Our latest paper introduces IMPALA (Importance-Weighted Actor-Learner), a new and efficient distributed architecture capable of solving many tasks at the same time. We also introduce DMLab-30, a new set of visually-unified environments designed to test IMPALA and other architectures.
From smartphone assistants to image recognition and translation, machine learning already helps us in our everyday lives. But it can also help us to tackle some of the world’s most challenging physical problems -- such as energy consumption. Large-scale commercial and industrial systems like data centres consume a lot of energy, and while much has been done to stem the growth of energy use, there remains a lot more to do given the world’s increasing need for computing power.Read more
We founded DeepMind to make the world a better place by developing technologies that help address some of society's toughest challenges.Read more
Humans excel at solving a wide variety of challenging problems, from low-level motor control through to high-level cognitive tasks. Our goal at DeepMind is to create artificial agents that can achieve a similar level of performance and generality. Like a human, our agents learn for themselves to achieve successful strategies that lead to the greatest long-term rewards.Read more
We founded DeepMind to solve intelligence and use it to make the world a better place by developing technologies that help address some of society's toughest challenges. It was clear to us that we should focus on healthcare because it’s an area where we believe we can make a real difference to people’s lives across the world.Read more