintelligent machines, theory of mind, There's no reason for any individual to have a computer in his home - Ken Olsen, Turing test, Vernor Vinge, Watson beat the top human players on Jeopardy!

AlphaGo is possibly the world’s best Go player, but it can’t do anything else; it can’t even play checkers, tic-tac-toe, or Candy Land. Google Translate can render an English movie review into Chinese, but it can’t tell you if the reviewer liked the movie or not, and it certainly can’t watch and review the movie itself. The terms narrow and weak are used to contrast with strong, human-level, general, or full-blown AI (sometimes called AGI, or artificial general intelligence)—that is, the AI that we see in movies, that can do most everything we humans can do, and possibly much more. General AI might have been the original goal of the field, but achieving it has turned out to be much harder than expected. Over time, efforts in AI have become focused on particular well-defined tasks—speech recognition, chess playing, autonomous driving, and so on. Creating machines that perform such functions is useful and often lucrative, and it could be argued that each of these tasks individually requires “intelligence.”

A Aaronson, Scott abstraction; in Bongard problems; in convolutional neural networks; in human cognition; in letter-string analogy problems activation maps activations: in encoder-decoder systems; formula for computing; in neural networks; in neurons; in recurrent neural networks; in word2vec active-symbol architecture active symbols adversarial examples: for computer vision; for deep Q-learning systems; for natural-language processing systems; for self-driving cars; for speech-recognition systems adversarial learning AGI, see general or human-level AI Agüera y Arcas, Blaise AI, see artificial intelligence AI Singularity, see Singularity AI spring AI winter AlexNet algorithm Allen, Paul Allen Institute for Artificial Intelligence; science questions data set AlphaGo; intelligence of; learning in AlphaGo Fan AlphaGo Lee AlphaGo Zero AlphaZero Amazon Mechanical Turk; origin of name American Civil Liberties Union (ACLU) analogy: in humans; letter-string microworld; relationship to categories and concepts; using word vectors; in visual situations; see also Copycat artificial general intelligence, see general or human-level AI artificial intelligence: beneficial; bias in; creativity in; definition of; explainability; general or human-level; moral; origin of term; regulation of; relationship to deep learning and machine learning; “right to explanation”; spring; strong; subsymbolic; symbolic; unemployment due to; weak; winter Asimov, Isaac; fundamental Rules of Robotics Atari video games; see also Breakout automated image captioning autonomous vehicles, see self-driving cars B back-propagation; in convolutional neural networks; in deep reinforcement learning barrier of meaning Barsalou, Lawrence beneficial AI Bengio, Yoshua bias; in face recognition; in word vectors big data bilingual evaluation understudy (BLEU) board positions; in checkers; in chess; in Go Bongard, Mikhail Bongard problems Bored Yann LeCun Bostrom, Nick Brackeen, Brian Breakout; deep Q-learning for; transfer learning on Brin, Sergey brittleness of AI systems Brooks, Rodney C CaptionBot Centre for the Study of Existential Risk checkers; see also Samuel’s checkers-playing program chess; see also Deep Blue Clark, Andy Clarke, Arthur C.


pages: 1,034 words: 241,773

Enlightenment Now: The Case for Reason, Science, Humanism, and Progress by Steven Pinker

3D printing, access to a mobile phone, affirmative action, Affordable Care Act / Obamacare, agricultural Revolution, Albert Einstein, Alfred Russel Wallace, anti-communist, Anton Chekhov, Arthur Eddington, artificial general intelligence, availability heuristic, Ayatollah Khomeini, basic income, Berlin Wall, Bernie Sanders, Black Swan, Bonfire of the Vanities, business cycle, capital controls, Capital in the Twenty-First Century by Thomas Piketty, carbon footprint, clean water, clockwork universe, cognitive bias, cognitive dissonance, Columbine, conceptual framework, correlation does not imply causation, creative destruction, crowdsourcing, cuban missile crisis, Daniel Kahneman / Amos Tversky, dark matter, decarbonisation, deindustrialization, dematerialisation, demographic transition, Deng Xiaoping, distributed generation, diversified portfolio, Donald Trump, Doomsday Clock, double helix, effective altruism, Elon Musk, en.wikipedia.org, end world poverty, endogenous growth, energy transition, European colonialism, experimental subject, Exxon Valdez, facts on the ground, Fall of the Berlin Wall, first-past-the-post, Flynn Effect, food miles, Francis Fukuyama: the end of history, frictionless, frictionless market, germ theory of disease, Gini coefficient, Hans Rosling, hedonic treadmill, helicopter parent, Hobbesian trap, humanitarian revolution, Ignaz Semmelweis: hand washing, income inequality, income per capita, Indoor air pollution, Intergovernmental Panel on Climate Change (IPCC), invention of writing, Jaron Lanier, Joan Didion, job automation, Johannes Kepler, John Snow's cholera map, Kevin Kelly, Khan Academy, knowledge economy, l'esprit de l'escalier, Laplace demon, life extension, long peace, longitudinal study, Louis Pasteur, Martin Wolf, mass incarceration, meta analysis, meta-analysis, Mikhail Gorbachev, minimum wage unemployment, moral hazard, mutually assured destruction, Naomi Klein, Nate Silver, Nathan Meyer Rothschild: antibiotics, Nelson Mandela, New Journalism, Norman Mailer, nuclear winter, obamacare, open economy, Paul Graham, peak oil, Peter Singer: altruism, Peter Thiel, precision agriculture, prediction markets, purchasing power parity, Ralph Nader, randomized controlled trial, Ray Kurzweil, rent control, Republic of Letters, Richard Feynman, road to serfdom, Robert Gordon, Rodney Brooks, rolodex, Ronald Reagan, Rory Sutherland, Saturday Night Live, science of happiness, Scientific racism, Second Machine Age, secular stagnation, self-driving car, sharing economy, Silicon Valley, Silicon Valley ideology, Simon Kuznets, Skype, smart grid, sovereign wealth fund, stem cell, Stephen Hawking, Steven Pinker, Stewart Brand, Stuxnet, supervolcano, technological singularity, Ted Kaczynski, The Rise and Fall of American Growth, the scientific method, The Signal and the Noise by Nate Silver, The Spirit Level, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, Thomas Kuhn: the structure of scientific revolutions, Thomas Malthus, total factor productivity, union organizing, universal basic income, University of East Anglia, Unsafe at Any Speed, Upton Sinclair, uranium enrichment, urban renewal, War on Poverty, We wanted flying cars, instead we got 140 characters, women in the workforce, working poor, World Values Survey, Y2K

Indeed, we know of one highly advanced form of intelligence that evolved without this defect. They’re called women. The second fallacy is to think of intelligence as a boundless continuum of potency, a miraculous elixir with the power to solve any problem, attain any goal.23 The fallacy leads to nonsensical questions like when an AI will “exceed human-level intelligence,” and to the image of an ultimate “Artificial General Intelligence” (AGI) with God-like omniscience and omnipotence. Intelligence is a contraption of gadgets: software modules that acquire, or are programmed with, knowledge of how to pursue various goals in various domains.24 People are equipped to find food, win friends and influence people, charm prospective mates, bring up children, move around in the world, and pursue other human obsessions and pastimes.

Understanding does not obey Moore’s Law: knowledge is acquired by formulating explanations and testing them against reality, not by running an algorithm faster and faster.25 Devouring the information on the Internet will not confer omniscience either: big data is still finite data, and the universe of knowledge is infinite. For these reasons, many AI researchers are annoyed by the latest round of hype (the perennial bane of AI) which has misled observers into thinking that Artificial General Intelligence is just around the corner.26 As far as I know, there are no projects to build an AGI, not just because it would be commercially dubious but because the concept is barely coherent. The 2010s have, to be sure, brought us systems that can drive cars, caption photographs, recognize speech, and beat humans at Jeopardy!, Go, and Atari computer games. But the advances have not come from a better understanding of the workings of intelligence but from the brute-force power of faster chips and bigger data, which allow the programs to be trained on millions of examples and generalize to similar new ones.

Homicide. https://www.ons.gov.uk/peoplepopulationandcommunity/crimeandjustice/compendium/focusonviolentcrimeandsexualoffences/yearendingmarch2016/homicide. Ohlander, J. 2010. The decline of suicide in Sweden, 1950–2000. Ph.D. dissertation, Pennsylvania State University. Olfson, M., Druss, B. G., & Marcus, S. C. 2015. Trends in mental health care among children and adolescents. New England Journal of Medicine, 372, 2029–38. Omohundro, S. M. 2008. The basic AI drives. In P. Wang, B. Goertzel, & S. Franklin, eds., Artificial general intelligence 2008: Proceedings of the first AGI conference. Amsterdam: IOS Press. Oreskes, N., & Conway, E. 2010. Merchants of doubt: How a handful of scientists obscured the truth on issues from tobacco smoke to global warming. New York: Bloomsbury Press. Ortiz-Ospina, E., Lee, L., & Roser, M. 2016. Suicide. Our World in Data. https://ourworldindata.org/suicide/. Ortiz-Ospina, E., & Roser, M. 2016a.


Global Catastrophic Risks by Nick Bostrom, Milan M. Cirkovic

affirmative action, agricultural Revolution, Albert Einstein, American Society of Civil Engineers: Report Card, anthropic principle, artificial general intelligence, Asilomar, availability heuristic, Bill Joy: nanobots, Black Swan, carbon-based life, cognitive bias, complexity theory, computer age, coronavirus, corporate governance, cosmic microwave background, cosmological constant, cosmological principle, cuban missile crisis, dark matter, death of newspapers, demographic transition, Deng Xiaoping, distributed generation, Doomsday Clock, Drosophila, endogenous growth, Ernest Rutherford, failed state, feminist movement, framing effect, friendly AI, Georg Cantor, global pandemic, global village, Gödel, Escher, Bach, hindsight bias, Intergovernmental Panel on Climate Change (IPCC), invention of agriculture, Kevin Kelly, Kuiper Belt, Law of Accelerating Returns, life extension, means of production, meta analysis, meta-analysis, Mikhail Gorbachev, millennium bug, mutually assured destruction, nuclear winter, P = NP, peak oil, phenotype, planetary scale, Ponzi scheme, prediction markets, RAND corporation, Ray Kurzweil, reversible computing, Richard Feynman, Ronald Reagan, scientific worldview, Singularitarianism, social intelligence, South China Sea, strong AI, superintelligent machines, supervolcano, technological singularity, technoutopianism, The Coming Technological Singularity, Tunguska event, twin studies, uranium enrichment, Vernor Vinge, War on Poverty, Westphalian system, Y2K

The value judgement is that this outcome satisfies or is preferable to current conditions. Given a different empirical belief about the actual real­ world consequences of a communist system, the decision may undergo a corresponding change. We would expect a true A I , an Artificial General Intelligence, to be capable of changing its empirical beliefs (or its probabilistic world model, etc.). If somehow Charles Babbage had lived before Nicolaus Copernicus, somehow computers had been invented before telescopes, and somehow the programmers of that day and age successfully created an Artificial General Intelligence, it would not follow that the AI would believe forever after that the Sun orbited the Earth. The AI might transcend the factual error of its programmers, provided that the programmers understood inference rather better than they understood astronomy.

We cannot rely on having distant advance warning before AI is created; past technological revolutions usually did not telegraph themselves to people alive at the time, whatever was said afterwards Artificial Intelligence in global risk 327 in hindsight. The mathematics and techniques of Friendly A I will not materialize from nowhere when needed; it takes years to lay firm foundations. Furthermore, we need to solve the Friendly AI challenge before Artificial General Intelligence is created, not afterwards; I should not even have to point this out. There will be difficulties for Friendly AI because the field of AI itself is in a state oflow consensus and high entropy. But that does not mean we do not need to worry about Friendly A I . It means there will be difficulties. The two statements, sadly, are not remotely equivalent. The possibility of sharp jumps in intelligence also implies a higher standard for Friendly AI techniques.

Artificial Intelligence: A Modern Approach, pp. 962-964 (NJ: Prentice Hall). Sandberg, A. ( 1999) . The physics of information processing superobjects: daily life mong the Jupiter brains. ]. Evol. Techno!., 5. http:/ jftp.nada.kth.sejpubjhomejasaj workjBrainsjBrains2 Schmidhuber, J. (2003). Goede! machines: self-referential universal problem solvers making provably optimal self-improvements. In Goertzel, B. and Pennachin, C. (eds.), Artificial General Intelligence, (New York: Springer-Verlag) . Sober, E. ( 1984). The Nature of Selection (Cambridge, MA: M IT Press). Tooby, J . and Cosmides, L. ( 1992). The psychological foundations ofculture. In Barkow, J . H . , Cosmides, L. and Tooby, J. (eds.), The Adapted Mind: Evolutionary Psychology and the Generation of Culture, (New York: Oxford University Press). Artificial Intelligence in global risk 345 Vinge, V.


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

"Robert Solow", A Declaration of the Independence of Cyberspace, AI winter, airport security, Apple II, artificial general intelligence, Asilomar, augmented reality, autonomous vehicles, basic income, Baxter: Rethink Robotics, Bill Duvall, bioinformatics, Brewster Kahle, Burning Man, call centre, cellular automata, Chris Urmson, Claude Shannon: information theory, Clayton Christensen, clean water, cloud computing, collective bargaining, computer age, computer vision, crowdsourcing, Danny Hillis, DARPA: Urban Challenge, data acquisition, Dean Kamen, deskilling, don't be evil, Douglas Engelbart, Douglas Engelbart, Douglas Hofstadter, Dynabook, Edward Snowden, Elon Musk, Erik Brynjolfsson, factory automation, From Mathematics to the Technologies of Life and Death, future of work, Galaxy Zoo, Google Glasses, Google X / Alphabet X, Grace Hopper, Gunnar Myrdal, Gödel, Escher, Bach, Hacker Ethic, haute couture, hive mind, hypertext link, indoor plumbing, industrial robot, information retrieval, Internet Archive, Internet of things, invention of the wheel, Jacques de Vaucanson, Jaron Lanier, Jeff Bezos, job automation, John Conway, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, Kevin Kelly, knowledge worker, Kodak vs Instagram, labor-force participation, loose coupling, Marc Andreessen, Mark Zuckerberg, Marshall McLuhan, medical residency, Menlo Park, Mitch Kapor, Mother of all demos, natural language processing, new economy, Norbert Wiener, PageRank, pattern recognition, pre–internet, RAND corporation, Ray Kurzweil, Richard Stallman, Robert Gordon, Rodney Brooks, Sand Hill Road, Second Machine Age, self-driving car, semantic web, shareholder value, side project, Silicon Valley, Silicon Valley startup, Singularitarianism, skunkworks, Skype, social software, speech recognition, stealth mode startup, Stephen Hawking, Steve Ballmer, Steve Jobs, Steve Wozniak, Steven Levy, Stewart Brand, strong AI, superintelligent machines, technological singularity, Ted Nelson, telemarketer, telepresence, telepresence robot, Tenerife airport disaster, The Coming Technological Singularity, the medium is the message, Thorstein Veblen, Turing test, Vannevar Bush, Vernor Vinge, Watson beat the top human players on Jeopardy!, Whole Earth Catalog, William Shockley: the traitorous eight, zero-sum game

When pressed, the computer scientists, roboticists, and technologists offer conflicting views. Some want to replace humans with machines; some are resigned to the inevitability—“I for one, welcome our insect overlords” (later “robot overlords”) was a meme that was popularized by The Simpsons—and some of them just as passionately want to build machines to extend the reach of humans. The question of whether true artificial intelligence—the concept known as “Strong AI” or Artificial General Intelligence—will emerge, and whether machines can do more than mimic humans, has also been debated for decades. Today there is a growing chorus of scientists and technologists raising new alarms about the possibility of the emergence of self-aware machines and their consequences. Discussions about the state of AI technology today veer into the realm of science fiction or perhaps religion. However, the reality of machine autonomy is no longer merely a philosophical or hypothetical question.

., 191–192 aging, of humans, 93–94, 236–237, 245, 327–332 “Alchemy and Artificial Intelligence” (Dreyfus), 177 Allen, Paul, 267, 268, 337 Alone Together (Turkle), 173, 221–222 Amazon, 97–98, 206, 247 Ambler (robot), 33, 202 Anderson, Chris, 88 Andreessen, Marc, 69 Apocalypse AI (Geraci), 85, 116–117 Apple. see also Siri (Apple) early history of, 7, 8, 214, 279–281, 307 iPhone, 23, 93, 239, 275, 281 iPod, 194, 275, 281 Jobs and, 13, 35, 112, 131, 194, 214, 241, 281–282, 320–323 Knowledge Navigator, 188, 300, 304, 305–310, 317, 318 labor force of, 83–84 Rubin and, 240 Sculley and, 35, 280, 300, 305, 306, 307, 317 Architecture Machine, The (Negroponte), 191 Architecture Machine Group, 306–307, 308–309 Arkin, Ronald, 333–335 Armer, Paul, 74 Aronson, Louise, 328 Artificial General Intelligence, 26 artificial intelligence (AI). see artificial intelligence (AI) history; autonomous vehicles; intelligence augmentation (IA) versus AI; labor force; robotics advancement; Siri (Apple) artificial intelligence (AI) history, 95–158. see also intelligence augmentation (IA) versus AI AI commercialization, 156–158 AI terminology, xii, 105–109 AI Winter, 16, 130–131, 140 Breiner and, 125–135 deep learning neural networks, 150–156, 151 early neural networks, 141–150 expert systems, 134–141, 285 McCarthy and, 109–115 Moravec and, 115–125 Silicon Valley inception, 95–99, 100, 256 SRI inception, 99–105 Strong artificial intelligence, 12, 26, 272 “Artificial Intelligence” (Lighthill), 130 “Artificial Intelligence of Hubert L.


pages: 551 words: 174,280

The Beginning of Infinity: Explanations That Transform the World by David Deutsch

agricultural Revolution, Albert Michelson, anthropic principle, artificial general intelligence, Bonfire of the Vanities, conceptual framework, cosmological principle, dark matter, David Attenborough, discovery of DNA, Douglas Hofstadter, Eratosthenes, Ernest Rutherford, first-past-the-post, Georg Cantor, global pandemic, Gödel, Escher, Bach, illegal immigration, invention of movable type, Isaac Newton, Islamic Golden Age, Jacquard loom, Johannes Kepler, John Conway, John von Neumann, Joseph-Marie Jacquard, Kenneth Arrow, Loebner Prize, Louis Pasteur, pattern recognition, Pierre-Simon Laplace, Richard Feynman, Search for Extraterrestrial Intelligence, Stephen Hawking, supervolcano, technological singularity, Thales of Miletus, The Coming Technological Singularity, the scientific method, Thomas Malthus, Thorstein Veblen, Turing test, Vernor Vinge, Whole Earth Review, William of Occam, zero-sum game

But my guess is that when we do understand them, artificially implementing evolution and intelligence and its constellation of associated attributes will then be no great effort. TERMINOLOGY Quale (plural qualia) The subjective aspect of a sensation. Behaviourism Instrumentalism applied to psychology. The doctrine that science can (or should) only measure and predict people’s behaviour in response to stimuli. SUMMARY The field of artificial (general) intelligence has made no progress because there is an unsolved philosophical problem at its heart: we do not understand how creativity works. Once that has been solved, programming it will not be difficult. Even artificial evolution may not have been achieved yet, despite appearances. There the problem is that we do not understand the nature of the universality of the DNA replication system. 8 A Window on Infinity Mathematicians realized centuries ago that it is possible to work consistently and usefully with infinity.

Anything that is copied, for whatever reason, he calls a replicator. What I call a replicator he calls an ‘active replicator’. *These are not the ‘parallel universes’ of the quantum multiverse, which I shall describe in Chapter 11. Those universes all obey the same laws of physics and are in constant slight interaction with each other. They are also much less speculative. * Hence what I am calling ‘AI’ is sometimes called ‘AGI’: Artificial General Intelligence. *First, they announce to the existing guests, ‘For each natural number N, will the guest in room number N please move immediately to room number N (N +1)/2.’ Then they announce, ‘For all natural numbers N and M, will the Nth passenger from the Mth train please go to room number [(N + M)2 + N – M/2.’ *In the story as told by Plato in his Apology, Chaerophon asks the Oracle whether there is anyone wiser than Socrates, and is told no.


pages: 381 words: 78,467

100 Plus: How the Coming Age of Longevity Will Change Everything, From Careers and Relationships to Family And by Sonia Arrison

23andMe, 8-hour work day, Albert Einstein, Anne Wojcicki, artificial general intelligence, attribution theory, Bill Joy: nanobots, bioinformatics, Clayton Christensen, dark matter, disruptive innovation, East Village, en.wikipedia.org, epigenetics, Frank Gehry, Googley, income per capita, indoor plumbing, Jeff Bezos, Johann Wolfgang von Goethe, Kickstarter, Law of Accelerating Returns, life extension, personalized medicine, Peter Thiel, placebo effect, post scarcity, Ray Kurzweil, rolodex, Silicon Valley, Simon Kuznets, Singularitarianism, smart grid, speech recognition, stem cell, Stephen Hawking, Steve Jobs, Steve Wozniak, Steven Levy, Thomas Malthus, upwardly mobile, World Values Survey, X Prize

This means it is both modifiable and reparable, but the biggest problem Goertzel sees is that we have too much data and not enough human bandwidth to analyze everything. “The human brain simply was not evolved for the integrative analysis of a massive number of complexly-interrelated, highdimensional biological datasets,” he writes.15 “In the short term, the most feasible path to working around this problem is to supplement human biological scientists with increasingly advanced AI software, gradually moving toward the goal of an AGI (Artificial General Intelligence) bioscientist.”16 Just as Google is a form of artificial intelligence that allows for fast searching of the Internet, a software program that could “read” biological studies and help to sort the data for human scientists would make the task of finding repair mechanisms for the human body that much easier. Another proponent of this idea is maven Ray Kurzweil. In 1999 President Bill Clinton awarded Ray Kurzweil the National Medal of Technology, the highest honor for technological achievement bestowed by the president of the United States on America’s leading innovators.


pages: 345 words: 75,660

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

"Robert Solow", Ada Lovelace, AI winter, Air France Flight 447, Airbus A320, artificial general intelligence, autonomous vehicles, basic income, Bayesian statistics, Black Swan, blockchain, call centre, Capital in the Twenty-First Century by Thomas Piketty, Captain Sullenberger Hudson, collateralized debt obligation, computer age, creative destruction, Daniel Kahneman / Amos Tversky, data acquisition, data is the new oil, deskilling, disruptive innovation, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, everywhere but in the productivity statistics, Google Glasses, high net worth, ImageNet competition, income inequality, information retrieval, inventory management, invisible hand, job automation, John Markoff, Joseph Schumpeter, Kevin Kelly, Lyft, Minecraft, Mitch Kapor, Moneyball by Michael Lewis explains big data, Nate Silver, new economy, On the Economy of Machinery and Manufactures, pattern recognition, performance metric, profit maximization, QWERTY keyboard, race to the bottom, randomized controlled trial, Ray Kurzweil, ride hailing / ride sharing, Second Machine Age, self-driving car, shareholder value, Silicon Valley, statistical model, Stephen Hawking, Steve Jobs, Steven Levy, strong AI, The Future of Employment, The Signal and the Noise by Nate Silver, Tim Cook: Apple, Turing test, Uber and Lyft, uber lyft, US Airways Flight 1549, Vernor Vinge, Watson beat the top human players on Jeopardy!, William Langewiesche, Y Combinator, zero-sum game

The school would modify the task of offering incentives like scholarships and financial aid due to increased certainty about who will succeed. Finally, the school would adjust other elements of the work flow to take advantage of being able to provide instantaneous school admission decisions. 13 Decomposing Decisions Today’s AI tools are far from the machines with human-like intelligence of science fiction (often referred to as “artificial general intelligence” or AGI, or “strong AI”). The current generation of AI provides tools for prediction and little else. This view of AI does not diminish it. As Steve Jobs once remarked, “One of the things that really separates us from the high primates is that we’re tool builders.” He used the example of the bicycle as a tool that had given people superpowers in locomotion above every other animal.


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

3D printing, Albert Michelson, Amazon Web Services, artificial general intelligence, basic income, Bernie Sanders, bitcoin, Black Swan, blockchain, Burning Man, buy low sell high, Claude Shannon: information theory, cloud computing, Computer Numeric Control, conceptual framework, crowdsourcing, cryptocurrency, data acquisition, disruptive innovation, Donald Trump, double helix, Edward Snowden, Elon Musk, Ferguson, Missouri, fiat currency, financial innovation, Flash crash, frictionless, game design, Gerolamo Cardano, informal economy, interchangeable parts, Internet Archive, Internet of things, Isaac Newton, Jeff Bezos, John Harrison: Longitude, Joi Ito, Khan Academy, Kickstarter, Mark Zuckerberg, microbiome, Nate Silver, Network effects, neurotypical, Oculus Rift, pattern recognition, peer-to-peer, pirate software, pre–internet, prisoner's dilemma, Productivity paradox, race to the bottom, RAND corporation, random walk, Ray Kurzweil, Ronald Coase, Ross Ulbricht, Satoshi Nakamoto, self-driving car, SETI@home, side project, Silicon Valley, Silicon Valley startup, Simon Singh, Singularitarianism, Skype, slashdot, smart contracts, Steve Ballmer, Steve Jobs, Steven Levy, Stewart Brand, Stuxnet, supply-chain management, technological singularity, technoutopianism, The Nature of the Firm, the scientific method, The Signal and the Noise by Nate Silver, There's no reason for any individual to have a computer in his home - Ken Olsen, Thomas Kuhn: the structure of scientific revolutions, universal basic income, unpaid internship, uranium enrichment, urban planning, WikiLeaks

And yet, we would encourage a less pessimistic view: we no more understand what ultimate uses our new technologies will serve than the “animated pictures” audiences of 1896 could have predicted Citizen Kane. The point of this book isn’t to scare you with dread visions of the future. It’s as useful to entertain visions of life on Kepler-62e. Because “Artificial Intelligence” is used as a label for everything from Siri to Tesla automobiles, we now describe this kind of problem-solving AI as “narrow” or “specialized” AI, to differentiate it from AGI—artificial general intelligence. Artificial intelligence expert Ben Goertzel suggests that an AGI would be a machine that could apply to college, be admitted, and then get a degree. There are many differences between a specialized AI and an AGI but neither is programmed. They are “trained” or they “learn.” Specialized AIs are carefully trained by engineers who tweak the data and algorithms, and keep testing them until they do the specific things that are required of them.


pages: 252 words: 79,452

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

3D printing, Ada Lovelace, AI winter, Airbnb, Albert Einstein, artificial general intelligence, brain emulation, clean water, cognitive dissonance, computer age, cosmological principle, dark matter, disruptive innovation, double helix, Edward Snowden, effective altruism, Elon Musk, Extropian, friendly AI, global pandemic, impulse control, income inequality, invention of the wheel, Jacques de Vaucanson, John von Neumann, knowledge economy, Law of Accelerating Returns, life extension, lifelogging, Lyft, Mars Rover, means of production, Norbert Wiener, Peter Thiel, profit motive, Ray Kurzweil, RFID, self-driving car, sharing economy, Silicon Valley, Silicon Valley ideology, Singularitarianism, Skype, Stephen Hawking, Steve Wozniak, superintelligent machines, technological singularity, technoutopianism, The Coming Technological Singularity, Travis Kalanick, trickle-down economics, Turing machine, uber lyft, Vernor Vinge

(He was the coauthor, with Google’s research director Peter Norvig, of Artificial Intelligence: A Modern Approach, the book most widely used as a core AI text in university computer science courses.) In 2014, Russell and three other scientists—Stephen Hawking, Max Tegmark, and Nobel laureate physicist Frank Wilczek—had published a stern warning, in of all venues The Huffington Post, about the dangers of AI. The idea, common among those working on AI, that because an artificial general intelligence is widely agreed to be several decades from realization we can just keep working on it and solve safety problems if and when they arise is one that Russell and his esteemed coauthors attack as fundamentally wrongheaded. “If a superior alien civilization sent us a text message saying ‘We’ll arrive in a few decades,’ would we just reply, ‘OK, call us when you get here—we’ll leave the lights on’?


pages: 282 words: 81,873

Live Work Work Work Die: A Journey Into the Savage Heart of Silicon Valley by Corey Pein

23andMe, 4chan, affirmative action, Affordable Care Act / Obamacare, Airbnb, Amazon Mechanical Turk, Anne Wojcicki, artificial general intelligence, bank run, barriers to entry, Benevolent Dictator For Life (BDFL), Bernie Sanders, bitcoin, Build a better mousetrap, California gold rush, cashless society, colonial rule, computer age, cryptocurrency, data is the new oil, disruptive innovation, Donald Trump, Douglas Hofstadter, Elon Musk, Extropian, gig economy, Google bus, Google Glasses, Google X / Alphabet X, hacker house, hive mind, illegal immigration, immigration reform, Internet of things, invisible hand, Isaac Newton, Jeff Bezos, job automation, Kevin Kelly, Khan Academy, Law of Accelerating Returns, Lean Startup, life extension, Lyft, Mahatma Gandhi, Marc Andreessen, Mark Zuckerberg, Menlo Park, minimum viable product, move fast and break things, move fast and break things, mutually assured destruction, obamacare, passive income, patent troll, Paul Graham, peer-to-peer lending, Peter H. Diamandis: Planetary Resources, Peter Thiel, platform as a service, plutocrats, Plutocrats, Ponzi scheme, post-work, Ray Kurzweil, regulatory arbitrage, rent control, RFID, Robert Mercer, rolodex, Ronald Reagan, Ross Ulbricht, Ruby on Rails, Sam Altman, Sand Hill Road, Scientific racism, self-driving car, sharing economy, side project, Silicon Valley, Silicon Valley startup, Singularitarianism, Skype, Snapchat, social software, software as a service, source of truth, South of Market, San Francisco, Startup school, stealth mode startup, Steve Jobs, Steve Wozniak, TaskRabbit, technological singularity, technoutopianism, telepresence, too big to fail, Travis Kalanick, tulip mania, Uber for X, uber lyft, ubercab, upwardly mobile, Vernor Vinge, X Prize, Y Combinator

The person most closely associated with this concept is the author, inventor, and tech executive Ray Kurzweil. Kurzweil is now known primarily as a purveyor of far-out ideas, of which the Singularity is only one, but his early pronouncements are remarkably restrained in comparison. In a 1984 conference speech, he lamented the overly optimistic predictions of AI researchers, who were forever claiming that the holy grail of the field, “artificial general intelligence”—a computerized mind equivalent to that of a human, in capabilities if not in design—was just a decade or two away, only to be proven wrong time and again. Such romanticism, he said, had created a “credibility problem” that plagued the field. In 1990, MIT Press published Kurzweil’s first book, The Age of Intelligent Machines, which collected predictions from more than twenty authors.


pages: 347 words: 97,721

Only Humans Need Apply: Winners and Losers in the Age of Smart Machines by Thomas H. Davenport, Julia Kirby

AI winter, Andy Kessler, artificial general intelligence, asset allocation, Automated Insights, autonomous vehicles, basic income, Baxter: Rethink Robotics, business intelligence, business process, call centre, carbon-based life, Clayton Christensen, clockwork universe, commoditize, conceptual framework, dark matter, David Brooks, deliberate practice, deskilling, digital map, disruptive innovation, Douglas Engelbart, Edward Lloyd's coffeehouse, Elon Musk, Erik Brynjolfsson, estate planning, fixed income, follow your passion, Frank Levy and Richard Murnane: The New Division of Labor, Freestyle chess, game design, general-purpose programming language, global pandemic, Google Glasses, Hans Lippershey, haute cuisine, income inequality, index fund, industrial robot, information retrieval, intermodal, Internet of things, inventory management, Isaac Newton, job automation, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Joi Ito, Khan Academy, knowledge worker, labor-force participation, lifelogging, longitudinal study, loss aversion, Mark Zuckerberg, Narrative Science, natural language processing, Norbert Wiener, nuclear winter, pattern recognition, performance metric, Peter Thiel, precariat, quantitative trading / quantitative finance, Ray Kurzweil, Richard Feynman, risk tolerance, Robert Shiller, Robert Shiller, Rodney Brooks, Second Machine Age, self-driving car, Silicon Valley, six sigma, Skype, social intelligence, speech recognition, spinning jenny, statistical model, Stephen Hawking, Steve Jobs, Steve Wozniak, strong AI, superintelligent machines, supply-chain management, transaction costs, Tyler Cowen: Great Stagnation, Watson beat the top human players on Jeopardy!, Works Progress Administration, Zipcar

Vendors like IBM, Cognitive Scale, SAS, and Tibco are adding new cognitive functions and integrating them into solutions. Deloitte is working with companies like IBM and Cognitive Scale to create not just a single application, but a broad “Intelligent Automation Platform.” Even when progress is made on these types of integration, the result will still fall short of the all-knowing “artificial general intelligence” or “strong AI” that we discussed in Chapter 2. That may well be coming, but not anytime soon. Still, these short-term combinations of tools and methods may well make automation solutions much more useful. Broadening Application of the Same Tools —In addition to employing broader types of technology, organizations that are stepping forward are using their existing technology to address different industries and business functions.


pages: 502 words: 124,794

Nexus by Ramez Naam

artificial general intelligence, bioinformatics, Brownian motion, crowdsourcing, Golden Gate Park, hive mind, low earth orbit, mandatory minimum, Menlo Park, pattern recognition, the scientific method, upwardly mobile

Kade idly flipped through a guide book himself. Thailand did look amazingly beautiful, with jungles and waterfalls and beaches, and temple after temple after temple. If only I was coming here for a vacation, he thought. The conference guide yielded up a plethora of fascinating talks: Neural Substrates of Symbolic Reasoning, Intelligence and Prospects for Increasing It, Emotive-Loop Programming: A New Path to Artificial General Intelligence. How could they even hold these talks? In the US the topics of half of them would be classified as Emerging Technological Threats. No wonder the international meeting trumps the US neuroscience meetings these days, Kade thought. The cutting edge stuff isn't legal at home any more. He looked over at Sam. She was part of the reason he was here. She was part of the organization blackmailing him.


pages: 428 words: 121,717

Warnings by Richard A. Clarke

active measures, Albert Einstein, algorithmic trading, anti-communist, artificial general intelligence, Asilomar, Asilomar Conference on Recombinant DNA, Bernie Madoff, cognitive bias, collateralized debt obligation, complexity theory, corporate governance, cuban missile crisis, data acquisition, discovery of penicillin, double helix, Elon Musk, failed state, financial thriller, fixed income, Flash crash, forensic accounting, friendly AI, 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, nuclear winter, pattern recognition, personalized medicine, phenotype, Ponzi scheme, Ray Kurzweil, 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, technological singularity, The Future of Employment, the scientific method, The Signal and the Noise by Nate Silver, Tunguska event, uranium enrichment, Vernor Vinge, Watson beat the top human players on Jeopardy!, women in the workforce, Y2K

Navy at http://www.navy.mil/submit/display.asp?story_id=75298, accessed July 7 2013. 2. Just as artificial intelligence is an overbroad term, so are the terms used to refer to its two constituent halves, weak and strong. Weak AI is often also called narrow AI. 3. Arthur Samuel offered this definition of machine learning in 1959. 4. Superintelligence is a term made popular by philosopher Nick Bostrom and is often also called artificial general intelligence. 5. Referenced from Luke Muehlhauser’s fantastic work which was a great guide for the authors. 6. James Barrat, Our Final Invention: Artificial Intelligence and the End of the Human Era (New York: Thomas Dunne Books, 2013). Barrat’s book was an important source for the authors. 7. Nick Bostrom, “Ethical Issues in Advanced Artificial Intelligence,” http://www.nickbostrom.com/ethics/ai.html (accessed Nov. 9, 2016).


pages: 381 words: 120,361

Sunfall by Jim Al-Khalili

airport security, artificial general intelligence, augmented reality, Carrington event, cosmological constant, cryptocurrency, dark matter, David Attenborough, Fellow of the Royal Society, Intergovernmental Panel on Climate Change (IPCC), Internet of things, invisible hand, Kickstarter, mass immigration, megacity, MITM: man-in-the-middle, off grid, pattern recognition, Silicon Valley, smart cities, sorting algorithm, South China Sea, stem cell, Stephen Hawking, Turing test

Most of the time, of course, the Sentinels did a far better job at cybersecurity than any human ever could, since they were able to carry out tasks billions, and often trillions, of times faster, as well as being in constant communication with each other, exchanging the information content of an entire library of books in less than a nanosecond. She still vividly remembered with nostalgic fondness a hiking trip with her father in the Alborz Mountains when she was thirteen. She recalled being cold and tired, but the scenery had been stunning. They had spent hours discussing AI and the way the world was changing. Her father explained to her that the notion of artificial general intelligence, when machines could do everything humans could, required AIs to be sentient, to develop self-awareness. Otherwise, they would stay just very clever zombies, with no true understanding of what they were doing. All the time this remained the case, humans could keep one step ahead of them. True machine consciousness, he’d said – she recalled this was the first time she’d heard the term ‘the singularity’ – would not be achieved for many decades.


pages: 412 words: 128,042

Extreme Economies: Survival, Failure, Future – Lessons From the World’s Limits by Richard Davies

agricultural Revolution, air freight, Anton Chekhov, artificial general intelligence, autonomous vehicles, barriers to entry, big-box store, cashless society, clean water, complexity theory, deindustrialization, eurozone crisis, failed state, financial innovation, illegal immigration, income inequality, informal economy, James Hargreaves, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, joint-stock company, large denomination, Livingstone, I presume, Malacca Straits, mandatory minimum, manufacturing employment, means of production, megacity, meta analysis, meta-analysis, new economy, off grid, oil shale / tar sands, pension reform, profit motive, randomized controlled trial, school choice, school vouchers, Scramble for Africa, side project, Silicon Valley, Simon Kuznets, Skype, spinning jenny, The Chicago School, the payments system, trade route, Travis Kalanick, uranium enrichment, urban planning, wealth creators, white picket fence, working-age population, Y Combinator, young professional

By making the ballot box obsolete, Estonia’s e-democracy has drawn young people into voting, and since a politician’s assets are all traceable via the system, it cuts the potential for graft. As Siim Sikkut, the government’s top tech guru, puts it, ‘You can’t bribe a computer.’ The aim for many of those seeking to crack the holy grail of artificial intelligence is to use computers to strengthen democratic systems too, says Ahti Heinla, inventor of the delivery robot. Teams across the world are racing to create something known as an artificial general intelligence, or AGI. This would be a computerized mind so powerful that it could reason, planning what to learn and building its own digital brain in a strategic way, rather than being taught what to do by its human masters. People pursuing this kind of research think an AGI might help humans solve intractable problems such as the politics of nuclear disarmament or the economics of trade deals.


pages: 486 words: 150,849

Evil Geniuses: The Unmaking of America: A Recent History by Kurt Andersen

affirmative action, Affordable Care Act / Obamacare, airline deregulation, airport security, always be closing, American ideology, American Legislative Exchange Council, anti-communist, Apple's 1984 Super Bowl advert, artificial general intelligence, autonomous vehicles, basic income, Bernie Sanders, blue-collar work, Bonfire of the Vanities, bonus culture, Burning Man, call centre, Capital in the Twenty-First Century by Thomas Piketty, Cass Sunstein, centre right, computer age, coronavirus, corporate governance, corporate raider, COVID-19, Covid-19, creative destruction, Credit Default Swap, cryptocurrency, deindustrialization, Donald Trump, Elon Musk, ending welfare as we know it, Erik Brynjolfsson, feminist movement, financial deregulation, financial innovation, Francis Fukuyama: the end of history, future of work, game design, George Gilder, Gordon Gekko, greed is good, High speed trading, hive mind, income inequality, industrial robot, interchangeable parts, invisible hand, Isaac Newton, James Watt: steam engine, Jane Jacobs, Jaron Lanier, Jeff Bezos, jitney, Joan Didion, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Joseph Schumpeter, knowledge worker, low skilled workers, Lyft, Mark Zuckerberg, market bubble, mass immigration, mass incarceration, Menlo Park, Naomi Klein, new economy, Norbert Wiener, Norman Mailer, obamacare, Peter Thiel, Picturephone, plutocrats, Plutocrats, post-industrial society, Powell Memorandum, pre–internet, Ralph Nader, Right to Buy, road to serfdom, Robert Bork, Robert Gordon, Robert Mercer, Ronald Reagan, Saturday Night Live, Seaside, Florida, Second Machine Age, shareholder value, Silicon Valley, Social Responsibility of Business Is to Increase Its Profits, Steve Jobs, Stewart Brand, strikebreaker, The Death and Life of Great American Cities, The Future of Employment, The Rise and Fall of American Growth, The Wealth of Nations by Adam Smith, Tim Cook: Apple, too big to fail, trickle-down economics, Tyler Cowen: Great Stagnation, Uber and Lyft, uber lyft, union organizing, universal basic income, Unsafe at Any Speed, urban planning, urban renewal, very high income, wage slave, Wall-E, War on Poverty, Whole Earth Catalog, winner-take-all economy, women in the workforce, working poor, young professional, éminence grise

To me one of the most interesting recent accomplishments is an AI that designed new AI software as well as or better than engineers could design it. All of that is why the funding of AI start-ups quadrupled just between 2015 and 2018, to $40 billion, and why the total investment put into in AI businesses in 2019 reached $70 billion. The debate among technologists tends to focus on when they’ll manage to create artificial general intelligence, machines able to figure out any problem and carry out any cognitive task that a person can. People at Facebook and Google and Stanford and elsewhere say they’ll do it by the mid-2020s, that they’ll then have machines “better than human level at all of the primary human senses” and “general cognition” (Zuckerberg), true “human-level A.I.” (the head of Google’s DeepMind). The state of the art right now is “narrow AI” or “weak AI,” software that can merely beat human champions at Jeopardy or predict the shapes of cellular proteins or drive cars.


pages: 669 words: 210,153

Tools of Titans: The Tactics, Routines, and Habits of Billionaires, Icons, and World-Class Performers by Timothy Ferriss

Airbnb, Alexander Shulgin, artificial general intelligence, asset allocation, Atul Gawande, augmented reality, back-to-the-land, Ben Horowitz, Bernie Madoff, Bertrand Russell: In Praise of Idleness, Black Swan, blue-collar work, Boris Johnson, Buckminster Fuller, business process, Cal Newport, call centre, Charles Lindbergh, Checklist Manifesto, cognitive bias, cognitive dissonance, Colonization of Mars, Columbine, commoditize, correlation does not imply causation, David Brooks, David Graeber, diversification, diversified portfolio, Donald Trump, effective altruism, Elon Musk, fault tolerance, fear of failure, Firefox, follow your passion, future of work, Google X / Alphabet X, Howard Zinn, Hugh Fearnley-Whittingstall, Jeff Bezos, job satisfaction, Johann Wolfgang von Goethe, John Markoff, Kevin Kelly, Kickstarter, Lao Tzu, lateral thinking, life extension, lifelogging, Mahatma Gandhi, Marc Andreessen, Mark Zuckerberg, Mason jar, Menlo Park, Mikhail Gorbachev, MITM: man-in-the-middle, Nelson Mandela, Nicholas Carr, optical character recognition, PageRank, passive income, pattern recognition, Paul Graham, peer-to-peer, Peter H. Diamandis: Planetary Resources, Peter Singer: altruism, Peter Thiel, phenotype, PIHKAL and TIHKAL, post scarcity, post-work, premature optimization, QWERTY keyboard, Ralph Waldo Emerson, Ray Kurzweil, recommendation engine, rent-seeking, Richard Feynman, risk tolerance, Ronald Reagan, selection bias, sharing economy, side project, Silicon Valley, skunkworks, Skype, Snapchat, social graph, software as a service, software is eating the world, stem cell, Stephen Hawking, Steve Jobs, Stewart Brand, superintelligent machines, Tesla Model S, The Wisdom of Crowds, Thomas L Friedman, Wall-E, Washington Consensus, Whole Earth Catalog, Y Combinator, zero-sum game

Libin, Phil: “So just imagine it’s me with a big glass of whiskey. And the caption will say, ‘Evernote helps you remember. Suntory helps you forget.’” MacAskill, Will: “It would be outside the Gates Foundation, or maybe outside Bill Gates’s house . . . where ultimately, he’s going to donate $100 billion. And it would say, ‘Bill, you have spoken about the risks and potential upside in the long run from development of artificial general intelligence, yet you’re not doing anything about it yet. You haven’t gotten involved.’” MacKenzie, Brian: “‘Ego is how we want the world to see us. Confidence is how we see ourselves.’” McCarthy, Nicholas: “‘Anything is possible.’ I wholeheartedly believe that. Why wouldn’t I think that? Because for a guy who’s from a non-classical background, from a non-money background, from a very small village in England—no one’s really done a great deal where I’m from—and with one arm as well, and the age that I started, to then enter this arena of highbrow classical music and honing your craft to the highest level . . .


pages: 761 words: 231,902

The Singularity Is Near: When Humans Transcend Biology by Ray Kurzweil

additive manufacturing, AI winter, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Albert Einstein, anthropic principle, Any sufficiently advanced technology is indistinguishable from magic, artificial general intelligence, Asilomar, augmented reality, autonomous vehicles, Benoit Mandelbrot, Bill Joy: nanobots, bioinformatics, brain emulation, Brewster Kahle, Brownian motion, business cycle, business intelligence, c2.com, call centre, carbon-based life, cellular automata, Claude Shannon: information theory, complexity theory, conceptual framework, Conway's Game of Life, coronavirus, cosmological constant, cosmological principle, cuban missile crisis, data acquisition, Dava Sobel, David Brooks, Dean Kamen, disintermediation, double helix, Douglas Hofstadter, en.wikipedia.org, epigenetics, factory automation, friendly AI, George Gilder, Gödel, Escher, Bach, informal economy, information retrieval, invention of the telephone, invention of the telescope, invention of writing, iterative process, Jaron Lanier, Jeff Bezos, job automation, job satisfaction, John von Neumann, Kevin Kelly, Law of Accelerating Returns, life extension, lifelogging, linked data, Loebner Prize, Louis Pasteur, mandelbrot fractal, Marshall McLuhan, Mikhail Gorbachev, Mitch Kapor, mouse model, Murray Gell-Mann, mutually assured destruction, natural language processing, Network effects, new economy, Norbert Wiener, oil shale / tar sands, optical character recognition, pattern recognition, phenotype, premature optimization, randomized controlled trial, Ray Kurzweil, remote working, reversible computing, Richard Feynman, Robert Metcalfe, Rodney Brooks, scientific worldview, Search for Extraterrestrial Intelligence, selection bias, semantic web, Silicon Valley, Singularitarianism, speech recognition, statistical model, stem cell, Stephen Hawking, Stewart Brand, strong AI, superintelligent machines, technological singularity, Ted Kaczynski, telepresence, The Coming Technological Singularity, Thomas Bayes, transaction costs, Turing machine, Turing test, Vernor Vinge, Y2K, Yogi Berra

Yudkowsky formed the Singularity Institute for Artificial Intelligence (SIAI) to develop "Friendly AI," intended to "create cognitive content, design features, and cognitive architectures that result in benevolence" before near-human or better-than-human Als become possible. SIAI has developed The SIAI Guidelines on Friendly AI: "Friendly AI," http://www.singinst.org/friendly/. Ben Goertzel and his Artificial General Intelligence Research Institute have also examined issues related to developing friendly AI; his current focus is on developing the Novamente AI Engine, a set of learning algorithms and architectures. Peter Voss, founder of Adaptive A.I., Inc., has also collaborated on friendly-AI issues: http://adaptiveai.com/. 46. Integrated Fuel Cell Technologies, http://ifctech.com. Disclosure: The author is an early investor in and adviser to IFCT. 47.