I don't play a lot of Go either, but I've read some on the topic.<p>There are a few problems with modeling Go in AI. One of the biggest is that in Chess, you can more easily evaluate intermediary positions by material advantage. Positioning still matters, and there's plenty of times where a sacrifice is worth it, but material advantage is a serviceable enough quantitative measure. Go does not see captures as commonly, and emphasizes positioning more, which makes it harder to quantify.<p>The other problem is that (if I remember right) Go has more possibilities, so it's harder to look ahead as far. All grid intersections on the board are possible moves, and the board is bigger, while for chess the range of moves is more constrained. But really, this isn't the important part, because the forecast horizon of both games are finite, which means in both games at some point you have to stop looking at possibilities and evaluate board position of leaf nodes. And chess has a less-imperfect method for that.