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