Each chapter is relatively self-contained and can be used as a unit of study. The algorithms are described in English and in a pseudocode designed to be readable by anyone who has done a little programming. The explanations have been kept elementary without sacrificing depth of coverage or mathematical rigor.

    The meme is also one of the most important--and controversial--concepts to emerge since 'The Origin of the Species' appeared nearly 150 years ago.In 'The Meme Machine' Susan Blackmore boldly asserts: "Just as the design of our bodies can be understood only in terms of natural selection, so the design of our minds can be understood only in terms of memetic selection." Indeed, Blackmore shows that once our distant ancestors acquired the crucial ability to imitate, a second kind of natural selection began, a survival of the fittest amongst competing ideas and behaviors. Ideas and behaviors that proved most adaptive - making tools, for example, or using language--survived and flourished, replicating themselves in as many minds as possible. These memes then passed themselves on from generation to generation by helping to ensure that the genes of those who acquired them also survived and reproduced. Applying this theory to many aspects of human life, Blackmore offers brilliant explanations for why we live in cities, why we talk so much, why we can't stop thinking, why we behave altruistically, how we choose our mates, and much more.With controversial implications for our religious beliefs, our free will, our very sense of "self," 'The Meme Machine' offers a provocative theory everyone will soon be talking about.

    By reconstructing authentic, complex AI programs using state-of-the-art Common Lisp, the book teaches students and professionals how to build and debug robust practical programs, while demonstrating superior programming style and important AI concepts. The author strongly emphasizes the practical performance issues involved in writing real working programs of significant size. Chapters on troubleshooting and efficiency are included, along with a discussion of the fundamentals of object-oriented programming and a description of the main CLOS functions. This volume is an excellent text for a course on AI programming, a useful supplement for general AI courses and an indispensable reference for the professional programmer.

    In captivity, octopuses have been known to identify individual human keepers, raid neighboring tanks for food, turn off lightbulbs by spouting jets of water, plug drains, and make daring escapes. How is it that a creature with such gifts evolved through an evolutionary lineage so radically distant from our own? What does it mean that evolution built minds not once but at least twice? The octopus is the closest we will come to meeting an intelligent alien. What can we learn from the encounter?In Other Minds, Peter Godfrey-Smith, a distinguished philosopher of science and a skilled scuba diver, tells a bold new story of how subjective experience crept into being—how nature became aware of itself. As Godfrey-Smith stresses, it is a story that largely occurs in the ocean, where animals first appeared. Tracking the mind’s fitful development, Godfrey-Smith shows how unruly clumps of seaborne cells began living together and became capable of sensing, acting, and signaling. As these primitive organisms became more entangled with others, they grew more complicated. The first nervous systems evolved, probably in ancient relatives of jellyfish; later on, the cephalopods, which began as inconspicuous mollusks, abandoned their shells and rose above the ocean floor, searching for prey and acquiring the greater intelligence needed to do so. Taking an independent route, mammals and birds later began their own evolutionary journeys.But what kind of intelligence do cephalopods possess? Drawing on the latest scientific research and his own scuba-diving adventures, Godfrey-Smith probes the many mysteries that surround the lineage. How did the octopus, a solitary creature with little social life, become so smart? What is it like to have eight tentacles that are so packed with neurons that they virtually “think for themselves”? What happens when some octopuses abandon their hermit-like ways and congregate, as they do in a unique location off the coast of Australia?By tracing the question of inner life back to its roots and comparing human beings with our most remarkable animal relatives, Godfrey-Smith casts crucial new light on the octopus mind—and on our own.

    Although we are unaware of it, our brains sift through and discard billions of pieces of data in order to allow us to understand the world around us. In fact, most of what we call thought is actually the unconscious discarding of information. What our consciousness rejects constitutes the most valuable part of ourselves, the "Me" that the "I" draws on for most of our actions--fluent speech, riding a bicycle, anything involving expertise. No wonder that, in this age of information, so many of us feel empty and dissatisfied. As engaging as it is insightful, this important book encourages us to rely more on what our instincts and our senses tell us so that we can better appreciate the richness of human life.

    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.

    That was before one pioneering work documented the underground computer revolution that was about to change our world forever. With groundbreaking profiles of Bill Gates, Steve Wozniak, MIT's Tech Model Railroad Club, and more, Steven Levy's Hackers brilliantly captured a seminal moment when the risk-takers and explorers were poised to conquer twentieth-century America's last great frontier. And in the Internet age, the hacker ethic-first espoused here-is alive and well.

    They can navigate more data than a doctor or lawyer and act with greater precision. For many years we’ve taken solace in the notion that they can’t create. But now that algorithms can learn and adapt, does the future of creativity belong to machines, too?It is hard to imagine a better guide to the bewildering world of artificial intelligence than Marcus du Sautoy, a celebrated Oxford mathematician whose work on symmetry in the ninth dimension has taken him to the vertiginous edge of mathematical understanding. In The Creativity Code he considers what machine learning means for the future of creativity. The Pollockizer can produce drip paintings in the style of Jackson Pollock, Botnik spins off fanciful (if improbable) scenes inspired by J. K. Rowling, and the music-composing algorithm Emmy managed to fool a panel of Bach experts. But do these programs just mimic, or do they have what it takes to create? Du Sautoy argues that to answer this question, we need to understand how the algorithms that drive them work―and this brings him back to his own subject of mathematics, with its puzzles, constraints, and enticing possibilities.While most recent books on AI focus on the future of work, The Creativity Code moves us to the forefront of creative new technologies and offers a more positive and unexpected vision of our future cohabitation with machines. It challenges us to reconsider what it means to be human―and to crack the creativity code.

    In this revision, the author takes a structured approach to explaining how networks function.

    What they don't realize—and what Daniel Hillis's short book brilliantly demonstrates—is that computers' seemingly complex operations can be broken down into a few simple parts that perform the same simple procedures over and over again.Computer wizard Hillis offers an easy-to-follow explanation of how data is processed that makes the operations of a computer seem as straightforward as those of a bicycle. Avoiding technobabble or discussions of advanced hardware, the lucid explanations and colorful anecdotes in The Pattern on the Stone go straight to the heart of what computers really do.Hillis proceeds from an outline of basic logic to clear descriptions of programming languages, algorithms, and memory. He then takes readers in simple steps up to the most exciting developments in computing today—quantum computing, parallel computing, neural networks, and self-organizing systems.Written clearly and succinctly by one of the world's leading computer scientists, The Pattern on the Stone is an indispensable guide to understanding the workings of that most ubiquitous and important of machines: the computer.

    In this virtuosic work of popular science, neuroscientist and best-selling author Dean Buonomano investigates the intricate relationship between the brain and time: What is time? Why does time seem to speed up or slow down? Is our sense that time flows an illusion? Buonomano presents his own influential theory of how the brain tells time, and he illuminates such concepts as free will, consciousness, spacetime, and relativity from the perspective of a neuroscientist. Drawing on physics, evolutionary biology, and philosophy, Your Brain Is a Time Machine reveals that the brain’s ultimate purpose may be to predict the future, and thus that your brain is a time machine.

    He explains what the mind is, how it evolved, and how it allows us to see, think, feel, laugh, interact, enjoy the arts, and ponder the mysteries of life. And he does it with the wit that prompted Mark Ridley to write in the New York Times Book Review, "No other science writer makes me laugh so much. . . . [Pinker] deserves the superlatives that are lavished on him."  The arguments in the book are as bold as its title. Pinker rehabilitates some unfashionable ideas, such as that the mind is a computer and that human nature was shaped by natural selection, and challenges fashionable ones, such as that passionate emotions are irrational, that parents socialize their children, and that nature is good and modern society corrupting. Winner of the Los Angeles Times Book Prize A New York Times Notable Book of the Year and Publishers Weekly Best Book of 1997 Featured in Time magazine, the New York Times Magazine, The New Yorker, Nature, Science, Lingua Franca, and Science Times Front-page reviews in the Washington Post Book World, the Boston Globe Book Section, and the San Diego Union Book Review

    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.

    Is it in our genes? The structure of our brains? Our genome may determine our eye color and even aspects of our personality. But our friendships, failures, and passions also shape who we are. The question is: how?Sebastian Seung, a dynamic professor at MIT, is on a quest to discover the biological basis of identity. He believes it lies in the pattern of connections between the brain’s neurons, which change slowly over time as we learn and grow. The connectome, as it’s called, is where our genetic inheritance intersects with our life experience. It’s where nature meets nurture.Seung introduces us to the dedicated researchers who are mapping the brain’s connections, neuron by neuron, synapse by synapse. It is a monumental undertaking—the scientific equivalent of climbing Mount Everest—but if they succeed, it could reveal the basis of personality, intelligence, memory, and perhaps even mental disorders. Many scientists speculate that people with anorexia, autism, and schizophrenia are "wired differently," but nobody knows for sure. The brain’s wiring has never been clearly seen.In sparklingly clear prose, Seung reveals the amazing technological advances that will soon help us map connectomes. He also examines the evidence that these maps will someday allow humans to "upload" their minds into computers, achieving a kind of immortality.Connectome is a mind-bending adventure story, told with great passion and authority. It presents a daring scientific and technological vision for at last understanding what makes us who we are. Welcome to the future of neuroscience.

    Polya, How to Solve It will show anyone in any field how to think straight. In lucid and appealing prose, Polya reveals how the mathematical method of demonstrating a proof or finding an unknown can be of help in attacking any problem that can be reasoned out--from building a bridge to winning a game of anagrams. Generations of readers have relished Polya's deft--indeed, brilliant--instructions on stripping away irrelevancies and going straight to the heart of the problem.