Understanding AlphaZero Neural Network's SuperHuman Chess Ability - MarkTechPost
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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
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Understanding AlphaZero Neural Network's SuperHuman Chess Ability - MarkTechPost
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Understanding AlphaZero Neural Network's SuperHuman Chess Ability - MarkTechPost
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