AlphaGo is decidedly not brute force, under any meaningful definition of the term. It's monte carlo tree search, augmented by a neutral network to give stronger

AlphaGo is decidedly not brute force, under any meaningful definition of the term. It's monte carlo tree search, augmented by a neutral network to give stronger priors on which branches are worth exploring. There is an explore/exploit trade-off to manage, which takes it out of the realm of brute force. The previous best go programs used Monte Carlo tree search alone, or with worse heuristics for the priors. Alpha Go improves drastically on the priors, which is arguably exactly the part of the problem that one would attribute to understanding the game: Of the available moves, which ones look the best? They used a fantastic amount of compute for their solution, but, as has uniformly been the case for neutral networks, the compute required for both training and inference has dropped rapidly after the initial research result.