Dennett, Gerald Edelman, and Roger Penrose, all firing volleys in what has come to be called the consciousness wars. Now, in The Conscious Mind, philosopher David J. Chalmers offers a cogent analysis of this heated debate as he unveils a major new theory of consciousness, one that rejects the prevailing reductionist trend of science, while offering provocative insights into the relationship between mind and brain.Writing in a rigorous, thought-provoking style, the author takes us on a far-reaching tour through the philosophical ramifications of consciousness. Chalmers convincingly reveals how contemporary cognitive science and neurobiology have failed to explain how and why mental events emerge from physiological occurrences in the brain. He proposes instead that conscious experience must be understood in an entirely new light--as an irreducible entity (similar to such physical properties as time, mass, and space) that exists at a fundamental level and cannot be understood as the sum of its parts. And after suggesting some intriguing possibilities about the structure and laws of conscious experience, he details how his unique reinterpretation of the mind could be the focus of a new science. Throughout the book, Chalmers provides fascinating thought experiments that trenchantly illustrate his ideas. For example, in exploring the notion that consciousness could be experienced by machines as well as humans, Chalmers asks us to imagine a thinking brain in which neurons are slowly replaced by silicon chips that precisely duplicate their functions--as the neurons are replaced, will consciousness gradually fade away? The book also features thoughtful discussions of how the author's theories might be practically applied to subjects as diverse as artificial intelligence and the interpretation of quantum mechanics.All of us have pondered the nature and meaning of consciousness. Engaging and penetrating, The Conscious Mind adds a fresh new perspective to the subject that is sure to spark debate about our understanding of the mind for years to come.

    Explaining why the whole is sometimes smarter than the sum of its parts, Johnson presents surprising examples of feedback, self-organization, and adaptive learning. How does a lively neighborhood evolve out of a disconnected group of shopkeepers, bartenders, and real estate developers? How does a media event take on a life of its own? How will new software programs create an intelligent World Wide Web? In the coming years, the power of self-organization -- coupled with the connective technology of the Internet -- will usher in a revolution every bit as significant as the introduction of electricity. Provocative and engaging, Emergence puts you on the front lines of this exciting upheaval in science and thought.

    Turing Mathematician Alan Turing invented an imaginary computer known as the Turing Machine; in an age before computers, he explored the concept of what it meant to be "computable," creating the field of computability theory in the process, a foundation of present-day computer programming.The book expands Turing's original 36-page paper with additional background chapters and extensive annotations; the author elaborates on and clarifies many of Turing's statements, making the original difficult-to-read document accessible to present day programmers, computer science majors, math geeks, and others.Interwoven into the narrative are the highlights of Turing's own life: his years at Cambridge and Princeton, his secret work in cryptanalysis during World War II, his involvement in seminal computer projects, his speculations about artificial intelligence, his arrest and prosecution for the crime of "gross indecency," and his early death by apparent suicide at the age of 41.

    Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

    -Byte, September 1995 I can't begin to tell you how many pleasurable hours of study and recreation they have afforded me! I have pored over them in cars, restaurants, at work, at home... and even at a Little League game when my son wasn't in the line-up. -Charles Long If you think you're a really good programmer... read [Knuth's] Art of Computer Programming... You should definitely send me a resume if you can read the whole thing. -Bill Gates It's always a pleasure when a problem is hard enough that you have to get the Knuths off the shelf. I find that merely opening one has a very useful terrorizing effect on computers. -Jonathan Laventhol The first revision of this third volume is the most comprehensive survey of classical computer techniques for sorting and searching. It extends the treatment of data structures in Volume 1 to consider both large and small databases and internal and external memories. The book contains a selection of carefully checked computer methods, with a quantitative analysis of their efficiency. Outstanding features of the second edition include a revised section on optimum sorting and new discussions of the theory of permutations and of universal hashing. Ebook (PDF version) produced by Mathematical Sciences Publishers (MSP), http: //msp.org

    When we are awake we identify with our body, but if we let our mind wander or daydream, we project a mentally imagined self into the remembered past or anticipated future. As we fall asleep, the impression of being a bounded self distinct from the world dissolves, but the self reappears in the dream state. If we have a lucid dream, we no longer identify only with the self within the dream. Our sense of self now includes our dreaming self, the "I" as dreamer. Finally, as we meditate--either in the waking state or in a lucid dream--we can observe whatever images or thoughts arise and how we tend to identify with them as "me." We can also experience sheer awareness itself, distinct from the changing contents that make up our image of the self.Contemplative traditions say that we can learn to let go of the self, so that when we die we can witness its dissolution with equanimity. Thompson weaves together neuroscience, philosophy, and personal narrative to depict these transformations, adding uncommon depth to life's profound questions. Contemplative experience comes to illuminate scientific findings, and scientific evidence enriches the vast knowledge acquired by contemplatives.

    It covers the basics of computer programming, including variables and values, functions, conditionals and control flow, program development and debugging. Later chapters cover basic algorithms and data structures.

    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.

    It was the dawn of a new era in artificial intelligence: a machine capable of beating the reigning human champion at this most cerebral game. That moment was more than a century in the making, and in this breakthrough book, Kasparov reveals his astonishing side of the story for the first time. He describes how it felt to strategize against an implacable, untiring opponent with the whole world watching, and recounts the history of machine intelligence through the microcosm of chess, considered by generations of scientific pioneers to be a key to unlocking the secrets of human and machine cognition. Kasparov uses his unrivaled experience to look into the future of intelligent machines and sees it bright with possibility. As many critics decry artificial intelligence as a menace, particularly to human jobs, Kasparov shows how humanity can rise to new heights with the help of our most extraordinary creations, rather than fear them. Deep Thinking is a tightly argued case for technological progress, from the man who stood at its precipice with his own career at stake.

    But humans are more than computers, and the cutting-edge research in cognitive science is increasingly focused on the more mysterious, creative aspects of the mind. The Way We Think is a landmark synthesis that exemplifies this new direction. The theory of conceptual blending is already widely known in laboratories throughout the world; this book is its definitive statement. Gilles Fauconnier and Mark Turner argue that all learning and all thinking consist of blends of metaphors based on simple bodily experiences. These blends are then themselves blended together into an increasingly rich structure that makes up our mental functioning in modern society. A child's entire development consists of learning and navigating these blends. The Way We Think shows how this blending operates; how it is affected by (and gives rise to) language, identity, and concept of category; and the rules by which we use blends to understand ideas that are new to us. The result is a bold, exciting, and accessible new view of how the mind works.

    But as the field evolves, designers enter the field from many disciplines. Practitioners today have enough experience in UI design that they have been exposed to design rules, but it is essential that they understand the psychology behind the rules in order to effectively apply them. In "Designing with the Mind in Mind," Jeff Johnson, author of the best selling "GUI Bloopers," provides designers with just enough background in perceptual and cognitive psychology that UI design guidelines make intuitive sense rather than being just a list of rules to follow. * The first practical, all-in-one source for practitioners on user interface design rules and why, when and how to apply them.* Provides just enough background into the reasoning behind interface design rules that practitioners can make informed decisions in every project.* Gives practitioners the insight they need to make educated design decisions when confronted with tradeoffs, including competing design rules, time constrictions, or limited resources.

    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.

    In this book, Anthony Chemero describes thisnonrepresentational approach (which he terms radical embodied cognitive science), puts it in historical and conceptual context, and applies it to traditional problemsin the philosophy of mind. Radical embodied cognitive science is a direct descendantof the American naturalist psychology of William James and John Dewey, and followsthem in viewing perception and cognition to be understandable only in terms ofaction in the environment. Chemero argues that cognition should be described interms of agent-environment dynamics rather than in terms of computation andrepresentation. After outlining this orientation to cognition, Chemero proposes amethodology: dynamical systems theory, which would explain things dynamically andwithout reference to representation. He also advances a background theory: Gibsonianecological psychology, "shored up" and clarified. Chemero then looks atsome traditional philosophical problems (reductionism, epistemological skepticism, metaphysical realism, consciousness) through the lens of radical embodied cognitivescience and concludes that the comparative ease with which it resolves theseproblems, combined with its empirical promise, makes this approach to cognitivescience a rewarding one. "Jerry Fodor is my favorite philosopher," Chemerowrites in his preface, adding, "I think that Jerry Fodor is wrong about nearlyeverything." With this book, Chemero explains nonrepresentational, dynamical, ecological cognitive science as clearly and as rigorously as Jerry Fodor explainedcomputational cognitive science in his classic work The Language ofThought.

    Hoffman aptly demonstrates the mysterious constructive powers of our eye-brain machines using lots of simple drawings and diagrams to illustrate basic rules of the visual road. Many of the examples are familiar optical illusions--perspective-confounding cubes, a few lines that add up to a more complex shape than seems right. Hoffman also takes a cue from Oliver Sacks, employing anecdotes about people with various specific visual malfunctions to both further his mechanical explanation of visual intelligence and drive home how important this little-understood aspect of cognition can be in our lives. An especially intriguing example involves a boy, blind from birth, who is surgically given the power to see. At first, he is completely unable to visually distinguish objects familiar by touch, such as the cat and the dog. Other poignant examples show clearly how image construction is normally linked to our emotional well-being and sense of place. Visual Intelligence is a fascinating, confounding look (as it were) at an aspect of human physiology and psychology that very few of us think about much at all. --Therese Littleton

    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.