Winner of the Pulitzer Prize, this book applies Godel's seminal contribution to modern mathematics to the study of the human mind and the development of artificial intelligence.

How much do we humans enjoy our current status as the most intelligent beings on earth? Enough to try to stop our own inventions from surpassing us in smarts? If so, we'd better pull the plug right now, because if Ray Kurzweil is right, we've only got until about 2020 before computers outpace the human brain in computational power. Kurzweil, artificial intelligence expert and author of The Age of Intelligent Machines, shows that technological evolution moves at an exponential pace. Further, he asserts, in a sort of swirling postulate, time speeds up as order increases, and vice versa. He calls this the "Law of Time and Chaos," and it means that although entropy is slowing the stream of time down for the universe overall, and thus vastly increasing the amount of time between major events, in the eddy of technological evolution the exact opposite is happening, and events will soon be coming faster and more furiously. This means that we'd better figure out how to deal with conscious machines as soon as possible--they'll soon not only be able to beat us at chess, they'll likely demand civil rights, and they may at last realize the very human dream of immortality.

A computer in your shoe? Maybe so. Neil Gershenfeld, director of MIT's Media Lab, joins the ranks of techno-prognosticators with When Things Start to Think, and his focus is on how the future of computing will fit into our physical realities. This sensorial focus allows Gershenfeld to explore such science fictional ideas as wearable computers, nanotech circuitry implants, as well as such concerns as emotions, money, and civil rights in the new age of artificial intelligence. Gershenfeld provides a historical overview of the development of computers and extrapolates a world in which we will be forced to deal with things that think all the time. This can't help but reshape our society in ways we must try to imagine. You may be surprised at how far along this road we are--Gershenfeld is in exactly the right place to tell this story, and it's a whole lot of fun (and a little scary) to ride this wave with him. --Adam Fisher 

Data mining techniques are used to power intelligent software, both on and off the Internet. Data Mining: Practical Machine Learning Tools explains the magic behind information extraction in a book that succeeds at bringing the latest in computer science research to any IS manager or developer. In addition, this book provides an opportunity for the authors to showcase their powerful reusable Java class library for building custom data mining software.

This text is remarkable with its comprehensive review of recent research on machine learning, all told in a very approachable style. (While there is plenty of math in some sections, the authors' explanations are always clear.) The book tours the nature of machine learning and how it can be used to find predictive patterns in data comprehensible to managers and developers alike. And they use sample data (for such topics as weather, contact lens prescriptions, and flowers) to illustrate key concepts. 

After setting out to explain the types of machine learning models (like decision trees and classification rules), the book surveys algorithms used to implement them, plus strategies for improving performance and the reliability of results.  Later the book turns to the authors' downloadable Weka (rhymes with "Mecca") Java class library, which lets you experiment with data mining hands-on and gets you started with this technology in custom applications. Final sections look at the bright prospects for data mining and machine learning on the Internet (for example, in Web search engines). 

Precise but never pedantic, this admirably clear title delivers a real-world perspective on advantages of data mining and machine learning. Besides a programming how-to, it can be read profitably by any manager or developer who wants to see what leading-edge machine learning techniques can do for their software. --Richard Dragan

This is science fiction without the fiction--and more mind-bending than anything you ever saw on Star Trek. Moravec, a professor of robotics at Carnegie Mellon University, envisions a not-too-distant future in which robots of superhuman intelligence have picked up the evolutionary baton from their human creators and headed out into space to colonize the universe.

This isn't anything that a million sci-fi paperbacks haven't already envisioned. The difference lies in Moravec's practical-minded mapping of the technological, economic, and social steps that could lead to that vision. Starting with the modest accomplishments of contemporary robotics research, he projects a likely course for the next 40 years of robot development, predicting the rise of superintelligent, creative, emotionally complex cyberbeings and the end of human labor by the middle of the next century.

After Moravec makes this point, his projections start to get really wild: robot corporations will take up residence in outer space with rogue cyborgs; planet-size robots will cruise the solar system looking for smaller bots to assimilate; and eventually every atom in the entire galaxy will be transformed into data-storage space, with a full-scale simulation of human civilization running as a subroutine somewhere.

His last chapter, which mingles the latest in avant-garde physics with hints of Borges's most intoxicating metaphysical conceits, is a breathtaking piece of hallucinatory eschatology. Moravec concludes by reminding us that even the wildest long-range predictions about the technological future never turn out to be as unhinged as they should have been. --Julian Dibbell