Rémi Coulom spent the last decade building software that can play the ancient game of Go better than practically any other machine on earth. He calls his creation Crazy Stone. Early last year, at the climax of a tournament in Tokyo, it challenged the Go grandmaster Norimoto Yoda, one of the world’s top human players, and it performed remarkably well. In what’s known as the Electric Sage Battle, Crazy Stone beat the grandmaster. But the win came when the comptuer was given a four stone advantage.
Go—the Eastern version of chess in which two players compete with polished stones on 19-by-19-line grid—remains the exception to games where computers beat humans.
In the mid-’90s, a computer program called Chinook beat the world’s top player at the game of checkers. A few years later, IBM’s Deep Blue supercomputer shocked the chess world when it wiped the proverbial floor with world champion Gary Kasparov. And more recently, another IBM machine, Watson, topped the best humans at Jeopardy!, the venerable TV trivia game. Machines have also mastered Othello, Scrabble, backgammon, and poker.
Building a machine that can win at Go isn’t just a matter of computing power. That’s why programs like Coulom’s haven’t cracked it. Crazy Stone relies upon what’s called a Monte Carlo tree search, a system that essentially analyzes the outcomes of every possible move. This is how machines mastered checkers and chess and other games. They looked further ahead than the humans they beat. But with Go, there are too many possibilities to consider. In chess, on any given turn, the average number of possible moves is 35. With Go, it’s 250. And after each of those 250 possible moves, there are another 250. And so on. It’s impossible for a tree search to consider the results of every move (at least not in a reasonable amount of time).
Cracking Go remains enormously difficult. But modern AI is getting closer. When Hassabis reveals his “big surprise,” we’ll know just how close it has come.