Here we share our experience of working with JAX, outline why we find it useful for our AI research, how we are supporting JAX development, and give an overview of the ecosystem we are building to support researchers everywhere.Read More
AlphaFold: a solution to a 50-year-old grand challenge in biology
In a major scientific advance, the latest version of our AI systemAlphaFoldhas been recognised as a solution to the protein folding problem by the organisers of the biennial Critical Assessment of protein Structure Prediction (CASP).Read More
Using Unity to Help Solve Intelligence
We present our use of Unity, a widely recognised and comprehensive game engine, to create more diverse, complex, virtual simulations. We describe the concepts and components developed to simplify the authoring of these environments, intended for use predominantly in the field of reinforcement learning.Read More
FermiNet: Quantum Physics and Chemistry from First Principles
Weve developed a new neural network architecture, the Fermionic Neural Network or FermiNet, which is well-suited to modeling the quantum state of large collections of electrons, the fundamental building blocks of chemical bonds.Read More
Traffic prediction with advanced Graph Neural Networks
Working with our partners at Google Maps, we used advanced machine learning techniques including Graph Neural Networks, to improve the accuracy of real time ETAs by up to 50%.Read More
RL Unplugged: Benchmarks for Offline Reinforcement Learning
We propose a benchmark called RL Unplugged to evaluate and compare offline RL methods. RL Unplugged includes data from a diverse range of domains including games (e.g., Atari benchmark) and simulated motor control problems (e.g. DM Control Suite). The datasets include domains that are partially or fully observable, use continuous or discrete actions, and have stochastic vs. deterministic dynamics.Read More
Applying for technical roles
We answer the Women in Machine Learning community’s questions about applying for a job in industry.Read More
dm_control: Software and Tasks for Continuous Control
The dm_control software package is a collection of Python libraries and task suites for reinforcement learning agents in an articulated-body simulation. A MuJoCo wrapper provides convenient bindings to functions and data structures. The PyMJCF and Composer libraries enable procedural model manipulation and task authoring.Read More