Alignment Problem

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

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

Norton Special Sales at specialsales@wwnorton.com or 800-233-4830 Jacket design: Gregg Kulick Jacket illustrations: (top) Courtesy of Legado Cajal, Instituto Cajal (CSIC), Madrid; (bottom) Gregg Kulick Book design by Chris Welch Production manager: Lauren Abbate The Library of Congress has cataloged the printed edition as follows: Names: Christian, Brian, 1984– author. Title: The alignment problem : machine learning and human values / Brian Christian. Description: First edition. | New York, NY : W.W. Norton & Company, [2020] | Includes bibliographical references and index. Identifiers: LCCN 2020029036 | ISBN 9780393635829 (hardcover) | ISBN 9780393635836 (epub) Subjects: LCSH: Artificial intelligence—Moral and ethical aspects. | Artificial intelligence—Social aspects. | Machine learning—Safety measures. | Software failures. | Social values. Classification: LCC Q334.7 .C47 2020 | DDC 174/.90063—dc23 LC record available at https://lccn.loc.gov/2020029036 W.

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Ghahramani, Zoubin. “Probabilistic Machine Learning and Artificial Intelligence.” Nature 521, no. 7553 (2015): 452–59. Ghorbani, Amirata, Abubakar Abid, and James Zou. “Interpretation of Neural Networks Is Fragile.” In Proceedings of the AAAI Conference on Artificial Intelligence 33 (2019): 3681–88. Gielniak, Michael J., and Andrea L. Thomaz. “Generating Anticipation in Robot Motion.” In 2011 RO-MAN, 449–54. IEEE, 2011. Giusti, Alessandro, Jérôme Guzzi, Dan C. Cireşan, Fang-Lin He, Juan P. Rodríguez, Flavio Fontana, Matthias Faessler, et al. “A Machine Learning Approach to Visual Perception of Forest Trails for Mobile Robots.”

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., Sarah Dean, Esther Rolf, Max Simchowitz, and Moritz Hardt. “Delayed Impact of Fair Machine Learning.” In Proceedings of the 35th International Conference on Machine Learning, 2018. Liu, Si, Risheek Garrepalli, Thomas G. Dietterich, Alan Fern, and Dan Hendrycks. “Open Category Detection with PAC Guarantees.” In Proceedings of the 35th International Conference on Machine Learning, 2018. Liu, Si, Risheek Garrepalli, Alan Fern, and Thomas G. Dietterich. “Can We Achieve Open Category Detection with Guarantees?” In Workshops at the Thirty-Second AAAI Conference on Artificial Intelligence, 2018. Lockhart, Ted. Moral Uncertainty and Its Consequences.

Architects of Intelligence by Martin Ford

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

About a quarter of those interviewed in this book are women, and that number is likely significantly higher than what would be found across the entire field of AI or machine learning. A recent study found that women represent about 12 percent of leading researchers in machine learning. (https://www.wired.com/story/artificial-intelligence-researchers-gender-imbalance) A number of the people I spoke to emphasized the need for greater representation for both women and members of minority groups. As you will learn from her interview in this book, one of the foremost women working in artificial intelligence is especially passionate about the need to increase diversity in the field. Stanford University’s Fei-Fei Li co-founded an organization now called AI4ALL (http://ai-4-all.org/) to provide AI-focused summer camps geared especially to underrepresented high school students.

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In recent years, he has focused on AI and machine learning and worked on the development of TensorFlow, Google’s widely-used open source software for deep learning. He currently guides Google’s future path in AI as director of artificial intelligence and head of the Google Brain project. MARTIN FORD: As the Director of AI at Google and head of Google Brain, what’s your vision for AI research at Google? JEFF DEAN: Overall, I view our role as to advance the state of the art in machine learning, to try and build more intelligent systems by developing new machine learning algorithms and techniques, and to build software and hardware infrastructure that allows us to make faster progress on these approaches and allow other people to also apply these approaches to problems they care about.

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One especially important audience, however, consists of young people who might consider a future career in artificial intelligence. There is currently a massive shortage of talent in the field, especially among those with skills in deep learning, and a career in AI or machine learning promises to be exciting, lucrative and consequential. As the industry works to attract more talent into the field, there is widespread recognition that much more must be done to ensure that those new people are more diverse. If artificial intelligence is indeed poised to reshape our world, then it is crucial that the individuals who best understand the technology—and are therefore best positioned to influence its direction—be representative of society as a whole.

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

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

Instead they take in information from others in a remarkably subtle and sensitive way, making complex inferences about where the information comes from and how trustworthy it is and systematically integrating their own experiences with what they are hearing.* “Artificial intelligence” and “machine learning” sound scary. And in some ways they are. These systems are being used to control weapons, for example, and we really should be scared about that. Still, natural stupidity can wreak far more havoc than artificial intelligence; we humans will need to be much smarter than we have been in the past to properly regulate the new technologies. But there is not much basis for either the apocalyptic or the utopian vision of AIs replacing humans. Until we solve the basic paradox of learning, the best artificial intelligences will be unable to compete with the average human four-year-old.

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If they can’t infer what we value, there’s no way for them to act in support of those values—and they may well act in ways that contravene them. Value alignment is the subject of a small but growing literature in artificial-intelligence research. One of the tools used for solving this problem is inverse-reinforcement learning. Reinforcement learning is a standard method for training intelligent machines. By associating particular outcomes with rewards, a machine-learning system can be trained to follow strategies that produce those outcomes. Wiener hinted at this idea in the 1950s, but the intervening decades have developed it into a fine art. Modern machine-learning systems can find extremely effective strategies for playing computer games—from simple arcade games to complex real-time strategy games—by applying reinforcement-learning algorithms.

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But, at least for now, we have almost no idea at all how the sort of creativity we see in children is possible.” Everyone’s heard about the new advances in artificial intelligence, and especially machine learning. You’ve also heard utopian or apocalyptic predictions about what those advances mean. They have been taken to presage either immortality or the end of the world, and a lot has been written about both of those possibilities. But the most sophisticated AIs are still far from being able to solve problems that human four-year-olds accomplish with ease. In spite of the impressive name, artificial intelligence largely consists of techniques to detect statistical patterns in large data sets.

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

AI winter, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Albert Einstein, Alignment Problem, AlphaGo, Amazon Mechanical Turk, artificial general intelligence, autonomous vehicles, Big Tech, Black Swan, Bletchley Park, Boeing 737 MAX, business intelligence, Charles Babbage, Claude Shannon: information theory, Computing Machinery and Intelligence, conceptual framework, correlation does not imply causation, data science, deep learning, DeepMind, driverless car, Elon Musk, Ernest Rutherford, Filter Bubble, Geoffrey Hinton, Georg Cantor, Higgs boson, hive mind, ImageNet competition, information retrieval, invention of the printing press, invention of the wheel, Isaac Newton, Jaron Lanier, Jeff Hawkins, John von Neumann, Kevin Kelly, Large Hadron Collider, Law of Accelerating Returns, Lewis Mumford, Loebner Prize, machine readable, machine translation, Nate Silver, natural language processing, Nick Bostrom, Norbert Wiener, PageRank, PalmPilot, paperclip maximiser, pattern recognition, Peter Thiel, public intellectual, Ray Kurzweil, retrograde motion, self-driving car, semantic web, Silicon Valley, social intelligence, speech recognition, statistical model, Stephen Hawking, superintelligent machines, tacit knowledge, technological singularity, TED Talk, The Coming Technological Singularity, the long tail, the scientific method, The Signal and the Noise by Nate Silver, The Wisdom of Crowds, theory of mind, Turing machine, Turing test, Vernor Vinge, Watson beat the top human players on Jeopardy!, Yochai Benkler

It’s impossible to make sense of language relying on induction, correctly understood (that is, not packing into it other forms of inference). 14. Marcus and Davis, Rebooting AI. 292 N O T E S T O PA G E S 131 – 14 3 15. Russell, ­Human Compatible. 16. Pearl, The Book of Why, 36. Chapter 11: Machine Learning and Big Data 1. Stuart Russell, ­Human Compatible: Artificial Intelligence and the Prob­lem of Control (New York: Viking, 2019). 2. Tom Mitchell, Machine Learning (New York: McGraw-­H ill Education, 1997), 2. 3. We trust the filters in large part ­because they are deliberately permissive: spam is more likely to get into inboxes then legitimate messages are to get thrown out.

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computer (Watson) by, 220–226, 230–231 ImageNet competitions, 135, 145, 155, 165, 243 image recognition, 278–279 imitation game, 9, 51 I ndex The Imitation Game (film), 21 incompleteness theorems, 12–15 induction, 115–121, 171–172; abduction and, 161; in artificial intelligence, 273–274; in life situations, 125–126; limits to, 278–279; machine learning as, 133; not strategy for artificial general intelligence, 173; prob­lems of, 122–124; regularity in, 126–129 inductive inference, 189 inference, 4, 104, 280–281, 283n1; abductive inference, 99–102, 162–163; in artificial intelligence, 103; combining types of, 218–219, 231; guesses as, 160; in history of science, 103–105; in knowledge bases, 181, 182; monotonic, 167; non-­monotonic, 167–168; as trust, 129–130; types of, 171 inference engines, 182–184 information technology, 249, 252 ingenuity: Gödel on, 12; Turing on, 11, 17, 18 innovation: decline in, 269; funding in control over, 270 insight, 103 instincts, 184 intelligence.

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I’m hopeful that the current turn away from the Singularity ­toward practical concerns about ceding real authority to AI—to, let’s face it, mindless machines—­w ill eventually result in a renewed appreciation for ­human intelligence and value. Considering the alignment prob­lem might give rise to considerations of augmentation—­how we can best use increasingly power­f ul ­idiots savants to further our own objectives, including in the pursuit of scientific pro­g ress. 280 T he ­F uture of the M yth I N CONCLUSION The inference framework I’ve presented in this book clarifies the proj­ect of expanding current artificial intelligence into artificial general intelligence: it must bridge to a distinct type of inference, currently not programmable.

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

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

When thinking about lock-in, the key technology is artificial intelligence.35 Writing gave ideas the power to influence society for thousands of years; artificial intelligence could give them influence that lasts millions. I’ll discuss when this might occur later; for now let’s focus on why advanced artificial intelligence would be of such great longterm importance. Artificial General Intelligence Artificial intelligence (AI) is a branch of computer science that aims to design machines that can mimic or replicate human intelligence. Because of the success of machine learning as a paradigm, we’ve made enormous progress in AI over the last ten years.

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151 Armageddon (film), 106 Arrhenius, Svante, 42 artificial general intelligence (AGI) averting civilisational stagnation, 156 longterm importance of, 80–83 predicting the arrival of, 89–91 prioritising threats to improve on, 228 the pursuit of immortality, 83–86 reducing future uncertainty, 228–229 surpassing human abilities, 86–88 values lock-in, 92–95 artificial intelligence (AI) addressing neglected problems, 231 AI safety, 244 alignment problem, 87 artificial general intelligence, 80–83 defining, 80 future threats and benefits, 6 missing moments of plasticity, 43 prioritising future solutions, 228–229 uncertainty over the future, 224–226 value lock-in, 79 arts and literature preserving and projecting, 22–23 the value of non-wellbeing goods, 214–215 asteroids, collision with, 105–107, 113 Atari, 82–83 Atlantis, 12 Australia: effects of all-out nuclear war, 131 average view of wellbeing, 177–179, 179(fig.)

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See value lock-in lock-in paradox, 101–102 Long Peace, 114 long reflection, 98–99 longtermism arguments for and against, 4–7, 257–261 concerns for future generations, 10–12 contingency of moral norms, 71–72 empowering future generations, 9 expedition into uncharted terrain, 6–7 longterm consequences of small actions, 173–175 perspective on civilisational stagnation, 159–163 population ethics, 168–171 the size of the future, 19 understanding the implications of, 229–230 values changes, 53–55 lottery winners, 203 Lustig, Richard, 203 lying, negative effects of, 241 Lyons, Oren, 11 Macaulay, Zachary, 69 MacFarquhar, Larissa, 168 machine learning artificial general intelligence development, 80–81 predicting AGI completion, 90–91 See also artificial general intelligence; artificial intelligence mammals evolution of, 4, 13(fig.) lifespan, 13, 13(fig.) megafauna, 29–30 Mao Zedong, 218–219 Marlowe, Frank, 206–207 Mars rovers, 189 mathematics Islamic Golden Age, 143 noncontingency, 32–33 Mauritania: abolition of slavery, 69–70 McKibben, Bill, 43 medicine: expected value theory in decisionmaking, 38 megafauna, 29–30 megatherium, 29 Mercy for Animals, 72–73 Metaculus forecasting platform, 113, 116 metaphors of humanity, history, and longtermism, 6–7 Middle Ages: history of civilisational stagnation, 157 migration.

The Ethical Algorithm: The Science of Socially Aware Algorithm Design by Michael Kearns, Aaron Roth

23andMe, affirmative action, algorithmic bias, algorithmic trading, Alignment Problem, Alvin Roth, backpropagation, Bayesian statistics, bitcoin, cloud computing, computer vision, crowdsourcing, data science, deep learning, DeepMind, Dr. Strangelove, Edward Snowden, Elon Musk, fake news, Filter Bubble, general-purpose programming language, Geoffrey Hinton, Google Chrome, ImageNet competition, Lyft, medical residency, Nash equilibrium, Netflix Prize, p-value, Pareto efficiency, performance metric, personalized medicine, pre–internet, profit motive, quantitative trading / quantitative finance, RAND corporation, recommendation engine, replication crisis, ride hailing / ride sharing, Robert Bork, Ronald Coase, self-driving car, short selling, sorting algorithm, sparse data, speech recognition, statistical model, Stephen Hawking, superintelligent machines, TED Talk, telemarketer, Turing machine, two-sided market, Vilfredo Pareto

Our training notwithstanding, our research and interest in the topics described here have not been formulated in a vacuum of abstraction and mathematics. We’ve both always been interested in applying that approach to problems in machine learning and artificial intelligence. We are also neither adverse to nor inexperienced in experimental, data-driven work in machine learning—often as a test of the practicality and limitations of our theories, but not always. And it was the very trends we describe in these pages—the explosive growth of consumer data enabled by the Internet, and the accompanying rise in machine learning for automated decision-making—that made us and our colleagues aware of and concerned about the potential collateral damage.

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We don’t need one scientist running a thousand experiments and only misleadingly reporting the results from one of them, because the same thing happens if a thousand scientists each run only one experiment (each in good faith), but only the one with the most surprising result ends up being published. The Sport of Machine Learning The dangers of p-hacking are by no means limited to the traditional sciences: they extend to machine learning as well. Sandy Pentland, a professor at MIT, was quoted in The Economist as saying that “according to some estimates, three-quarters of published scientific papers in the field of machine learning are bunk.” To see a particularly egregious example, let’s go back to 2015, when the market for machine learning talent was heating up. The techniques of deep learning had recently reemerged from relative obscurity (its previous incarnation was called backpropagation in neural networks, which we discussed in the introduction), delivering impressive results in computer vision and image recognition.

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See lending and creditworthiness crime, 14–15, 62, 92–93 crowdsourcing, 104 cryptography, 31–34, 37 Cuddy, Amy, 141–42 cultural biases, 57 Dalenius, Tore, 35 data administrators, 45–47 data analysis procedures, 164 data collection, 58 dating preferences, 94–97, 100–101 decision-making process, 3–4, 11, 190–91 decision trees, 154–55, 159–60, 164, 173 deep learning algorithms, 133, 146–47 DeepMind, 179 deep neural networks, 174–75 defection, 99–100, 115, 128 demographics, 7, 193 derived models, 6 diagnostics, 27–29 dietary research, 143–45, 158 differential privacy to combat overfitting data, 167 commercial deployment of, 47–49 and correlation equilibrium, 114–15 described, 36–39 design of ethical algorithms, 193, 195 and differing notions of fairness, 85 and embarrassing polls, 40, 43–45 and fairness vs. accuracy of models, 63 and game-theoretic algorithm design, 135 limitations of, 50–56 and trust in data administrators, 45–47 Dijkstra’s algorithm, 104, 109 diminishing marginal returns, 186–88 disability status, 86–89 discrimination and algorithmic violations of fairness and privacy, 96 and data collection bias, 90–93 and “fairness gerrymandering,” 86–89 and fairness issues in machine learning, 65–66 and fairness vs. accuracy of models, 63 and game-theoretic algorithm design, 134–35 and “merit” in algorithmic fairness, 75 and recent efforts to address machine learning issues, 15 and self-play in machine learning, 132–33 and statistical parity, 69 and supervised machine learning, 64 and unique challenges of algorithms, 7 and user preferences, 96–97. See also gender data and bias; racial data and bias disease prediction models, 30–33, 54, 96 diversity, 125–26 divorce proceedings, 40–41, 48, 51–52 DNA data, 30–31, 54–56 Doll, Richard, 34 doomsday scenarios, 184–85 downstream effects, 194 driving times.

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

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

In that connection—human beings giving and receiving love—I caught a glimpse of how humans will find work and meaning in the age of artificial intelligence. I believe that the skillful application of AI will be China’s greatest opportunity to catch up with—and possibly surpass—the United States. But more important, this shift will create an opportunity for all people to rediscover what it is that makes us human. 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.

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In that touchscreen device and that unmet desire for human contact, I saw the first sketches of a blueprint for coexistence between people and artificial intelligence. Yes, intelligent machines will increasingly be able to do our jobs and meet our material needs, disrupting industries and displacing workers in the process. But there remains one thing that only human beings are able to create and share with one another: love. With all of the advances in machine learning, the truth remains that we are still nowhere near creating AI machines that feel any emotions at all. Can you imagine the elation that comes from beating a world champion at the game you’ve devoted your whole life to mastering?

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v=UF8uR6Z6KLc&t=785s. space race of the 1960s: John R. Allen and Amir Husain, “The Next Space Race Is Artificial Intelligence: And the United States Is Losing,” Foreign Policy, November 3, 2017, http://foreignpolicy.com/2017/11/03/the-next-space-race-is-artificial-intelligence-and-america-is-losing-to-china/. Cold War arms race: Zachary Cohen, “US Risks Losing Artificial Intelligence Arms Race to China and Russia,” CNN, November 29, 2017, https://www.cnn.com/2017/11/29/politics/us-military-artificial-intelligence-russia-china/index.html. 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.

Machine Translation by Thierry Poibeau

Alignment Problem, AlphaGo, AltaVista, augmented reality, call centre, Claude Shannon: information theory, cloud computing, combinatorial explosion, crowdsourcing, deep learning, DeepMind, easy for humans, difficult for computers, en.wikipedia.org, geopolitical risk, Google Glasses, information retrieval, Internet of things, language acquisition, machine readable, machine translation, Machine translation of "The spirit is willing, but the flesh is weak." to Russian and back, natural language processing, Necker cube, Norbert Wiener, RAND corporation, Robert Mercer, seminal paper, Skype, speech recognition, statistical model, technological singularity, Turing test, wikimedia commons

Facebook bought out different companies specialized in machine translation (such as Jibbigo in 2013 for voice messages in particular). Apple and Google are also regularly buying startups in the communication and information technology domains. Most importantly, all these large companies are hiring engineers and researchers (mainly in machine learning and artificial intelligence) in order to produce their own machine translation solution. They are also opening new research centers worldwide in order to attract the best talent everywhere. New Applications of Machine Translation The machine translation market is growing fast. Over the last few years we have witnessed the emergence of new applications, particularly on mobile devices.

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Since the advent of computers (after the Second World War), this research program has materialized through the design of machine translation tools—in other words, computer programs capable of automatically producing in a target language the translation of a text in a source language. This research program is very ambitious: it is even one of the most fundamental in the field of artificial intelligence. The analysis of languages cannot be separated from the analysis of knowledge and reasoning, which explains the interest in this field shown by philosophers and specialists of artificial intelligence as well as the cognitive sciences. This brings to mind the test proposed by Turing3 in 1950: the test is successfully completed if a person dialoguing (through a screen) with a computer is unable to say whether her discussion partner is a computer or a human being.

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In fact, it is possible to generalize the approach so to consider the problem of translation as an alignment problem at the level of sequences of words, and not at the level of isolated words only. The goal is to translate at the phrase level (i.e., sequences of several words): this would enable the context to be better taken into account and would thus offer translations of better quality than simple word-for-word equivalences. It is possible to generalize the approach so as to consider the problem of translation as an alignment problem at the level of sequences of words, and not at the level of isolated words only.

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

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

Watson heralded a new age and portended machines that would finally begin to parse language and truly engage with humans, but 2011 would also mark the beginning of a dramatic shift in the underlying technology of artificial intelligence. Watson relied on machine learning algorithms that used statistical techniques to make sense of information, but over the next few years, another kind of machine learning—based directly on the perceptron conceived by Frank Rosenblatt more than half a century earlier—would once again come to the forefront and then rapidly rise to dominate the field of artificial intelligence. CONNECTIONIST VS. SYMBOLIC AI AND THE RISE OF DEEP LEARNING Even as the general field of artificial intelligence traced its boom-and-bust path over the decades, the research focus swung between two general philosophies that emphasized contrasting approaches to building more intelligent machines.

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In other words, this preliminary version of AlphaFold was not yet accurate enough to be a truly useful research tool.80 The fact that DeepMind was able to refine its technology to the point where a number of scientists declared the protein folding problem to be “solved” just two years later is, I think, an especially vivid indication of just how rapidly specific applications of artificial intelligence are likely to continue advancing. Aside from using machine learning to discover new drugs and other chemical compounds, the most promising general application of artificial intelligence to scientific research may be in the assimilation and understanding of the continuously exploding volume of published research. In 2018 alone, more than three million scientific papers were published in more than 40,000 separate journals.81 Making sense of information on that scale is so far beyond the capability of any individual human mind that artificial intelligence is arguably the only tool at our disposal that could lead to some sort of holistic comprehension.

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BIAS, FAIRNESS AND TRANSPARENCY IN MACHINE LEARNING ALGORITHMS As artificial intelligence and machine learning are deployed more and more widely, it’s critical that the results and recommendations produced by these algorithms are perceived as fair and that the reasoning behind them can be adequately explained. If you’re using a deep learning system to maximize the energy efficiency of some industrial machine, then you are probably not particularly concerned about the details that drive an algorithmic outcome; you simply want the optimal result. But when machine learning is applied to areas like criminal justice, hiring decisions or the processing of home mortgage applications—in other words, to high-stakes decisions that directly impact the rights and future well-being of human beings—it’s essential that algorithmic outcomes can be shown to be unbiased across demographic groups and that the analysis that led to those outcomes is transparent and just.

Artificial Intelligence: A Guide for Thinking Humans by Melanie Mitchell

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

A recent AI survey paper summed it up: “Because we don’t deeply understand intelligence or know how to produce general AI, rather than cutting off any avenues of exploration, to truly make progress we should embrace AI’s ‘anarchy of methods.’”14 But since the 2010s, one family of AI methods—collectively called deep learning (or deep neural networks)—has risen above the anarchy to become the dominant AI paradigm. In fact, in much of the popular media, the term artificial intelligence itself has come to mean “deep learning.” This is an unfortunate inaccuracy, and I need to clarify the distinction. AI is a field that includes a broad set of approaches, with the goal of creating machines with intelligence. Deep learning is only one such approach. Deep learning is itself one method among many in the field of machine learning, a subfield of AI in which machines “learn” from data or from their own “experiences.” To better understand these various distinctions, it’s important to understand a philosophical split that occurred early in the AI research community: the split between so-called symbolic and subsymbolic AI.

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The original goals of AI—computers that could converse with us in natural language, describe what they saw through their camera eyes, learn new concepts after seeing only a few examples—are things that young children can easily do, but, surprisingly, these “easy things” have turned out to be harder for AI to achieve than diagnosing complex diseases, beating human champions at chess and Go, and solving complex algebraic problems. As Minsky went on, “In general, we’re least aware of what our minds do best.”27 The attempt to create artificial intelligence has, at the very least, helped elucidate how complex and subtle are our own minds. 2 Neural Networks and the Ascent of Machine Learning Spoiler alert: Multilayer neural networks—the extension of perceptrons that was dismissed by Minsky and Papert as likely to be “sterile”—have instead turned out to form the foundation of much of modern artificial intelligence. Because they are the basis of several of the methods I’ll describe in later chapters, I’ll take some time here to describe how these networks work.

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Nagy, “Neural Networks—Then and Now,” IEEE Transactions on Neural Networks 2, no. 2 (1991): 316–18. 24.  Minsky and Papert, Perceptrons, 231–32. 25.  J. Lighthill, “Artificial Intelligence: A General Survey,” in Artificial Intelligence: A Paper Symposium (London: Science Research Council, 1973). 26.  Quoted in C. Moewes and A. Nürnberger, Computational Intelligence in Intelligent Data Analysis (New York: Springer, 2013), 135. 27.  M. L. Minsky, The Society of Mind (New York: Simon & Schuster, 1987), 29. 2: Neural Networks and the Ascent of Machine Learning   1.  The activation value y at each hidden and output unit is typically computed by taking the dot product between the vector x of inputs to the unit and the vector w of weights on the connections to that unit, and applying the sigmoid function to the result: y = 1/(1 + e−(x.w)).

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

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

The trick is figuring out how to encourage the good hacks while stopping the bad ones, and knowing the difference between the two. Hacking will become even more disruptive as we increasingly implement artificial intelligence (AI) and autonomous systems. These are computer systems, which means they will inevitably be hacked in the same ways that all computer systems are. They affect social systems—already AI systems make loan, hiring, and parole decisions—which means those hacks will consequently affect our economic and political systems. More significantly, machine-learning processes that underpin all of modern AI will result in the computers performing the hacks. Extrapolating further, AI systems will soon start discovering new hacks.

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Luke Halpin and Doug Dannemiller (2019), “Artificial intelligence: The next frontier for investment management firms,” Deloitte, https://www2.deloitte.com/content/dam/Deloitte/global/Documents/Financial-Services/fsi-artificial-intelligence-investment-mgmt.pdf. Peter Salvage (March 2019), “Artificial intelligence sweeps hedge funds,” BNY Mellon, https://www.bnymellon.com/us/en/insights/all-insights/artificial-intelligence-sweeps-hedge-funds.html. 244the precautionary principle: Maciej Kuziemski (1 May 2018), “A precautionary approach to artificial intelligence,” Project Syndicate, https://www.project-syndicate.org/commentary/precautionary-principle-for-artificial-intelligence-by-maciej-kuziemski-2018-05. 60.

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Public services, business transactions, and even basic social interactions are now mediated by digital systems that make predictions and decisions just like humans do, but they do it faster, more consistently, and less accountably than humans. Our machines increasingly make decisions for us, but they don’t think like we do, and the interaction of our minds with these artificial intelligences points the way to an exciting and dangerous future for hacking: in the economy, the law, and beyond. PART 7 HACKING AI SYSTEMS 50 Artificial Intelligence and Robotics Artificial intelligence—AI—is an information technology. It consists of software, it runs on computers, and it is already deeply embedded into our social fabric, both in ways we understand and in ways we don’t.

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

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

Wells, 1898 People, Power, and Profits: Progressive Capitalism for an Age of Discontent, Joseph Stiglitz, 2019 The Sixth Extinction: An Unnatural History, Elizabeth Kolbert, 2014 Utopia for Realists: The Case for a Universal Basic Income, Open Borders, and a 15-hour Workweek, 2014, and Humankind: A Hopeful History, 2019, Rutger Bregman Notes from an Apocalypse: A Personal Journey to the End of the World and Back, Mark O’Connell, 2020 The Better Angels of Our Nature: Why Violence Has Declined, Steven Pinker, 2011 Blockchain Chicken Farm: And Other Stories of Tech in China’s Countryside, Xiaowei Wang, 2020 Life 3.0: Being Human in the Age of Artificial Intelligence, Max Tegmark, 2017 The Alignment Problem: How Can Machines Learn Human Values?, Brian Christian, 2021 I Love, Therefore I Am There is no reading list. There is everything you are. Illustration and Text Credits p.13 Babbage’s Difference Engine No 1, 1824–1832 © Science & Society Picture Library / Getty Images; p.14 Punch cards on a Jacquard loom © John R.

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The smarter AI gets, the smarter robots will get. At present there are serious technical issues to overcome. All artificial intelligence is narrow AI – programmed, specific, problem-solving that doesn’t transfer well to other domains. It’s not AGI, where the system would operate more like a human brain does. A robot with a map of your kitchen wouldn’t ‘know’ why a table is where it is – and it will be confused if the table moves. Statistical knowledge is not the same thing as general understanding. Machine learning can get around this by throwing more data at the problem (train your AI on bigger data-sets), but we aren’t solving the underlying issue.

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* * * Bloom points out that most humans are fixated on space without boundaries. Think about it: land-grab, colonisation, urban creep, loss of habitat, the current fad for seasteading (sea cities with vast oceans at their disposal). And space itself – the go-to fascination of rich men: Richard Branson, Elon Musk, Jeff Bezos. When I think about artificial intelligence, and what is surely to follow – artificial general intelligence, or superintelligence – it seems to me that what this affects most, now and later, isn’t space but time. The brain uses chemicals to transmit information. A computer uses electricity. Signals travel at high speeds through the nervous system (neurons fire 200 times a second, or 200 hertz) but computer processors are measured in gigahertz – billions of cycles per second.

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

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

GO TO NOTE REFERENCE IN TEXT “By 2030, China’s AI theories” Graham Webster et al., “Full Translation: China’s ‘New Generation Artificial Intelligence Development Plan,’ ” DigiChina, Stanford University, Aug. 1, 2017, digichina.stanford.edu/​work/​full-translation-chinas-new-generation-artificial-intelligence-development-plan-2017. GO TO NOTE REFERENCE IN TEXT Indeed, Tsinghua publishes more Benaich and Hogarth, State of AI; Neil Savage, “The Race to the Top Among the World’s Leaders in Artificial Intelligence,” Nature Index, Dec. 9, 2020, www.nature.com/​articles/​d41586-020-03409-8; “Tsinghua University May Soon Top the World League in Science Research,” Economist, Nov. 17, 2018, www.economist.com/​china/​2018/​11/​17/​tsinghua-university-may-soon-top-the-world-league-in-science-research.

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See Laura Cooper and Preeti Singh, “Private Equity Backs Record Volume of Tech Deals,” Wall Street Journal, Jan. 3, 2022, www.wsj.com/​articles/​private-equity-backs-record-volume-of-tech-deals-11641207603. GO TO NOTE REFERENCE IN TEXT Investment in AI technologies See, for example, Artificial Intelligence Index Report 2021, although the numbers have certainly grown in the generative AI boom since then. GO TO NOTE REFERENCE IN TEXT PwC forecasts AI will add “Sizing the Prize—PwC’s Global Artificial Intelligence Study: Exploiting the AI Revolution,” PwC, 2017, www.pwc.com/​gx/​en/​issues/​data-and-analytics/​publications/​artificial-intelligence-study.html. GO TO NOTE REFERENCE IN TEXT McKinsey forecasts a $4 trillion boost Jacques Bughin et al., “Notes from the AI Frontier: Modeling the Impact of AI on the World Economy,” McKinsey, Sept. 4, 2018, www.mckinsey.com/​featured-insights/​artificial-intelligence/​notes-from-the-ai-frontier-modeling-the-impact-of-ai-on-the-world-economy; Michael Ciu, “The Bio Revolution: Innovations Transforming Economies, Societies, and Our Lives,” McKinsey Global Institute, May 13, 2020, www.mckinsey.com/​industries/​pharmaceuticals-and-medical-products/​our-insights/​the-bio-revolution-innovations-transforming-economies-societies-and-our-lives.

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GO TO NOTE REFERENCE IN TEXT The most ambitious legislation “The Artificial Intelligence Act,” Future of Life Institute, artificialintelligenceact.eu. GO TO NOTE REFERENCE IN TEXT Some argue it’s too focused See, for example, “FLI Position Paper on the EU AI Act,” Future of Life Institute, Aug. 4, 2021, futureoflife.org/​wp-content/​uploads/​2021/​08/​FLI-Position-Paper-on-the-EU-AI-Act.pdf?x72900; and David Matthews, “EU Artificial Intelligence Act Not ‘Futureproof,’ Experts Warn MEPs,” Science Business, March 22, 2022, sciencebusiness.net/​news/​eu-artificial-intelligence-act-not-futureproof-experts-warn-meps.

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

3D printing, Abraham Maslow, access to a mobile phone, affirmative action, Affordable Care Act / Obamacare, agricultural Revolution, Albert Einstein, Alfred Russel Wallace, Alignment Problem, An Inconvenient Truth, anti-communist, Anton Chekhov, Arthur Eddington, artificial general intelligence, availability heuristic, Ayatollah Khomeini, basic income, Berlin Wall, Bernie Sanders, biodiversity loss, Black Swan, Bonfire of the Vanities, Brexit referendum, business cycle, capital controls, Capital in the Twenty-First Century by Thomas Piketty, carbon footprint, carbon tax, Charlie Hebdo massacre, classic study, clean water, clockwork universe, cognitive bias, cognitive dissonance, Columbine, conceptual framework, confounding variable, correlation does not imply causation, creative destruction, CRISPR, crowdsourcing, cuban missile crisis, Daniel Kahneman / Amos Tversky, dark matter, data science, decarbonisation, degrowth, deindustrialization, dematerialisation, demographic transition, Deng Xiaoping, distributed generation, diversified portfolio, Donald Trump, Doomsday Clock, double helix, Eddington experiment, Edward Jenner, effective altruism, Elon Musk, en.wikipedia.org, end world poverty, endogenous growth, energy transition, European colonialism, experimental subject, Exxon Valdez, facts on the ground, fake news, Fall of the Berlin Wall, first-past-the-post, Flynn Effect, food miles, Francis Fukuyama: the end of history, frictionless, frictionless market, Garrett Hardin, germ theory of disease, Gini coefficient, Great Leap Forward, Hacker Conference 1984, Hans Rosling, hedonic treadmill, helicopter parent, Herbert Marcuse, Herman Kahn, Hobbesian trap, humanitarian revolution, Ignaz Semmelweis: hand washing, income inequality, income per capita, Indoor air pollution, Intergovernmental Panel on Climate Change (IPCC), invention of writing, Jaron Lanier, Joan Didion, job automation, Johannes Kepler, John Snow's cholera map, Kevin Kelly, Khan Academy, knowledge economy, l'esprit de l'escalier, Laplace demon, launch on warning, life extension, long peace, longitudinal study, Louis Pasteur, Mahbub ul Haq, Martin Wolf, mass incarceration, meta-analysis, Michael Shellenberger, microaggression, Mikhail Gorbachev, minimum wage unemployment, moral hazard, mutually assured destruction, Naomi Klein, Nate Silver, Nathan Meyer Rothschild: antibiotics, negative emissions, Nelson Mandela, New Journalism, Norman Mailer, nuclear taboo, nuclear winter, obamacare, ocean acidification, Oklahoma City bombing, open economy, opioid epidemic / opioid crisis, paperclip maximiser, Paris climate accords, Paul Graham, peak oil, Peter Singer: altruism, Peter Thiel, post-truth, power law, precautionary principle, precision agriculture, prediction markets, public intellectual, purchasing power parity, radical life extension, Ralph Nader, randomized controlled trial, Ray Kurzweil, rent control, Republic of Letters, Richard Feynman, road to serfdom, Robert Gordon, Rodney Brooks, rolodex, Ronald Reagan, Rory Sutherland, Saturday Night Live, science of happiness, Scientific racism, Second Machine Age, secular stagnation, self-driving car, sharing economy, Silicon Valley, Silicon Valley ideology, Simon Kuznets, Skype, smart grid, Social Justice Warrior, sovereign wealth fund, sparse data, stem cell, Stephen Hawking, Steve Bannon, Steven Pinker, Stewart Brand, Stuxnet, supervolcano, synthetic biology, tech billionaire, technological determinism, technological singularity, Ted Kaczynski, Ted Nordhaus, TED Talk, The Rise and Fall of American Growth, the scientific method, The Signal and the Noise by Nate Silver, The Spirit Level, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, Thomas Kuhn: the structure of scientific revolutions, Thomas Malthus, total factor productivity, Tragedy of the Commons, union organizing, universal basic income, University of East Anglia, Unsafe at Any Speed, Upton Sinclair, uranium enrichment, urban renewal, W. E. B. Du Bois, War on Poverty, We wanted flying cars, instead we got 140 characters, women in the workforce, working poor, World Values Survey, Y2K

See also note 25 above. 27. Shallowness and brittleness of current AI: Brooks 2015; Davis & Marcus 2015; Lanier 2014; Marcus 2016; Schank 2015. 28. Naam 2010. 29. Robots turning us into paper clips and other Value Alignment Problems: Bostrom 2016; Hanson & Yudkowsky 2008; Omohundro 2008; Yudkowsky 2008; P. Torres, “Fear Our New Robot Overlords: This Is Why You Need to Take Artificial Intelligence Seriously,” Salon, May 14, 2016. 30. Why we won’t be turned into paper clips: B. Hibbard, “Reply to AI Risk,” http://www.ssec.wisc.edu/~billh/g/AIRisk_Reply.html; R. Loosemore, “The Maverick Nanny with a Dopamine Drip: Debunking Fallacies in the Theory of AI Motivation,” Institute for Ethics and Emerging Technologies, July 24, 2014, http://ieet.org/index.php/IEET/more/loosemore20140724; A.

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See also Holocaust Anton, Michael (“Publius Decius Mus”), 448, 449 anxiety, 283 adulthood and, 288–9 “collapse anxiety,” 292 depression as comorbid with, 283 and institutions, loss of faith in, 286 media practices of encouraging, 287 as motivation to solve problems, 287 postwar increase in, 284 prevalence of depression and, 282–3, 476n74 sex differences in, 285 strategems for coping with, 287 women’s gains in autonomy and, 285 Appiah, Kwame Anthony, 443 Aquino, Corazon, 91 Arab countries classical Arab civilization, 439, 442 clerical meddling in education, 234 slavery/racism and, 397 See also Muslim countries Arab Spring (2011), 203, 228, 370 archaeology, 407 Argentina, 200, 315 Ariely, Dan, 353 Aristotle, eudaemonia, 267 Arkhipov, Vasili, 479n93 Armenia, 158 artificial intelligence (AI) “Artificial General Intelligence” (AGI), 297, 298 and Enlightenment thinkers, 386 as existential threat, putative, 296–300, 477n20 job losses and, 118, 300 Value Alignment Problem, 299–300 arts and culture availability of, 260–61 and consilience with science, 407–9 depicting traditionalism vs. modernity, 284 ideological innumeracy and, 48 Nietzsche as influence on, 445, 446–7 vs. science, 34, 389–90 Aryans, romantic heroism and, 33, 398, 444 Asafu-Adjaye, John, 122 Asia authoritarian regimes, rise of, 200 carbon emissions of, 144 famine in, 69, 78 globalization and, 111, 112, 117 IQ gains in, 241 life expectancy in, 53–4, 54, 55 military governments of, 200 postcolonial governments of, 78 undernourishment in, 72 See also individual countries and subregions Asians, hate crimes against, 219, 220 Asiri, Abdullah al-, 303 Assad, Bashar al-, 159 Astell, Mary, 252 atheism and atheists charitable acts by, 432 dangers of self-labeling as, 435 definition of, 430 moral realism of, 429 “New Atheism,” 430 numbers of, 435, 436, 437–8, 489n68, 490n65 rising Intelligence Quotient test scores and, 438 wars by, 429–30 Athens (ancient), 212 Atkins, Peter, 17 Auden, W.

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This is the danger that we will be subjugated, intentionally or accidentally, by artificial intelligence (AI), a disaster sometimes called the Robopocalypse and commonly illustrated with stills from the Terminator movies. As with Y2K, some smart people take it seriously. Elon Musk, whose company makes artificially intelligent self-driving cars, called the technology “more dangerous than nukes.” Stephen Hawking, speaking through his artificially intelligent synthesizer, warned that it could “spell the end of the human race.”19 But among the smart people who aren’t losing sleep are most experts in artificial intelligence and most experts in human intelligence.20 The Robopocalypse is based on a muzzy conception of intelligence that owes more to the Great Chain of Being and a Nietzschean will to power than to a modern scientific understanding.21 In this conception, intelligence is an all-powerful, wish-granting potion that agents possess in different amounts.

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

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

The most plausible existential risk would come from success in AI researchers’ grand ambition of creating agents with a general intelligence that surpasses our own. But how likely is that to happen, and when? In 2016, a detailed survey was conducted of more than 300 top researchers in machine learning.84 Asked when an AI system would be “able to accomplish every task better and more cheaply than human workers,” on average they estimated a 50 percent chance of this happening by 2061 and a 10 percent chance of it happening as soon as 2025.85 FIGURE 5.1 Measures of progress and interest in artificial intelligence. The faces show the very rapid recent progress in generating realistic images of “imagined” people. The charts show longterm progress in chess AI surpassing the best human grand masters (measured in Elo), as well as the recent rise in academic activity in the field—measured by papers posted on arXiv, and attendance at conferences.86 This should be interpreted with care.

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The international body responsible for the continued prohibition of bioweapons (the Biological Weapons Convention) has an annual budget of just $1.4 million—less than the average McDonald’s restaurant.54 The entire spending on reducing existential risks from advanced artificial intelligence is in the tens of millions of dollars, compared with the billions spent on improving artificial intelligence capabilities.55 While it is difficult to precisely measure global spending on existential risk, we can state with confidence that humanity spends more on ice cream every year than on ensuring that the technologies we develop do not destroy us.56 In scientific research, the story is similar.

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While exploring society’s current vulnerabilities or the dangers from recent techniques, the biosecurity community also emits dangerous information (something I’ve had to be acutely aware of while writing this section).70 This makes the job of those trying to protect us even harder. UNALIGNED ARTIFICIAL INTELLIGENCE In the summer of 1956 a small group of mathematicians and computer scientists gathered at Dartmouth College to embark on the grand project of designing intelligent machines. They explored many aspects of cognition including reasoning, creativity, language, decision-making and learning. Their questions and stances would come to shape the nascent field of artificial intelligence (AI). The ultimate goal, as they saw it, was to build machines rivaling humans in their intelligence.71 As the decades passed and AI became an established field, it lowered its sights.

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

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

“Zero-Hours Contracts Have a Devastating Impact on Career Progression—Labour Is Right to Ban Them.” Conversation, September 24. https://theconversation.com/zero-hours-contracts-have-a-devastating-impact-on-career-progression-labour-is-right-to-ban-them-123066. Neapolitan, Richard E., and Xia Jiang. 2018. Artificial Intelligence: With an Introduction to Machine Learning, 2nd ed. London: Chapman and Hall/CRC. Neeson, J. M. 1993. Commoners, Common Right, Enclosure and Social Change in England, 1700‒1820. Cambridge: Cambridge University Press. Noble, David. 1977. America by Design: Science, Technology, and the Rise of Corporate Capitalism.

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Medium, November 27, https://medium.com/@francois.chollet/the-impossibility-of-intelligence-explosion-5be4a9eda6ec. Chollet, François. 2019. “On the Measure of Intelligence.” Working paper, https://arxiv.org/pdf/1911.01547.pdf?ref=https://githubhelp.com. Christian, Brian. 2020. The Alignment Problem: Machine Learning and Human Values. New York: W.W. Norton. Chudek, Maciej, Sarah Heller, Susan Birch, and Joseph Henrich. 2012. “Prestige-Biased Cultural Learning: Bystander’s Differential Attention to Potential Models Influences Children’s Learning.” Evolution and Human Behavior 33, no. 1: 46‒56. Cialdini, Robert B. 2006.

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If a company is installing, say, new computers, this must mean that the higher revenues they generate more than make up for the costs. But in a world in which shared visions guide our actions, there is no guarantee that this is indeed the case. If everybody becomes convinced that artificial-intelligence technologies are needed, then businesses will invest in artificial intelligence, even when there are alternative ways of organizing production that could be more beneficial. Similarly, if most researchers are working on a particular way of advancing machine intelligence, others may follow faithfully, or even blindly, in their footsteps.

Practical C Programming, 3rd Edition by Steve Oualline

Alignment Problem, Dennis Ritchie, Free Software Foundation, functional programming, Grace Hopper, index card, Ken Thompson, linear programming, off-by-one error, systems thinking

Although trees are supposed to be fast, this program was so slow that you would think I used a linked list. Why? Hint: Graphically construct a tree using the words “able,” “baker,” “cook,” “delta,” and “easy,” and look at the result. (Click here for the answer Section 17.11) Data Structures for a Chess Program One of the classic problems in artificial intelligence is the game of chess. As this book goes to press, the Grandmaster who beat the world’s best chess-playing computer last year has lost to the computer this year (1997). We are going to design a data structure for a chess-playing program. In chess, you have several possible moves that you can make.

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The program can automatically fix the file problem: const long int MAGIC = 0x11223344L /* file identification number*/ const long int SWAP_MAGIC = 0x22114433L /* magic-number byte swapped */ FILE *in_file; /* file containing binary data */ long int magic; /* magic number from file */ in_file = fopen("data", "rb"); fread((char *)&magic, sizeof(magic), 1, in_file); switch (magic) { case MAGIC: /* No problem */ break; case SWAP_MAGIC: printf("Converting file, please wait\n"); convert_file(in_file); break; default: fprintf(stderr,"Error:Bad magic number %lx\n", magic); exit (8); } Alignment Problem Some computers limit the address that can be used for integers and other types of data. For example, the 68000 series require that all integers start on a 2-byte boundary. If you attempt to access an integer using an odd address, you will generate an error. Some processors have no alignment rules, while some are even more restrictive—requiring integers to be aligned on a 4-byte boundary.

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. ==, How to Learn C == operator (equal), if Statement, Debugging, General > operator (greater than), if Statement >= operator (greater than or equal to), if Statement \\>\\> (right shift), Bit Operators, The Left- and Right-Shift Operators (<<, >>) \\n (newline character), Characters ^ (exclusive or), Bit Operators, The Bitwise Exclusive or (^) __MSDOS__ (pre-defined symbol), Conditional Compilation __STDC_ (pre-defined symbol), Conditional Compilation __TURBOC_ (pre-defined symbol), Conditional Compilation {} (curly braces), if Statement | (or operator), Bitwise or (|) | (or), Bit Operators ~ (complement operator), The Ones Complement Operator (Not) (~) ~ (complement), Bit Operators A accuracy, floating point, Accuracy, Determining Accuracy addition operator (+), Simple Expressions addition, floating point, Floating Addition/Subtraction address of operator (&), Simple Pointers address operator (&), Simple Pointers alignment restrictions, Alignment Problem ambiguous code, else Statement and operator (&), Bit Operators, The and Operator (&) and portability, Byte Order Problem Appendix A, ASCII Table, Characters argc, Command-Line Arguments argv, Command-Line Arguments array, declarations, How C Works arrays, Arrays, Arrays of Structures and pointers, Pointers and Arrays index, Arrays infinite, A Program to Use Infinite Arrays, Using the Infinite Array initializing, Initializing Variables multiple dimensional, Initializing Variables of structures, Arrays of Structures arrays, dimension, Arrays arrays, element, Arrays ASCII characters, Characters files, Binary and ASCII Files versus binary files, Binary and ASCII Files assembly language, How Programming Works assembly language translation, How Programming Works assignment statement, How C Works assignment statements, General author, Style auto, Scope and Class automatic parameter changes, Pointers and Arrays variables, Scope and Class B binary files, Binary and ASCII Files, Byte Order Problem I/O, Binary I/O mode for fopen (b), The End-of-Line Puzzle trees, Trees bit, Bit Operations bit fields, Bit Fields or Packed Structures bit operations, Bit Operations bit operators, Bit Operators Bit values, Setting, Clearing, and Testing Bits bitmapped graphics, Bitmapped Graphics bitmaps, Bitmapped Graphics bits, Integers bits, clearing, Setting, Clearing, and Testing Bits bits, defining, Setting, Clearing, and Testing Bits bits, setting, Setting, Clearing, and Testing Bits bits, testing, Setting, Clearing, and Testing Bits bitwise and (&), Bit Operators, The and Operator (&) bitwise compement (~), Bit Operators bitwise complement (~), The Ones Complement Operator (Not) (~) bitwise exclusive or (^), Bit Operators, The Bitwise Exclusive or (^) bitwise left shift (), Bit Operators bitwise or (|), Bit Operators, Bitwise or (|) bitwise right shift (\\>\\>), Bit Operators, The Left- and Right-Shift Operators (<<, >>) bitwise shift left (), The Left- and Right-Shift Operators (<<, >>) blank modifier, The extern Modifier boolean algebra, Bit Operations bottom-up programming, Structured Programming Bourne shell, #define Statement branching statements, Decision and Control Statements break statement, break Statement, switch Statement breakpoints, Debugging a Binary Search buffered file problems, Buffering Problems byte, Bit Operations bytes, Byte Order Problem C C language, Brief History of C C tools, Electronic Archaeology C++ language, Brief History of C C++, how to learn, How to Learn C Calc (program), Specification, Prototype calc (programmed with switch statements), switch Statement calculation operators, Simple Expressions call graphs, Electronic Archaeology carriage return, The End-of-Line Puzzle case labels, switch Statement case statement, switch Statement PASCAL, switch Statement cb program, Electronic Archaeology cc command, #define Statement, Conditional Compilation cc program, C Preprocessor cd command, Setting Up cdb debugger, Interactive Debuggers cflow program, Electronic Archaeology char, Characters (as integer), Types of Integers character constants, Characters characters, Characters chess, data structures for, Data Structures for a Chess Program class, Scope and Class class, derrived, Line Counter Submodule (lc) class, variable, Scope and Class classes, How This Book is Organized clause conditional, Decision and Control Statements clearing bits, Setting, Clearing, and Testing Bits close, Unbuffered I/O COBOL, How Programming Works code commenting out, Conditional Compilation design, Programming Process, Code Design format, Indentation and Code Format, Simplicity maintaining, Style code design, Specification coding, Coding command-line arguments, Command-Line Arguments command-line arguments, sample program, Command-Line Arguments commands, Interactive Debuggers dbx, Interactive Debuggers commenting out code, Conditional Compilation comments, How to Learn C, Style boxes, Style program, Add Comments comments, author, Style comments, file formats, Style comments, functions, Functions comments, heading, Style, Basic Program Structure comments, in data files, Designing File Formats comments, notes, Style comments, procedures, Functions comments, purpose, Style comments, references, Style comments, restrictions, Style comments, revision history, Style comments, units, Common Coding Practices compile command, Conditional Compilation compiler, How C Works compilers, Compiler conditional, Conditional Compilation complement (~), Bit Operators, The Ones Complement Operator (Not) (~) complex data types, How C Works computation operators, Simple Expressions concatenating strings, Strings conditional clause, Decision and Control Statements conditionals, General const, Constant Declarations const pointers, const Pointers const vs.

Inviting Disaster by James R. Chiles

air gap, Airbus A320, airline deregulation, Alignment Problem, Apollo 11, Apollo 13, Boeing 747, crew resource management, cuban missile crisis, Exxon Valdez, flying shuttle, Gene Kranz, Maui Hawaii, megaproject, Milgram experiment, Neil Armstrong, North Sea oil, Piper Alpha, Recombinant DNA, Richard Feynman, Richard Feynman: Challenger O-ring, risk tolerance, Ted Sorensen, time dilation

I suggest that companies think of their time on the machine frontier as a privilege, bestowed by the rest of us. It’s a valuable location because working the frontier offers more opportunity for profit and growth than old technological territory. In our era, some frontiers with seemingly great potential include genetic engineering, artificial intelligence, and deepwater exploration for oil and gas. But holding a place on the machine frontier is not a constitutional right. Losing the privilege could happen if a laboratory lets something loose onto the citizenry or if some cost-cutting decision causes the destruction of a valued habitat. One mishap might not cause public support to collapse, but two incidents could be more than enough.

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Cooper never visited the project again, relying on mail and telegrams to keep him in touch with the construction of his design, a design that had been forced to the edges of safety by financial difficulties in the Quebec Bridge Company. When signs of trouble began appearing—steel ribs not lining up, because of the higher-than-estimated weight of the structure—Cooper wasn’t there to see it firsthand, as he had been with the Eads Bridge. When the steel began buckling and the alignment problems grew worse by the day, Cooper heard about it but could do no more than send worried telegrams. His last telegram tried to order the crews to add no more weight to the bridge until after investigation, but he’d sent it via the steel fabricator’s factory, and the message didn’t make it to the construction site in time.

Python for Data Analysis by Wes McKinney

Alignment Problem, backtesting, Bear Stearns, cognitive dissonance, crowdsourcing, data science, Debian, duck typing, Firefox, functional programming, Google Chrome, Guido van Rossum, index card, machine readable, random walk, recommendation engine, revision control, sentiment analysis, Sharpe ratio, side project, sorting algorithm, statistical model, type inference

MovieLens 1M Data Set GroupLens Research (http://www.grouplens.org/node/73) provides a number of collections of movie ratings data collected from users of MovieLens in the late 1990s and early 2000s. The data provide movie ratings, movie metadata (genres and year), and demographic data about the users (age, zip code, gender, and occupation). Such data is often of interest in the development of recommendation systems based on machine learning algorithms. While I will not be exploring machine learning techniques in great detail in this book, I will show you how to slice and dice data sets like these into the exact form you need. The MovieLens 1M data set contains 1 million ratings collected from 6000 users on 4000 movies. It’s spread across 3 tables: ratings, user information, and movie information.

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Data Munging Topics Many helpful data munging tools for financial applications are spread across the earlier chapters. Here I’ll highlight a number of topics as they relate to this problem domain. Time Series and Cross-Section Alignment One of the most time-consuming issues in working with financial data is the so-called data alignment problem. Two related time series may have indexes that don’t line up perfectly, or two DataFrame objects might have columns or row labels that don’t match. Users of MATLAB, R, and other matrix-programming languages often invest significant effort in wrangling data into perfectly aligned forms. In my experience, having to align data by hand (and worse, having to verify that data is aligned) is a far too rigid and tedious way to work.

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Preparation Cleaning, munging, combining, normalizing, reshaping, slicing and dicing, and transforming data for analysis. Transformation Applying mathematical and statistical operations to groups of data sets to derive new data sets. For example, aggregating a large table by group variables. Modeling and computation Connecting your data to statistical models, machine learning algorithms, or other computational tools Presentation Creating interactive or static graphical visualizations or textual summaries In this chapter I will show you a few data sets and some things we can do with them. These examples are just intended to pique your interest and thus will only be explained at a high level.

HBase: The Definitive Guide by Lars George

Alignment Problem, Amazon Web Services, bioinformatics, create, read, update, delete, Debian, distributed revision control, domain-specific language, en.wikipedia.org, fail fast, fault tolerance, Firefox, FOSDEM, functional programming, Google Earth, information security, Kickstarter, place-making, revision control, smart grid, sparse data, web application

Ganglia and its graphs are a great tool to go back in time and find what caused a problem. However, they are only helpful when dealing with quantitative data—for example, for performing postmortem analysis of a cluster problem. In the next section, you will see how to complement the graphing with a qualitative support system. Figure 10-5. Graphs that can help align problems with related events * * * [112] Ganglia is a distributed, scalable monitoring system suitable for large cluster systems. See its project website for more details on its history and goals. [113] See the RRDtool project website for details. JMX The Java Management Extensions technology is the standard for Java applications to export their status.

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Because of this, companies have become focused on delivering more targeted information, such as recommendations or online ads, and their ability to do so directly influences their success as a business. Systems like Hadoop[6] now enable them to gather and process petabytes of data, and the need to collect even more data continues to increase with, for example, the development of new machine learning algorithms. Where previously companies had the liberty to ignore certain data sources because there was no cost-effective way to store all that information, they now are likely to lose out to the competition. There is an increasing need to store and analyze every data point they generate. The results then feed directly back into their e-commerce platforms and may generate even more data.

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Facebook, for example, is adding more than 15 TB of data into its Hadoop cluster every day[9] and is subsequently processing it all. One source of this data is click-stream logging, saving every step a user performs on its website, or on sites that use the social plug-ins offered by Facebook. This is an ideal case in which batch processing to build machine learning models for predictions and recommendations is appropriate. Facebook also has a real-time component, which is its messaging system, including chat, wall posts, and email. This amounts to 135+ billion messages per month,[10] and storing this data over a certain number of months creates a huge tail that needs to be handled efficiently.