Posts in category: Deepmind

In our recent paper we explore how multi-agent deep reinforcement learning can serve as a model of complex social interactions, like the formation of social norms. This new class of models could provide a path to create richer, more detailed simulations of the world.Read More

In our recent paper we explore how multi-agent deep reinforcement learning can serve as a model of complex social interactions, like the formation of social norms. This new class of models could provide a path to create richer, more detailed simulations of the world.Read More

We’re excited to share DeepMind’s first significant milestone in demonstrating how artificial intelligence research can drive and accelerate new scientific discoveries.Read More

Using neural networks to model electron interactions in molecules and materials, advancing the state-of-the-art towards the long-standing challenge of the density functional in quantum chemistryRead More

Solving some of the major challenges of the 21st Century, such as producing clean electricity or developing high temperature superconductors, will require us to design new materials with specific properties. To do this on a computer requires the simulation of electrons, the subatomic particles that govern how atoms bond to form molecules and are also responsible for the flow of electricity in solids.Read More

We show that imitation learning of human-human interactions in a simulated world, in conjunction with self-supervised learning, is sufficient to produce a multimodal interactive agent, which we call MIA, that successfully interacts with non-adversarial humans 75% of the time. We further identify architectural and algorithmic techniques that improve performance, such as hierarchical action selection.Read More

We explore an alternate path for improving language models: we augment transformers with retrieval over a database of text passages including web pages, books, news and code. We call our method RETRO, for “Retrieval Enhanced TRansfOrmers”.Read More

We show that imitation learning of human-human interactions in a simulated world, in conjunction with self-supervised learning, is sufficient to produce a multimodal interactive agent, which we call MIA, that successfully interacts with non-adversarial humans 75% of the time. We further identify architectural and algorithmic techniques that improve performance, such as hierarchical action selection.Read More

We explore an alternate path for improving language models: we augment transformers with retrieval over a database of text passages including web pages, books, news and code. We call our method RETRO, for “Retrieval Enhanced TRansfOrmers”.Read More

Language, and its role in demonstrating and facilitating comprehension – or intelligence – is a fundamental part of being human. It gives people the ability to communicate thoughts and concepts, express ideas, create memories, and build mutual understanding. These are foundational parts of social intelligence. It’s why our teams at DeepMind study aspects of language processing and communication, both in artificial agents and in humans.Read More