Understanding AlphaZero Neural Network's SuperHuman Chess Ability

As a common and (sometimes) proven belief, deep learning systems seem to learn uninterpretable representations and are far from human understanding. Recently, some studies have highlighted the fact that this may not always be applicable, and some networks may be able to learn human-readable representations. Unfortunately, this ability could merely come from the fact that these networks are exposed to human-generated data. So, to demonstrate their ability to learn like humans (and not that they are simply memorizing human-created labels), it is necessary to test them without any label.  Following this idea, the DeepMind and Google Brain teams, together with

AlphaZero - Chessprogramming wiki

Understanding AlphaZero Neural Network's SuperHuman Chess Ability - MarkTechPost

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Electronics, Free Full-Text

DeepMind's AlphaZero algorithm taught itself to play Go, chess, and shogi with superhuman performance and then beat state-of-the-art programs specializing in each game. The ability of AlphaZero to adapt to various game

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