Written by the man who started the adventure, Behind Deep Blue reveals the inside story of what happened behind the scenes at the two historic Deep Blue vs. Kasparov matches. This is also the story behind the quest to create the mother of all chess machines. The book unveils how a modest student project eventually produced a multimillion dollar supercomputer, from the development of the scientific ideas through technical setbacks, rivalry in the race to develop the ultimate chess machine, and wild controversies to the final triumph over the world's greatest human player.In nontechnical, conversational prose, Feng-hsiung Hsu, the system architect of Deep Blue, tells us how he and a small team of fellow researchers forged ahead at IBM with a project they'd begun as students at Carnegie Mellon in the mid-1980s: the search for one of the oldest holy grails in artificial intelligence--a machine that could beat any human chess player in a bona fide match. Back in 1949 science had conceived the foundations of modern chess computers but not until almost fifty years later--until Deep Blue--would the quest be realized.Hsu refutes Kasparov's controversial claim that only human intervention could have allowed Deep Blue to make its decisive, "uncomputerlike" moves. In riveting detail he describes the heightening tension in this war of brains and nerves, the "smoldering fire" in Kasparov's eyes. Behind Deep Blue is not just another tale of man versus machine. This fascinating book tells us how man as genius was given an ultimate, unforgettable run for his mind, no, not by the genius of a computer, but of man as toolmaker.

    This book covers it all! Encrypting your private files, securing your PC, masking your online footsteps, and all while giving you peace of mind with TOTAL 100% ANONYMITY. Don't waste months scouring the internet for info. Just read this! You'll be hooked in five minutes. It's all here: CIA techniques, how the NSA catches Tor users, Truecrypt and the FBI, nuking tracking cookies, private browsing, preventing identity theft. I will show you: -How to Be Anonymous Online -Step by Step Guides for Tor, Freenet, I2P, VPNs, Usenet and more -Browser Fingerprinting -Anti-Hacking and Counter-Forensic Techniques -Photo & Video Metadata -How to Encrypt Files (I make this super simple) -How to Defeat NSA Spying -How to Browse the Deep Web -How to Protect Your Identity -How to Hide Anything! You've probably read How to Be Invisible by J. J. Luna and Incognito Toolkit by Rob Robideau, and while they are fine books, you need this companion piece to take it to the next level!

    Enjoy a journey through the eyes of a geek working at one of the most important companies in the world as he walks you through events both large and small. Just don't get caught in the Redmond reality distortion field! What others are saying about "A Microsoft Life": "Any self-respecting geek needs to read this book. Stepto provides an enjoyable and entertaining insight of life inside Microsoft." - Larry "Major Nelson" Hryb, Director of Programming for Xbox LIVE "Anyone who lived through the adolescent years of the computer revolution will alternate between laughing and crying (from laughing) at these great stories from inside the monolith. - Ken Denmead, NYT Bestselling Author, and editor of Geekdad.com

    Its enable us to sense, measure, and understand aspects of our existence in ways never before possible. The Human Face of Big Data captures, in glorious photographs and moving essays, an extraordinary revolution sweeping, almost invisibly, through business, academia, government, healthcare, and everyday life. It's already enabling us to provide a healthier life for our children. To provide our seniors with independence while keeping them safe. To help us conserve precious resources like water and energy. To alert us to tiny changes in our health, weeks or years before we develop a life-threatening illness. To peer into our own individual genetic makeup. To create new forms of life.  And soon, as many predict, to re-engineer our own species. And we've barely scratched the surface . . . Over the past decade, Rick Smolan and Jennifer Erwitt, co-founders of Against All Odds Productions, have produced a series of ambitious global projects in collaboration with hundreds of the world's leading photographers, writers, and graphic designers. Their Day in the Life projects were credited for creating a mass market for large-format illustrated books (rare was the coffee table book without one).  Today their projects aim at sparking global conversations about emerging topics ranging from the Internet (24 Hours in Cyberspace), to Microprocessors (One Digital Day), to how the human race is learning to heal itself, (The Power to Heal) to the global water crisis (Blue Planet Run). This year Smolan and Erwitt dispatched photographers and writers in every corner of the globe to explore the world of “Big Data” and to determine if it truly does, as many in the field claim, represent a brand new toolset for humanity, helping address the biggest challenges facing our species. The book features 10 essays by noted writers:Introduction: OCEANS OF DATA by Dan GardnerChapter 1: REFLECTIONS IN A DIGITAL MIRROR by Juan Enriquez, CEO, BiotechnomomyChapter 2: OUR DATA OURSELVES by Kate Green, the EconomistChapter 3: QUANTIFYING MYSELF by AJ Jacobs, EsquireChapter 4: DARK DATA by Marc Goodman, Future Crime InstituteChapter 5:  THE SENTIENT SENSOR MESH by Susan Karlin, Fast CompanyChapter 6: TAKING THE PULSE OF THE PLANET by Esther Dyson, EDventureChapter 7: CITIZEN SCIENCE by Gareth Cook, the Boston GlobeChapter 8: A DEMOGRAPH OF ONE by Michael Malone, Forbes magazineChapter 9: THE ART OF DATA by Aaron Koblin, Google Artist in ResidenceChapter 10: DATA DRIVEN by Jonathan Harris, Cowbird The book will also feature stunning info graphics from NIGEL HOLMES.1) GOOGLING GOOGLE: all the ways Google uses Data to help humanity2) DATA IS THE NEW OIL3) THE WORLD ACCORDING TO TWITTER4) AUCTIONING EYEBALLS: The world of Internet advertising5) FACEBOOK: A Billion Friends

    In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases

    This guide also helps you understand the many data-mining techniques in use today.Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making.Understand how data science fits in your organization—and how you can use it for competitive advantageTreat data as a business asset that requires careful investment if you’re to gain real valueApproach business problems data-analytically, using the data-mining process to gather good data in the most appropriate wayLearn general concepts for actually extracting knowledge from dataApply data science principles when interviewing data science job candidates

    This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python.You'll work with a case study throughout the book to help you learn the entire data analysis process—from collecting data and generating statistics to identifying patterns and testing hypotheses. Along the way, you'll become familiar with distributions, the rules of probability, visualization, and many other tools and concepts.Develop your understanding of probability and statistics by writing and testing codeRun experiments to test statistical behavior, such as generating samples from several distributionsUse simulations to understand concepts that are hard to grasp mathematicallyLearn topics not usually covered in an introductory course, such as Bayesian estimationImport data from almost any source using Python, rather than be limited to data that has been cleaned and formatted for statistics toolsUse statistical inference to answer questions about real-world data

    However, most discussions of Bayesian inference rely on intensely complex mathematical analyses and artificial examples, making it inaccessible to anyone without a strong mathematical background. Now, though, Cameron Davidson-Pilon introduces Bayesian inference from a computational perspective, bridging theory to practice-freeing you to get results using computing power. Bayesian Methods for Hackers illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, and Matplotlib. Using this approach, you can reach effective solutions in small increments, without extensive mathematical intervention. Davidson-Pilon begins by introducing the concepts underlying Bayesian inference, comparing it with other techniques and guiding you through building and training your first Bayesian model. Next, he introduces PyMC through a series of detailed examples and intuitive explanations that have been refined after extensive user feedback. You'll learn how to use the Markov Chain Monte Carlo algorithm, choose appropriate sample sizes and priors, work with loss functions, and apply Bayesian inference in domains ranging from finance to marketing. Once you've mastered these techniques, you'll constantly turn to this guide for the working PyMC code you need to jumpstart future projects. Coverage includes - Learning the Bayesian "state of mind" and its practical implications - Understanding how computers perform Bayesian inference - Using the PyMC Python library to program Bayesian analyses - Building and debugging models with PyMC - Testing your model's "goodness of fit" - Opening the "black box" of the Markov Chain Monte Carlo algorithm to see how and why it works - Leveraging the power of the "Law of Large Numbers" - Mastering key concepts, such as clustering, convergence, autocorrelation, and thinning - Using loss functions to measure an estimate's weaknesses based on your goals and desired outcomes - Selecting appropriate priors and understanding how their influence changes with dataset size - Overcoming the "exploration versus exploitation" dilemma: deciding when "pretty good" is good enough - Using Bayesian inference to improve A/B testing - Solving data science problems when only small amounts of data are available Cameron Davidson-Pilon has worked in many areas of applied mathematics, from the evolutionary dynamics of genes and diseases to stochastic modeling of financial prices. His contributions to the open source community include lifelines, an implementation of survival analysis in Python. Educated at the University of Waterloo and at the Independent University of Moscow, he currently works with the online commerce leader Shopify.

    With the sophisticated algorithms in this book, you can write smart programs to access interesting datasets from other web sites, collect data from users of your own applications, and analyze and understand the data once you've found it.Programming Collective Intelligence takes you into the world of machine learning and statistics, and explains how to draw conclusions about user experience, marketing, personal tastes, and human behavior in general -- all from information that you and others collect every day. Each algorithm is described clearly and concisely with code that can immediately be used on your web site, blog, Wiki, or specialized application. This book explains:Collaborative filtering techniques that enable online retailers to recommend products or media Methods of clustering to detect groups of similar items in a large dataset Search engine features -- crawlers, indexers, query engines, and the PageRank algorithm Optimization algorithms that search millions of possible solutions to a problem and choose the best one Bayesian filtering, used in spam filters for classifying documents based on word types and other features Using decision trees not only to make predictions, but to model the way decisions are made Predicting numerical values rather than classifications to build price models Support vector machines to match people in online dating sites Non-negative matrix factorization to find the independent features in a dataset Evolving intelligence for problem solving -- how a computer develops its skill by improving its own code the more it plays a game Each chapter includes exercises for extending the algorithms to make them more powerful. Go beyond simple database-backed applications and put the wealth of Internet data to work for you. "Bravo! I cannot think of a better way for a developer to first learn these algorithms and methods, nor can I think of a better way for me (an old AI dog) to reinvigorate my knowledge of the details."-- Dan Russell, Google "Toby's book does a great job of breaking down the complex subject matter of machine-learning algorithms into practical, easy-to-understand examples that can be directly applied to analysis of social interaction across the Web today. If I had this book two years ago, it would have saved precious time going down some fruitless paths."-- Tim Wolters, CTO, Collective Intellect

    

    During that week, you will learn almost everything modern machine learning has to offer. The author and other practitioners have spent years learning these concepts.Companion wiki — the book has a continuously updated wiki that extends some book chapters with additional information: Q&A, code snippets, further reading, tools, and other relevant resources.Flexible price and formats — choose from a variety of formats and price options: Kindle, hardcover, paperback, EPUB, PDF. If you buy an EPUB or a PDF, you decide the price you pay!Read first, buy later — download book chapters for free, read them and share with your friends and colleagues. Only if you liked the book or found it useful in your work, study or business, then buy it.

    The author Kevin Warwick, a pioneer in the field, examines issues of what it means to be man or machine and looks at advances in robotics which have blurred the boundaries. Topics covered include:how intelligence can be defined whether machines can 'think' sensory input in machine systems the nature of consciousness the controversial culturing of human neurons.Exploring issues at the heart of the subject, this book is suitable for anyone interested in AI, and provides an illuminating and accessible introduction to this fascinating subject.

    Mirroring his theory, Minsky boldly casts The Society of Mind as an intellectual puzzle whose pieces are assembled along the way. Each chapter -- on a self-contained page -- corresponds to a piece in the puzzle. As the pages turn, a unified theory of the mind emerges, like a mosaic. Ingenious, amusing, and easy to read, The Society of Mind is an adventure in imagination.

    He traces AI's origins in ancient myth, through literary classics like Frankenstein, to today's sci-fi blockbusters, arguing that a fascination with AI is hardwired into the human psyche. He explains AI's history, technology, and potential; its manifestations in intelligent machines; its connections to neurology and consciousness, as well as—perhaps most tellingly—what AI reveals about us as human beings.In Our Own Image argues that we are on the brink of a fourth industrial revolution—poised to enter the age of Artificial Intelligence as science fiction becomes science fact. Ultimately, Zarkadakis observes, the fate of AI has profound implications for the future of science and humanity itself.

    For the first time in history, the secrets of the living brain are being revealed by a battery of high tech brain scans devised by physicists. Now what was once solely the province of science fiction has become a startling reality. Recording memories, telepathy, videotaping our dreams, mind control, avatars, and telekinesis are not only possible; they already exist. The Future of the Mind gives us an authoritative and compelling look at the astonishing research being done in top laboratories around the world—all based on the latest advancements in neuroscience and physics. One day we might have a "smart pill" that can enhance our cognition; be able to upload our brain to a computer, neuron for neuron; send thoughts and emotions around the world on a "brain-net"; control computers and robots with our mind; push the very limits of immortality; and perhaps even send our consciousness across the universe. Dr. Kaku takes us on a grand tour of what the future might hold, giving us not only a solid sense of how the brain functions but also how these technologies will change our daily lives. He even presents a radically new way to think about "consciousness" and applies it to provide fresh insight into mental illness, artificial intelligence and alien consciousness. With Dr. Kaku's deep understanding of modern science and keen eye for future developments, The Future of the Mind is a scientific tour de force--an extraordinary, mind-boggling exploration of the frontiers of neuroscience.