[vegarab]

You are talking about AlphaGo. AlphaZero was not given any prior knowledge of the game and is trained exclusively through self-play -- and it outperforms Monte Carlo tree search-based systems such as AlphaGo and Stockfish in chess 100-0 with a fraction of the training time.

AlphaZero is also capable of playing Chess, Shogi and Go at a super-super-human.

[TwoBit]

As impressive as AlphaZero surely is, I don't think it ever got a proper comparison to Stockfish. It was running on a veritable supercomputer while Stockfish was running in a crippled mode on crippled hardware.

[tastroder]

Not working in this area but the abstract of the AlphaZero paper [0] seems to disagree about your /any prior knowledge/ point: "Starting from random play, and given no domain knowledge except the game rules, AlphaZero achieved within 24 hours a superhuman level of play in the games of chess and shogi (Japanese chess) as well as Go, and convincingly defeated a world-champion program in each case."

[0] https://arxiv.org/abs/1712.01815

[vegarab]

This is my point exactly. The model is trained without any prior domain knowledge at all. It only has access to a game world where the constrains in the world is a representation of the game's rules.

[sjg007]

You can view these as optimized pattern recognizer regexes. You start with a blank fully connected graph and it eventually converge on a useful function. That graph has many paths encoded in it that represents specific optimal game play.

[vegarab]

Isn't this how the neurons and synapses in our brain work, though?

[sjg007]

Maybe... there’s some other properties of biological neurons we don’t capture in NNs currently.