Personally I think its not nearly as impressive as state-space comparisons would have you believe. The initial difficulty in cracking Go was because we were using standard game AI techniques that works on games with a much smaller state space. Once we developed methods specifically for Go, we started making progress in leaps and bounds. But this doesn&#x27;t represent fundamental progress in the AI of games, but rather a recognition of using the wrong tool for the job.<p>The difference between Chess and Go is critical here. I don&#x27;t know if there&#x27;s any formal analysis of this sort, but the difference seems to be that in Go multiple paths to the same board state are very similar in evaluation. And so sets of moves can be evaluated as a batch or stochastically. I think it was an algorithm that exploited this property that saw one of the first significant jumps in computer Go strength. Contrast this with chess where different paths to a given board state vary so widely in evaluation that you must evaluate them all. I suspect it is this property that allows Google&#x27;s technique to be effective in evaluation positions. But I don&#x27;t see it extending to games that aren&#x27;t similar to Go in this regard, like chess.