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TR: Fair enough. But you could have said that last year when Garry won. Yet this year he lost. What made the difference?

HSU: The most obvious differences are that Deep Blue was twice as fast this year because it had new central-processing-unit chips, as well as twice as many chips designed only for the purpose of playing chess.
But for the match those hardware advances weren’t as critical as two other considerations. First, we addressed the knowledge gap. Garry is a remarkable human being, with vast stores of knowledge and intuition about chess gained over 30 years of playing. Last year Deep Blue went into the match as a newborn baby: it had just been built and didn’t know much about chess. So afterward we asked International Grand Master Joel Benjamin to come in with us and essentially take the machine to chess school. Actually, we went to chess school and used what we learned to completely reprogram the machine’s basic software code and redesign the chess chips to incorporate much more chess knowledge. By this year’s match, in Joel’s words, Deep Blue had started to play human-level chess.

Second, we addressed the question of continued learning on Garry’s part. For a computer scientist, the idea of building a machine to compete with the world chess champion is like climbing Mt. Everest. Unfortunately for us last year, the human Mt. Everest grew 100 feet a day while the match was proceeding: Garry has a human being’s ability to adapt to what Deep Blue is doing. We knew that Deep Blue would never be as adaptive as a human, since that’s not the way a computer is constructed. But we built software tools that allowed us to go in between the games and adjust Deep Blue’s programming much faster than we could before. That turned out to be critical. The situation was like competing in the Indy 500, where you go to the pit stop and use your own high-speed tool to change the wheel.

TR: As you note, Deep Blue isn’t as adaptive as a person. You and your colleagues have emphasized again and again that the computer operates by numerical brute force. Why not try to simulate human cognition and adaptability?

HSU: While people are very good at pattern recognition, concept formation, and so on, those tasks are very difficult for computers. Computers can complement humans, however, because they’re good at calculations. So from an engineering point of view, if you want to attack chess problems by computer, you figure out how to use the ability of the machine to calculate fast.

The ability to compute quickly is quite useful in many other fields. One application is called data mining. Big organizations use this technique to extract select information from a vast number of details-for instance, businesses employ it to analyze financial markets. Data mining could also help solve a myriad of problems for individuals, such as the information overload people are now experiencing in the wake of increased access to, among other entities, the Internet. Just as we used our special-purpose chess chips to speed up Deep Blue-employing many of them in parallel-we can create computer systems good for data mining the World Wide Web. Such technology could find and present you with information in a nutshell so that you don’t have to spend your whole life surfing the Web.

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