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.

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

The invention of a new move by AI in Go led to discussions about the implications of computers generating ideas that humans had never considered, marking the beginning of the AI revolution.

In 2016, we recognized that new and powerful algorithms were emerging, but we didn't fully grasp their implications until AI invented a new move in a 2,500-year-old game, Go, which had never been seen before.

The impact of AlphaGo and AlphaZero has been positive for the Go community, as players now use AI to enhance their learning.

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.

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.

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

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.