[jscahefer]

think of it this way: The search space for chess is much smaller, so we can lean very heavily on brute forcing in our A.I. implementations.

The search space for Go is much larger, so while brute force searches are critical in tight fighting, and in endgame play, something more has to happen to play go well in the middle game.

Chess fell to a much earlier generation of A.I. While Go held out until A.I. as a field had advanced as well several generations/decades as well.

[geebee]

Agreed. The part I object to is the unqualified statement that go requires a high degree of intuition, whereas chess doesn't. As humans play the game, I think it's safe to say that this is generally inaccurate. Both games, for humans, rely very heavily a high degree of intuition.

I would tend to agree that there is something interesting and new at work here, though, in that computers didn't get better than humans at go simply by applying the same brute force algorithm, just with more processing power. It does suggest that at least some of what we previously thought required "intuition" can be modeled through a random forest (I think that's what they're using, if not RF, then some other combination of ML).

[chillacy]

In the video for the second match, a Google employee mentions that a neural net they call the policy net (trained on a large sample of historical games) provides intuitive moves, while another NN evaluates board strength. They apply the policy net to find multiple interesting moves, then continue to apply the net to anticipate the opponents moves to generate a tree of possible moves. It then just settles on which move to make that gives it the best odds of winning

Starts at 42:00 https://www.youtube.com/watch?v=l-GsfyVCBu0