Research Team

Pioneering intelligent systems, with scientific rigour


The DeepMind Research team brings together multidisciplinary, collaborative teams to develop cutting-edge AI research. By combining extraordinary intellectual freedom and scientific rigour with access to top resources and a structured, supportive culture, we have established an unparalleled track record of AI breakthroughs.

Our team

Our pioneering scientists and engineers have taught agents to cooperate, play world-class chess, diagnose eye disease, and predict the complex 3D shapes of proteins. Combined with a strong focus on safety, ethics, and robustness, the team works to create systems that can provide extraordinary benefits to society.

Research themes

We bring together knowledge from diverse disciplines to research the entire spectrum of intelligence from deep learning and neuroscience to robotics and safety.

Control & robotics

General purpose learning systems must be able to cope with the richness and complexity of the real world. These topics drive the control and robotics teams at DeepMind, which aim to create mechanical systems that can learn how to perform complex manipulation tasks with minimal prior knowledge. The shared ambition is to create systems that are data-efficient, reliable, and robust.

Read our control & robotics publications

Significant breakthroughs

We're proud of our track record of breakthroughs in fundamental AI research, published in journals like Nature, Science, and more.

WaveNet: A generative model for raw audio

WaveNet generates realistic human-sounding speech that reduced the gap between computer and human performance by over 50%, when it was first introduced. It now powers the voice of the Google Assistant.

Giving doctors a headstart on acute kidney injury

Our technology is helping doctors diagnose acute kidney injury (AKI) up to 48-hours earlier than current methods. With early detection, patients get better preventative care, avoiding invasive procedures, and reducing costs.

More accurately identifying breast cancer

We worked with Google Health, Northwestern University, Cancer Research UK and Royal Surrey County Hospital to develop an AI system that can better identify breast cancer in X-rays across populations and systems.

AlphaStar plays StarCraft II at Grandmaster level

AlphaStar is the first AI to reach the top league of StarCraft II without any restrictions. Understanding the potential and limitations of open-ended learning like this is a critcial step towards creating robust systems for real-world domains.

AlphaZero: Shedding new light on chess, shogi, and Go

AlphaZero learned to play three famously complex games, becoming the strongest player in history for each. Learning entirely from scratch, it developed its own distinctive style that continues to inspire human grandmasters.

DQN: Human-level control of Atari games

A great challenge in AI is building flexible systems that can take on a wide range of tasks. Our Deep Q-Network (DQN) made progress on this goal when it learned how to play 49 different Atari games using only raw pixels and the score as inputs.

A neural network with dynamic memory

The differentiable neural computer (DNC) can use its external memory to answer questions about complex structured data, such as stories, family trees, or a map of the London Underground.

AlphaGo defeats Lee Sedol in the game of Go

While becoming the first computer program to defeat a professional human Go player, AlphaGo taught the world new knowledge about perhaps the most studied and contemplated game in history.

GQN: Neural scene representation and rendering

The Generative Query Network (GQN) allows computers to learn about a generated scene purely from observation, much like how infants learn to understand the world.