> As far as I can tell from my (limited) background on chess/go/etc algorithms, they seem to include lots of hard-coded scenarios (like a bunch of chess opening

> As far as I can tell from my (limited) background on chess/go/etc algorithms, they seem to include lots of hard-coded scenarios (like a bunch of chess openings) and brute force searching. When playing chess at an advanced level, you need to know a fair number of different openings (that even have names). Chess AI programs have a huge library of openings, endings and other game situations that are evaluated. In contrast, Go does not have a "library" of openings the same way chess does. There are simply too many ways a game can progress. However, in Go, there are many "micro patterns" that repeat over and over again, and you have to learn some of these even at a beginner level. There are numerous recurring formations, like the "Ladder", where one player inevitably loses and it should be recognized after 3-5 stones are in a specific form. So Go has these patterns that recur, but they aren't as clear and simple as in chess. While a ladder can be recognized after just a few stones, the outcome may depend on a stone on the other side of the board, that may be have been there for 100 turns. This is what makes pattern recognition tricky for Go AI.