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At the heart of human intelligence rests a fundamental puzzle: How are we incredibly smart and stupid at the same time? No existing machine can match the power and flexibility of human perception, language, and reasoning. Yet, we routinely commit errors that reveal the failures of our thought processes. 'What Makes Us Smart' makes sense of this paradox by arguing that our cognitive errors are not haphazard. Rather, they are the inevitable consequences of a brain optimized for efficient inference and decision making within the constraints of time, energy, and memory - in other words, data and resource limitations. Framing human intelligence in terms of these constraints, Samuel Gershman shows how a deeper computational logic underpins the 'stupid' errors of human cognition.

Cognition --- Cognitive psychology. --- Age factors. --- Psychology, Cognitive --- Cognitive science --- Psychology --- Age factors in cognition --- Ability, Influence of age on --- Cognition. --- Intellect. --- Human intelligence --- Intelligence --- Mind --- Ability --- Thought and thinking --- Accuracy and precision. --- Action potential. --- Ad hoc hypothesis. --- Ad hominem. --- Adaptive bias. --- Almost surely. --- Alternative hypothesis. --- Altruism. --- Ambiguity. --- Analogy. --- Anecdote. --- Approximation. --- Attractiveness. --- Bayes' theorem. --- Bayesian inference. --- Bayesian probability. --- Bayesian. --- Behavior. --- Circular reasoning. --- Cognitive flexibility. --- Cognitive style. --- Commitment device. --- Confidence. --- Confirmation bias. --- Conspiracy theory. --- Controllability. --- Counterintuitive. --- Credibility. --- Decision-making. --- Effectiveness. --- Efficacy. --- Efficiency. --- Efficient coding hypothesis. --- Efficient frontier. --- Estimation. --- Expected value. --- Explanation. --- Fair coin. --- Fair market value. --- Gimmick. --- Guessing. --- Heuristic. --- Hot Hand. --- Human intelligence. --- Hypothesis. --- Illusion of control. --- Inductive bias. --- Inference. --- Intelligent design. --- Learnability. --- Lightness (philosophy). --- Likelihood function. --- Logical extreme. --- Logical reasoning. --- Moral hazard. --- Motivated reasoning. --- Mutual exclusivity. --- Natural approach. --- Normative. --- Observation. --- Observational learning. --- Of Miracles. --- Opportunity cost. --- Optimism bias. --- Optimism. --- Our Choice. --- Pairwise comparison. --- Perfect rationality. --- Physical attractiveness. --- Point estimation. --- Politeness. --- Positive feedback. --- Predictability. --- Prediction. --- Predictive coding. --- Predictive power. --- Principle of rationality. --- Prior probability. --- Probability. --- Prosocial behavior. --- Quantity. --- Rational agent. --- Rational choice theory. --- Rationality. --- Reason. --- Reinforcement learning. --- Result. --- Self-control. --- Sophistication. --- Spontaneous recovery. --- Strong inference. --- Suggestion. --- Theory. --- Thought. --- Truth value. --- Uncertainty. --- Utility. --- Value of information. --- With high probability. --- PSYCHOLOGY / Cognitive Psychology & Cognition --- COMPUTERS / Logic Design

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An essential guide to recognizing bogus numbers and misleading dataNumbers are often intimidating, confusing, and even deliberately deceptive-especially when they are really big. The media loves to report on millions, billions, and trillions, but frequently makes basic mistakes or presents such numbers in misleading ways. And misunderstanding numbers can have serious consequences, since they can deceive us in many of our most important decisions, including how to vote, what to buy, and whether to make a financial investment. In this short, accessible, enlightening, and entertaining book, leading computer scientist Brian Kernighan teaches anyone-even diehard math-phobes-how to demystify the numbers that assault us every day.With examples drawn from a rich variety of sources, including journalism, advertising, and politics, Kernighan demonstrates how numbers can mislead and misrepresent. In chapters covering big numbers, units, dimensions, and more, he lays bare everything from deceptive graphs to speciously precise numbers. And he shows how anyone-using a few basic ideas and lots of shortcuts-can easily learn to recognize common mistakes, determine whether numbers are credible, and make their own sensible estimates when needed.Giving you the simple tools you need to avoid being fooled by dubious numbers, Millions, Billions, Zillions is an essential survival guide for a world drowning in big-and often bad-data.

Numbers, Complex. --- Data mining. --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Complex numbers --- Imaginary quantities --- Quantities, Imaginary --- Algebra, Universal --- Quaternions --- Vector analysis --- A picture is worth a thousand words. --- AARP. --- American Medical Association. --- Approximation. --- Arithmetic mean. --- Arithmetic. --- Associated Press. --- Baby boomers. --- Back-of-the-envelope calculation. --- Barrel (unit). --- Birth rate. --- Blogger (service). --- Body surface area. --- Breast cancer. --- Calculation. --- Celsius. --- Centenarian. --- Computation. --- Consumer Reports. --- Corporate tax. --- Correlation does not imply causation. --- Daniel Kahneman. --- Darrell Huff. --- Dilbert. --- Dot-com bubble. --- Economics. --- Edward Tufte. --- Error. --- Estimation. --- Exabyte. --- Exponential growth. --- FLOPS. --- Factoid. --- Fermi problem. --- Gigabyte. --- Half Gone. --- Headline. --- Hectare. --- Home computer. --- How to Lie with Statistics. --- Hulu. --- Identity theft. --- Inception. --- Inflation. --- Innumeracy (book). --- Jeff Bezos. --- John Maynard Keynes. --- Just in case. --- Kilobit. --- Kilogram. --- Life expectancy. --- Little's law. --- Millionth. --- Mortality rate. --- My Local. --- Naomi Wolf. --- National Rifle Association. --- Net worth. --- Newspaper. --- Newsweek. --- Nobel Prize. --- Order of magnitude. --- Outright. --- Percentage point. --- Percentage. --- Petabit. --- Petabyte. --- Population growth. --- Pound sterling. --- Power of 10. --- Quadrillion. --- Quantity. --- Ranking (information retrieval). --- Result. --- Round number. --- Rule of 72. --- Sampling bias. --- School bus. --- Scientific notation. --- Square foot. --- Square yard. --- Strategic Petroleum Reserve (United States). --- Tax cut. --- Tax. --- Technology. --- Terabit. --- The Beauty Myth. --- The Colbert Report. --- The New York Times. --- The Wisdom of Crowds. --- The World's Billionaires. --- U.S. News & World Report. --- Ultra-high-definition television. --- Unemployment. --- W. E. B. Du Bois. --- Warren Buffett. --- With high probability. --- Year. --- Your Computer (British magazine). --- Zettabyte. --- Mathematics --- Mathematics in mass media --- Critical thinking --- Statistics --- Big data --- Million (The number) --- Billion (The number) --- Evaluation --- Methodology