Understanding AlphaZero Neural Network's SuperHuman Chess Ability
Por um escritor misterioso
Descrição
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
Mastering the game of Go with deep neural networks and tree search
DeepMind's AlphaZero beats state-of-the-art chess and shogi game engines
DeepMind, Google Brain & World Chess Champion Explore How AlphaZero Learns Chess Knowledge, by Synced, SyncedReview
Game Changer: AlphaZero's Groundbreaking Chess Strategies and the Promise of AI by Matthew Sadler
Understanding AlphaZero Neural Network's SuperHuman Chess Ability - MarkTechPost
What's Inside AlphaZero's Chess Brain?
Chess AI: A Brief History
Move over AlphaGo: AlphaZero taught itself to play three different games
The Evolution of AlphaGo to MuZero, by Connor Shorten
DeepMind's new MuZero AI develops 'superhuman' chess skills by making plans - SiliconANGLE
AI, Trends and Yearly API Usage Cleanup - The Building Coder
de
por adulto (o preço varia de acordo com o tamanho do grupo)