Understanding AlphaZero Neural Network's SuperHuman Chess Ability
Por um escritor misterioso
Last updated 01 junho 2024
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
The Artificial Intelligence Gambit - WorldQuant
DeepMind's AlphaZero beats state-of-the-art chess and shogi game engines
CB article on AlphaZero suggests AlphaZero is playing a different kind of chess. : r/chess
DeepMind's New Milestones on the Road to Artificial General Intelligence - The New Stack
AlphaZero algorithm can pick up victory moves in chess
Electronics, Free Full-Text
Living under AI supremacy: Five lessons learned from chess
Google AI Achieves Alien Superhuman Mastery of Chess and Go in Mere Hours - The New Stack
Computer chess - Wikipedia
How AlphaZero Learns Chess
MuZero, AlphaZero, and AlphaDev: Optimizing computer systems - Google DeepMind
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