For me the most interesting part of this match was the part where one of the DeepMind team confirmed that because AlphaGo optimizes for probability of winning rather than expected score difference, games where it has the advantage will look close. It changes how you should interpret the apparent closeness of a game
I was worried about something like this after the first game. I wasn’t sure if expert Go players could discern the difference between AlphaGo playing slightly better than a 9dan versus playing massively better than a 9dan due to how the AI was set up and how difficult it might be to look at players better than the ones already at the top.
Almost all algorithms for almost all games play to win. That isn’t anything special about AlphaGo. Maybe there’s something special about Go or about this algorithm that makes is harder to assess, but that isn’t a reason.
Qiaochu Yuan, or him quoting someone.
This appears to be a general property of the Monte Carlo search algorithm, which AlphaGo employs.
I was worried about something like this after the first game. I wasn’t sure if expert Go players could discern the difference between AlphaGo playing slightly better than a 9dan versus playing massively better than a 9dan due to how the AI was set up and how difficult it might be to look at players better than the ones already at the top.
Almost all algorithms for almost all games play to win. That isn’t anything special about AlphaGo. Maybe there’s something special about Go or about this algorithm that makes is harder to assess, but that isn’t a reason.