Superhuman AI in the game Go

For a few years now, AI achieved superhuman game playing abilities for Go.

It was quite a milestone for AI. When I was in graduate school, people used to joke that AI for Go was where careers go to die. The game has a massive search space, so had thwarted efforts for decades.

So AlphaGo and similar efforts that beat top-ranked Go players was a very big deal indeed when it happened back in 2016. But now, a amateur-level human player just beat a top-ranked AI at playing Go. He won 14 of 15 games.

Most of the reporting on this has been that the player used an exploit, one hole in the AI strategy, that will easily be closed. But I think this will be harder to fix than most people expect.

AlphaGo and similar techniques work by using deep learning to guide the game tree search, focusing it on moves used by experts. This result says you can't do that, that you need to consider more possible moves.

The human won here by doing moves the AI didn't expect, then exploiting the result. It's not that there is just one hole. It's that doing moves outside of what the AI expects, anything outside of what it has seen in the training data, can result in a bad playing by the AI, which can then be exploited by the human.

Solving that means considering more moves by the opponent, which explodes the game tree search, making the search massively exponential again. I suspect it's going to be hard to fix.