PHOTO:Mark Leong

In 1957, Herbert A. Simon, a pioneer in artificial intelligence and later a Nobel Laureate in economics, predicted that in 10 years a computer would surpass humans in what was then regarded as the premier battleground of wits: the game of chess. Though the project took four times as long as he expected, in 1997 my colleagues and I at IBM fielded a computer called Deep Blue that defeated Garry Kasparov, the highest-rated chess player ever.

You might have thought that we had finally put the question to rest—but no. Many people argued that we had tailored our methods to solve just this one, narrowly defined problem, and that it could never handle the manifold tasks that serve as better touchstones for human intelligence. These critics pointed to weiqi, an ancient Chinese board game, better known in the West by the Japanese name of Go, whose combinatorial complexity was many orders of magnitude greater than that of chess. Noting that the best Go programs could not even handle the typical novice, they predicted that none would ever trouble the very best players.

Ten years later, the best Go programs still can't beat good human players. Nevertheless, I believe that a world-champion-level Go machine can be built within 10 years, based on the same method of intensive analysis—brute force, basically—that Deep Blue employed for chess. I've got more than a small personal stake in this quest. At my lab at Microsoft Research Asia, in Beijing, I am organizing a graduate student project to design the hardware and software elements that will test the ideas outlined here. If they prove out, then the way will be clear for a full-scale project to dethrone the best human players.

Such a result would further vindicate brute force as a general approach to computing problems, if further vindication were needed. Even now, the method is being applied to such forbidding challenges as protein folding, scheduling, and the many-body problem.

Many of the early computer-chess researchers hailed from the fields of psychology or artificial intelligence and believed that chess ­programs should mimic human thinking. Specifically, they wanted computers to examine only playing sequences that were meaningful according to some human reasoning process. In computer chess this policy, known as selective search, never really made progress. The reason is that humans are extremely good at recognizing patterns; it is one of the things that we do best.

It was only in the late 1970s, with the success of Northwestern University's Chess 4.x program, written by David Slate and Larry Atkins, that the engineering school of thought became dominant. The idea was to let computers do what they do best, namely, calculate. A simple legal-move generator finds all the permissible moves in a position, considers all the possible responses, and then repeats the cycle. Each cycle is called a ply, each generation of new possibilities is called a node—that is, a branching point in a rapidly widening tree of analysis. The branches terminate in “leaf,” or end positions.

Carried to its logical extreme, the tree would grow until it exhausted every legal continuation, leaving the program nothing to do but examine the end positions to see which of them were wins—that is, checkmates—and which were draws, then work backward along the branching structure to choose the line that led to the best outcome, assuming that both sides play perfectly. Such exhaustive analysis is impractical, though, because it would produce a tree containing about 10⁶⁰ positions. That's about a thousand times the number of hydrogen atoms in the sun.