Posts in category: Deepmind

Perceiver IO, a more general version of the Perceiver architecture, can produce a wide variety of outputs from many different inputs.Read More

Most architectures used by AI systems today are specialists. A 2D residual network may be a good choice for processing images, but at best it’s a loose fit for other kinds of data — such as the Lidar signals used in self-driving cars or the torques used in robotics. What’s more, standard architectures are often designed with only one task in mind, often leading engineers to bend over backwards to reshape, distort, or otherwise modify their inputs and outputs in hopes that a standard architecture can learn to handle their problem correctly. Dealing with more than one kind of data, like the sounds and images that make up videos, is even more complicated and usually involves complex, hand-tuned systems built from many different parts, even for simple tasks. As part of DeepMind’s mission of solving intelligence to advance science and humanity, we want to build systems that can solve problems that use many types of inputs and outputs, so we began to explore a more general and versatile architecture that can handle all types of data.Read More

In new work, algorithmic advances and new training environments lead to agents which exhibit general heuristic behaviours.Read More

In recent years, artificial intelligence agents have succeeded in a range of complex game environments. For instance, AlphaZero beat world-champion programs in chess, shogi, and Go after starting out with knowing no more than the basic rules of how to play. Through reinforcement learning (RL), this single system learnt by playing round after round of games through a repetitive process of trial and error. But AlphaZero still trained separately on each game — unable to simply learn another game or task without repeating the RL process from scratch. The same is true for other successes of RL, such as Atari, Capture the Flag, StarCraft II, Dota 2, and Hide-and-Seek. DeepMind’s mission of solving intelligence to advance science and humanity led us to explore how we could overcome this limitation to create AI agents with more general and adaptive behaviour. Instead of learning one game at a time, these agents would be able to react to completely new conditions and play a whole universe of games and tasks, including ones never seen before.Read More

Many novel machine learning innovations contribute to AlphaFold’s current level of accuracy. We give a high-level overview of the system below; for a technical description of the network architecture see our AlphaFold methods paper and especially its extensive Supplementary Information.Read More

In partnership with EMBL-EBI, were incredibly proud to be launching the AlphaFold Protein Structure Database.Read More

Here we introduce Melting Pot, a scalable evaluation suite for multi-agent reinforcement learning. Melting Pot assesses generalisation to novel social situations involving both familiar and unfamiliar individuals, and has been designed to test a broad range of social interactions such as: cooperation, competition, deception, reciprocation, trust, stubbornness and so on. Melting Pot offers researchers a set of 21 MARL “substrates” (multi-agent games) on which to train agents, and over 85 unique test scenarios on which to evaluate these trained agents.Read More

To help combat racism and advance racial equity, we’ve made donations to organisations that support Black communities in the AI/ML space.Read More

Sports analytics is in the midst of a remarkably important era, offering interesting opportunities for AI researchers and sports leaders alike.Read More

Our research explored a new approach to an old problem: we reformulated principal component analysis (PCA), a type of eigenvalue problem, as a competitive multi-agent game we call EigenGame.Read More