So it’s exceedingly difficult to emulate that style of play. Sometimes they have to calculate enormously deeply to decide what to do and sometimes they won’t, but they have very sophisticated evaluation of position and searching mechanisms for deciding which options to explore. The human style is fairly well studied, it’s not completely understood but there have been studies by psychologists going back decades, and the consensus is that strong chess players or grandmasters will look at just a small number of moves and positions as they consider what they’re going to do. I think we were not trying to emulate human style at all - only to the extent that humans played well in most positions so we wanted to play well in most positions. Was the goal to emulate a human play style, or to develop a system that could win at all costs? And right toward the end we brought in additional grandmasters as sparring partners to assess how well our system was doing. It wasn’t important for us in the early stages of developing this to be really strong chess players, and we certainly weren’t, but when it got down to the final stages of preparation there are lots of little details about how the game is played and standard grandmaster practice, so we found it was helpful to bring in one particular grandmaster, Joel Benjamin, to consult with us for a period of time. I think it was important to have some knowledge of chess. To what extent was it necessary to be proficient in chess yourself - was it a question of putting the rules in and working from there, or did the work need to be informed by your own experience?
There’s a famous 1949 paper by Claude Shannon, who was a world-renowned mathematician, and he set out in this paper the process for what it would look like to create a chess computer, and saying that this is a grand challenge-class problem. I’d always maintained an interest throughout my education, so when I joined IBM I saw this as an opportunity to finish this off and show that it could be done.īut separately from a personal interest, it’s something that had been set out as a challenge for computer science right from the earliest days. That got me interested in what it would take to create a computer that could play at a high level. But I could recognize that people who were really good at chess had something that I just didn’t have. I certainly did have an interest in the area of chess, I was a chess player before I was a computer scientist - I was champion of my province in Alberta, Canada at one point. On a human level, what was your motivation for getting into this in the first place? Was it personal interest in chess, or was it more of an abstract challenge for computing? "It’s exceedingly difficult to emulate style of play." We actually lost in 1996 but came back the next year with a new and improved system and won in 1997. So we combined some AI-type advances in algorithms, in search and evaluation, together with a large supercomputer-level machine to produce ultimately world champion-level chess. Our approach was that we realised that a pure brute-force approach wouldn’t be good enough to beat the world champion, but on the other hand having a lot of computing power did make a difference and there was a documented relationship between the strength of a program and how fast it could calculate. We had actually started work on a chess program while we were graduate students at Carnegie Mellon University, and IBM hired the three of us to come and build the next chess machine, which became known as Deep Blue.
Deep Blue’s famous victory was nearly 20 years ago - how did you approach the challenge of solving chess and beating Kasparov back then?
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