Our brains, therefore, are designed not just to hunt and gather, but also to help us get ahead socially, often via deception and self-deception. But while we may be self-interested schemers, we benefit by pretending otherwise. The less we know about our own ugly motives, the better - and thus we don't like to talk or even think about the extent of our selfishness. This is "the elephant in the brain." Such an introspective taboo makes it hard for us to think clearly about our nature and the explanations for our behavior. The aim of this book, then, is to confront our hidden motives directly - to track down the darker, unexamined corners of our psyches and blast them with floodlights. Then, once everything is clearly visible, we can work to better understand ourselves: Why do we laugh? Why are artists sexy? Why do we brag about travel? Why do we prefer to speak rather than listen?Our unconscious motives drive more than just our private behavior; they also infect our venerated social institutions such as Art, School, Charity, Medicine, Politics, and Religion. In fact, these institutions are in many ways designed to accommodate our hidden motives, to serve covert agendas alongside their "official" ones. The existence of big hidden motives can upend the usual political debates, leading one to question the legitimacy of these social institutions, and of standard policies designed to favor or discourage them. You won't see yourself - or the world - the same after confronting the elephant in the brain.

    However, according to Hofstadter, the formal system that underlies all mental activity transcends the system that supports it. If life can grow out of the formal chemical substrate of the cell, if consciousness can emerge out of a formal system of firing neurons, then so too will computers attain human intelligence. Gödel, Escher, Bach is a wonderful exploration of fascinating ideas at the heart of cognitive science: meaning, reduction, recursion, and much more.

    Their visionary answers flow from the frontiers of psychology, philosophy, economics, physics, sociology, and more. Surprising and enlightening, these insights will revolutionize the way you think about yourself and the world.Contributors include:Daniel Kahneman on the “focusing illusion”Jonah Lehrer on controlling attentionRichard Dawkins on experimentationAubrey De Grey on conquering our fear of the unknownMartin Seligman on the ingredients of well-beingNicholas Carr on managing “cognitive load”Steven Pinker on win-win negotiatingDaniel Goleman on understanding our connection to the natural worldMatt Ridley on tapping collective intelligenceLisa Randall on effective theorizingBrian Eno on “ecological vision”J. Craig Venter on the multiple possible origins of life  Helen Fisher on temperamentSam Harris on the flow of thoughtLawrence Krauss on living with uncertainty

    Sipser's candid, crystal-clear style allows students at every level to understand and enjoy this field. His innovative "proof idea" sections explain profound concepts in plain English. The new edition incorporates many improvements students and professors have suggested over the years, and offers updated, classroom-tested problem sets at the end of each chapter.

    The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. These models can also be learned automatically from data, allowing the approach to be used in cases where manually constructing a model is difficult or even impossible. Because uncertainty is an inescapable aspect of most real-world applications, the book focuses on probabilistic models, which make the uncertainty explicit and provide models that are more faithful to reality.Probabilistic Graphical Models discusses a variety of models, spanning Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to deal with dynamical systems and relational data. For each class of models, the text describes the three fundamental cornerstones: representation, inference, and learning, presenting both basic concepts and advanced techniques. Finally, the book considers the use of the proposed framework for causal reasoning and decision making under uncertainty. The main text in each chapter provides the detailed technical development of the key ideas. Most chapters also include boxes with additional material: skill boxes, which describe techniques; case study boxes, which discuss empirical cases related to the approach described in the text, including applications in computer vision, robotics, natural language understanding, and computational biology; and concept boxes, which present significant concepts drawn from the material in the chapter. Instructors (and readers) can group chapters in various combinations, from core topics to more technically advanced material, to suit their particular needs.

    The solution is to rein in the market, right? Radical Markets turns this thinking--and pretty much all conventional thinking about markets, both for and against--on its head. The book reveals bold new ways to organize markets for the good of everyone. It shows how the emancipatory force of genuinely open, free, and competitive markets can reawaken the dormant nineteenth-century spirit of liberal reform and lead to greater equality, prosperity, and cooperation.Eric Posner and Glen Weyl demonstrate why private property is inherently monopolistic, and how we would all be better off if private ownership were converted into a public auction for public benefit. They show how the principle of one person, one vote inhibits democracy, suggesting instead an ingenious way for voters to effectively influence the issues that matter most to them. They argue that every citizen of a host country should benefit from immigration--not just migrants and their capitalist employers. They propose leveraging antitrust laws to liberate markets from the grip of institutional investors and creating a data labor movement to force digital monopolies to compensate people for their electronic data.Only by radically expanding the scope of markets can we reduce inequality, restore robust economic growth, and resolve political conflicts. But to do that, we must replace our most sacred institutions with truly free and open competition--Radical Markets shows how.

    Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach.The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package—PMTK (probabilistic modeling toolkit)—that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

    Busy people fail to manage their time efficiently for the same reasons the poor and those maxed out on credit cards fail to manage their money. The dynamics of scarcity reveal why dieters find it hard to resist temptation, why students and busy executives mismanage their time, and why sugarcane farmers are smarter after harvest than before. Once we start thinking in terms of scarcity and the strategies it imposes, the problems of modern life come into sharper focus.Mullainathan and Shafir discuss how scarcity affects our daily lives, recounting anecdotes of their own foibles and making surprising connections that bring this research alive. Their book provides a new way of understanding why the poor stay poor and the busy stay busy, and it reveals not only how scarcity leads us astray but also how individuals and organizations can better manage scarcity for greater satisfaction and success.http://us.macmillan.com/scarcity/Send...

    Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not.Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you're familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format.With this book, you'll learn:Why exploratory data analysis is a key preliminary step in data scienceHow random sampling can reduce bias and yield a higher quality dataset, even with big dataHow the principles of experimental design yield definitive answers to questionsHow to use regression to estimate outcomes and detect anomaliesKey classification techniques for predicting which categories a record belongs toStatistical machine learning methods that "learn" from dataUnsupervised learning methods for extracting meaning from unlabeled data

    These sections together make an appropriate one-term advanced/graduate level course (mathematical prerequisites: vector analysis and simple partial-differential equations). The book is printed to make it easy for readers to identify these sections.• The remaining Track 2 material provides a wealth of advanced topics instructors can draw from to flesh out a two-term course, with Track 1 sections serving as prerequisites.

    Employing an irresistible cast of dragon-riding Vikings, lizard-throwing giants, and feuding aliens, the renowned illustrator Grady Klein and the award-winning statistician Alan Dabney teach you how to collect reliable data, make confident statements based on limited information, and judge the usefulness of polls and the other numbers that you're bombarded with every day. If you want to go beyond the basics, they've created the ultimate resource: "The Math Cave," where they reveal the more advanced formulas and concepts.Timely, authoritative, and hilarious, The Cartoon Introduction to Statistics is an essential guide for anyone who wants to better navigate our data-driven world.

    Many of our wisest policy-makers, activists, scientists, and philosophers agree that the only problem with empathy is that we don’t have enough of it.Nothing could be farther from the truth, argues Yale researcher Paul Bloom. In AGAINST EMPATHY, Bloom reveals empathy to be one of the leading motivators of inequality and immorality in society. Far from helping us to improve the lives of others, empathy is a capricious and irrational emotion that appeals to our narrow prejudices. It muddles our judgment and, ironically, often leads to cruelty. We are at our best when we are smart enough not to rely on it, but to draw instead upon a more distanced compassion.Basing his argument on groundbreaking scientific findings, Bloom makes the case that some of the worst decisions made by individuals and nations—who to give money to, when to go to war, how to respond to climate change, and who to imprison—are too often motivated by honest, yet misplaced, emotions. With precision and wit, he demonstrates how empathy distorts our judgment in every aspect of our lives, from philanthropy and charity to the justice system; from medical care and education to parenting and marriage. Without empathy, Bloom insists, our decisions would be clearer, fairer, and—yes—ultimately more moral.Brilliantly argued, urgent and humane, AGAINST EMPATHY shows us that, when it comes to both major policy decisions and the choices we make in our everyday lives, limiting our impulse toward empathy is often the most compassionate choice we can make.

    The award-winning author Melanie Mitchell, a leading computer scientist, now reveals AI’s turbulent history and the recent spate of apparent successes, grand hopes, and emerging fears surrounding it.In Artificial Intelligence, Mitchell turns to the most urgent questions concerning AI today: How intelligent—really—are the best AI programs? How do they work? What can they actually do, and when do they fail? How humanlike do we expect them to become, and how soon do we need to worry about them surpassing us? Along the way, she introduces the dominant models of modern AI and machine learning, describing cutting-edge AI programs, their human inventors, and the historical lines of thought underpinning recent achievements. She meets with fellow experts such as Douglas Hofstadter, the cognitive scientist and Pulitzer Prize–winning author of the modern classic Gödel, Escher, Bach, who explains why he is “terrified” about the future of AI. She explores the profound disconnect between the hype and the actual achievements in AI, providing a clear sense of what the field has accomplished and how much further it has to go.Interweaving stories about the science of AI and the people behind it, Artificial Intelligence brims with clear-sighted, captivating, and accessible accounts of the most interesting and provocative modern work in the field, flavored with Mitchell’s humor and personal observations. This frank, lively book is an indispensable guide to understanding today’s AI, its quest for “human-level” intelligence, and its impact on the future for us all.

    In Filters Against Folly, Garrett Hardin shows how the filters of literacy—understanding what words really mean, numeracy—being able to quantify information, and ecolacy—assessment of complex interactions over time, can allow us to make sensible judgments about ecological issues.

    A comprehensive introduction to the subject, this book shows in detail how such problems can be solved numerically with great efficiency. The focus is on recognizing convex optimization problems and then finding the most appropriate technique for solving them. The text contains many worked examples and homework exercises and will appeal to students, researchers and practitioners in fields such as engineering, computer science, mathematics, statistics, finance, and economics.