The transition from AlphaGo to AlphaZero illustrates the potential of removing human biases from AI training, resulting in more powerful and effective models.

AlphaGo's creative moves have changed the Go community, demonstrating that AI can play in ways humans have never considered.

DeepMind's work on reinforcement learning, particularly with AlphaGo and AlphaZero, has shown that algorithmic advancements can lead to substantial improvements in AI performance, often requiring far less training than traditional methods.

Self-play mechanisms in AI, where systems play against themselves, have led to significant advancements in reinforcement learning, exemplified by systems like AlphaGo and AlphaZero.

AlphaZero learns to play games like Go and chess through self-play, without any human data, by using trial and error to discover winning strategies.

AlphaZero's success in two-player games highlights the need for a general understanding of the world and language to develop more advanced AI models.

In AlphaZero, we found that removing human data allowed the system to learn more effectively, achieving higher performance than models that relied on human knowledge.

AlphaGo's move 37 demonstrated a creative strategy that was unexpected by human players, showcasing the potential for AI to generate novel ideas beyond human understanding.

AlphaGo demonstrated creativity by developing new strategies, such as the famous move 37 in game two of the championship match, which had never been seen before in hundreds of years of Go play.