Reimagining the mouse pointer for the AI era

Partnering with industry leaders to accelerate AI transformation

Google DeepMind partners with global consultancies to bring the power of frontier AI to organizations around the world.

We’re expanding our presence in Singapore to advance AI in the Asia-Pacific region

Google DeepMind opens a new Singapore research lab, accelerating AI progress in the Asia-Pacific region.

From Atari to EVE Online: Building on 15 Years of AI Research in Games

Google DeepMind partners with game studios to prototype breakthrough AI gameplay.

Strengthening Singapore’s AI Future: A New National Partnership

Google DeepMind and Singapore partner to apply frontier AI to address complex challenges across health, education, and sustainability and more.

Announcing our partnership with the Republic of Korea

Google DeepMind and Korea partner to accelerate scientific breakthroughs using frontier AI models

Google DeepMind and A24 announce first-of-its-kind research partnership

The all new le Chat: Your AI assistant for life and work

Hugging Face and Google partner for open AI collaboration

Introducing Activation Atlases

We’ve created activation atlases (in collaboration with Google researchers), a new technique for visualizing what interactions between neurons can represent. As AI systems are deployed in increasingly sensitive contexts, having a better understanding of their internal decision-making processes will let us identify weaknesses and investigate failures.

PuzzleMate: Benchmarking MLLMs for Egocentric Puzzle Assistance

arXiv:2609.14473v1 Announce Type: new Abstract: Personal AI assistants hold the potential to evolve from digital interfaces into embodied companions capable of guiding users through complex physical...

The RAIL Principles for Neurosymbolic AI: Reasoning, Assurances, Interfacing and Learning

arXiv:2608. 04285v1 Announce Type: new Abstract: Neurosymbolic AI systems that integrate machine learning and symbolic reasoning are rapidly gaining attention.

Learning from human preferences

One step towards building safe AI systems is to remove the need for humans to write goal functions, since using a simple proxy for a complex goal, or getting the complex goal a bit wrong, can lead to undesirable and even dangerous behavior. In collaboration with DeepMind’s safety team, we’ve developed an algorithm which can infer what humans want by being told which of two proposed behaviors is better.