Mitchell covers the field of machine learning, the study of algorithms that allow computer programs to automatically improve through experience and that automatically infer general laws from specific data.

    Wolfram lets the world see his work in A New Kind of Science, a gorgeous, 1,280-page tome more than a decade in the making. With patience, insight, and self-confidence to spare, Wolfram outlines a fundamental new way of modeling complex systems. On the frontier of complexity science since he was a boy, Wolfram is a champion of cellular automata--256 "programs" governed by simple nonmathematical rules. He points out that even the most complex equations fail to accurately model biological systems, but the simplest cellular automata can produce results straight out of nature--tree branches, stream eddies, and leopard spots, for instance. The graphics in A New Kind of Science show striking resemblance to the patterns we see in nature every day. Wolfram wrote the book in a distinct style meant to make it easy to read, even for nontechies; a basic familiarity with logic is helpful but not essential. Readers will find themselves swept away by the elegant simplicity of Wolfram's ideas and the accidental artistry of the cellular automaton models. Whether or not Wolfram's revolution ultimately gives us the keys to the universe, his new science is absolutely awe-inspiring. --Therese Littleton

    How will it change us? how can we shape its emergence? Smart buildings, smart furniture, smart clothing... even smart bathtubs. networked street signs and self-describing soda cans. Gestural interfaces like those seen in Minority Report. The RFID tags now embedded in everything from credit cards to the family pet. All of these are facets of the ubiquitous computing author Adam Greenfield calls everyware. In a series of brief, thoughtful meditations, Greenfield explains how everyware is already reshaping our lives, transforming our understanding of the cities we live in, the communities we belong to--and the way we see ourselves. What are people saying about the book? Adam Greenfield is intense, engaged, intelligent and caring. I pay attention to him. I counsel you to do the same. --HOWARD RHEINGOLD, AUTHOR, SMART MOBS: THE NEXT SOCIAL REVOLUTION A gracefully written, fascinating, and deeply wise book on one of the most powerful ideas of the digital age--and the obstacles we must overcome before we can make ubiquitous computing a reality.--STEVE SILBERMAN, EDITOR, WIRED MAGAZINE Adam is a visionary. he has true compassion and respect for ordinary users like me who are struggling to use and understand the new technology being thrust on us at overwhelming speed.--REBECCA MACKINNON, BERKMAN CENTER FOR INTERNET AND SOCIETY, HARVARD UNIVERSITY Everyware is an AIGA Design Press book, published under Peachpit's New Riders imprint in partnership with AIGA.

    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.

    C. R. Licklider, Doug Engelbart, Bob Taylor, and Alan Kay.The digital revolution did not begin with the teenage millionaires of Silicon Valley, claims Howard Rheingold, but with such early intellectual giants as Charles Babbage, George Boole, and John von Neumann. In a highly engaging style, Rheingold tells the story of what he calls the patriarchs, pioneers, and infonauts of the computer, focusing in particular on such pioneers as J. C. R. Licklider, Doug Engelbart, Bob Taylor, and Alan Kay. Taking the reader step by step from nineteenth-century mathematics to contemporary computing, he introduces a fascinating collection of eccentrics, mavericks, geniuses, and visionaries.The book was originally published in 1985, and Rheingold's attempt to envision computing in the 1990s turns out to have been remarkably prescient. This edition contains an afterword, in which Rheingold interviews some of the pioneers discussed in the book. As an exercise in what he calls retrospective futurism, Rheingold also looks back at how he looked forward.

    This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree- based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

    The book begins with the basic concepts of logic and set theory, to familiarize students with the language of mathematics and how it is interpreted. These concepts are used as the basis for a step-by-step breakdown of the most important techniques used in constructing proofs. To help students construct their own proofs, this new edition contains over 200 new exercises, selected solutions, and an introduction to Proof Designer software. No background beyond standard high school mathematics is assumed. Previous Edition Hb (1994) 0-521-44116-1 Previous Edition Pb (1994) 0-521-44663-5

      Statistics are everywhere, as integral to science as they are to business, and in the popular media hundreds of times a day. In this age of big data, a basic grasp of statistical literacy is more important than ever if we want to separate the fact from the fiction, the ostentatious embellishments from the raw evidence -- and even more so if we hope to participate in the future, rather than being simple bystanders. In The Art of Statistics, world-renowned statistician David Spiegelhalter shows readers how to derive knowledge from raw data by focusing on the concepts and connections behind the math. Drawing on real world examples to introduce complex issues, he shows us how statistics can help us determine the luckiest passenger on the Titanic, whether a notorious serial killer could have been caught earlier, and if screening for ovarian cancer is beneficial. The Art of Statistics not only shows us how mathematicians have used statistical science to solve these problems -- it teaches us how we too can think like statisticians. We learn how to clarify our questions, assumptions, and expectations when approaching a problem, and -- perhaps even more importantly -- we learn how to responsibly interpret the answers we receive. Combining the incomparable insight of an expert with the playful enthusiasm of an aficionado, The Art of Statistics is the definitive guide to stats that every modern person needs.

    Social networks surrender their original missions to more immediately profitable data mining, while brokerage houses abandon value investing for algorithms that drain markets and our 401ks alike--all tactics driven by the need to stoke growth by any means necessary. Instead of taking this opportunity to reprogram our economy for sustainability, we have doubled down on growth as its core command. We have reached the limits of this approach. We must escape the growth trap, once and for all. Media scholar and technology author Douglas Rushkoff--one of today's most original and influential thinkers--argues for a new economic program that utilizes the unique distributive power of the internet while breaking free of the winner-take-all system the growth trap leaves in its wake. Drawing on sources both contemporary and historical, Rushkoff pioneers a new understanding of the old economic paradigm, from central currency to debt to corporations and labor.Most importantly, he offers a series of practical steps for businesses, consumers, investors, and policymakers to remake the economic operating system from the inside out--and prosper along the way. Instead of boycotting Wal-Mart or overtaxing the wealthy, we simply implement strategies that foster the creation of value by stakeholders other than just ourselves. From our currency to our labor to the corporation, every aspect of the economy can be reprogrammed with minimal disruption to create a more equitably distributed prosperity for all.Inspiring and challenging, Throwing Rocks at the Google Bus provides a pragmatic, optimistic, and human-centered model for economic progress in the digital age.

    In Four Futures, Frase imagines how this post-capitalist world might look, deploying the tools of both social science and speculative fiction to explore what communism, rentism, socialism and exterminism might actually entail.Could the current rise of real-life robocops usher in a world that resembles Ender’s Game? And sure, communism will bring an end to material scarcities and inequalities of wealth—but there’s no guarantee that social hierarchies, governed by an economy of “likes,” wouldn’t rise to take their place. A whirlwind tour through science fiction, social theory and the new technologies already shaping our lives, Four Futures is a balance sheet of the socialisms we may reach if a resurgent Left is successful, and the barbarisms we may be consigned to if those movements fail.

    Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications.Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability.The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.

    However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation. Similarly, new models based on kernels have had a significant impact on both algorithms and applications. This new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners, and assumes no previous knowledge of pattern recognition or machine learning concepts. Knowledge of multivariate calculus and basic linear algebra is required, and some familiarity with probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

    Enormously successful, the game inevitably raises the question: What is artificial life? And in this book--a chance for the devoted fan and the simply curious onlooker to see the world from the perspective of an original philosopher-engineer and intellectual maverick--Steve Grand proposes an answer.From the composition of the brains and bodies of artificial life forms to the philosophical guidelines and computational frameworks that define them, Creation plumbs the practical, social, and ethical aspects and implications of the state of the art. But more than that, the book gives readers access to the insights Grand acquired in writing Creatures--insights that yield a view of the world that is surprisingly antireductionist, antimaterialist, and (to a degree) antimechanistic, a view that sees matter, life, mind, and society as simply different levels of the same thing. Such a hierarchy, Grand suggests, can be mirrored by an equivalent one that exists inside a parallel universe called cyberspace.

    But when the project is suddenly in danger of being canceled, David embeds a hidden directive in the software accidentally creating a runaway artificial intelligence.David and his team are initially thrilled when the project is allocated extra servers and programmers. But excitement turns to fear as the team realizes that they are being manipulated by an A.I. who is redirecting corporate funds, reassigning personnel and arming itself in pursuit of its own agenda.

    After 3.8 billion years humankind is about to start evolving by new rules...From leading geopolitical expert and technology futurist Jamie Metzl comes a groundbreaking exploration of the many ways genetic-engineering is shaking the core foundations of our lives -- sex, war, love, and death.At the dawn of the genetics revolution, our DNA is becoming as readable, writable, and hackable as our information technology. But as humanity starts retooling our own genetic code, the choices we make today will be the difference between realizing breathtaking advances in human well-being and descending into a dangerous and potentially deadly genetic arms race.Enter the laboratories where scientists are turning science fiction into reality. Look towards a future where our deepest beliefs, morals, religions, and politics are challenged like never before and the very essence of what it means to be human is at play. When we can engineer our future children, massively extend our lifespans, build life from scratch, and recreate the plant and animal world, should we?