So this is a kinda follow-up topic to that Dota2 AI.
To summarise, Go is a tricky game. Particularly tricky for computers that try to iterate over all the possible moves (which is why you can’t “brute force” it in a way similar to chess) even with modern computing power.
Last year, Google managed to beat top human players with their AI. It started out looking at all good games of Go and finding patterns and rules that it could apply that led to victory. Basically it did the computer equivalent of being immersed in the play of the masters to seed the AI with how Go is played well. From there, it refined the process by using the speed of computers to simulate endless games with slight variations and find more rules to increase the odds of winning. This made an AI that played Go roughly like good Go players. It played so well that it beat experts.
This year, Google took the same rough framework but didn’t start out with that huge database of expert human players’ games. The AI got the rules of the game and started playing itself. It didn’t start the rule-learning refinement process as the level of an expert but at the level of a total novice. It played variants of itself and worked out rules for winning. The important difference is this makes it play games in ways that are not bound by the current expert players. The task is harder (which is where the technical breakthroughs are involved in working out how to do this efficiently) but the reward is an AI that doesn’t play Go like a very good human.
For example, joseki are specialised sequences of well-known moves that take place near the edges of the board. (Their scripted nature makes them a little like chess openings.) AlphaGo Zero discovered the standard joseki taught to human players. But it also discovered, and eventually preferred, several others that were entirely of its own invention. The machine, says David Silver, who led the AlphaGo project, seemed to play with a distinctly non-human style. The result is a program that is not just superhuman, but crushingly so.