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Books : Computers & Internet : Computer Science : Artificial Intelligence
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For over three decades, Ray Kurzweil has been one of the most respected and provocative advocates of the role of technology in our future. In his classic The Age of Spiritual Machines, he argued that computers would soon rival the full range of human intelligence at its best. Now he examines the next step in this inexorable evolutionary process: the union of human and machine, in which the knowledge and skills embedded in our brains will be combined with the vastly greater capacity, speed, and knowledge-sharing ability of our creations.
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An eye-opening look at the new computer revolution and the coming transformation of our economy, society, and culture.
A hundred years ago, companies stopped producing their own power with steam engines and generators and plugged into the newly built electric grid. The cheap power pumped out by electric utilities not only changed how businesses operated but also brought the modern world into existence. Today a similar revolution is under way. Companies are dismantling their private computer systems and tapping into rich services delivered over the Internet. This time it's computing that's turning into a utility. The shift is already remaking the computer industry, bringing new competitors like Google to the fore and threatening traditional stalwarts like Microsoft and Dell. But the effects will reach much further. Cheap computing will ultimately change society as profoundly as cheap electricity did. In this lucid and compelling book, Nicholas Carr weaves together history, economics, and technology to explain why computing is changing—and what it means for all of us.
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Want to tap the power behind search rankings, product recommendations, social bookmarking, and online matchmaking? This fascinating book demonstrates how you can build Web 2.0 applications to mine the enormous amount of data created by people on the Internet. 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 adataset 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
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What do we mean when we say "I"? Can thought arise out of matter? Can a self, a soul, a consciousness, an "I" arise out of mere matter? If it cannot, then how can you or I be here? I Am a Strange Loop argues that the key to understanding selves and consciousness is the "strange loop"--a special kind of abstract feedback loop inhabiting our brains. Deep down, a human brain is a chaotic seething soup of particles, on a higher level it is a jungle of neurons, and on a yet higher level it is a network of abstractions that we call "symbols." The most central and complex symbol in your brain or mine is the one we both call "I." The "I" is the nexus in our brain where the levels feed back into each other and flip causality upside down, with symbols seeming to have free will and to have gained the paradoxical ability to push particles around, rather than the reverse. For each human being, this "I" seems to be the realest thing in the world. But how can such a mysterious abstraction be real--or is our "I" merely a convenient fiction? Does an "I" exert genuine power over the particles in our brain, or is it helplessly pushed around by the all-powerful laws of physics? These are the mysteries tackled in I Am a Strange Loop, Douglas R. Hofstadter's first book-length journey into philosophy since Godel, Escher, Bach. Compulsively readable and endlessly thought-provoking, this is the book Hofstadter's many readers have long been waiting for.
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Learning OpenCV puts you right in the middle of the rapidly expanding field of computer vision. Written by the creators of OpenCV, the widely used free open-source library, this book introduces you to computer vision and demonstrates how you can quickly build applications that enable computers to "see" and make decisions based on the data. Computer vision is everywhere -- in security systems, manufacturing inspection systems, medical image analysis, Unmanned Aerial Vehicles, and more. It helps robot cars drive by themselves, stitches Google maps and Google Earth together, checks the pixels on your laptop's LCD screen, and makes sure the stitches in your shirt are OK. OpenCV provides an easy-to-use computer vision infrastructure along with a comprehensive library containing more than 500 functions that can run vision code in real time. With Learning OpenCV, any developer or hobbyist can get up and running with the framework quickly, whether it's to build simple or sophisticated vision applications. The book includes: A thorough introduction to OpenCV Getting input from cameras Transforming images Shape matching Pattern recognition, including face detection Segmenting images Tracking and motion in 2 and 3 dimensions Machine learning algorithmsHands-on exercises at the end of each chapter help you absorb the concepts, and an appendix explains how to set up an OpenCV project in Visual Studio. OpenCV is written in performance optimized C/C++ code, runs on Windows, Linux, and Mac OS X, and is free for commercial and research use under a BSD license. Getting machines to see is a challenging but entertaining goal. If you're intrigued by the possibilities, Learning OpenCV gets you started onbuilding computer vision applications of your own.
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Will the Geeks inherit the earth?
If computers become twice as fast and twice as capable every two years, how long is it before they’re as intelligent as humans? More intelligent? And then in two more years, twice as intelligent? How long before you won’t be able to tell if you are texting a person or an especially ingenious chatterbot program designed to simulate intelligent human conversation?
According to Richard Dooling in Rapture for the Geeks—maybe not that long. It took humans millions of years to develop opposable thumbs (which we now use to build computers), but computers go from megabytes to gigabytes in five years; from the invention of the PC to the Internet in less than fifteen. At the accelerating rate of technological development, AI should surpass IQ in the next seven to thirty-seven years (depending on who you ask). We are sluggish biological sorcerers, but we’ve managed to create whiz-bang machines that are evolving much faster than we are.
In this fascinating, entertaining, and illuminating book, Dooling looks at what some of the greatest minds have to say about our role in a future in which technology rapidly leaves us in the dust. As Dooling writes, comparing human evolution to technological evolution is “worse than apples and oranges: It’s appliances versus orangutans.” Is the era of Singularity, when machines outthink humans, almost upon us? Will we be enslaved by our supercomputer overlords, as many a sci-fi writer has wondered? Or will humans live lives of leisure with computers doing all the heavy lifting?
With antic wit, fearless prescience, and common sense, Dooling provocatively examines nothing less than what it means to be human in what he playfully calls the age of b.s. (before Singularity)—and what life will be like when we are no longer alone with Mother Nature at Darwin’s card table. Are computers thinking and feeling if they can mimic human speech and emotions? Does processing capability equal consciousness? What happens to our quaint beliefs about God when we’re all worshipping technology? What if the human compulsion to create ever more capable machines ultimately leads to our own extinction? Will human ingenuity and faith ultimately prevail over our technological obsessions? Dooling hopes so, and his cautionary glimpses into the future are the best medicine to restore our humanity.
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An engaging explanation of the science behind Malcolm Gladwell’s bestselling Blink
Gerd Gigerenzer is one of the researchers of behavioral intuition responsible for the science behind Malcolm Gladwell’s bestseller Blink. Gladwell showed us how snap decisions often yield better results than careful analysis. Now, Gigerenzer explains why our intuition is such a powerful decision-making tool. Drawing on a decade of research at the Max Plank Institute, Gigerenzer demonstrates that our gut feelings are actually the result of unconscious mental processes—processes that apply rules of thumb that we’ve derived from our environment and prior experiences. The value of these unconscious rules lies precisely in their difference from rational analysis—they take into account only the most useful bits of information rather than attempting to evaluate all possible factors. By examining various decisions we make—how we choose a spouse, a stock, a medical procedure, or the answer to a million-dollar game show question—Gigerenzer shows how gut feelings not only lead to good practical decisions, but also underlie the moral choices that make our society function.
In the tradition of Blink and Freakonomics, Gut Feelings is an exploration of the myriad influences and factors (nature and nurture) that affect how the mind works, grounded in cutting-edge research and conveyed through compelling real-life examples.
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The dramatic growth in practical applications for machine learning over the last ten years has been accompanied by many important developments in the underlying algorithms and techniques. For example, 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 techniques. The practical applicability of Bayesian methods has been greatly enhanced by the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation, while new models based on kernels have had a significant impact on both algorithms and applications.
This completely 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. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.
The book is suitable for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. Extensive support is provided for course instructors, including more than 400 exercises, graded according to difficulty. Example solutions for a subset of the exercises are available from the book web site, while solutions for the remainder can be obtained by instructors from the publisher. The book is supported by a great deal of additional material, and the reader is encouraged to visit the book web site for the latest information.
Coming soon:
*For students, worked solutions to a subset of exercises available on a public web site (for exercises marked "www" in the text)
*For instructors, worked solutions to remaining exercises from the Springer web site
*Lecture slides to accompany each chapter
*Data sets available for download
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Network+ Guide to Networks, Fourth Edition is designed to prepare users for CompTIA's newly-revised 2005 Network+ certification exam and will also offer mapping features to the exam objectives.
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Now, completely updated for LabVIEW 6i. Reflects the latest enhancements in National Instruments' LabVIEW 6i. Designed for non-experts. Softcover. CD-ROM included.
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Programming Legend Charles Petzold unlocks the secrets of the extraordinary and prescient 1936 paper by Alan M. TuringMathematician 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.
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Loads of pictures and very frank discussion make this book a pleasure to read, and a real learning tool.
— Craig Maloney, Slashdot Contributor
The author gives lots of practical advice, some of which would be useful even to experienced tinkerers. It is very thorough.
— Edward Chin, The Canadian Linux Users' Exchange
Learning robotics by yourself isnt easy, but it helps when the encouragement comes from an expert whos spent years in the field. Not only does Author David Cook assist you in understanding the component parts of robot development, but he also presents valuable techniques that prepare you to achieve new discoveries on your own.
Cook begins with the anatomy of a homemade robot and gives you the best advice on how to proceed successfully. General sources for tools and parts are provided in a consolidated list, and specific parts are recommended throughout the book. Also, basic safety precautions and essential measuring and numbering systems are promoted throughout.
Specific tools and parts covered include digital multimeters, motors, wheels, resistors, LEDs, photoresistors, transistors, chips, gears, nut drivers, batteries, and more. Robot Building for Beginners is an inspiring book that provides an essential base of practical knowledge for anyone getting started in amateur robotics.
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This valuable little book offers a thorough introduction to the open-source electronics prototyping platform that's taking the design and hobbyist world by storm. Getting Started with Arduino gives you lots of ideas for Arduino projects and helps you get going on them right away. From getting organized to putting the final touches on your prototype, all the information you need is right in the book.
Inside, you'll learn about:- Interaction design and physical computing
- The Arduino hardware and software development environment
- Basics of electricity and electronics
- Prototyping on a solderless breadboard
- Drawing a schematic diagram
And more. With inexpensive hardware and open-source software components that you can download free, getting started with Arduino is a snap. To use the introductory examples in this book, all you need is a USB Arduino, USB A-B cable, and an LED.
Join the tens of thousands of hobbyists who have discovered this incredible (and educational) platform. Written by the co-founder of the Arduino project, with illustrations by Elisa Canducci, Getting Started with Arduino gets you in on the fun! This 128-page book is a greatly expanded follow-up to the author's original short PDF that's available on the Arduino website.
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As with any burgeoning technology that enjoys commercial attention, the use of data mining is surrounded by a great deal of hype. Exaggerated reports tell of secrets that can be uncovered by setting algorithms loose on oceans of data. But there is no magic in machine learning, no hidden power, no alchemy. Instead there is an identifiable body of practical techniques that can extract useful information from raw data. This book describes these techniques and shows how they work.
The book is a major revision of the first edition that appeared in 1999. While the basic core remains the same, it has been updated to reflect the changes that have taken place over five years, and now has nearly double the references. The highlights for the new edition include thirty new technique sections; an enhanced Weka machine learning workbench, which now features an interactive interface; comprehensive information on neural networks; a new section on Bayesian networks; plus much more.
* Algorithmic methods at the heart of successful data mining-including tried and true techniques as well as leading edge methods
* Performance improvement techniques that work by transforming the input or output
* Downloadable Weka, a collection of machine learning algorithms for data mining tasks, including tools for data pre-processing, classification, regression, clustering, association rules, and visualization-in a new, interactive interface
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During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book.
This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for ``wide'' data (p bigger than n), including multiple testing and false discovery rates.
Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting.