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We work on some of the most complex and interesting challenges in AI.

Explore our research across
Deep learning
Theory and foundations
Control and robotics
Unsupervised learning and generative models
Reinforcement learning
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Highlighted research

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Visual representation of AlphaFold.
AlphaFold
AlphaFold can accurately predict 3D models of protein structures and has the potential to accelerate research in every field of biology.
Find out more
Visual representation of WaveNet generating system voices on devices.
WaveNet
WaveNet creates more natural-sounding speech for products used by millions of people around the world.
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Featured publications

Visual representation of AlphaFold.
Restoring and attributing ancient texts using deep neural networks
Restoring, placing, and dating ancient texts through collaboration between AI and historians.
Nature
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Visual representation of WaveNet generating system voices on devices.
Magnetic control of tokamak plasmas through deep reinforcement learning
Successfully controlling the nuclear fusion plasma in a tokamak with deep reinforcement learning.
Nature
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Visual representation of WaveNet generating system voices on devices.
Advancing mathematics by guiding human intuition with AI
Applying AI toward discovering new insights in two areas of pure mathematics: topology and representation theory.
Nature
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Publications

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Evaluating Multimodal Interactive Agents
Josh Abramson, Arun Ahuja, Federico Carnevale, Petko Georgiev, Alex Goldin, Jessica Landon, Timothy Lillicrap, Alistair Muldal, Adam Santoro, Tamara von Glehn, Gregory Wayne, Nathaniel Wong, Chen Yan, Blake Richards *, Alden Hung *
arXiv
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Dynamic language understanding: adaptation to new knowledge in parametric and semi-parametric models
Elena Gribovskaya, Angeliki Lazaridou, Tomáš Kočiský
arXiv
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Contextual Information-Directed Sampling
Botao Hao, Tor Lattimore, Chao Qin *
ICML
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Selection-Inference: Iterative Interpretable Reasoning Module
Antonia Creswell, Irina Higgins, Murray Shanahan
arXiv
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CLIP-CLOP: CLIP-Guided Collage and Photomontage
Piotr Mirowski, Dylan Banarse, Mateusz Malinowski, Simon Osindero, Chrisantha Fernando
ICCC
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Emergent Bartering Behaviour in Multi-Agent Reinforcement Learning
Mike Johanson, Edward Hughes, Finbarr Timbers, Joel Leibo
arXiv
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Normalizing flows for atomic solids
Peter Wirnsberger, George Papamakarios, Borja Ibarz, Sébastien Racaniere, Andy Ballard, Alexander Pritzel, Charles Blundell
Machine Learning: Science and Technology
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Constraint-based graph network simulator
Yulia Rubanova, Alvaro Sanchez, Tobias Pfaff, Peter Battaglia
ICML
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Biased Gradient Estimate with Drastic Variance Reduction for Meta Reinforcement Learning
Yunhao Tang
ICML
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A Generalist Agent
Scott Reed, Konrad Żołna, Emilio Parisotto, Sergio Gómez Colmenarejo, Alexander Novikov, Gabriel Barth-Maron, Mai Giménez, Yury Sulsky, Jackie Kay, Jost Tobias Springenberg, Tom Eccles, Jake Bruce, Ali Razavi, Ashley Edwards, Nicolas Heess, Yutian Chen, Raia Hadsell, Oriol Vinyals, Mahyar Bordbar, and Nando de Freitas
arXiv
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Active offline policy selection
Yutian Chen, Ksenia Konyushkova, Tom Paine, Caglar Gulcehre, Cosmin Paduraru, Daniel J. Mankowitz, Misha Denil, Nando de Freitas
NeurIPS
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Learning transferable motor skills with hierarchical latent mixture policies
Dushyant Rao, Fereshteh Sadeghi, Leonard Hasenclever, Markus Wulfmeier, Martina Zambelli, Giulia Vezzani, Dhruva TB, Yusuf Aytar, Josh Merel *, Nicolas Heess, Raia Hadsell
ICLR
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The prospects and opportunities of protein structure prediction with AI
Kathryn Tunyasuvunakool
Nature Reviews Molecular Cell Biology
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Fair Normalizing Flows
Mislav Balunovic *, Anian Ruoss, Martin Vechev *
ICLR
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Measuring CLEVRness: Black-box Testing of Visual Reasoning Models
Spyridon Mouselinos *, Henryk Michalewski *, Mateusz Malinowski
ICLR
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Data Distributional Properties Drive Emergent Few-Shot Learning in Transformers
Stephanie Chan, Adam Santoro, Andrew Lampinen, Jane Wang, Aaditya Singh, Pierre Harvey Richemond, Jay Mcclelland, Felix Hill
arXiv
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A Brief Guide to Designing and Evaluating Human-Centered Interactive Machine Learning
Kory Mathewson, Patrick Pilarski
arXiv
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An empirical analysis of compute-optimal large language model training
Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Laurent Sifre
arXiv
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Offline Distillation for Robot Lifelong Learning with Imbalanced Experience
Wenxuan Zhou *, Steven Bohez, Jan Humplik, Abbas Abdolmaleki, Dushyant Rao, Markus Wulfmeier, Tuomas Haarnoja, Nicolas Heess
arXiv
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Learning heuristics for A*
Danilo Numeroso *, Davide Bacciu *, Petar Veličković
ICLR
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From eye-blinks to state construction: diagnostic benchmarks for online representation learning
Banafsheh Rafiee *, Zaheer Abbas, Sina Ghiassian *, Raksha Kumaraswamy *, Elliot Ludvig *, Richard Sutton, Adam White
Adaptive Behavior
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Continuous Neural Algorithmic Planners
Yu He *, Petar Veličković, Pietro Liò *, Andreea Deac *
ICLR
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Towards better visual explanations for deep image classifiers
Agnieszka Grabska-Barwinska, Amal Rannen-Triki, Omar Rivasplata *, Andras Gyorgy
NeurIPS Workshop
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Imitate and Repurpose: Learning Reusable Robot Movement Skills From Human and Animal Behaviors
Steven Bohez, Saran Tunyasuvunakool, Philemon Brakel, Fereshteh Sadeghi, Leonard Hasenclever, Yuval Tassa, Emilio Parisotto, Jan Humplik, Tuomas Haarnoja, Roland Hafner, Markus Wulfmeier, Michael Neunert, Ben Moran, Noah Siegel, Andrea Huber, Francesco Romano, Nathan Batchelor, Federico Casarini, Josh Merel *, Raia Hadsell, Nicolas Heess
arXiv
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Investigating the Properties of Neural Network Representations in Reinforcement Learning
Han Wang *, Erfan Miahi *, Martha White *, Zaheer Abbas, Marlos C. Machado, Adam White, Vincent Liu *, Raksha Kumaraswamy *
arXiv
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