New AlphaGo AI learns without help from humans

What's new: AlphaGo's initial iteration was trained on a database of human Go games whereas the newer AlphaGo Zero's artificial neural networks use the current state of the game as input. Through trial and error and feedback in the form of winning, the AI learned how to play. It then used that same network to choose its next move whereas AlphaGo used a separate network. This reinforcement learning strategy, which was used extensively by AlphaGo as well, has its roots in psychology: the neural network learns from rewards like humans do. The DeepMind researchers wrote: "the self-learned player performed much better overall, defeating the human-trained player within the first 24h of training. This suggests that AlphaGo Zero may be learning a strategy that is qualitatively different to human play."