Go is a traditional Chinese game that is played by two players and involves placing stones on a board in order to capture the opponent’s stones.
Although the rules are quite simple, and there is perfect information, the strategies involved are quite complex. The very best software available today that plays this game is not able to beat professional players for many reasons, including the number of possible moves and the large size of the board (compared to chess).
Now Demis Hassabis, who was behind DeepMind (purchased by Google in 2014), has hinted that his team has managed to get a machine to play the board game Go. Previously, the team has demonstrated an AI learning to play old video games like Breakout. The algorithms have learned to play without help better than most humans, so this team knows a thing or two about designing algorithms to learn to play games.
I’m a little skeptical though about how much conquering Go will advance the field of AI. Back in the 1990s, much excitement and publicity was generated when IBM’s Deep Blue managed to beat the reigning world chess champion, Gary Kasparov. However, this feat was more down to processing power and Deep Blue’s ability to evaluate millions of positions per second than any real advance in machine intelligence.
Certainly Kasparov didn’t play that way and so Deep Blue didn’t really give any great insights into human intelligence. While it may be interesting, beating a human at Go may well turn out to be another example of Artificial Narrow Intelligence.