Understanding AlphaZero Neural Network's SuperHuman Chess Ability - MarkTechPost

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 Chess: How It Works, What Sets It Apart, and What It Can Tell Us, by Maxim Khovanskiy

The future is here – AlphaZero learns chess

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DeepMind's AlphaZero crushes chess

Understanding AlphaZero Neural Network's SuperHuman Chess Ability - MarkTechPost

AlphaZero Chess: How It Works, What Sets It Apart, and What It Can Tell Us, by Maxim Khovanskiy

Move over AlphaGo: AlphaZero taught itself to play three different games

A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play

Acquisition of Chess Knowledge in AlphaZero