Lee Sedol, the 18-time world Go champion, struck a small victory for humankind over the weekend by finally defeating Google’s masterful Go-playing computer, AlphaGo. Sadly for Sedol, and for those cheering humanity’s champion, by that game the best-of-five series was already lost. However, his fightback highlights the way that human ingenuity can overcome brute-force machine learning.
The website Go Game Guru has a very interesting analysis of the fourth game in the historic match between AlphaGo and Sedol:
AlphaGo is very good at estimating its probability of winning. It appears to be able to do this even more accurately than the best human players. With the help of this skill, AlphaGo seems to be able to manage risk more precisely than humans can, and is completely happy to accept losses as long as its probability of winning remains favorable.
The Japanese have a name for this style of play, as it closely resembles the prevalent style of Japanese professionals over the previous few decades. They call it "souba" Go, which means something like "market price."
What Lee and his [team] had realized, was that they needed to completely upend the market.
In other words, Sedol did something that doesn’t involve reasoning over the position of pieces on the Go board. He considered how AlphaGo works, and sought to exploit weaknesses in its design.
AlphaGo is undoubtedly a fantastically clever piece of AI. The program combines two well-established AI techniques—machine learning and tree analysis—to master a game that previously seemed too complex and subtle for machines. So Sedol chose to pursue a strategy that made AlphaGo’s ability to judge risk less useful. And, on turn 79, he came up with an incredibly clever move to turn the game decisively in his favor.
Sadly, it’s too late for Sedol to claim the $1 million purse. But it’ll be interesting to see if Sedol can find further weaknesses in AlphaGo in the fifth and final game.