AlphaGo Zero: The Most Significant Research Advance in AI

#AlphaGoZero: The Most Significant Research Advance in #AI  #AlphaGo

  • The previous version of AlphaGo beat the human world champion in 2016.
  • The new AlphaGo Zero beat the previous version by 100 games to 0, and learned Go completely on its own.
  • Here is the Nature paper explaining technical details (also PDF version: Mastering the Game of Go without Human Knowledge One of the main reasons for success was the use of a novel form of Reinforcement learning in which AlphaGo learned by playing itself.The system starts with a neural net that…
  • It plays millions of games against itself and tuned the neural network to predict next move and the eventual winner of the games.The updated neural network was merged with the Monte Carlo Tree Search algorithm to create a new and stronger version of AlphaGo Zero, and the process resumed.
  • In each iteration, the performance improved by a small amount, but because it can play millions of games a day, AlphaGo Zero surpassed thousands of years of human knowledge of Go in just 3 days., from DeepMind post This is a hugely significant advance for AI and Machine Learning research.Here…


The previous version of AlphaGo beat the human world champion in 2016. The new AlphaGo Zero beat the previous version by 100 games to 0, and learned Go completely on its own. We examine what this means for AI.
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