AI winter

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pages: 413 words: 119,587

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

Winograd walked away from AI in part because of a series of challenging conversations with a group of philosophers at the University of California. A member of a small group of AI researchers, he engaged in a series of weekly seminars with Berkeley philosophers Hubert Dreyfus and John Searle. The philosophers convinced him that there were real limits to the capabilities of intelligent machines. Winograd’s conversion coincided with the collapse of a nascent artificial intelligence industry known as the “AI Winter.” Several decades later, Winograd, who was faculty advisor for Google cofounder Larry Page at Stanford, famously counseled the young graduate student to focus on the problem of Web search rather than self-driving cars. In the intervening decades Winograd had become acutely aware of the importance of the designer’s point of view. The separation of the fields of AI and human-computer interaction, or HCI, is partly a question of approach, but it’s also an ethical stance about designing humans either into or out of the systems we create.

When the commercial market for artificial intelligence software failed to materialize quickly enough, inside the company he struggled, most bitterly with board member Pierre Lamond, a venture capitalist who was a veteran of the semiconductor industry with no software experience. Ultimately Breiner lost his battle and Lamond brought in an outside corporate manager who moved the company headquarters to Texas, where the manager lived. Syntelligence itself would confront directly what would be become known as the “AI Winter.” One by one the artificial intelligence firms of the early 1980s were eclipsed either because they failed financially or because they returned to their roots as experimental efforts or consulting companies. The market failure became an enduring narrative that came to define artificial intelligence, with a repeated cycle of hype and failure fueled by overly ambitious scientific claims that are inevitably followed by performance and market disappointments.

A generation of true believers, steeped in the technocratic and optimistic artificial intelligence literature of the 1960s, clearly played an early part in the collapse. Since then the same boom-and-bust cycle has continued for decades, even as AI has advanced.38 Today the cycle is likely to repeat itself again as a new wave of artificial intelligence technologies is being heralded by some as being on the cusp of offering “thinking machines.” The first AI Winter had actually come a decade earlier in Europe. Sir Michael James Lighthill, a British applied mathematician, led a study in 1973 that excoriated the field for not delivering on the promises and predictions, such as the early SAIL prediction of a working artificial intelligence in a decade. Although it had little impact in the United States, the Lighthill report, “Artificial Intelligence: A General Survey,” led to the curtailment of funding in England and a dispersal of British researchers from the field.


pages: 523 words: 61,179

Human + Machine: Reimagining Work in the Age of AI by Paul R. Daugherty, H. James Wilson

3D printing, AI winter, algorithmic trading, Amazon Mechanical Turk, augmented reality, autonomous vehicles, blockchain, business process, call centre, carbon footprint, cloud computing, computer vision, correlation does not imply causation, crowdsourcing, digital twin, disintermediation, Douglas Hofstadter, en.wikipedia.org, Erik Brynjolfsson, friendly AI, future of work, industrial robot, Internet of things, inventory management, iterative process, Jeff Bezos, job automation, job satisfaction, knowledge worker, Lyft, natural language processing, personalized medicine, precision agriculture, Ray Kurzweil, recommendation engine, RFID, ride hailing / ride sharing, risk tolerance, Rodney Brooks, Second Machine Age, self-driving car, sensor fusion, sentiment analysis, Shoshana Zuboff, Silicon Valley, software as a service, speech recognition, telepresence, telepresence robot, text mining, the scientific method, uber lyft

Mike Murphy, “Siemens is building a swarm of robot spiders to 3D-print objects together,” Quartz, April 29, 2016, https://qz.com/672708/siemens-is-building-a-swarm-of-robot-spiders-to-3d-print-objects-together/. c. Robotiq, “Inertia Switch Case Study – Robotiq 2-Finger Adaptive Gripper – ROBOTIQ,” YouTube video, 1:32 minutes, posted July 28, 2014, https://www.youtube.com/watch?v=iJftrfiGyfs. Kindler, Gentler Robots During the second AI “winter,” Rodney Brooks challenged one of the fundamental ideas that had driven previous AI research—namely, the reliance on predetermined symbols and relationships between symbols to help computers make sense of the world (see the sidebar “Two AI Winters”). He claimed a much more robust approach: instead of cataloging the world in advance and representing it with symbols, why not survey it with sensors instead? “The world is its own best model,” he wrote in a famous 1990 paper called “Elephants Don’t Play Chess.” (Brooks would later found iRobot, maker of the robotic vacuum Roomba, as well as Rethink Robotics.

AI helps both robots and people play to their strengths, and in the process, the assembly line changes shape. Two AI Winters The path to human-machine collaboration—a hallmark of the third wave of process improvement—was far from smooth. AI was initially greeted with considerable enthusiasm, only to be followed by results that didn’t live up to the initial hype, and then more progress, leading to a second wave of hype then disappointment. Those down periods have become known as AI’s two “winters.” The field of AI began in the 1950s, and during the decades that followed any research progress came only in fits and starts. By the 1970s, funding had dissipated so much that the era became known as the first AI winter. Then, during a few years in the 1980s, some researchers made progress in so-called expert systems—computer systems loaded with code that allowed a machine to perform a kind of rudimentary reasoning using “if-then” rules rather than following a strict, predetermined algorithm.

Then, during a few years in the 1980s, some researchers made progress in so-called expert systems—computer systems loaded with code that allowed a machine to perform a kind of rudimentary reasoning using “if-then” rules rather than following a strict, predetermined algorithm. But the desktop computer revolution was under way, and attention was diverted toward personal computers as they became increasingly affordable and practical for the average person. Again, money for AI dried up, and the second AI winter descended. It wasn’t until the 2000s that AI began to draw major investment again. One way that assembly lines can be reconfigured is through AI itself. Engineers at the Fraunhofer Institute of Material Flow and Logistics (IML) have been testing embedded sensors to create self-adapting assembly lines in car plants. Essentially, the line itself can modify the steps in its process to fit the demands of various features and add-ons for highly customizable cars.


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

MARTIN FORD: Lately, I’ve heard a number of people express the view that deep learning is over-hyped and might soon “hit a wall” in terms of continued progress. There have even been suggestions that a new AI Winter could be on the horizon. Do you think that’s a real risk? Could disillusionment lead to a big drop off in investment? ANDREW NG: No, I don’t think there’ll be another AI winter, but I do think there needs to be a reset of expectations about AGI. In the earlier AI winters, there was a lot of hype about technologies that ultimately did not really deliver. The technologies that were hyped were really not that useful, and the amount of value created by those earlier generations of technology was vastly less than expected. I think that’s what caused the AI winters. In the current era, if you look at the number of people actually working on deep learning projects to date, it’s much greater than six months ago, and six months ago, it was much greater than six months before that.

We see it also in machine translation that works very well for literal language, where it’s had a lot of examples, but not for the kind of language you see in novels or anything that’s literary or alliterative. MARTIN FORD: Do you think there will be a backlash against all the hype surrounding deep learning when its limitations are more widely recognized? BARBARA GROSZ: I have survived numerous AI Winters in the past and I’ve come away from them feeling both fearful and hopeful. I’m fearful that people, once they see the limitations of deep learning will say, “Oh, it doesn’t really work.” But I’m hopeful that, because deep learning is so powerful for so many things, and in so many areas, that there won’t be an AI Winter around deep learning. I do think, however, that to avoid an AI Winter for deep learning, people in the field need to put deep learning in its correct place, and be clear about its limitations. I said at one point that “AI systems are best if they’re designed with people in mind.”

I also hope we’re going to make some convincing breakthrough before the people funding all this research get tired, because that’s what happened in previous decades. MARTIN FORD: You’ve warned that AI is being overhyped and that this might even lead to another “AI Winter.” Do you really think there’s a risk of that? Deep learning has become so central to the business models of Google, Facebook, Amazon, Tencent, and all these other incredibly wealthy corporations. So, it seems hard to imagine that investment in the technology would fall off dramatically. YANN LECUN: I don’t think we’re going to see an AI winter in the way we saw before because there is a big industry around it and there are real applications that are bringing real revenue to these companies. There’s still a huge amount of investment, with the hope that, for example, self-driving cars are going to be working in the next five years and that medical imaging is going to be radically revolutionized.


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

Herbert Simon said in 1965 that “machines will be capable, within twenty years, of doing any work a man can do,” (5) and Marvin Minksy said two years later that “Within a generation . . . the problem of creating ‘artificial intelligence’ will substantially be solved.” (6) These were hugely over-optimistic claims which with hindsight look like hubris. But hindsight is a wonderful thing, and it is unfair to criticise harshly the pioneers of AI for under-estimating the difficulty of replicating the feats which the human brain is capable of. AI winters and springs When it became apparent that AI was going to take much longer to achieve its goals than originally expected, the funding taps were turned off. There were rumblings of discontent among funding bodies from the late 1960s, and they crystallised in a report written in 1973 by mathematician James Lighthill for the British Science Research Council. A particular problem identified in the Lighthill report is the “combinatorial problem”, whereby a simple problem involving two or three variables becomes vast and possibly intractable when the number of variables is increased.

A particular problem identified in the Lighthill report is the “combinatorial problem”, whereby a simple problem involving two or three variables becomes vast and possibly intractable when the number of variables is increased. Thus simple AI applications which looked impressive in laboratory settings became useless when applied to real-world situations. From 1974 until around 1980 it was very hard for AI researchers to obtain funding, and this period of relative inactivity which became known as the first AI winter. This bust was followed in the 1980s by another boom, thanks to the advent of expert systems, and the Japanese Fifth Generation Computer Systems project. Expert systems limit themselves to solving narrowly-defined problems from single domains of expertise (for instance, litigation) using vast data banks. They avoid the messy complications of everyday life, and do not tackle the perennial problem of trying to inculcate common sense.

The reason was (again) the under-estimation of the difficulties of the tasks being addressed, and also the fact that desktop computers and what we now call servers overtook mainframes in speed and power, rendering very expensive legacy machines redundant. The boom and bust phenomenon was familiar to economists, with famous examples being Tulipmania in 1637 and the South Sea Bubble in 1720. It has also been a feature of technology introduction since the industrial revolution, seen in canals, railways and telecoms, as well as in the dot-com bubble of the late 1990s. The second AI winter thawed in the early 1990s, and AI research has been increasingly well funded since then. Some people are worried that the present excitement (and concern) about the progress in AI is merely the latest boom phase, characterised by hype and alarmism, and will shortly be followed by another damaging bust, in which thousands of AI researchers will find themselves out of a job, promising projects will be halted, and important knowledge and insights lost.


pages: 370 words: 107,983

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

Ada Lovelace, affirmative action, AI winter, Alfred Russel Wallace, Amazon Mechanical Turk, animal electricity, autonomous vehicles, Black Swan, British Empire, cellular automata, citizen journalism, Claude Shannon: information theory, combinatorial explosion, corporate personhood, correlation coefficient, crowdsourcing, Daniel Kahneman / Amos Tversky, desegregation, discovery of DNA, Douglas Hofstadter, Elon Musk, Fellow of the Royal Society, feminist movement, Filter Bubble, Flash crash, Gerolamo Cardano, gig economy, Gödel, Escher, Bach, invention of the wheel, invisible hand, Jacquard loom, Jacques de Vaucanson, John Harrison: Longitude, John von Neumann, Kenneth Arrow, low skilled workers, Mark Zuckerberg, mass immigration, meta analysis, meta-analysis, mutually assured destruction, natural language processing, new economy, On the Economy of Machinery and Manufactures, p-value, pattern recognition, Paul Samuelson, performance metric, Pierre-Simon Laplace, precariat, profit maximization, profit motive, Silicon Valley, social intelligence, statistical model, Stephen Hawking, stochastic process, telemarketer, The Bell Curve by Richard Herrnstein and Charles Murray, The Future of Employment, the scientific method, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, theory of mind, Thomas Bayes, Thomas Malthus, traveling salesman, Turing machine, Turing test, twin studies, Vilfredo Pareto, Von Neumann architecture, women in the workforce

This made the implementation of the physical symbol system hypothesis, and reasoning as search, commercially unviable, leading to complete failure of the AI enterprise in the 1970s and the dawn of what is now known as the ‘AI winter’. Seeking to evaluate what the UK was getting for its investment in AI research in 1973, Sir James Lighthill reported to Parliament that there had been an utter failure of the field to advance on its ‘grandiose objectives’. Soon afterwards in the USA, ARPA (Advanced Research Projects Agency), the agency now known as DARPA (Defence Advanced Research Projects Agency), received a similar report from the American Study Group. As a result, public funding for AI research was dramatically cut, and, by the end of the 1980s, almost all of those early AI start-ups had collapsed. It would be wrong to say that expert systems died completely in the AI winter. In reality the term ‘expert system’ was simply replaced with the moniker ‘decision support system’ in reports and research proposals.

Gallup also reports drops in confidence versus historical averages for institutions as varied as banking, criminal justice, medicine, organized labour, big business, police, print media, broadcast media, public schools and organized religion.3 Meanwhile, faith in the opinion of the masses is soaring. What was once derided as ‘lowest common denominator’ is now hailed ‘the wisdom of crowds’. This ‘wisdom’ isn’t only being exploited by populist politicians; it is the life blood of today’s AI. The fact that expertise proved hard to fathom for expert systems is exactly what led to the AI winter. Since then there have been no significant technical breakthroughs on modelling how smart people reason. While modelling the reasoning process of a single human expert proved overwhelmingly expensive, the Internet heralded in a new era whereby the great mass of people in the emergent online world offered up a sea of information for free; and, in many cases, even paid (sometimes explicitly, but largely through subscriptions and views of advertising) to have their data monitored and mined.

This is another way of looking at the central question of AI: is it possible to divide the labour of human decision-making (in general, or even in a particular expert domain) into a set of computational atoms and mechanical rules to process them, such that they can be performed on a Babbage-style computer which was, after all, built on the concept of the computational division of labour? Given the potential economic advantages, finding an answer of ‘yes’ may be a self-fulfilling prophecy. However, the AI winter of the 1970s and 1980s seemed to demonstrate that atomizing human decision-making was simply too complex and expensive for practicality, and that the answer was a firm ‘no’. But the advent of the Internet, with its web-like social structure, cheap crowdsourcing potential and explosive scalability, has heralded a paradigm shift, much like steam technology did in the eighteenth century. Now it looks very possible that big data algorithms could replace a slew of white-collar intellectual jobs ranging from clerks, social workers and lawyers to general practitioners, policemen and judges.


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!

Phase 2: The promised breakthroughs don’t occur, or are much less impressive than promised. Government funding and venture capital dry up. Start-up companies fold, and AI research slows. This pattern became familiar to the AI community: “AI spring,” followed by overpromising and media hype, followed by “AI winter.” This has happened, to various degrees, in cycles of five to ten years. When I got out of graduate school in 1990, the field was in one of its winters and had garnered such a bad image that I was even advised to leave the term “artificial intelligence” off my job applications. Easy Things Are Hard The cold AI winters taught practitioners some important lessons. The simplest lesson was noted by John McCarthy, fifty years after the Dartmouth conference: “AI was harder than we thought.”26 Marvin Minsky pointed out that in fact AI research had uncovered a paradox: “Easy things are hard.”

Such negative speculations were at least part of the reason that funding for neural network research dried up in the late 1960s, at the same time that symbolic AI was flush with government dollars. In 1971, at the age of forty-three, Frank Rosenblatt died in a boating accident. Without its most prominent proponent, and without much government funding, research on perceptrons and other subsymbolic AI methods largely halted, except in a few isolated academic groups. AI Winter In the meantime, proponents of symbolic AI were writing grant proposals promising impending breakthroughs in areas such as speech and language understanding, commonsense reasoning, robot navigation, and autonomous vehicles. By the mid-1970s, while some very narrowly focused expert systems were successfully deployed, the more general AI breakthroughs that had been promised had not materialized.

In analyzing the limitations of these systems, researchers were discovering how much the human experts writing the rules actually rely on subconscious knowledge—what you might call common sense—in order to act intelligently. This kind of common sense could not easily be captured in programmed rules or logical deduction, and the lack of it severely limited any broad application of symbolic AI methods. In short, after a cycle of grand promises, immense funding, and media hype, symbolic AI was facing yet another AI winter. According to the proponents of connectionism, the key to intelligence was an appropriate computational architecture—inspired by the brain—and the ability of the system to learn on its own from data or from acting in the world. Rumelhart, McClelland, and their team constructed connectionist networks (in software) as scientific models of human learning, perception, and language development. While these networks did not exhibit anywhere near human-level performance, the various networks described in the Parallel Distributed Processing books and elsewhere were interesting enough as AI artifacts that many people took notice, including those at funding agencies.


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

Shaw, and Herbert Simon, which was able to find proofs for theorems that had stumped mathematicians such as Bertrand Russell, and early programs from the MIT Artificial Intelligence Laboratory, which could answer SAT questions (such as analogies and story problems) at the level of college students.163 A rash of AI companies occurred in the 1970s, but when profits did not materialize there was an AI "bust" in the 1980s, which has become known as the "AI winter." Many observers still think that the AI winter was the end of the story and that nothing has since come of the AI field. Yet today many thousands of AI applications are deeply embedded in the infrastructure of every industry. Most of these applications were research projects ten to fifteen years ago; People who ask, "Whatever happened to AI?" remind me of travelers to the rain forest who wonder, "Where are all the many species that are supposed to live here?"

Heather Havenstein writes that the "inflated notions spawned by science fiction writers about the convergence of humans and machines tarnished the image of AI in the 1980s because AI was perceived as failing to live up to its potential." Heather Havenstein, "Spring Comes to AI Winter: A Thousand Applications Bloom in Medicine, Customer Service, Education and Manufacturing," Computerworld, February 14, 2005, http://www.computerworld.com/softwaretopics/software/story/0,10801,99691,00.html. This tarnished image led to "AI Winter," defined as "a term coined by Richard Gabriel for the (circa 1990–94?) crash of the wave of enthusiasm for the AI language Lisp and AI itself, following a boom in the 1980s." Duane Rettig wrote: "... companies rode the great AI wave in the early 80's, when large corporations poured billions of dollars into the AI hype that promised thinking machines in 10 years.

Duane Rettig wrote: "... companies rode the great AI wave in the early 80's, when large corporations poured billions of dollars into the AI hype that promised thinking machines in 10 years. When the promises turned out to be harder than originally thought, the AI wave crashed, and Lisp crashed with it because of its association with AI. We refer to it as the AI Winter." Duane Rettig quoted in "AI Winter," http://c2.com/cgi/wiki?AiWinter. 163. The General Problem Solver (GPS) computer program, written in 1957, was able to solve problems through rules that allowed the GPS to divide a problem's goals into subgoals, and then check if obtaining a particular subgoal would bring the GPS closer to solving the overall goal. In the early 1960s Thomas Evan wrote ANALOGY, a "program [that] solves geometric-analogy problems of the form A:B::C:? taken from IQ tests and college entrance exams." Boicho Kokinov and Robert M.


pages: 402 words: 110,972

Nerds on Wall Street: Math, Machines and Wired Markets by David J. Leinweber

AI winter, algorithmic trading, asset allocation, banking crisis, barriers to entry, Big bang: deregulation of the City of London, business cycle, butter production in bangladesh, butterfly effect, buttonwood tree, buy and hold, buy low sell high, capital asset pricing model, citizen journalism, collateralized debt obligation, corporate governance, Craig Reynolds: boids flock, creative destruction, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, Danny Hillis, demand response, disintermediation, distributed generation, diversification, diversified portfolio, Emanuel Derman, en.wikipedia.org, experimental economics, financial innovation, fixed income, Gordon Gekko, implied volatility, index arbitrage, index fund, information retrieval, intangible asset, Internet Archive, John Nash: game theory, Kenneth Arrow, load shedding, Long Term Capital Management, Machine translation of "The spirit is willing, but the flesh is weak." to Russian and back, market fragmentation, market microstructure, Mars Rover, Metcalfe’s law, moral hazard, mutually assured destruction, Myron Scholes, natural language processing, negative equity, Network effects, optical character recognition, paper trading, passive investing, pez dispenser, phenotype, prediction markets, quantitative hedge fund, quantitative trading / quantitative finance, QWERTY keyboard, RAND corporation, random walk, Ray Kurzweil, Renaissance Technologies, risk tolerance, risk-adjusted returns, risk/return, Robert Metcalfe, Ronald Reagan, Rubik’s Cube, semantic web, Sharpe ratio, short selling, Silicon Valley, Small Order Execution System, smart grid, smart meter, social web, South Sea Bubble, statistical arbitrage, statistical model, Steve Jobs, Steven Levy, Tacoma Narrows Bridge, the scientific method, The Wisdom of Crowds, time value of money, too big to fail, transaction costs, Turing machine, Upton Sinclair, value at risk, Vernor Vinge, yield curve, Yogi Berra, your tax dollars at work

Gr eatest Hits of Computation in Finance 39 Figure 2.4 Overly exuberant Wall Street Computer Review covers. Source: Wall Street Computer Review (now Wall Street & Technology), June 1987 and June 1990. Figure 2.5 The AI industry apologizes to the world, sort of. Source: Published with permission from Parallel Simulation Technology, LLC. All rights reserved. Marvin Minsky, MIT’s AI übermaven, declared an “AI winter” at the major AI conference of 1987. It got so bad that one AI vendor used the image in Figure 2.5 at the same venue. I don’t mean to pick on expert systems or any particular technology here. Neural nets, wavelets, chaos, genetic algorithms, fuzzy logic, and any number of others could be tarred with the same brush used here (though without the charming illustrations). Nor do I mean to 40 Nerds on Wall Str eet imply that these ideas are utterly without merit.

The ability to change large, complex data structures on the fly allowed LISP to deal with the complexity of problems like symbolic integration, but the need to clean up after those changes created the need for garbage collection.2 When we ran our first, very simple LISP trading systems demonstrations (crossover rules, for the most part) using recorded data for our visitors from Wall Street, we saw their eyes glaze over when, in the middle of the simulated run, the machine would take a break for a few minutes and we would offer more coffee. A Little AI Goes a Long Way on Wall Str eet 161 My colleague Dale Prouty, a brilliant Caltech Ph.D. physicist whose metabolism seemed to make his own caffeine, and I quickly realized there was no way LISP systems would fit in trading. Similar realizations, in other contexts, contributed to the AI winter, described in Chapter 2. Dale had heard that PaineWebber’s equity block desk was looking for proposals for an “intelligent hedging advisory system” for the desk. Ideally, the block traders would “go home flat,” with no net long or short exposure to the market, to sectors, or to other common equity factors. This was not always possible, so the firm had more overnight risk exposure than it wanted.

Tight integration with both market data and electronic execution channels, combined with an appropriate, accessible user interface and a high level of support contributed to a major AI success story with MarketMind/QuantEx. The transactions flowing through these systems produced more revenue on a busy day than many other AI applications generated over their entire operational lifetimes. Prehistory of Artificial Intelligence on Wall Street Summer 1987. AI godfather Marvin Minsky warns American Association for Artificial Intelligence (AAAI) Conference attendees in Seattle that “the AI winter will soon be upon us.” This isn’t news to most of them. Many of the pioneer firms have been pared down to near invisibility. AI stocks have dropped so low that Ferraris are being traded in for Yugos in Palo Alto and Cambridge. On Wall Street, the expert systems that were last year’s breakthrough of the century are this year’s R&D write-off. LISP3 machines can be had in lower Manhattan for 10 cents on the dollar.


pages: 309 words: 114,984

The Digital Doctor: Hope, Hype, and Harm at the Dawn of Medicine’s Computer Age by Robert Wachter

"Robert Solow", activist fund / activist shareholder / activist investor, Affordable Care Act / Obamacare, AI winter, Airbnb, Atul Gawande, Captain Sullenberger Hudson, Checklist Manifesto, Chuck Templeton: OpenTable:, Clayton Christensen, collapse of Lehman Brothers, computer age, creative destruction, crowdsourcing, deskilling, disruptive innovation, en.wikipedia.org, Erik Brynjolfsson, everywhere but in the productivity statistics, Firefox, Frank Levy and Richard Murnane: The New Division of Labor, Google Glasses, Ignaz Semmelweis: hand washing, Internet of things, job satisfaction, Joseph Schumpeter, Kickstarter, knowledge worker, lifelogging, medical malpractice, medical residency, Menlo Park, minimum viable product, natural language processing, Network effects, Nicholas Carr, obamacare, pattern recognition, peer-to-peer, personalized medicine, pets.com, Productivity paradox, Ralph Nader, RAND corporation, Second Machine Age, self-driving car, Silicon Valley, Silicon Valley startup, six sigma, Skype, Snapchat, software as a service, Steve Jobs, Steven Levy, the payments system, The Wisdom of Crowds, Thomas Bayes, Toyota Production System, Uber for X, US Airways Flight 1549, Watson beat the top human players on Jeopardy!, Yogi Berra

Locations are approximate in e-readers, and you may need to page down one or more times after clicking a link to get to the indexed material. accountable care organizations, 59, 188 ACOs. See accountable care organizations Adams, Timothy, 231 Adler-Milstein, Julia, 248 Affordable Care Act (ACA), 15, 16, 239 See also Obamacare AI. See artificial intelligence (AI) AI winter, 102, 107 AIDS activists, 195–196 alerts, 134, 143–153, 251 ignoring, 135–137 ways to safely reduce number of alerts, 145–146 Althaus, Deb, 86 Altmann, Erik, 83 American College of Surgeons, 36 APIs, 192–193, 216 application programming interfaces. See APIs Arenson, Ron, 59, 61 Arizona General Hospital, 73 artificial intelligence (AI) AI winter, 102, 107 computers replacing the physician’s brain, 93–104 little AI, 113 See also medical AI athenahealth, 89, 215, 217, 226–231, 233 Augmedix, 180, 240, 241, 242 auscultation, 32, 33 automation hazards of overreliance on, 162–163 irony of, 162 Avrin, David, 50, 51 Bainbridge, Lisanne, 162 Baker, Stephen, 53 bar-coded medication administration (BCMA), 130 Baron, Richard, 68–69, 76–77 Batalden, Paul, 19 Bates, David, 222, 224 Baucus, Max, 16 Bayes, Thomas, 99 Bayes’ theorem, 99 bedside teaching, 35 Bell, Joseph, 97 Benioff, Marc, 233 Berwick, Don, 232 Beth Israel Deaconess Medical Center, 172, 176, 178, 186, 231 big data, 7, 115–123 biometric identifiers, 190 Birkmeyer, John, 79 Blair, Tony, 10, 17 blood tests, 32 bloodletting, 33 Bloom, Paul, 156 Blumenfeld, Barry, 67 Blumenthal, David, 15, 205–208, 235–236, 243–244, 268 Bolten, Josh, 11–12 Boston Children’s Hospital, 144 Brailer, David, 10–14, 18–19, 68, 207, 212 Brigham & Women’s Hospital, 87, 88, 187 Brown, Eric, 103, 118–119, 123 Brynjolfsson, Erik, 94, 250 productivity paradox, 244–245 bundled payments, 59 Burton, Matthew, 1–8, 10, 113 Bush, George W., 9–10, 11–12, 17 Bush, Jonathan, 89, 226–233 Carr, Nicholas, 275 case-mix adjustment, 40 Cedars-Sinai Medical Center, 67–68 CellScope, 240–241, 242 Cerner, 8, 86, 187, 222, 231 Chan, Benjamin, 139–141, 149–153, 155–157 Chang, Paul, 53, 62 the chart, 44–45 The Checklist Manifesto (Gawande), 121–122 Christensen, Clay, 12, 61, 217, 229 clinical research, 263–264 clinical trials, 33 clinicopathologic correlation, 31 Clinton, Hillary, 11 Clinton, William “Bill”, 9, 189 Code Blue, 2–4 Codman, Ernest, 36 cognitive computing, 146 cognitive load, 150–151 complementary innovations, 245 computer systems, replacing the physician’s brain, 93–104 computerized decision support for clinicians, 248, 251, 260 computerized provider order entry (CPOE), 130 “Connecting for Health” initiative, 10, 17 cookbook medicine, 120 Cramer, Jim, 233 creative destruction, 250–251 The Creative Destruction of Medicine (Topol), 250 CT scans, 50–51 quality of images, 52–53 stacking, 53 data.

The attitude was captured by one early computing pioneer in a 1971 paean to his computer: “It is immune from fatigue and carelessness; and it works day and night, weekends and holidays, without coffee breaks, overtime, fringe benefits or human courtesy.” By the mid-1980s, disappointment had set in. The tools that had seemed so promising a decade earlier were, by and large, unable to manage the complexity of clinical medicine, and they garnered few clinician advocates and miniscule commercial adoption. The medical AI movement skidded to a halt, marking the start of a 20-year period that insiders still refer to as the “AI winter.” Ted Shortliffe, one of the field’s longstanding leaders, has said that the early experience with programs like INTERNIST, DXplain, and MYCIN reminded him of a cartoon that showed an obviously distressed patient who had just been interviewed by a physician. Evidently the poor man had come from an archery field, because protruding from his back was a two-foot-long arrow. The doctor had turned to his office computer and, after examining the screen, proclaimed, “Rapid pulse, sweating, shallow breathing… .

First, during Isabel’s hospitalization, he was amazed that the doctors weren’t using computers to prompt them to consider all possible diagnoses. Second, “medicine is beautifully written down. There are very few industries that have textbooks, journals, and all this data on the Internet.” And third, “from my background in finance, I knew about some clever searching software.” I asked Maude whether he was aware of the AI winter—the junkyard of failed computerized diagnostic programs built in the 1970s and 1980s—when he decided to build a medical AI program. He was, but he tried using one of the existing programs and thought, “Well, it’s pretty rubbish.” “I think I’m sort of naturally bloody-minded,” he said, using the British slang for cantankerous. “I thought, ‘I’m just going to make Isabel better.’” Joining the long line of people who have underappreciated the complexity of this problem, he predicted that his quest would take “maybe three to five years.”


Driverless: Intelligent Cars and the Road Ahead by Hod Lipson, Melba Kurman

AI winter, Air France Flight 447, Amazon Mechanical Turk, autonomous vehicles, barriers to entry, butterfly effect, carbon footprint, Chris Urmson, cloud computing, computer vision, connected car, creative destruction, crowdsourcing, DARPA: Urban Challenge, digital map, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Google Earth, Google X / Alphabet X, high net worth, hive mind, ImageNet competition, income inequality, industrial robot, intermodal, Internet of things, job automation, Joseph Schumpeter, lone genius, Lyft, megacity, Network effects, New Urbanism, Oculus Rift, pattern recognition, performance metric, precision agriculture, RFID, ride hailing / ride sharing, Second Machine Age, self-driving car, Silicon Valley, smart cities, speech recognition, statistical model, Steve Jobs, technoutopianism, Tesla Model S, Travis Kalanick, Uber and Lyft, uber lyft, Unsafe at Any Speed

Over the past several decades, neural network research has come in and out of ideological favor in university computer science departments. AI researchers who dedicated themselves to its pursuit knew they were perhaps embarking on a risky career path. During the periods in which research on neural networks was not viewed as worthwhile, federal research funding would dry up. During these lean years that came to be known as “AI winters,” neural network researchers faced a difficult choice. They could choose to continue their work despite the chilly research climate, or they could shift gears and embrace another more fiscally and professionally rewarding form of artificial-intelligence research. Neural networks To understand the politics that have dogged the development of deep learning, it helps to understand the broader history of neural networks.

The New Yorker admired the Perceptron as a significant technological accomplishment, and in 1958 the New York Times went so far as to call it a revolution, publishing an article with the headline “New Navy Device Learns by Doing.” Rosenblatt’s Perceptron earned him a place in artificial-intelligence history. In my introductory course on machine learning, the first homework assignment is to build a software reincarnation of Rosenblatt’s machine. Yet Rosenblatt’s success, and the resulting media frenzy, brought on the wrath of computer scientists from competing schools of thought. The first AI winter If more researchers in the brand-new field of artificial intelligence had followed Rosenblatt’s approach, we would have succeeded in automating perception decades earlier. Yet shortly after its successful introduction, the Perceptron lost its allure. One reason was that the computing power and sensor data needed to properly train a neural network were still insufficient. The second reason was another sort of people problem, that of political rivalry.

But when presented with pictures depicting somewhat similar four-legged animals, the network’s performance would deteriorate to just above randomness, somewhat like a student circling just any answer to get through a multiple-choice exam. Nevertheless, hope springs eternal. Better digital-camera technology combined with the timely release of Werbos’s backprop algorithm sparked new interest in the field of neural-network research, effectively ending the long AI winter of the 1960s and 1970s. If you dig through research papers from the late 1980s and 1990s, you’ll find the relics of this brief period of euphoria. Researchers attempted to apply neural networks to classify everything under the sun: images, text, and sound. Neural-network research, now renamed connectionism, showed up in a wide range of applications, from estimating an applicant’s credit card worthiness to medical diagnosis.


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

James Lighthill, a British applied mathematician at Cambridge, was the report’s lead author; his most damning criticism was that those early AI techniques—teaching a computer to play checkers, for example—would never scale up to solve bigger, real-world problems.30 In the wake of the reports, elected officials in the US and UK demanded answers to a new question: Why are we funding the wild ideas of theoretical scientists? The US government, including DARPA, pulled funding for machine translation projects. Companies shifted their priorities away from time-intensive basic research on general AI to more immediate programs that could solve problems. If the early years following the Dartmouth workshop were characterized by great expectations and optimism, the decades after those damning reports became known as the AI Winter. Funding dried up, students shifted to other fields of study, and progress came to a grinding halt. Even McCarthy became much more conservative in his projections. “Humans can do this kind of thing very readily because it’s built into us,” McCarthy said.31 But we have a much more difficult time understanding how we understand speech—the physical and cognitive processes that make language recognition possible.

If I gave you an additional piece of information—the bird is a penguin—then you might not put a top on it. Therefore, whether or not the birdcage requires a top depends on a few things: the information I give you and all of the associations you already have with the word “bird,” like the fact that most birds fly. We have built-in assumptions and context. Getting AI to respond the same way we do would require a lot more explicit information and instruction.32 The AI Winter would go on to last for three decades.33 What Came Next: Learning to Play Games While funding had dried up, many of the Dartmouth researchers continued their work on AI—and they kept teaching new students. Meanwhile, Moore’s law continued to be accurate, and computers became ever more powerful. By the 1980s, some of those researchers figured out how to commercialize aspects of AI—and there was now enough compute power and a growing network of researchers who were finding that their work had commercial viability.

James Lighthill, “Artificial Intelligence: A General Survey,” Chilton Computing, July 1972, http://www.chilton-computing.org.uk/inf/literature/reports/lighthill_report/p001.htm. 31. “Mind as Society with Marvin Minsky, PhD,” transcript from “Thinking Allowed, Conversations on the Leading Edge of Knowledge and Discovery, with Dr. Jeffrey Mishlove,” The Intuition Network, 1998, http://www.intuition.org/txt/minsky.htm. 32. Ibid. 33. The AI Winter included new predictions—this time in the form of warnings—for the future. In his book Computer Power and Human Reason, Weizenbaum argued that while artificial intelligence may be possible, we should never allow computers to make important decisions because computers will always lack human qualities such as compassion and wisdom. Weizenbaum makes the crucial distinction between deciding and choosing.


pages: 252 words: 74,167

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!

As the psychologist Steven Pinker summed it up: ‘The main lesson of [the first] thirty-five years of AI research is that the hard problems are easy and the easy problems are hard.’ Changing Ambitions Facing these kinds of challenges, Good Old-Fashioned AI started to run into problems. From the 1970s, the field cooled off as the optimism of previous decades dissipated. Budgets were brutally slashed, plunging Artificial Intelligence into the first of several so-called ‘AI Winters’. In the United States, even the lovable SHAKEY the robot project shuddered to a halt when it became clear that it was not the robotic James Bond spy its funders at the Defense Department had hoped for. Forget spying, SHAKEY couldn’t even replace regular troops on the battlefield! One researcher who worked on the project remembers some military types coming in for a last-ditch look at SHAKEY rolling around the laboratory at its research institute, SRI International.

To create a hologram, people bounce multiple beams of light off an object and then record these bits of information in a giant database. The brain does the same, only with networks of neurons instead of beams of light. This observation led Hinton to study physiology and psychology at Cambridge and then Artificial Intelligence at the University of Edinburgh in Scotland. He arrived in the chilly city of Edinburgh in the mid-1970s, appropriately enough just at the time the first AI winter was setting in. Despite the blow that Good Old-Fashioned AI had just suffered, Hinton’s doctoral supervisor desperately tried to steer him away from neural networks. ‘He kept trying to get me to give up on them and switch to symbolic AI,’ he says. ‘We kept making deals where I would get to do neural nets for a little bit longer.’ Hinton didn’t get much support elsewhere. His fellow students thought he was crazy to be studying neural networks after Minsky and Papert had so totally decimated the field.

_r=1 2 Rogers, Adam, ‘We Asked a Robot to Write an Obit for AI Pioneer Marvin Minsky’, Wired, 26 January 2016: wired.com/2016/01/we-asked-a-robot-to-write-an-obit-for-ai-pioneer-marvin-minsky/ 3 Minsky, Marvin, Society of Mind (New York: Simon and Schuster, 1986). 4 HAL 90210, ‘No Go: Facebook Fails to Spoil Google’s Big AI Day’, Guardian, 28 January 2016: theguardian.com/technology/2016/jan/28/go-playing-facebook-spoil-googles-ai-deepmind 5 Moyer, Christopher, ‘How Google’s AlphaGo Beat a Go World Champion,’ Atlantic, 28 March 2016: http://www.theatlantic.com/technology/archive/2016/03/the-invisible-opponent/475611 6 ‘US Military Shelves Google Robot Plan Over “Noise Concerns”’, BBC News, 30 December 2015: bbc.co.uk/news/technology-35201183 7 Collins, Ben, ‘Meet the Robot Writing “Friends” Sequels’, Daily Beast, 20 January 2016: thedailybeast.com/articles/2016/01/20/meet-the-robot-writing-friends-sequels.html Index The page references in this index correspond to the printed edition from which this ebook was created. To find a specific word or phrase from the index, please use the search feature of your ebook reader. 2001: A Space Odyssey (1968) 2, 228, 242–4 2045 Initiative 217 accountability issues 240–4, 246–8 Active Citizen 120–2 Adams, Douglas 249 Advanced Research Projects Agency (ARPA) 19–20, 33 Affectiva 131 Age of Industry 6 Age of Information 6 agriculture 150–1, 183 AI Winters 27, 33 airlines, driverless 144 algebra 20 algorithms 16–17, 59, 67, 85, 87, 88, 145, 158–9, 168, 173, 175–6, 183–4, 186, 215, 226, 232, 236 evolutionary 182–3, 186–8 facial recognition 10–11, 61–3 genetic 184, 232, 237, 257 see also back-propagation AliveCor 87 AlphaGo (AI Go player) 255 Amazon 153, 154, 198, 236 Amy (AI assistant) 116 ANALOGY program 20 Analytical Engine 185 Android 59, 114, 125 animation 168–9 Antabi, Bandar 77–9 antennae 182, 183–5 Apple 6, 35, 56, 65, 90–1, 108, 110–11, 113–14, 118–19, 126–8, 131–2, 148–9, 158, 181, 236, 238–9, 242 Apple iPhone 108, 113, 181 Apple Music 158–9 Apple Watch 66, 199 architecture 186 Artificial Artificial Intelligence (AAI) 153, 157 Artificial General Intelligence (AGI) 226, 230–4, 239–40, 254 Artificial Intelligence (AI) 2 authentic 31 development problems 23–9, 32–3 Good Old-Fashioned (Symbolic) 22, 27, 29, 34, 36, 37, 39, 45, 49–52, 54, 60, 225 history of 5–34 Logical Artificial Intelligence 246–7 naming of 19 Narrow/Weak 225–6, 231 new 35–63 strong 232 artificial stupidity 234–7 ‘artisan economy’ 159–61 Asimov, Isaac 227, 245, 248 Athlone Industries 242 Atteberry, Kevan J. 112 Automated Land Vehicle in a Neural Network (ALVINN) 54–5 automation 141, 144–5, 150, 159 avatars 117, 193–4, 196–7, 201–2 Babbage, Charles 185 back-propagation 50–3, 57, 63 Bainbridge, William Sims 200–1, 202, 207 banking 88 BeClose smart sensor system 86 Bell Communications 201 big business 31, 94–6 biometrics 77–82, 199 black boxes 237–40 Bletchley Park 14–15, 227 BMW 128 body, machine analogy 15 Bostrom, Nick 235, 237–8 BP 94–95 brain 22, 38, 207–16, 219 Brain Preservation Foundation 219 Brain Research Through Advanced Innovative Neurotechnologies 215–16 brain-like algorithms 226 brain-machine interfaces 211–12 Breakout (video game) 35, 36 Brin, Sergey 6–7, 34, 220, 231 Bringsjord, Selmer 246–7 Caenorhabditis elegans 209–10, 233 calculus 20 call centres 127 Campbell, Joseph 25–6 ‘capitalisation effect’ 151 cars, self-driving 53–56, 90, 143, 149–50, 247–8 catering 62, 189–92 chatterbots 102–8, 129 Chef Watson 189–92 chemistry 30 chess 1, 26, 28, 35, 137, 138–9, 152–3, 177, 225 Cheyer, Adam 109–10 ‘Chinese Room, the’ 24–6 cities 89–91, 96 ‘clever programming’ 31 Clippy (AI assistant) 111–12 clocks, self-regulating 71–2 cognicity 68–9 Cognitive Assistant that Learns and Organises (CALO) 112 cognitive psychology 12–13 Componium 174, 176 computer logic 8, 10–11 Computer Science and Artificial Intelligence Laboratory (CSAIL) 96–7 Computer-Generated Imagery (CGI) 168, 175, 177 computers, history of 12–17 connectionists 53–6 connectomes 209–10 consciousness 220–1, 232–3, 249–51 contact lenses, smart 92 Cook, Diane 84–6 Cook, Tim 91, 179–80 Cortana (AI assistant) 114, 118–19 creativity 163–92, 228 crime 96–7 curiosity 186 Cyber-Human Systems 200 cybernetics 71–4 Dartmouth conference 1956 17–18, 19, 253 data 56–7, 199 ownership 156–7 unlabelled 57 death 193–8, 200–1, 206 Deep Blue 137, 138–9, 177 Deep Knowledge Ventures 145 Deep Learning 11–12, 56–63, 96–7, 164, 225 Deep QA 138 DeepMind 35–7, 223, 224, 245–6, 255 Defense Advanced Research Projects Agency (DARPA) 33, 112 Defense Department 19, 27–8 DENDRAL (expert system) 29–31 Descartes, René 249–50 Dextro 61 DiGiorgio, Rocco 234–5 Digital Equipment Corporation (DEC) 31 Digital Reasoning 208–9 ‘Digital Sweatshops’ 154 Dipmeter Advisor (expert system) 31 ‘do engines’ 110, 116 Dungeons and Dragons Online (video game) 197 e-discovery firms 145 eDemocracy 120–1 education 160–2 elderly people 84–6, 88, 130–1, 160 electricity 68–9 Electronic Numeric Integrator and Calculator (ENIAC) 12, 13, 92 ELIZA programme 129–30 Elmer and Elsie (robots) 74–5 email filters 88 employment 139–50, 150–62, 163, 225, 238–9, 255 eNeighbor 86 engineering 182, 183–5 Enigma machine 14–15 Eterni.me 193–7 ethical issues 244–8 Etsy 161 Eurequa 186 Eve (robot scientist) 187–8 event-driven programming 79–81 executives 145 expert systems 29–33, 47–8, 197–8, 238 Facebook 7, 61–2, 63, 107, 153, 156, 238, 254–5 facial recognition 10–11, 61–3, 131 Federov, Nikolai Fedorovich 204–5 feedback systems 71–4 financial markets 53, 224, 236–7 Fitbit 94–95 Flickr 57 Floridi, Luciano 104–5 food industry 141 Ford 6, 230 Foxbots 149 Foxconn 148–9 fraud detection 88 functional magnetic resonance imaging (fMRI) 211 Furbies 123–5 games theory 100 Gates, Bill 32, 231 generalisation 226 genetic algorithms 184, 232, 237, 257 geometry 20 glial cells 213 Go (game) 255 Good, Irving John 227–8 Google 6–7, 34, 58–60, 67, 90–2, 118, 126, 131, 155–7, 182, 213, 238–9 ‘Big Dog’ 255–6 and DeepMind 35, 245–6, 255 PageRank algorithm 220 Platonic objects 164, 165 Project Wing initiative 144 and self-driving cars 56, 90, 143 Google Books 180–1 Google Brain 61, 63 Google Deep Dream 163–6, 167–8, 184, 186, 257 Google Now 114–16, 125, 132 Google Photos 164 Google Translate 11 Google X (lab) 61 Government Code and Cypher School 14 Grain Marketing Adviser (expert system) 31 Grímsson, Gunnar 120–2 Grothaus, Michael 69, 93 guilds 146 Halo (video game) 114 handwriting recognition 7–8 Hank (AI assistant) 111 Hawking, Stephen 224 Hayworth, Ken 217–21 health-tracking technology 87–8, 92–5 Healthsense 86 Her (film, 2013) 122 Herd, Andy 256–7 Herron, Ron 89–90 High, Rob 190–1 Hinton, Geoff 48–9, 53, 56, 57–61, 63, 233–4 hive minds 207 holograms 217 HomeChat app 132 homes, smart 81–8, 132 Hopfield, John 46–7, 201 Hopfield Nets 46–8 Human Brain Project 215–16 Human Intelligence Tasks (HITs) 153, 154 hypotheses 187–8 IBM 7–11, 136–8, 162, 177, 189–92 ‘IF THEN’ rules 29–31 ‘If-This-Then-That’ rules 79–81 image generation 163–6, 167–8 image recognition 164 imagination 178 immortality 204–7, 217, 220–1 virtual 193–8, 201–4 inferences 97 Infinium Robotics 141 information processing 208 ‘information theory’ 16 Instagram 238 insurance 94–5 Intellicorp 33 intelligence 208 ambient 74 ‘intelligence explosion’ 228 top-down view 22, 25, 246 see also Artificial Intelligence internal combustion engine 140–1, 150–1 Internet 10, 56 disappearance 91 ‘Internet of Things’ 69, 70, 83, 249, 254 invention 174, 178, 179, 182–5, 187–9 Jawbone 78–9, 92–3, 254 Jennings, Ken 133–6, 138–9, 162, 189 Jeopardy!


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

Or could something stop it? AGI defeaters cluster around two ideas: economics and software complexity. The first, economics, considers that funds won’t be available to get from narrow AI to the far more complex and powerful cognitive architectures of AGI. Few AGI efforts are well-funded. This prompts a subset of researchers to feel that their field is stuck in the endless stall of a so-called AI winter. They’ll escape if the government or a corporation like IBM or Google considers AGI a priority of the first order, and undertakes a Manhattan Project–sized effort to achieve it. During World War II, fast-tracking atomic weapons development cost the U.S. government about $2 billion dollars, in today’s valuation, and employed around 130,000 people. The Manhattan Project frequently comes up among researchers who want to achieve AGI soon.

Barring some other bottleneck, the world’s economy will be driven by the creation of strong artificial intelligence, and fueled by the growing global apprehension of all the ways it will change our lives. Up ahead we’ll explore another critical roadblock—software complexity. We’ll find out if the challenge of creating software architectures that match human-level intelligence is just too difficult to conquer, and whether or not all that stretches out ahead is a perpetual AI winter. Chapter Twelve The Last Complication How can we be so confident that we will build superintelligent machines? Because the progress of neuroscience makes it clear that our wonderful minds have a physical basis, and we should have learned by now that our technology can do anything that’s physically possible. IBM’s Watson, playing Jeopardy! as skillfully as human champions, is a significant milestone and illustrates the progress of machine language processing.

But today, if you suddenly removed all AI from these industries, you couldn’t get a loan, your electricity wouldn’t work, your car wouldn’t go, and most trains and subways would stop. Drug manufacturing would creak to a halt, faucets would run dry, and commercial jets would drop from the sky. Grocery stores wouldn’t be stocked, and stocks couldn’t be bought. And when were all these AI systems implemented? During the last thirty years, the so-called AI winter, a term used to describe a long decline in investor confidence, after early, overly optimistic AI predictions proved false. But there was no real winter. To avoid the stigma of the label “artificial intelligence,” scientists used more technical names like machine learning, intelligent agents, probabilistic inference, advanced neural networks, and more. And still the accreditation problem continues.


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 Fifth-Generation Project failed to meet its objectives, as did its counterparts in the United States and Europe. A second AI winter descended. At this point, a critic could justifiably bemoan “the history of artificial intelligence research to date, consisting always of very limited success in particular areas, followed immediately by failure to reach the broader goals at which these initial successes seem at first to hint.”21 Private investors began to shun any venture carrying the brand of “artificial intelligence.” Even among academics and their funders, “AI” became an unwanted epithet.22 Technical work continued apace, however, and by the 1990s, the second AI winter gradually thawed. Optimism was rekindled by the introduction of new techniques, which seemed to offer alternatives to the traditional logicist paradigm (often referred to as “Good Old-Fashioned Artificial Intelligence,” or “GOFAI” for short), which had focused on high-level symbol manipulation and which had reached its apogee in the expert systems of the 1980s.

The performance of these early systems also suffered because of poor methods for handling uncertainty, reliance on brittle and ungrounded symbolic representations, data scarcity, and severe hardware limitations on memory capacity and processor speed. By the mid-1970s, there was a growing awareness of these problems. The realization that many AI projects could never make good on their initial promises led to the onset of the first “AI winter”: a period of retrenchment, during which funding decreased and skepticism increased, and AI fell out of fashion. A new springtime arrived in the early 1980s, when Japan launched its Fifth-Generation Computer Systems Project, a well-funded public–private partnership that aimed to leapfrog the state of the art by developing a massively parallel computing architecture that would serve as a platform for artificial intelligence.

The brain-like qualities of neural networks contrasted favorably with the rigidly logic-chopping but brittle performance of traditional rule-based GOFAI systems—enough so to inspire a new “-ism,” connectionism, which emphasized the importance of massively parallel sub-symbolic processing. More than 150,000 academic papers have since been published on artificial neural networks, and they continue to be an important approach in machine learning. Evolution-based methods, such as genetic algorithms and genetic programming, constitute another approach whose emergence helped end the second AI winter. It made perhaps a smaller academic impact than neural nets but was widely popularized. In evolutionary models, a population of candidate solutions (which can be data structures or programs) is maintained, and new candidate solutions are generated randomly by mutating or recombining variants in the existing population. Periodically, the population is pruned by applying a selection criterion (a fitness function) that allows only the better candidates to survive into the next generation.


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

To understand why, we must first grasp the basics of the technology and how it is set to transform our world. A BRIEF HISTORY OF DEEP LEARNING Machine learning—the umbrella term for the field that includes deep learning—is a history-altering technology but one that is lucky to have survived a tumultuous half-century of research. Ever since its inception, artificial intelligence has undergone a number of boom-and-bust cycles. Periods of great promise have been followed by “AI winters,” when a disappointing lack of practical results led to major cuts in funding. Understanding what makes the arrival of deep learning different requires a quick recap of how we got here. Back in the mid-1950s, the pioneers of artificial intelligence set themselves an impossibly lofty but well-defined mission: to recreate human intelligence in a machine. That striking combination of the clarity of the goal and the complexity of the task would draw in some of the greatest minds in the emerging field of computer science: Marvin Minsky, John McCarthy, and Herbert Simon.

But years of ingrained prejudice against the neural networks approach led many AI researchers to overlook this “fringe” group that claimed outstanding results. The turning point came in 2012, when a neural network built by Hinton’s team demolished the competition in an international computer vision contest. After decades spent on the margins of AI research, neural networks hit the mainstream overnight, this time in the form of deep learning. That breakthrough promised to thaw the ice from the latest AI winter, and for the first time truly bring AI’s power to bear on a range of real-world problems. Researchers, futurists, and tech CEOs all began buzzing about the massive potential of the field to decipher human speech, translate documents, recognize images, predict consumer behavior, identify fraud, make lending decisions, help robots “see,” and even drive a car. PULLING BACK THE CURTAIN ON DEEP LEARNING So how does deep learning do this?

Index A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z A Africa, 138, 139, 169 age of data, 14, 18, 56 age of implementation Chinese entrepreneurs and, 16, 18, 25 Chinese government and, 18 data and, 17, 20, 55, 80 deep learning and, 13–14, 143 going light vs. going heavy, 71 AGI (artificial general intelligence), 140–44 AI. See artificial intelligence (AI) AI engineers, 14 Airbnb, 39, 49, 73 AI revolution deep learning and, 5, 25, 92, 94, 143 economic impact of, 151–52 speed of, 152–55 AI winters, 6–7, 8, 9, 10 algorithmic bias, 229 algorithms, AI AI revolution and, 152–53 computing power and, 14, 56 credit and, 112–13 data and, 14, 17, 56, 138 fake news detection by, 109 intelligence sharing and, 87 legal applications for, 115–16 medical diagnosis and, 114–15 as recommendation engines, 107–8 robot reporting, 108 white-collar workers and, 167, 168 Alibaba Amazon compared to, 109 Chinese startups and, 58 City Brain, 93–94, 117, 124, 228 as dominant AI player, 83, 91, 93–94 eBay and, 34–35 financial services spun off from, 73 four waves of AI and, 106, 107, 109 global markets and, 137 grid approach and, 95 Microsoft Research Asia and, 89 mobile payments transition, 76 New York Stock Exchange debut, 66–67 online purchasing and, 68 success of, 40 Tencent’s “Pearl Harbor attack” on, 60–61 Wang Xing and, 24 Alipay, 35, 60, 69, 73–74, 75, 112, 118 Alphabet, 92–93 AlphaGo, 1–4, 5, 6, 11, 199 AlphaGo Zero, 90 Altman, Sam, 207 Amazon Alibaba compared to, 109 Chinese market and, 39 data captured by, 77 as dominant AI player, 83, 91 four waves of AI and, 106 grid approach and, 95 innovation mentality at, 33 monopoly of e-commerce, 170 online purchasing and, 68 Wang Xing and, 24 warehouses, 129–30 Amazon Echo, 117, 127 Amazon Go, 163, 213 Anderson, Chris, 130 Andreesen Horowitz, 70 Ant Financial, 73 antitrust laws, 20, 28, 171, 229 Apollo project, 135 app constellation model, 70 Apple, 33, 75, 117, 126, 143, 177, 184 Apple Pay, 75, 76 app-within-an-app model, 59 ARM (British firm), 96 Armstrong, Neil, 3 artificial general intelligence (AGI), 140–44 artificial intelligence (AI) introduction to, ix–xi See also China; deep learning; economy and AI; four waves of AI; global AI story; human coexistence with AI; new world order artificial superintelligence.


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

Tracing all the steps between where we are now and that great convergence is a valuable exercise, because it helps to reveal the workplace realities that are most likely within the spans of our own careers. As we’ll see, there will remain plenty of opportunities to work with smart machines that don’t yet have it all. Ode to AI Spring For the sellers of smart machines, if we may slightly paraphrase Gerard Manley Hopkins, nothing is so beautiful as AI spring. The observation that artificial intelligence has its seasons of enthusiasm and also (in AI winter) of despair has become commonplace; by most accounts, the term “AI winter” was first coined as an allusion to nuclear winter, a level of devastation that seemed analogous when a slew of AI-related companies that had been founded in the 1970s all went bust in the early 1980s. By later in that same decade, a thaw was beginning. (In 1988, for example, Time magazine had AI back on its cover with an in-depth story called “Putting Knowledge to Work.”)

Accenture, 83, 102, 134, 183 Adrià, Ferran, 122 Aetna, 83 AI (film), 125 Ainge, Danny, 117 Allen, Robbie, 97 Allstate, 94, 103–4 Amazon Echo, 167 Amazon Robotics (Kiva Systems), 2–3 Amplify, 20 “Analytics 3.0,” 42–43 Analytics Revolution, The (Franks), 43 AnalytixInsight, 22 Anders, George, 120 Anthem, 15, 84 Aplin, Ken, 153–54 Apollo Guidance Computer, 67 Apple, 63 Archilochus, 171 architect jobs, 23, 24–25, 151 Ariely, Dan, 113 Armstrong, Stuart, 226, 249 Arnett, Thomas, 84 artificial intelligence, 7, 26, 33–58, 141, 163, 189. See also cognitive technologies; computers “AI winter,” 36 Apollo flights and, 67 Asimov’s three laws for, 244 augmentation of soft skills and, 123 automating administrative tasks and, 216 broadening the base of methods, 194 cancer cure and, 60–61 consequences of, 243–46, 249–50 cost to build, 155 in education, 230 endowing with emotions, 246 expert systems, 37 fighter pilots and, 66 human attributes, 124–27, 245 human control of, 244–45 investing by, 92–93 move from “narrow” to “general,” 35 programmers, 178 regulatory oversight of, 246–49 researchers and, 181 Ruby programming language, 222 self-awareness and AI spring, 54–57 social memory, 123 society-level decisions about, 226–28 term use and branches of, 37–38 Thiel on, 243 universe model and KIGEN, 59 warnings and predictions about, 225–26 Warren, 20 (see also IBM Watson) artists, 24, 119, 238 Asimov, Isaac, 244 Associated Press (AP) automated journalism, 96–98, 103, 104, 121, 222 investing in automated publishing, 97 Atlas, David, 121 Auerbach, Red, 116–17 Auerswald, Phil, 128 augmentation, 31–32, 59–88, 201–24, 234 “automation leader” for, 221–23 automation vs., 61–63, 128–29, 204–8, 223–24 cutting both ways, 70–74 defined, 64–65 example, spreadsheets, 69–70 example, underwriting, 77–84, 218–19 five options for, 76–77, 218 forms of, 65–69, 209 as goal, 228–29 in governance, 249–51 government policies and, 229–43 human-machine partnership, 68, 203, 234, 235, 237, 239, 250, 251 implementing, steps for, 208–23 key capabilities of humans and, 71–73 less work not likely with, 69–70 “moon shots,” 210, 215–16 as organizational priority, 203–4 preparing employees for, 219 proofs of concept or pilots, 220–21 reasons for augmentation, 204–8 smart machines and job security, 59–61 Stepping Aside, 77, 81, 85, 87, 108–30 Stepping Forward, 77, 83–86, 88, 176–200 Stepping In, 77, 81–82, 85, 87, 131–52 Stepping Narrowly, 77, 82, 85, 87–88, 153–75 Stepping Up, 76–77, 80, 84–86, 89–107 three forms, for specialization, 166–69 as wheels for the mind, 63–65 Augmentation Research Center, 64 “Augmenting Human Intellect” (Engelbart), 64 Automated Insights, 22, 97 Wordsmith, 96 automation, 1, 3–4, 5, 6, 8, 12–13 augmentation vs., 61–63, 128–29, 204–8, 223–24 business process management, 40 codified tasks and, 12–13, 14, 27–28, 30 content transmission and, 19–20 eras of, 2–5 government policies and, 229–43 income inequality and, 228–29 “isolation syndrome,” 24 job losses and, 1–6, 8, 30, 78, 150–51, 167, 223–24, 226, 227, 238 jobs resistant to, 153–75 process automation, 48–49 “race against the machine,” 8, 29 reductions in cost and time, 48, 49 regulated sectors and legal constraints on, 213–15 repetitive task, 42, 47–48, 49, 50 robotic process, 48–49, 187, 221, 222–23 “rule engines,” 47 sectors using, 1, 11–12, 13, 18, 74, 201–3 (see also specific industries) signs of coming automation, 19–22 Stepping Forward with, 176–200 Stepping In with, 134–52 Stepping Up and, 91–95 strategy of, as self-defeating, 204–8 strongest evidence of job threat, 19 Automation Anywhere, 48, 216 automotive sector, 1 Autor, David, 70–71 Balaporia, Zahir, 189–91 Bankrate.com, 96 Bathgate, Alastair, 156, 157 Baylor College of Medicine, 212 Beaudry, Paul, 6, 24 Belmont, Chris, 209 Berg company, 60–61 Berlin, Isaiah, 171 Bernanke, Ben, 28, 42, 73 Bernaski, Michael, 79, 80, 81, 82, 187 Bessen, James, 133, 233 Betterment, 86–87, 198 big-picture perspective, 71, 75, 76–77, 84, 91, 92, 99, 100, 155 Stepping Up and, 98–100 Binsted, Kim, 125 “black box,” 95, 134, 139, 148, 192, 198 Blanke, Jennifer, 7 Blue Prism, 49, 156, 216, 221 Bohrer, Abram, 159 Bostrom, Nick, 226, 227 Brackett, Glenn, 128 Braverman, Harry, 15–16 Breaking Bad (TV show), 172 Brem, Rachel, 181–82 Bridgewater Associates, 92–93 Brooks, David, 241 Brooks, Rodney, 170, 182 Brown, John Seely, 237 Brynjolfsson, Erik, 6, 8, 27, 74 Bryson, Joanna J., 226 Buehner, Carl, 120 Buffett, Warren, 244 Bush, Vannevar, 64, 248 Bustarret, Claire, 154 BYOD (Bring Your Own Device), 13 Cameron, James, 165–66 Carey, Greg, 154, 156, 172–73 Carr, Nick, 162 CastingWords, 168 Catanzaro, Sandro, 179–80, 193 Cathcart, Ron, 89–91, 95 Cerf, Vint, 248 Chambers, Joshua, 250 Charles Schwab, 88 chess, 74–76 Chi, Michelene, 163 Chicago Mercantile Exchange, 11–12 Chilean miners, 201–2 China, 239 Chiriac, Marcel, 217 Circle (Internet start-up), 146 Cisco, 43 Civilian Conservation Corps (CCC), 238 “Claiming our Humanity in the Digital Age,” 248 Class Dojo, 141 Cleveland Clinic, 54 Clifton, Jim, 8 Clinton, Bill, 108 Clockwork Universe, The (Dolnick), 169–70 Codelco/Codelco Digital, 40, 201–3 Cognex, 47 CognitiveScale, 45, 194, 209 cognitive technologies, 4–5, 32, 33–58.


pages: 499 words: 144,278

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

2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, 4chan, 8-hour work day, Ada Lovelace, AI winter, Airbnb, Amazon Web Services, Asperger Syndrome, augmented reality, Ayatollah Khomeini, barriers to entry, basic income, Bernie Sanders, bitcoin, blockchain, blue-collar work, Brewster Kahle, Brian Krebs, Broken windows theory, call centre, cellular automata, Chelsea Manning, clean water, cloud computing, cognitive dissonance, computer vision, Conway's Game of Life, crowdsourcing, cryptocurrency, Danny Hillis, David Heinemeier Hansson, don't be evil, don't repeat yourself, Donald Trump, dumpster diving, Edward Snowden, Elon Musk, Erik Brynjolfsson, Ernest Rutherford, Ethereum, ethereum blockchain, Firefox, Frederick Winslow Taylor, game design, glass ceiling, Golden Gate Park, Google Hangouts, Google X / Alphabet X, Grace Hopper, Guido van Rossum, Hacker Ethic, HyperCard, illegal immigration, ImageNet competition, Internet Archive, Internet of things, Jane Jacobs, John Markoff, Jony Ive, Julian Assange, Kickstarter, Larry Wall, lone genius, Lyft, Marc Andreessen, Mark Shuttleworth, Mark Zuckerberg, Menlo Park, microservices, Minecraft, move fast and break things, move fast and break things, Nate Silver, Network effects, neurotypical, Nicholas Carr, Oculus Rift, PageRank, pattern recognition, Paul Graham, paypal mafia, Peter Thiel, pink-collar, planetary scale, profit motive, ransomware, recommendation engine, Richard Stallman, ride hailing / ride sharing, Rubik’s Cube, Ruby on Rails, Sam Altman, Satoshi Nakamoto, Saturday Night Live, self-driving car, side project, Silicon Valley, Silicon Valley ideology, Silicon Valley startup, single-payer health, Skype, smart contracts, Snapchat, social software, software is eating the world, sorting algorithm, South of Market, San Francisco, speech recognition, Steve Wozniak, Steven Levy, TaskRabbit, the High Line, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, universal basic income, urban planning, Wall-E, Watson beat the top human players on Jeopardy!, WikiLeaks, women in the workforce, Y Combinator, Zimmermann PGP, éminence grise

Indeed, you need to know even more primitive concepts: What’s a “country”? What’s an “economy”? What does “falling apart” mean? This is what’s sometimes referred to as the problem of “common sense.” Our human ability to interact with the world is based on a ton of common-sense knowledge that we gradually absorb as we grow up, and in school. So the dream of a grand, self-learning AI quickly crashed. It produced an “AI winter”—a period where computer scientists and investors felt so burned by AI hype that they wouldn’t touch the field. It was too dangerous; you’d look like an idiot. Indeed, from the ’60s to the ’00s, AI went through several cycles of hype—a “summer,” when the money flooded in and people got excited about new techniques—only to overpromise and underdeliver, producing yet another winter. One of the few forms of AI that reliably worked and that made money was extremely simple and modest: “expert systems.”

Banks used LeCun’s work to create neural nets that could automatically read checks; some voice-recognition companies used neural nets to create the first clunky systems that could slowly, painstakingly recognize speech and type it out. But mostly, computer scientists regarded neural nets as another false promise of AI. After some small bursts of excitement in the ’80s, neural nets lapsed into their own “AI winter.” “Everyone was saying, ‘This is a lost field,’ ” Hans-Christian Boos told me; he was a young grad student fascinated by this crazy technique. But his peers told him to stay away; nothing would ever come of neural nets. They were wrong. Jeff Dean was one of the coders who discovered just how wrong they were. Dean is the head of Google AI, an AI division at the tech giant. A tall, wiry 50-year-old, Dean was an early hire in 1999.

That year, Hinton’s deep-learning neural net got only 15.3 percent of the images wrong. The next-best competitor had an error rate almost twice as high, of 26.2 percent. It was an AI moon shot. Another of Dean’s colleagues was equally impressed: a Stanford professor named Andrew Ng, then a part-time consultant for Google X. Like Dean, he’d tinkered with neural code while young, then set it aside during deep learning’s long AI winter. But in 2011, over a dinner with Dean, the two got excited about the idea of using Google’s enormous phalanxes of computers to see how powerful a neural net they could build. “When people think of AI, they think of sentience,” Ng tells me. “But when I think of AI, I think of automation. That’s the value of AI.” Or as he once put it in a tweet: “Pretty much anything that a normal human can do in <1 sec, we can now automate with AI.”


pages: 484 words: 104,873

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

Fascination with the idea of building a true thinking machine traces its origin at least as far back as 1950, when Alan Turing published the paper that ushered in the field of artificial intelligence. In the decades that followed, AI research was subjected to a boom-and-bust cycle in which expectations repeatedly soared beyond any realistic technical foundation, especially given the speed of the computers available at the time. When disappointment inevitably followed, investment and research activity collapsed and long, stagnant periods that have come to be called “AI winters” ensued. Spring has once again arrived, however. 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.

Never before have such deep-pocketed corporations viewed artificial intelligence as absolutely central to their business models—and never before has AI research been positioned so close to the nexus of competition between such powerful entities. A similar competitive dynamic is unfolding among nations. 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.

INDEX ABB Group, 10 accelerometer, 4–5 ACFR. See Australian Centre for Field Robotics (ACFR) adaptive learning systems, 143 Adenhart, Nick, 83 Ad Hoc Committee on the Triple Revolution, 30–31 administrative costs, higher education, 140–141 Aethon, Inc., 154 Affordable Care Act, 151, 165, 167n, 168n, 204, 279 aging populations, xvii, 220–223, 224 agriculture, ix, x–xi, 23–26 AI. 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.


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

We are currently experiencing the third wave of optimism. The first wave was largely funded by the US military, and one of its champions, Herbert Simon, claimed in 1965 that ‘machines will be capable, within twenty years, of doing any work a man can do.’ Claims like this turned out to be wildly unrealistic, and disappointment was crystallised by a damning government report in 1974. Funding was cut off, causing the first ‘AI winter’. ‘Interest was sparked again in the early 1980s, when Japan announced its ‘fifth generation’ computer research programme. ‘Expert systems’, which captured and deployed the specialised knowledge of human experts were also showing considerable promise. This second boom was extinguished in the late 1980s when the expensive, specialised computers which drove it were overtaken by smaller, general-purpose desktop machines manufactured by IBM and others.

But impressed by the continued progress of Moore’s Law, which observes that computer processing power is doubling every 18 months, more and more scientists now believe that humans may create an artificial intelligence sometime this century. One of the more optimistic, Ray Kurzweil, puts the date as close as 2029.’ As the lights came back up, Ross was standing again, poised in front of the seated guests. ‘So, Professor Montaubon. Since David and Matt’s dramatic adventure the media has been full of talk about artificial intelligence. Are we just seeing the hype again? Will we shortly be heading to into another AI winter?’ ‘I don’t think so,’ replied Montaubon, cheerfully. ‘It is almost certain that artificial intelligence will arrive much sooner than most people think. Before long we will have robots which carry out our domestic chores. And people will notice that as each year’s model becomes more eerily intelligent than the last, they are progressing towards a genuine, conscious artificial intelligence. It will happen first in the military space first, because that is where the big money is.’

His work was highly confidential and it would have been very controversial – if anyone knew about it. But that didn’t bother him: he was following his passion. He had been fascinated by the human brain for as long as he could remember, and developing artificial intelligence seemed the best way to understand our own intelligence. His career had begun in the early 1990s, just as interest in the field was picking up – recovering from the ‘AI winter’ brought on by the failure of the Japanese Fifth Generation programme in the 1980s. He had benefited enormously from the influx of funding, which provided superb facilities and equipment, and rapid promotion opportunities for anyone who was ambitious and pliable. Which he was. Thanks to hard work and talent, his progress up the academic ladder had been swift, and he felt his efforts had been rewarded two years ago when he was recruited by the South Korean army for a senior role in a top-secret project – developing the country’s most advanced artificial intelligence software.


pages: 371 words: 108,317

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

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

Betterment or Wealthfront: Rob Berger, “7 Robo Advisors That Make Investing Effortless,” Forbes, February 5, 2015. 80 percent of its revenue: Rick Summer, “By Providing Products That Consumers Use Across the Internet, Google Can Dominate the Ad Market,” Morningstar, July 17, 2015. 3 billion queries that Google conducts: Danny Sullivan, “Google Still Doing at Least 1 Trillion Searches Per Year,” Search Engine Land, January 16, 2015. Google CEO Sundar Pichai stated: James Niccolai, “Google Reports Strong Profit, Says It’s ‘Rethinking Everything’ Around Machine Learning,” ITworld, October 22, 2015. the AI winter: “AI Winter,” Wikipedia, accessed July 24, 2015. Billions of neurons in our brain: Frederico A. C. Azevedo, Ludmila R. B. Carvalho, Lea T. Grinberg, et al., “Equal Numbers of Neuronal and Non-Neuronal Cells Make the Human Brain an Isometrically Scaled-up Primate Brain,” Journal of Comparative Neurology 513, no. 5 (2009): 532–41. run neural networks in parallel: Rajat Raina, Anand Madhavan, and Andrew Y.

My prediction: By 2026, Google’s main product will not be search but AI. This is the point where it is entirely appropriate to be skeptical. For almost 60 years, AI researchers have predicted that AI is right around the corner, yet until a few years ago it seemed as stuck in the future as ever. There was even a term coined to describe this era of meager results and even more meager research funding: the AI winter. Has anything really changed? Yes. Three recent breakthroughs have unleashed the long-awaited arrival of artificial intelligence: 1. Cheap Parallel Computation Thinking is an inherently parallel process. Billions of neurons in our brain fire simultaneously to create synchronous waves of computation. To build a neural network—the primary architecture of AI software—also requires many different processes to take place simultaneously.


pages: 416 words: 112,268

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

The second AI bubble burst when these systems proved to be inadequate for many of the tasks to which they were applied. Again, the machines just weren’t smart enough. An AI winter ensued. My own AI course at Berkeley, currently bursting with over nine hundred students, had just twenty-five students in 1990. The AI community learned its lesson: smarter, obviously, was better, but we would have to do our homework to make that happen. The field became far more mathematical. Connections were made to the long-established disciplines of probability, statistics, and control theory. The seeds of today’s progress were sown during that AI winter, including early work on large-scale probabilistic reasoning systems and what later became known as deep learning. Beginning around 2011, deep learning techniques began to produce dramatic advances in speech recognition, visual object recognition, and machine translation—three of the most important open problems in the field.

If doubles are rolled (Doubles12), then the player rolls again, so D3 and D4 have non-zero values, and so on. In the situation described, the player lands on the yellow set if any of the three totals is 16, 17, or 19. Bayesian networks provide a way to build knowledge-based systems that avoids the failures that plagued the rule-based expert systems of the 1980s. (Indeed, had the AI community been less resistant to probability in the early 1980s, it might have avoided the AI winter that followed the rule-based expert system bubble.) Thousands of applications have been fielded, in areas ranging from medical diagnosis to terrorism prevention.3 Bayesian networks provide machinery for representing the necessary probabilities and performing the calculations to implement Bayesian updating for many complex tasks. Like propositional logic, however, they are quite limited in their ability to represent general knowledge.


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!

Machines, it seemed, would soon master language, recognize faces, and maneuver, as robots, in factories, hospitals, and homes. In short, computer scientists would create a superendowed class of electronic servants. This led, of course, to failed promises, to such a point that Artificial Intelligence became a term of derision. Bold projects to build bionic experts and conversational computers lost their sponsors. A long AI winter ensued, lasting through much of the ’80s and ’90s. What went wrong? In retrospect, it seems almost inconceivable that leading scientists, including Nobel laureates like Simon, believed it would be so easy. They certainly appreciated the complexity of the human brain. But they also realized that a lot of that complexity was tied up in dreams, memories, guilt, regrets, faith, desires, along with the controls to maintain the physical body.

Someone who has won the Boston Marathon might be contentedly weary. Another, in a divorce hearing, is anything but. One person may slack his jaw in an exaggerated way, as if to say “Know what I mean?” In this tiny negotiation, far beyond the range and capabilities of machines, two people can bridge the gap between the formal definition of a word and what they really want to say. It’s hard to nail down the exact end of AI winter. A certain thaw set in when IBM’s computer Deep Blue bested Garry Kasparov in their epic 1997 showdown. Until that match, human intelligence, with its blend of historical knowledge, pattern recognition, and the ability to understand and anticipate the behavior of the person across the board, ruled the game. Human grandmasters pondered a rich set of knowledge, jewels that had been handed down through the decades—from Bobby Fischer’s use of the Sozin Variation in his 1972 match with Boris Spassky to the history of the Queen’s Gambit Denied.


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

Work on AI in very specific environments (for instance, one that bred an artificial therapist) failed to generalize. The early 1980s brought hope that engineers could carefully program expert systems to replicate skilled domains like medical diagnosis, but these were costly to develop, cumbersome, and could not address the myriad of exceptions and possibilities, leading to what became known as an “AI winter.” Winter, however, appears to be over. More data, better models, and enhanced computers have enabled recent developments in machine learning to improve prediction. Improvements in the collection and storage of big data have provided feedstock for new machine learning algorithms. Compared to their older statistical counterparts, and facilitated by the invention of more suitable processors, the new machine learning models are significantly more flexible and generate better predictions—so much better that some people have returned to describing this branch of computer science as “artificial intelligence.”

Index accounts payable, 123–124, 162 action, in decision making, 74–76 Ada Support 90–91, 174 Adobe, 190 adoption, timing of, 17 adversarial machine learning, 187–188 advertising, 174–176 biases in, 195–198 effectiveness of, 198–199 gender discrimination and, 196–198 quality of, 198–199 AI. See artificial intelligence (AI) AI canvas, 134–139 AI-first strategy, 179–182 AI Insight, 14 AI moment, 7–8 AI neuroscience, 197–198 Air France Flight 447, 192 airline industry, 168–169, 170 airline pilots, 184–185, 192 airplanes, performance of, 182–183 airport lounges, 105–106 AI winter, 32 Alabama, hybrid corn adoption in, 158–160, 181 Alexa, 1, 2–3 Alibaba, 217, 218 Alipay, 219 AliveCor, 44 Allied bombing raids, WWII, 100–102 AlphaGo, 8, 187, 222 Amazon, 215 AI asset acquisition by, 217 Alexa, 1, 2–3 anticipatory shipping strategy, 16–17, 156–157 Echo, 220 fulfillment at, 105, 143, 144–145 Machine Learning, 203 Picking Challenge, 144 privacy policy, 190 The Americans (TV show), 103 analogies, 99 anticipatory shipping, 16–17, 156–157 Apple, 189–190, 217 Apple Watch, 44–45, 46, 48–49 application ranking, 127–129 artificial intelligence (AI), 31–32.See also tools, AI automation vs., 112 biases in, 195–198 cost reductions in, 7–20 diversity in machines for, 201–202 economics of, 8–9 general, or strong, 133, 221–223 limitations of, 133 machine learning as, 38–40 as magical, 8–9 superintelligent, 221–223 trade-offs with, 4 when to deploy, 184–187 arts, 117 Asimov, Isaac, 115 Atomwise, 134–138 AT&T, 215 autocorrect, 130 automatic teller machines (ATMs), 171–173 automation AI vs., 112 fulfillment and, 105, 143–145 job loss and, 210–212 job redesign and, 141–151 legal requirements for humans with, 115–117 in mining, 112–114 when not to use, 117–118 when to use, 114–117 work flow analysis and, 123–131, 142–145 automobile industry, 169–170, 171.


pages: 523 words: 148,929

Physics of the Future: How Science Will Shape Human Destiny and Our Daily Lives by the Year 2100 by Michio Kaku

agricultural Revolution, AI winter, Albert Einstein, Asilomar, augmented reality, Bill Joy: nanobots, bioinformatics, blue-collar work, British Empire, Brownian motion, cloud computing, Colonization of Mars, DARPA: Urban Challenge, delayed gratification, double helix, Douglas Hofstadter, en.wikipedia.org, friendly AI, Gödel, Escher, Bach, hydrogen economy, I think there is a world market for maybe five computers, industrial robot, Intergovernmental Panel on Climate Change (IPCC), invention of movable type, invention of the telescope, Isaac Newton, John Markoff, John von Neumann, life extension, Louis Pasteur, Mahatma Gandhi, Mars Rover, mass immigration, megacity, Mitch Kapor, Murray Gell-Mann, new economy, oil shale / tar sands, optical character recognition, pattern recognition, planetary scale, postindustrial economy, Ray Kurzweil, refrigerator car, Richard Feynman, Rodney Brooks, Ronald Reagan, Search for Extraterrestrial Intelligence, Silicon Valley, Simon Singh, social intelligence, speech recognition, stem cell, Stephen Hawking, Steve Jobs, telepresence, The Wealth of Nations by Adam Smith, Thomas L Friedman, Thomas Malthus, trade route, Turing machine, uranium enrichment, Vernor Vinge, Wall-E, Walter Mischel, Whole Earth Review, X Prize

Chess-playing machines could not win against a human expert, and could play only chess, nothing more. These early robots were like a one-trick pony, performing just one simple task. In fact, in the 1950s, real breakthroughs were made in AI, but because the progress was vastly overstated and overhyped, a backlash set in. In 1974, under a chorus of rising criticism, the U.S. and British governments cut off funding. The first AI winter set in. Today, AI researcher Paul Abrahams shakes his head when he looks back at those heady times in the 1950s when he was a graduate student at MIT and anything seemed possible. He recalled, “It’s as though a group of people had proposed to build a tower to the moon. Each year they point with pride at how much higher the tower is than it was the previous year. The only trouble is that the moon isn’t getting much closer.”

The Fifth Generation Project’s goal was, among others, to have a computer system that could speak conversational language, have full reasoning ability, and even anticipate what we want, all by the 1990s. Unfortunately, the only thing that the smart truck did was get lost. And the Fifth Generation Project, after much fanfare, was quietly dropped without explanation. Once again, the rhetoric far outpaced the reality. In fact, there were real gains made in AI in the 1980s, but because progress was again overhyped, a second backlash set in, creating the second AI winter, in which funding again dried up and disillusioned people left the field in droves. It became painfully clear that something was missing. In 1992 AI researchers had mixed feelings holding a special celebration in honor of the movie 2001, in which a computer called HAL 9000 runs amok and slaughters the crew of a spaceship. The movie, filmed in 1968, predicted that by 1992 there would be robots that could freely converse with any human on almost any topic and also command a spaceship.

AI researcher Richard Heckler says, “Today, you can buy chess programs for $49 that will beat all but world champions, yet no one thinks they’re intelligent.” But with Moore’s law spewing out new generations of computers every eighteen months, sooner or later the old pessimism of the past generation will be gradually forgotten and a new generation of bright enthusiasts will take over, creating renewed optimism and energy in the once-dormant field. Thirty years after the last AI winter set in, computers have advanced enough so that the new generation of AI researchers are again making hopeful predictions about the future. The time has finally come for AI, say its supporters. This time, it’s for real. The third try is the lucky charm. But if they are right, are humans soon to be obsolete? IS THE BRAIN A DIGITAL COMPUTER? One fundamental problem, as mathematicians now realize, is that they made a crucial error fifty years ago in thinking the brain was analogous to a large digital computer.


pages: 294 words: 96,661

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

AI has developed to a point where it touches our lives several times a day. This has been a long time coming, for progress in AI has slowed to a crawl several times throughout the last few decades as investment in the technology dried up due to disappointment over its slow development. These periods of drought were known as “AI winters,” and they lasted until some new technology or technique rekindled enthusiasm for AI, starting the cycle over again. AI has now proved itself in so many areas that it is unlikely that we will have another AI winter. This surge of progress has led the CEO of IBM, Ginni Rometty, to predict that by 2021 “cognitive AI will impact every decision made.” So how does AI work? Very broadly speaking, there are three different approaches to how to build AI. Let’s say you want to make an AI that tells farmers when to plant their seeds.


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

The late 1950s saw some of the first applied efforts with the writing of the first AI programs: Logic Theorist in 1956 and General Problem Solver in 1957, as well as the development of the AI programming language LISP in 1958. But while advances were made during this heyday, there were also many failures. Finally, in the early 1970s, frustrated with the lack of progress and under political pressure, much government funding was cut off, both in the United States and in Britain. This would become known as the first “AI winter,” a colloquialism that was used to denote those periods of political and corporate disillusionment that led to significant reductions in funding for AI projects. Subsequent periods of boom and bust would plague the field of AI research, but in many ways this was necessary. Just as environmental conditions create pressures in nature that contribute to the natural selection processes of evolution, so too do economic and societal realities contribute to the evolution of technology.

See Access-consciousness AARP (2010 study), 153 Abigail, 3–4, 161–162 Access-consciousness, 242–249, 270 ACLU, 145 adaptive learning technology, 117–118 addictive behaviors and digitized emotion, 220 adrenaline, 186, 221 Affdex, 66, 69 affect, 47 Affect in Speech, 57 Affectiva, 66, 68–72, 118, 275 Affective Computing Company (tACC), 72 Affective Computing (Picard), 47–48, 51 Affective Computing Research Group, Media Lab, 52–54, 57, 60 AI and social experiments, 195–198 AI Watson, 197 “AI winter,” 37–38 AIBO, 200 “AI-human symbiote,” 264 Air Force Research Lab, Wright-Patterson AFB, OH, 128–129 Aldebaran, 82, 112–113, 152 alexithymia, 34 Alone Together (Turkle), 199 AlphaGo, 68, 233 Alzheimer’s disease, 205 AM (deranged supercomputer), 232 American Psychiatric Association’s Diagnostic and Statistical Manual of Mental Disorders (DSM-5), 187 Amin, Wael, 59 amygdala, 19, 34, 221 anterior cingulate cortex (ACC), 19–20, 34, 247 anthropomorphism, 80–81 Apollo Program, 272 Apple, 75 application programming interfaces (APIs), 65, 72 Ardipethicus ramidus, 14 artificial intelligence, 52–53 development of, 35–36 foundations of, 36 term coined, 37 artificial neural networks (ANNs), 66, 251 artificially generated emotions, 102.


pages: 118 words: 35,663

Smart Machines: IBM's Watson and the Era of Cognitive Computing (Columbia Business School Publishing) by John E. Kelly Iii

AI winter, call centre, carbon footprint, crowdsourcing, demand response, discovery of DNA, disruptive innovation, Erik Brynjolfsson, future of work, Geoffrey West, Santa Fe Institute, global supply chain, Internet of things, John von Neumann, Mars Rover, natural language processing, optical character recognition, pattern recognition, planetary scale, RAND corporation, RFID, Richard Feynman, smart grid, smart meter, speech recognition, Turing test, Von Neumann architecture, Watson beat the top human players on Jeopardy!

John McCarthy, a professor at Stanford University, coined the term “artificial intelligence” in 1955, and Marvin Minsky, a professor at MIT, has produced a long series of advances in the field since the 1950s. He’s now focusing on giving machines the ability to perform humanlike commonsense reasoning.6 Today, Andrew Ng of Stanford is leading a team of scientists in an attempt to create algorithms that can learn based on principles that the brain might also employ. The field of artificial intelligence has advanced in starts and stops. Periods of soaring optimism have been followed by so-called AI winters, when seemingly promising avenues of research failed to produce the anticipated results. Put simply, this is hard stuff. So it’s no surprise that when Charles Lickel proposed Jeopardy! as the next grand challenge, his suggestion was met initially with reactions ranging from skepticism to outright derision. But he quickly won Paul Horn’s support. Paul thought the project could be very exciting—both to computer scientists and the public at large.7 In mid-2006 Charles gave the go-ahead to researcher David Ferrucci, who was an enthusiastic evangelist for the project, to explore whether building a machine that could win on Jeopardy!


pages: 331 words: 104,366

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

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

The Berlin Wall had fallen, the USSR wasn’t far behind, and a whole new world of challenges and opportunities was opening up for me and for chess. Machines would become an exciting part of this new era. Right around when Deep Thought became the first chess machine to become a real threat to Grandmasters at the end of the 1980s, artificial intelligence was experiencing a broad resurgence in the scientific and business worlds. The so-called AI winter that had descended after years of overpromising and subsequent disillusionment was lifting. The crisis for AI stemmed from the evaporation of the confidence so many experts in the 1970s had had in quickly discovering the secrets of cognition. Research projects and commercial AI ventures were shuttered throughout the 1980s and the AI movement had splintered. Basic science was out, practical systems were in.

Asimov’s Three Laws of Robotics: “A robot may not injure a human being or, through inaction, allow a human being to come to harm. A robot must obey the orders given it by human beings except where such orders would conflict with the First Law. A robot must protect its own existence as long as such protection does not conflict with the First or Second Laws.” Isaac Asimov, I, Robot (New York: Gnome Press, 1950). INDEX Adams, Douglas, 69, 70 Advanced Chess, 244–246 AI (artificial intelligence) AI winter/end, 95 ARPA/DARPA and, 96–99 attitudes towards, 9, 71, 255, 256–258 chess as Drosophila of, 74, 230, 234 context and, 75–77, 101–102 costs/limited resources and, 252, 253 descriptions/examples, 51, 75, 77 evolution, 51, 95–96 future and, 249–259 glitches/problems, 102–103 humans losing control (“the singularity”), 255, 256–258 language and, 99–100, 101, 102 movies and, 100–101, 255 optimism/expectations, 95–96, 99 origins and, 96–99 profits and, 103–104 questions and, 69–71, 72 “term” origins, 74 Turing’s idea of, 75 See also chess machines; computers; specific machines; technology/automation “AI effect,” 251–252 AI Magazine, 90 Alekhine, Alexander, 156 Aleksander, Igor, 222 “analyzing to the result,” 172–173 Anand, Viswanathan, 12, 47, 124, 131, 149, 171, 182, 235–238 anchoring effect, 242–244 anti-computer movement, 231 Apollo program, 36 ARPANET/Internet origins, 96–99 Ariely, Dan, 240 Aristotle, 27–28, 225 ARPA (Advanced Research Projects Agency) becoming/as DARPA, 97–99 Internet/World Wide Web origins and, 96–99 Ashley, Maurice, 165, 174, 189, 196, 198, 212 Asimov, Isaac, 249–250, 257 Atkin, Larry, 66 Atlantic, 103 Awonder Liang, 84 Babbage, Charles, 27, 152 Ban, Amir, 127 Behind Deep Blue (Hsu), 209 Belle, 38, 39, 63–64, 74, 89, 108, 133, 151, 153 Benjamin, Joel, 133, 136, 142, 143, 147, 160, 163, 189, 196, 219 Berliner, Hans, 39, 90–91, 98 Binet, Alfred, 74, 82 Blaine, David, 170–171 Bogart, Humphrey, 16 Bolt, Usain, 47 Bonaparte, Napoleon, 4, 11 Bostrom, Nick, 252, 256, 258 Botvinnik, Mikhail, 5, 20, 23, 52, 137, 156–158, 159, 175, 244 brain/human cognition understanding, 27–28 brain plasticity, 153 brain scans of chess players/results, 13–14 Breaking Dawn (movie), 16 Bronson, Charles, 218 Bronstein, David, 52, 54, 156 Brooks, David, 224 Browne, Walter, 206, 232 Brynjolfsson, Erik, 134 Bulletin of the Atomic Scientists, 45 Bushinsky, Shay, 127, 208, 209 Byrne, Robert, 175, 203 BYTE magazine, 56 Cameron, James, 255 Campbell, Murray, 39 Campbell, Murray after Deep Blue, 219 Deep Thought/Deep Blue and, 39, 64, 109, 115, 125, 139, 142, 143, 158, 160, 167, 175–176, 178, 179, 196, 210, 219 Carlsen, Magnus, 50, 66, 232, 233 Caxton, William, 11 chaturanga, 11 checkers machines, 73 chess age of GMs, 231–232 analysis of games, 136–138, 139 benefits for adults, 153 benefits for children, 152–153 cheaters and, 4 computer effects and, 50 democratizing, 49 descriptions, 17–18 drawing of lots, 170–171 draws/draw offer and, 145–147 as Drosophila of AI, 74, 230, 234 feuds/disputes and, 197–198 flattery of, 15–16 forced moves, 31 Internet effects, 61–62 joke, 186–187 movies and, 16–17 opening books importance (summary), 105–107 opening novelties, 62, 105–106, 181–182, 183, 198, 227–228, 237, 238 origins/evolution, 11–13 phases of game, 13–14 popularity, 8–9, 12, 19 ranking/rating players, 50, 66 rematches/overconfidence and, 157–158 rematches/rematch clause, 156–158 replying instantly vs. pausing, 182–183 “sharp positions” and, 144, 145 simultaneous blindfold play, 15–16 sports/comparisons and, 79–81 status of, 11 stereotype in West, 19 terminology, 66, 114 terminology for fast games, 114 chess machines “AI effect” and, 251–252 creators/programmers attitudes, 90–92 descriptions, 2, 48, 53, 81–82, 110, 111, 115, 116 future and, 73 glitches, 111 goal setting and, 121 “horizon effect” and, 87, 88 human/machine differences, 166, 168, 169, 177, 182–184, 186, 203–204, 210 human/machine partnerships and, 3, 235–238, 242–247 material value and, 37, 52–53, 72, 175 opening books (summary), 105, 106–107 “personalities,” 107 programmers style and, 126–127 public perception/change and, 93 speed and, 53–54, 116 sport vs. science, 108 tablebases and, 204–208 See also specific machines chess machines evolution alpha-beta search algorithm and, 38, 74, 99 chess selection reasons, 27, 28–30, 74, 99 descriptions/improvements, 3–5, 25–39, 54–56, 65–67, 74, 106–108 forecasts and, 36, 63–64 minimax algorithm and, 29–30, 38 Monte Carlo tree search, 121 move selection and, 29–33 “null move” technique, 120–121 number of possible moves and, 34, 35 programming improvements and, 37, 38 speed and, 36–37, 38, 53–54 times for moves and, 33–34, 35 Type A/Type B search techniques, 30–31, 33–35, 54–55, 63–64, 65 chess machines/humans playing anti-computer strategy, 66, 86, 88, 231 crashes/programmed crashes and effects, 208–209 energy use differences, 166 first game (with GM), 51–52 human adaptations and, 143, 144, 147, 148 human/machine differences, 86–87, 120–122, 143, 144, 147, 148, 166, 172, 208 human mind and, 120–122, 148, 172 human reactions and, 91–92 human strategies (summary), 86–92, 120–122 humans/children learning, 226–228, 230–234 learning, 230–234 technical problems/distractions and ethics, 130, 135, 142, 144–145, 169, 176, 177, 187–188, 198–200, 208–210 Turk machine (hoax), 3–4, 181, 199 See also specific individuals; specific machines chess machines/Kasparov attitude towards, 1, 5–7, 47, 124–125 Chess Genius (1994/1995), 123–124, 126 Deep Junior, 37, 67, 207, 254 Deep Thought and, 104, 107–112, 114, 115–116, 132, 139–140, 154, 162 Fritz, 122–123, 126, 127–128, 142 Hamburg “simuls” (1985), 1–2 preparation and, 127 summaries, 23, 37, 86 timing and, 63, 86 See also Deep Blue/Kasparov chess players control and, 17–18 defeat/recovery and, 80 “genius” label and, 14–15 geographic diversity of elite players, 233–234 innate talent/practice and, 49, 74, 82–85 negative stereotypes and, 17, 18–19, 21–22 “positional play,” 88–89 producing elite talent and, 49, 231–234 psychology and, 80–82, 86–87 See also specific individuals ChessBase, 48–49, 59, 62, 116, 131, 174, 178–179, 180 Clarke, Arthur C., 17, 18 Common Sense in Chess (Lasker), 81 computers analysis of human behavior and, 116 capabilities (PCs/early 1990s), 119 chess and, 119, 126, 153 chess preparation and, 59–60 Hopper game, 57–58 joke about, 69 predictions on, 25 “Turing test” and, 4, 18, 115–116 See also AI (artificial intelligence); chess machines; specific individuals Computers, Chess, and Cognition, 74 Condon, Joe, 63, 89 corporation ethics, 200 Cramton, Steven, 246 creativity/innovation Bell Labs and, 151 Belle machine and, 153 brain plasticity and, 153 diverse activities and, 153 education and, 234–235 “human plus machine” and, 228–235 imitation vs., 229–230 optimization and, 151–152 See also specific innovations Crook, Nigel/Artie (robot), 71 Cybernetics (Wiener), 29 DARPA (Defense Advanced Research Projects Agency), 97–99 de Groot, Adriaan, 74, 82 decision making anchoring effect, 240–242 chess and, 242–247 emotional state and, 238–239 “human plus machine” overview, 235–248 intuition and, 239–242 Deep Blue in 1990s, 37 Belle and, 151 computer chess programs vs.


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

And the same was true for the more pedestrian ambitions of getting machines to perform specific tasks. Despite all the efforts, machines could not beat a top player at chess. They could not translate more than a handful of sentences or identify anything but the simplest objects. And the story was the same for a great many other tasks, too. As progress faltered, researchers found themselves at a dead end. The late 1980s became known as the “AI winter”: funding dried up, research slowed, and interest in the field fell away. The first wave of AI, that had raised so many hopes, ended in failure. THE SECOND WAVE OF AI Things started looking up again for AI in 1997. That’s when a system called Deep Blue, owned by IBM, beat Garry Kasparov, then the world chess champion. It was a remarkable achievement, but even more remarkable was how the system did it.

In 2017, Tesla’s CEO Elon Musk expressed his hope that car production in the future will be so highly automated that “air friction” faced by robots would be a significant limiting factor.72 Just a few months later, under pressure as Tesla failed to meet production targets, he sheepishly tweeted, “yes, excessive automation at Tesla was a mistake.”73 However, to dwell for too long on any particular omission or exaggeration is to miss the bigger picture: machines are gradually encroaching on more and more tasks that, in the past, had required a rich range of human capabilities. Of course, this has not been a perfectly steady process. Over the years task encroachment has fallen into fallow periods when new obstacles were encountered, surged forward when limits to automation were overcome. Such ebb and flow will surely happen in the future as well. Perhaps new AI winters lie ahead, as today’s more feverish enthusiasms about new technologies collide with their actual limitations. But like past limits, many of these will fall away as fresh solutions and work-arounds are fashioned. Economists are wary of labeling any empirical regularity a “rule” or a “law,” but task encroachment has proven as law-like as any historical phenomenon can be. Barring catastrophe—nuclear war, perhaps, or widespread environmental collapse—it seems certain to continue.


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

Other highlights—and there are many—are noted in the timeline (Table 4.2). Those technologies, despite their prominence today, were not the heart of AI for the first decades of its existence. The field barreled along on the basis of logic-based expert systems, when pessimism took hold among computer scientists, who recognized that the tools weren’t working. That souring, along with a serious reduction of research output and grant support, led to the “AI winter,” as it became known, which lasted about twenty years. It started to come out of hibernation when the term “deep learning” was coined by Rina Dechter in 1986 and later popularized by Geoffrey Hinton, Yann LeCun and Yoshua Bengio. By the late 1980s, multilayered or deep neural networks (DNN) were gaining considerable interest, and the field came back to life. A seminal Nature paper in 1986 by David Rumelhart and Geoffrey Hinton on backpropagation provided an algorithmic method for automatic error correction in neural networks and reignited interest in the field.15 It turned out this was the heart of deep learning, adjusting the weights of the neurons of prior layers to achieve maximal accuracy for the network output.

chapter five DEEP LIABILITIES AIs are nowhere near as smart as a rat. —YANN LECUN I often tell my students not to be misled by the name “artificial intelligence”—there is nothing artificial about it. AI is made by humans, intended to behave by humans, and, ultimately, to impact human lives and human society. —FEI-FEI LI WHEN I VISITED FEI-FEI LI AT GOOGLE NEAR THE END OF 2017, with AI hype seemingly near its peak, she suggested that we may need another AI winter to get things to cool off, to do reality testing, and to “bubble wrap” the progress that has been made. There’s no question that we’ve seen hyperbole across the board, with forecasts of imminent doom, massive job loss, and replacement of doctors, just to name a few. But when I thought about and researched all the negative issues related to AI, I realized that I could write a whole book on this topic.


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

According to a 1979 collection of anecdotes, for example, researchers gave their English-to-Russian translation utility the phrase “The spirit is willing, but the flesh is weak.” The program responded with the Russian equivalent of “The whisky is agreeable, but the meat has gone bad.” This story is probably apocryphal, but it’s not an exaggeration. As a group, symbolic AI systems generated deeply underwhelming results, so much so that by the late 1980s, an “AI winter” had descended over the field as major corporate and governmental sources of research funding dried up. Over-ruled What explains the broad failure of symbolic approaches to AI? There are two main obstacles. One poses serious challenges for the field, and the other is apparently insurmountable. First, to put it simply, there are a lot of rules in the world, as adult language learners well know, and it’s generally not enough to know and follow most of them.

The promised breakthroughs did not come quickly, however, and in 1969 Marvin Minsky and Seymour Papert published a devastating critique titled Perceptrons: An Introduction to Computational Geometry. They showed mathematically that Rosenblatt’s design was incapable of accomplishing some basic classification tasks. For most in the field of artificial intelligence, this was enough to get them to turn away not only from Perceptrons, but from the broader concepts of neural networks and machine learning in general. The AI winter descended on both camps of researchers. Persistence with Perceptrons Pays Off A few teams carried on with machine learning because they remained convinced that the right way to get computers to think in humanlike ways was to build brain-inspired neural networks that could learn by example. These researchers came to understand and overcome the limitations of the Perceptron. They did this with a combination of sophisticated math, ever-more-powerful computer hardware, and a pragmatic approach that allowed them to take inspiration from how the brain works but not to be constrained by it.


pages: 742 words: 137,937

The Future of the Professions: How Technology Will Transform the Work of Human Experts by Richard Susskind, Daniel Susskind

23andMe, 3D printing, additive manufacturing, AI winter, Albert Einstein, Amazon Mechanical Turk, Amazon Web Services, Andrew Keen, Atul Gawande, Automated Insights, autonomous vehicles, Big bang: deregulation of the City of London, big data - Walmart - Pop Tarts, Bill Joy: nanobots, business process, business process outsourcing, Cass Sunstein, Checklist Manifesto, Clapham omnibus, Clayton Christensen, clean water, cloud computing, commoditize, computer age, Computer Numeric Control, computer vision, conceptual framework, corporate governance, creative destruction, crowdsourcing, Daniel Kahneman / Amos Tversky, death of newspapers, disintermediation, Douglas Hofstadter, en.wikipedia.org, Erik Brynjolfsson, Filter Bubble, full employment, future of work, Google Glasses, Google X / Alphabet X, Hacker Ethic, industrial robot, informal economy, information retrieval, interchangeable parts, Internet of things, Isaac Newton, James Hargreaves, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Joseph Schumpeter, Khan Academy, knowledge economy, lifelogging, lump of labour, Marshall McLuhan, Metcalfe’s law, Narrative Science, natural language processing, Network effects, optical character recognition, Paul Samuelson, personalized medicine, pre–internet, Ray Kurzweil, Richard Feynman, Second Machine Age, self-driving car, semantic web, Shoshana Zuboff, Skype, social web, speech recognition, spinning jenny, strong AI, supply-chain management, telepresence, The Future of Employment, the market place, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, transaction costs, Turing test, Watson beat the top human players on Jeopardy!, WikiLeaks, young professional

What was most remarkable, although we say so ourselves, was that we managed to build a legal problem-solver that was in significant respects a better performer than the lawyer (the domain expert) on the basis of whose knowledge it was built.105 After that project we broadened our interest and worked on expert systems in tax as well as systems that were for use by auditors. Here again, we were heavily involved in the development of systems that could undertake expert tasks at a high level.106 At the same time, we also kept close to parallel advances in medicine where substantial progress was being made. These early successes generated much excitement. Then came what is often referred to as the ‘AI winter’, the period during which AI seemed to stall. In the professions, certainly, thirty years on, there are far fewer operational expert systems of the sort we developed than we had expected. What went wrong? Why have so few expert systems in law, tax, and audit emerged since then? Why was this great early promise not fulfilled?107 One reason for the lack of uptake was commercial—these systems were very costly to develop (hugely time-consuming for the experts whose knowledge went into the systems), at a time when law and accounting firms were increasingly profitable and saw no reason to embrace innovative technologies that might undermine their winning streak.

These systems will provide high-quality advice and guidance, but not by reasoning or working in the same way as skilled specialists; nor by seeking to model human thoughts and reasoning processes; nor again by having common sense or general knowledge. These systems are high-performing but are not intelligent in the way that human beings are (we expand on this in section 7.1). On this view, we need to reappraise AI. For many commentators, the AI winter was a euphemism for AI’s demise. But it transpires that AI has not been expiring. It has instead been hibernating, conserving its energy, as it were, ticking over quietly in the background, waiting for enabling technologies to emerge and catch up with some of the original aspirations of the early AI scientists. In the thaw that has followed the winter, over the past few years, we have seen a series of significant developments—Big Data, Watson, robotics, and affective computing—that we believe point to a second wave of AI.


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

Herbert Simon said in 1965 that “machines will be capable, within twenty years, of doing any work a man can do,”[lxii] and two years later Marvin Minksy said that “Within a generation ... the problem of creating 'artificial intelligence' will substantially be solved.”[lxiii] These early claims turned out to be ill-founded, and later generations of researchers found their sources of funding dried up in so-called AI winters. Some leading figures in the field today are worried that a similar fate may befall them because, they say, excessive claims are being made about the capabilities of AI systems today, and what can be achieved in the short term. This seems to me an ungrounded fear. Machine intelligence is the target of enormous investments – by technology giants like Google and Facebook, by startups, by traditional companies like the automotive manufacturers, and by governments.


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

An attempt will be made to find how to make machines that use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves. We think that a significant advance can be made in one or more of these problems if a carefully selected group of scientists work on it together for a summer. This sort of hubris has been an intermittent characteristic of AI research, and has led to a series of “AI winters”—periods of drastically decreased funding following bursts of intense enthusiasm about the imminent solution to some or other problem which then turned out to be much more complicated than imagined. Repeated patterns, through the decades, of overpromising and underdelivering had led to a culture within AI whereby researchers were reluctant to look too far ahead. And this in turn has led to a difficulty in getting the field to engage seriously with the question of existential risk.


pages: 304 words: 125,363

Successful Lisp - About by Unknown

AI winter, general-purpose programming language, Paul Graham, Richard Stallman

If your own Lisp experience predates 1985 or so, you probably share this view. But in 1984, the year Big Brother never really became a reality (did it?), the year that the first bleeding-edge (but pathetic by today's standards) Macintosh started volume shipments, the Lisp world started changing. Unfortunately, most programmers never noticed; Lisp's fortune was tied to AI, which was undergoing a precipitous decline -- The AI Winter -- just as Lisp was coming of age. Some say this was bad luck for Lisp. I look at the resurgence of interest in other dynamic languages and the problems wrestled with by practicioners and vendors alike, and wonder whether Lisp wasn't too far ahead of its time. I changed my opinion of Lisp over the years, to the point where it's not only my favorite progamming language, but also a way of structuring much of my thinking about programming.


pages: 301 words: 85,263

New Dark Age: Technology and the End of the Future by James Bridle

AI winter, Airbnb, Alfred Russel Wallace, Automated Insights, autonomous vehicles, back-to-the-land, Benoit Mandelbrot, Bernie Sanders, bitcoin, British Empire, Brownian motion, Buckminster Fuller, Capital in the Twenty-First Century by Thomas Piketty, carbon footprint, cognitive bias, cognitive dissonance, combinatorial explosion, computer vision, congestion charging, cryptocurrency, data is the new oil, Donald Trump, Douglas Engelbart, Douglas Engelbart, Douglas Hofstadter, drone strike, Edward Snowden, fear of failure, Flash crash, Google Earth, Haber-Bosch Process, hive mind, income inequality, informal economy, Internet of things, Isaac Newton, John von Neumann, Julian Assange, Kickstarter, late capitalism, lone genius, mandelbrot fractal, meta analysis, meta-analysis, Minecraft, mutually assured destruction, natural language processing, Network effects, oil shock, p-value, pattern recognition, peak oil, recommendation engine, road to serfdom, Robert Mercer, Ronald Reagan, self-driving car, Silicon Valley, Silicon Valley ideology, Skype, social graph, sorting algorithm, South China Sea, speech recognition, Spread Networks laid a new fibre optics cable between New York and Chicago, stem cell, Stuxnet, technoutopianism, the built environment, the scientific method, Uber for X, undersea cable, University of East Anglia, uranium enrichment, Vannevar Bush, WikiLeaks

This idea was attacked by numerous researchers over the next decade, who held that intelligence was the product of the manipulation of symbols: essentially, some knowledge of the world was required to reason meaningfully about it. This debate between connectionists and symbolists was to define the artificial intelligence field for the next forty years, leading to numerous fallings out, and the notorious ‘AI winters’ in which no progress was made at all for many years. At heart, it was not merely a debate about what it means to be intelligent, but what is intelligible about intelligence. One of the more surprising advocates of early connectionism was Friedrich Hayek, best known today as the father of neoliberalism. Forgotten for many years, but making a recent comeback among Austrian-inclined neuroscientists, Hayek wrote The Sensory Order: An Inquiry into the Foundations of Theoretical Psychology in 1952, based on ideas he’d formulated in the 1920s.


pages: 324 words: 80,217

The Decadent Society: How We Became the Victims of Our Own Success by Ross Douthat

Affordable Care Act / Obamacare, AI winter, Bernie Sanders, bitcoin, Burning Man, Capital in the Twenty-First Century by Thomas Piketty, centre right, charter city, crack epidemic, crowdsourcing, David Graeber, Deng Xiaoping, Donald Trump, East Village, Elon Musk, Flynn Effect, Francis Fukuyama: the end of history, Francisco Pizarro, ghettoisation, gig economy, Haight Ashbury, helicopter parent, hive mind, Hyperloop, immigration reform, informal economy, Intergovernmental Panel on Climate Change (IPCC), Islamic Golden Age, Jeff Bezos, Joan Didion, Kevin Kelly, Kickstarter, knowledge worker, life extension, mass immigration, mass incarceration, means of production, megacity, move fast and break things, move fast and break things, multiplanetary species, New Journalism, Nicholas Carr, Norman Mailer, obamacare, Oculus Rift, open borders, out of africa, Panopticon Jeremy Bentham, Peter Thiel, plutocrats, Plutocrats, pre–internet, QAnon, quantitative easing, rent-seeking, Robert Bork, Robert Gordon, Ronald Reagan, secular stagnation, self-driving car, Silicon Valley, Silicon Valley ideology, Snapchat, social web, Steve Jobs, Steven Pinker, technoutopianism, the built environment, The Rise and Fall of American Growth, Tyler Cowen: Great Stagnation, wage slave, women in the workforce, Y2K

Meanwhile, travel speeds have dropped and transportation innovations have been delayed and delayed, and while we may get driverless cars in some form eventually (albeit perhaps a form that can’t drive in the rain or snow), the harder limits on machine intelligence have not yet been overcome. Indeed, beneath the hype and scaremongering about artificial intelligence, there is just as much evidence that we’re actually headed for an “AI winter”—in which research funding and public interest both dry up—as there is that we’ll all be in thrall to a superintelligence soon enough. Robots have obviously taken some jobs, with particularly disruptive consequences in some industries, and no doubt they will take more. But a society being fundamentally transformed by automation would have sharp productivity growth, of the kind we used to have and briefly enjoyed in the Internet’s first flower, rather than the productivity stagnation afflicting both the United States and Europe.


pages: 245 words: 83,272

Artificial Unintelligence: How Computers Misunderstand the World by Meredith Broussard

1960s counterculture, A Declaration of the Independence of Cyberspace, Ada Lovelace, AI winter, Airbnb, Amazon Web Services, autonomous vehicles, availability heuristic, barriers to entry, Bernie Sanders, bitcoin, Buckminster Fuller, Chris Urmson, Clayton Christensen, cloud computing, cognitive bias, complexity theory, computer vision, crowdsourcing, Danny Hillis, DARPA: Urban Challenge, digital map, disruptive innovation, Donald Trump, Douglas Engelbart, easy for humans, difficult for computers, Electric Kool-Aid Acid Test, Elon Musk, Firefox, gig economy, global supply chain, Google Glasses, Google X / Alphabet X, Hacker Ethic, Jaron Lanier, Jeff Bezos, John von Neumann, Joi Ito, Joseph-Marie Jacquard, life extension, Lyft, Mark Zuckerberg, mass incarceration, Minecraft, minimum viable product, Mother of all demos, move fast and break things, move fast and break things, Nate Silver, natural language processing, PageRank, payday loans, paypal mafia, performance metric, Peter Thiel, price discrimination, Ray Kurzweil, ride hailing / ride sharing, Ross Ulbricht, Saturday Night Live, school choice, self-driving car, Silicon Valley, speech recognition, statistical model, Steve Jobs, Steven Levy, Stewart Brand, Tesla Model S, the High Line, The Signal and the Noise by Nate Silver, theory of mind, Travis Kalanick, Turing test, Uber for X, uber lyft, Watson beat the top human players on Jeopardy!, Whole Earth Catalog, women in the workforce

I’m going to try to bring some clarity to the situation by defining machine learning and showing you an example of how someone might perform machine learning on a dataset. I’m going to explain machine learning a few different ways and also demonstrate some code. It’s going to get technical. If the technical parts get confusing, don’t worry; you can skim them first and return to them later. AI enjoyed a popularity bump in 2017 in contrast to many years of what people call an AI winter. In the mainstream, people mostly ignored AI for the first decade of the 2000s. The Internet was the popular thing technologically, then mobile devices, and those were the focus of our collective imagination. In the middle of the 2010s, however, people started talking about machine learning. Suddenly, AI was on fire again. AI startups were founded and acquired. IBM’s Watson beat a human player at Jeopardy!


pages: 339 words: 94,769

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

And I realized that all were engaged in writing a genre of book both unnamed and unrecognized by New York publishers. Since I had an MBA from Columbia Business School and a series of relative successes in business, I was dragooned into becoming an agent, initially for Gregory Bateson and John Lilly, whose books I sold quickly, and for sums that caught my attention, thus kick-starting my career as a literary agent. I never did meet Richard Feynman. THE LONG AI WINTERS This new career put me in close touch with most of the AI pioneers, and over the decades I rode with them on waves of enthusiasm, and into valleys of disappointment. In the early eighties the Japanese government mounted a national effort to advance AI. They called it the Fifth Generation; their goal was to change the architecture of computation by breaking “the von Neumann bottleneck” by creating a massively parallel computer.


pages: 411 words: 98,128

Bezonomics: How Amazon Is Changing Our Lives and What the World's Best Companies Are Learning From It by Brian Dumaine

activist fund / activist shareholder / activist investor, AI winter, Airbnb, Amazon Web Services, Atul Gawande, autonomous vehicles, basic income, Bernie Sanders, Black Swan, call centre, Chris Urmson, cloud computing, corporate raider, creative destruction, Danny Hillis, Donald Trump, Elon Musk, Erik Brynjolfsson, future of work, gig economy, Google Glasses, Google X / Alphabet X, income inequality, industrial robot, Internet of things, Jeff Bezos, job automation, Joseph Schumpeter, Kevin Kelly, Lyft, Marc Andreessen, Mark Zuckerberg, money market fund, natural language processing, pets.com, plutocrats, Plutocrats, race to the bottom, ride hailing / ride sharing, Sand Hill Road, self-driving car, shareholder value, Silicon Valley, Silicon Valley startup, Snapchat, speech recognition, Steve Jobs, Stewart Brand, supply-chain management, Tim Cook: Apple, too big to fail, Travis Kalanick, Uber and Lyft, uber lyft, universal basic income, wealth creators, web application, Whole Earth Catalog

By the 1980s, talking dolls, such as Worlds of Wonder’s Julie, could respond to simple questions from a child, but it wasn’t until the next decade that the first serious speech recognition software hit the market. A product called Dragon could process simple speech without the speaker having to pause awkwardly between each word. Despite this progress, over the next two decades, voice recognition as well as other types of AI programming largely disappointed its supporters, periodically entering into what the academic community dubbed AI winters—periods when progress and funding would dry up. The root cause wasn’t that scientists didn’t know how to write clever programs. It was that these AI programs required tremendous amounts of rare and expensive computing power. The fortunes of this technology changed as Moore’s law—which posits that computer processing power and speed doubles every two years—made crunching the mountains of data necessary for voice AI more affordable.


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

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

Rather than write a program based on logic and heuristics to handle all possible board positions, an impossible task given that there are 1020 possible backgammon board positions, we had the network learn to play through pattern recognition by watching a teacher play.10 Gerry went on to create the first backgammon program that played at world-championship levels by having the backgammon network play itself (a story that will be told in chapter 10). After my lecture, I learned that there was a front page article in the New York Times that morning about how government agencies were slashing The Rebirth of Artificial Intelligence 35 funding for artificial intelligence. Although this was the beginning of an AI winter for mainstream researchers, it didn’t affect me or the rest of my group, for whom the neural network spring had just begun. But our new approach to AI would take twenty-five years to deliver realworld applications in vision, speech, and language. Even in 1989, I should have known it would take this long. In 1978, when I was a graduate student at Princeton, I extrapolated Moore’s law for the exponential increase in computing power, doubling every 18 months, to see how long it would take to reach brain levels of computing power and concluded it would happen in 2015.


pages: 374 words: 111,284

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

"Robert Solow", 3D printing, agricultural Revolution, AI winter, Albert Einstein, anti-work, autonomous vehicles, basic income, Ben Bernanke: helicopter money, Bernie Sanders, blockchain, call centre, Capital in the Twenty-First Century by Thomas Piketty, Chris Urmson, computer age, conceptual framework, corporate governance, correlation does not imply causation, creative destruction, David Ricardo: comparative advantage, deindustrialization, deskilling, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, everywhere but in the productivity statistics, facts on the ground, financial intermediation, full employment, future of work, income inequality, income per capita, industrial robot, Internet of things, invention of the wheel, Isaac Newton, James Watt: steam engine, Jeff Bezos, job automation, job satisfaction, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, Joseph Schumpeter, Kevin Kelly, license plate recognition, Marc Andreessen, Mark Zuckerberg, market bubble, mega-rich, natural language processing, Network effects, new economy, Nicholas Carr, Paul Samuelson, Peter Thiel, positional goods, quantitative easing, RAND corporation, Ray Kurzweil, Richard Florida, ride hailing / ride sharing, rising living standards, road to serfdom, Robert Gordon, Robert Shiller, Robert Shiller, Second Machine Age, secular stagnation, self-driving car, Silicon Valley, Simon Kuznets, Skype, social intelligence, spinning jenny, Stanislav Petrov, Stephen Hawking, Steven Pinker, technological singularity, The Future of Employment, The Wealth of Nations by Adam Smith, Thomas Malthus, trade route, universal basic income, US Airways Flight 1549, Vernor Vinge, Watson beat the top human players on Jeopardy!, We wanted flying cars, instead we got 140 characters, wealth creators, winner-take-all economy, Y2K, Yogi Berra

These analysts are like latter-day Malthuses – only in reverse. (Mind you, their other achievements belie this comparison.) Indeed, the history of AI research is one of alternate periods of feast and famine as the experience of success in some field leads to a flood of investment, followed by failure, leading to funding being cut, or even drying up altogether. Periods of the latter are referred to as “AI winters.” In fact, machines that matched humans in general intelligence have been anticipated since the invention of computers in the 1940s. At that time, and at all times subsequently, this development was thought likely to occur about 20 years into the future. But the expected arrival date has been receding at the rate of about one year per year. Accordingly, many futurists still put the arrival date of machine general intelligence equivalent to humans 20 years into the future.26 The Ridley Scott film Blade Runner, which depicted a dystopian future in which artificial life forms beat humans in both strength and intelligence, appeared in 1982.


pages: 481 words: 125,946

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

However, in recent years the climate for ambitious artificial intelligence research has much improved, no doubt due to a string of stunning successes in the field. Not only have a number of longstanding challenges finally been met but there’s a growing sense among the community that the best is yet to come. We see this in our interactions with a wide range of researchers, and it can also be seen from the way in which media articles about artificial intelligence have changed in tone. If you hadn’t already noticed, the AI Winter is over and the AI Spring has begun. As with many trends, some people are a little too optimistic about the rate of progress, going as far as predicting that a solution to human-level artificial intelligence might be just around the corner. It’s not. Furthermore, given the negative portrayals of futuristic artificial intelligence in Hollywood, it’s perhaps not surprising that doomsday images still appear with some frequency in the media.


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

Funding bodies want to see results, and AI research only seemed to promise ever more difficult questions. Researchers began to actively avoided the term artificial intelligence and focus instead on solving more practical problems. True AI is only actually useful if it can solve real problems. SHRDLU may have been interesting, but at the end of the day it was quite useless. The resulting period is referred to as the “AI Winter”. One of Lilienthal’s controlled flights, 1890’s Public expired Historically, the same problem was encountered by early aviation pioneers. In 1891 Otto Lilienthal started to successfully fly his hang gliders. He made thousands of flights and could stay aloft in the wind for extended periods of time, often moving around to find the best positions to be photographed from. Lilienthal actively encouraged others to join him in the quest for flight, but nobody was interested.


pages: 482 words: 121,173

Tools and Weapons: The Promise and the Peril of the Digital Age by Brad Smith, Carol Ann Browne

Affordable Care Act / Obamacare, AI winter, airport security, Albert Einstein, augmented reality, autonomous vehicles, barriers to entry, Berlin Wall, Boeing 737 MAX, business process, call centre, Celtic Tiger, chief data officer, cloud computing, computer vision, corporate social responsibility, Donald Trump, Edward Snowden, en.wikipedia.org, immigration reform, income inequality, Internet of things, invention of movable type, invention of the telephone, Jeff Bezos, Mark Zuckerberg, minimum viable product, national security letter, natural language processing, Network effects, new economy, pattern recognition, precision agriculture, race to the bottom, ransomware, Ronald Reagan, Rubik’s Cube, school vouchers, self-driving car, Shoshana Zuboff, Silicon Valley, Skype, speech recognition, Steve Ballmer, Steve Jobs, The Rise and Fall of American Growth, Tim Cook: Apple, WikiLeaks, women in the workforce

And third, “A robot must protect its own existence as long as such protection does not conflict with the First or Second Law.” Isaac Asimov, “Runaround,” in I, Robot (New York: Doubleday, 1950). Back to note reference 2. In 1984–87, the focus was on advances in “expert systems” and their application to medicine, engineering, and science. There were even special computers made and built for AI. This was followed by a collapse and an “AI Winter,” as it was called, for several years into the mid-1990s. Back to note reference 3. W. Xiong, J. Droppo, X. Huang, F. Seide, M. Seltzer, A. Stolcke, D. Yu, and G. Zweing, Achieving Human Parity in Conversational Speech Recognition: Microsoft Research Technical Report MSR-TR-2016-71, February 2017, https://arxiv.org/pdf/1610.05256.pdf. Back to note reference 4. Terrence J.


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

Probably most people will do both. For example, I’m a little surprised at this workshop that there is not a larger contingent of the brain modeling crowd. I hope we will by next year’s conference, or workshop. For example, Henry Markram, the Swiss IBM, the Blue Brain project, that kind of thing. So I hope at the next workshop there will be a bigger contingent of that aspect. Obstacles? Well you just talked about AI Winters and Summers. I don’t know how many cycles it’s been through. Is it three, five? Is there a lesson to be learnt there? I guess one obvious one is just ignorance. Even if you take a purely engineering approach, how the hell do you build an intelligent machine? My sense is we don’t even know what the target is; we don’t even know how difficult it is to do that thing. So it’s very hard to figure out how long it’s going to take to get there.


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

In October 2017, Saudi Arabia granted “citizenship” to a humanoid robot named Sophia.1 This move was derided by commentators as a cynical media stunt and a particularly hypocritical act for a country which grants only limited rights to human women.2 Even so, the episode is significant because it was the first time that a country purported to grant a robot or AI entity any form of legal personality in its own right. Just days after the Saudi announcement, Tokyo’s Shibuya district announced that an AI system had been granted “residency”.3 In a seminal 1992 article, Lawrence B. Solum proposed a form of legal personhood4 for AI.5 When that paper was written, the world was still in the midst of the second “AI Winter”: a period when setbacks in AI development coupled with a lack of funding contributed to a period of relatively slow growth.6 For the following two decades, Solum’s ideas remained a mere thought experiment. Given the recent developments in the capabilities of AI and its growing use, it is now an appropriate time to reconsider this proposal.7 Legal personality for AI is no longer just a matter for academic debate.


pages: 720 words: 197,129

The Innovators: How a Group of Inventors, Hackers, Geniuses and Geeks Created the Digital Revolution by Walter Isaacson

1960s counterculture, Ada Lovelace, AI winter, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Albert Einstein, AltaVista, Apple II, augmented reality, back-to-the-land, beat the dealer, Bill Gates: Altair 8800, bitcoin, Bob Noyce, Buckminster Fuller, Byte Shop, c2.com, call centre, citizen journalism, Claude Shannon: information theory, Clayton Christensen, commoditize, computer age, crowdsourcing, cryptocurrency, Debian, desegregation, Donald Davies, Douglas Engelbart, Douglas Engelbart, Douglas Hofstadter, Dynabook, El Camino Real, Electric Kool-Aid Acid Test, en.wikipedia.org, Firefox, Google Glasses, Grace Hopper, Gödel, Escher, Bach, Hacker Ethic, Haight Ashbury, Howard Rheingold, Hush-A-Phone, HyperCard, hypertext link, index card, Internet Archive, Jacquard loom, Jaron Lanier, Jeff Bezos, jimmy wales, John Markoff, John von Neumann, Joseph-Marie Jacquard, Leonard Kleinrock, Marc Andreessen, Mark Zuckerberg, Marshall McLuhan, Menlo Park, Mitch Kapor, Mother of all demos, new economy, New Journalism, Norbert Wiener, Norman Macrae, packet switching, PageRank, Paul Terrell, pirate software, popular electronics, pre–internet, RAND corporation, Ray Kurzweil, RFC: Request For Comment, Richard Feynman, Richard Stallman, Robert Metcalfe, Rubik’s Cube, Sand Hill Road, Saturday Night Live, self-driving car, Silicon Valley, Silicon Valley startup, Skype, slashdot, speech recognition, Steve Ballmer, Steve Crocker, Steve Jobs, Steve Wozniak, Steven Levy, Steven Pinker, Stewart Brand, technological singularity, technoutopianism, Ted Nelson, The Coming Technological Singularity, The Nature of the Firm, The Wisdom of Crowds, Turing complete, Turing machine, Turing test, Vannevar Bush, Vernor Vinge, Von Neumann architecture, Watson beat the top human players on Jeopardy!, Whole Earth Catalog, Whole Earth Review, wikimedia commons, William Shockley: the traitorous eight

Gary Marcus, “Hyping Artificial Intelligence, Yet Again,” New Yorker, Jan. 1, 2014, citing “New Navy Device Learns by Doing” (UPI wire story), New York Times, July 8, 1958; “Rival,” New Yorker, Dec. 6, 1958. 3. Marvin Minsky and Seymour Papert, the original gurus of artificial intelligence, challenged some of Rosenblatt’s premises, after which the excitement surrounding the Perceptron faded and the entire field entered a decline known as the “AI winter.” See Danny Wilson, “Tantalizingly Close to a Mechanized Mind: The Perceptrons Controversy and the Pursuit of Artificial Intelligence,” undergraduate thesis, Harvard, December 2012; Frank Rosenblatt, “The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain,” Psychological Review, Fall 1958; Marvin Minsky and Seymour Papert, Perceptrons (MIT, 1969). 4. Author’s interview with Ginni Rometty. 5.