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.

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.

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.

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.

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.

We are also exploring AI's ability to learn quickly from prior experiences, similar to how humans adapt to new games or tasks.

Contemporary machine learning also faces the challenge of building systems that focus on relevant information in the current situation, as demonstrated in projects at DeepMind using Atari games.