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

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 DQN system from DeepMind represents one of the first general intelligences, capable of learning a variety of tasks from raw pixel data without prior knowledge of the game.

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.

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.

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

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.

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.