Anyone knows the algorithm of AlphaGo? I heard that it's related to neural networks. But don't know the details? So it does not find the solution to win in a deterministic way, right? I think so, because Lee Sedol won one game. Is it true that in chess the DeepBlue solves for the unique solution to win, so there is no way human beings can beat her?
Here's the AlphaGo team's paper that has all of the details (behind a paywall): http://www.nature.com/nature/journal/v529/n7587/full/nature16961.html
I gave a couple of tech talks about this recently. This one is about how AlphaGo works and the match with Fan Hui 2p: https://www.youtube.com/watch?v=HTDxpxmFRGo
I gave another talk last year about why it's hard to create a good Go AI. This one also briefly explains the rules: http://blog.fogcreek.com/go-and-artificial-intelligence-tech-talk/
AlphaGo relies heavily on Monte-Carlo Tree Search (MCTS), which is a form of decision making that utilizes random choices as an analog for creativity. Specifically, it allows the algorithm to "think" beyond the bounds of it's rational evaluation procedures. (The random choices are subsequently analyzed and weighted in order to determine which random choices are potentially useful.) Ability to successfully utilize this method to beat the strongest human players is partly a function of supercomputing, in that DeepMind has very fast processors and virtually unlimited memory.
Specifically: we cannot know if AlphaGo played optimally, only that it played more optimally than Lee Sedol in 4 out of 5 games.
Chess also remains unsolved, but the game has been so intensively studied, and DeepBlue so well developed, that it now seems impossible for a human to prevail.
In the case of Go, this means if you converted every atom in the universe into a computing device, and had the entire timespan of the universe to calculate, you still could not express the entire Go gametree. Chess is less complex, but still generally regarded as unsolvable.
The DeepMind paper published in Nature: Mastering the game of Go with deep neural networks and tree search is now freely available.