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."

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

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.

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

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