Deep reinforcement learning, which DeepMind is known for, allows systems to learn from scratch by trial and error, achieving superhuman performance in some tasks.

The breakthroughs in reinforcement learning at DeepMind, particularly in games like Capture the Flag and AlphaStar, have shown that we can achieve human-level performance in complex tasks.

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

At Google DeepMind, we've been applying AI across almost every branch of science, achieving breakthroughs in areas from mathematics to life sciences, including developing AlphaProof for solving Math Olympiad problems and AlphaFold for predicting molecular structures.

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

AI systems like DeepMind's AlphaProof are trained using reinforcement learning on both successful and failed formal proofs, demonstrating the potential for AI to assist in solving high-level mathematical problems.

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.

The incredible work being done by DeepMind will continue to drive exploration in scientific and mathematical questions, even as AI helps us find answers.