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Comment by pyentropy | Hacker News Reader

AlphaZero has coded all the rules for the respective three games, they do a tree search and their neural network output layer has exactly n neurons for max(n) possible moves. Although it's impressive they don't teach it heuristics and strategies, it's a very specific task.

What about pigeons predicting breast cancer with 99% probability, rats learning to drive cars, monkeys building tools?

Rodents stand a bigger chance at learning Go than AlphaZero spontaneously building stone tools and driving cars.

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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.

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.

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.

The natural environment encodes "all the rules" for real animals, too. You need some constraints or else there is nothing to be learned. One could say that every survival task is also specific , but is a slight variation of previously learned one.

> pigeons predicting breast cancer with 99%

pigeons contain 340M neurons (with dendrites and all, giving them higher computational capacity than ANN units).

> Rodents stand a bigger chance at learning Go

They probably don't ; probably because they can't understand the objective function and their brain capacity is limited

Scientist have just recently taught rats how to play hide-and-seek for fun. Other scientists have found out that slime mold will model the Japanese railroad system. I wouldn't be surprised if rodents (plural) instinctively have a go strategy once someone figures how to make an analog game for them.

its probably safe to assume that even if rodents are behaviorally trained to follow complex rules, they are mostly pattern-matching, and are lacking higher-level abstraction and communication models like humans do. If they did they would at least attempt to communicate with us, like we do with them. In such a case, an elephant that plays go by patternmatching is no different from a neural network that learned by patternmatching

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