Our AI method has accelerated and optimized chip design, and its superhuman chip layouts are used in hardware around the world.

We’re releasing two updated production-ready Gemini models

New video generation technology in YouTube Shorts will help millions of people realize their creative vision

Two new AI systems, ALOHA Unleashed and DemoStart, help robots learn to perform complex tasks that require dexterous movement

New AI system designs proteins that successfully bind to target molecules, with potential for advancing drug design, disease understanding and more.

Using deep learning to solve fundamental problems in computational quantum chemistry and explore how matter interacts with light

Food security, healthcare and exploring the cosmos are among the ways students of a new pan-African Master’s program aspire to apply AI

New research analyzes the misuse of multimodal generative AI today, in order to help build safer and more responsible technologies.

Announcing a comprehensive, open suite of sparse autoencoders for language model interpretability.

Breakthrough models AlphaProof and AlphaGeometry 2 solve advanced reasoning problems in mathematics

Exploring AGI, the challenges of scaling and the future of multimodal generative AI

Video-to-audio research uses video pixels and text prompts to generate rich soundtracks

How summits in Seoul, France and beyond can galvanize international cooperation on frontier AI safety

Our approach to analyzing and mitigating future risks posed by advanced AI models

We’re introducing a series of updates across the Gemini family of models, including the new 1.5 Flash, our lightweight model for speed and efficiency, and Project Astra, our vision for the future of AI assistants.

We’re introducing Veo, our most capable model for generating high-definition video, and Imagen 3, our highest quality text-to-image model. We’re also sharing new demo recordings created with our Music AI Sandbox.

Announcing our novel watermarking method for AI-generated text and video, and how we’re bringing SynthID to key Google products

Introducing a new AI model developed by Google DeepMind and Isomorphic Labs.

Developing next-gen AI agents, exploring new modalities, and pioneering foundational learning

Exploring the promise and risks of a future with more capable AI

As part of our multi-year collaboration with Liverpool FC, we develop a full AI system that can advise coaches on corner kicks

Introducing SIMA, a Scalable Instructable Multiworld Agent

Gemma is built for responsible AI development from the same research and technology used to create Gemini models.

The model delivers dramatically enhanced performance, with a breakthrough in long-context understanding across modalities.

We're bringing Gemini to more Google products

Advancing AI reasoning in mathematics

Introducing AutoRT, SARA-RT, and RT-Trajectory

In a series of experiments published in Nature Communications, we found evidence that human judgments are indeed systematically influenced by adversarial perturbations.

This has been a year of incredible progress in the field of Artificial Intelligence (AI) research and its practical applications.

In a paper published in Nature, we introduce FunSearch, a method for searching for “functions” written in computer code, and find new solutions in mathematics and computer science. FunSearch works by pairing a pre-trained LLM, whose goal is to provide creative solutions in the form of computer code, with an automated “evaluator”, which guards against hallucinations and incorrect ideas.

The Neural Information Processing Systems (NeurIPS) is the largest artificial intelligence (AI) conference in the world. NeurIPS 2023 will be taking place December 10-16 in New Orleans, USA.Teams from across Google DeepMind are presenting more than 150 papers at the main conference and workshops.

Making AI more helpful for everyone

We share the discovery of 2.2 million new crystals – equivalent to nearly 800 years’ worth of knowledge. We introduce Graph Networks for Materials Exploration (GNoME), our new deep learning tool that dramatically increases the speed and efficiency of discovery by predicting the stability of new materials.

Announcing our most advanced music generation model and two new AI experiments, designed to open a new playground for creativity

Experience AI's course and resources are expanding on a global scale

We introduce GraphCast, a state-of-the-art AI model able to make medium-range weather forecasts with unprecedented accuracy

Progress update: Our latest AlphaFold model shows significantly improved accuracy and expands coverage beyond proteins to other biological molecules, including ligands.

Introducing a context-based framework for comprehensively evaluating the social and ethical risks of AI systems

Robots are great specialists, but poor generalists. Typically, you have to train a model for each task, robot, and environment. Changing a single variable often requires starting from scratch. But what if we could combine the knowledge across robotics and create a way to train a general-purpose robot?

New AI tool classifies the effects of 71 million ‘missense’ mutations.

New tool helps watermark and identify synthetic images created by Imagen

Robotic Transformer 2 (RT-2) is a novel vision-language-action (VLA) model that learns from both web and robotics data, and translates this knowledge into generalised instructions for robotic control.

AI is a powerful technology that will transform our future, so how can we best apply it to help combat climate change and find sustainable solutions?

Exploring AI safety, adaptability, and efficiency for the real world

We’ve published our joint paper with Google Research in Nature Medicine, which proposes CoDoC (Complementarity-driven Deferral-to-Clinical Workflow), an AI system that learns when to rely on predictive AI tools or defer to a clinician for the most accurate interpretation of medical images.

New white paper investigates models and functions of international institutions that could help manage opportunities and mitigate risks of advanced AI.

Robots are quickly becoming part of our everyday lives, but they’re often only programmed to perform specific tasks well. While harnessing recent advances in AI could lead to robots that could help in many more ways, progress in building general-purpose robots is slower in part because of the time needed to collect real-world training data. Our latest paper introduces a self-improving AI agent for robotics, RoboCat, that learns to perform a variety of tasks across different arms, and then self-generates new training data to improve its technique.

It’s all about using our technology and research to help enrich people’s lives. Like YouTube — and its mission to give everyone a voice and show them the world.

Google Cloud empowers organizations to digitally transform themselves into smarter businesses. It offers cloud computing, data analytics, and the latest artificial intelligence (AI) and machine learning tools.

How MuZero, AlphaZero, and AlphaDev are optimizing the computing ecosystem that powers our world of devices.

New algorithms will transform the foundations of computing

New research proposes a framework for evaluating general-purpose models against novel threats

Next week marks the start of the 11th International Conference on Learning Representations (ICLR), taking place 1-5 May in Kigali, Rwanda. This will be the first major artificial intelligence (AI) conference to be hosted in Africa and the first in-person event since the start of the pandemic. Researchers from around the world will gather to share their cutting-edge work in deep learning spanning the fields of AI, statistics and data science, and applications including machine vision, gaming and robotics. We’re proud to support the conference as a Diamond sponsor and DEI champion.

Drawing from philosophy to identify fair principles for ethical AI...

DeepMind and the Brain team from Google Research will join forces to accelerate progress towards a world in which AI helps solve the biggest challenges facing humanity.

Solving novel problems and setting a new milestone in competitive programming.

Successful communication and cooperation have been crucial for helping societies advance throughout history. The closed environments of board games can serve as a sandbox for modelling and investigating interaction and communication – and we can learn a lot from playing them. In our recent paper, published today in Nature Communications, we show how artificial agents can use communication to better cooperate in the board game Diplomacy, a vibrant domain in artificial intelligence (AI) research, known for its focus on alliance building.

Game-playing artificial intelligence (AI) systems have advanced to a new frontier.

NeurIPS is the world’s largest conference in artificial intelligence (AI) and machine learning (ML), and we’re proud to support the event as Diamond sponsors, helping foster the exchange of research advances in the AI and ML community. Teams from across DeepMind are presenting 47 papers, including 35 external collaborations in virtual panels and poster sessions.

Most artificial intelligence (AI) researchers now believe that writing computer code which can capture the nuances of situated interactions is impossible. Alternatively, modern machine learning (ML) researchers have focused on learning about these types of interactions from data. To explore these learning-based approaches and quickly build agents that can make sense of human instructions and safely perform actions in open-ended conditions, we created a research framework within a video game environment.Today, we’re publishing a paper [INSERT LINK] and collection of videos, showing our early steps in building video game AIs that can understand fuzzy human concepts – and therefore, can begin to interact with people on their own terms.

Learning how to build upon knowledge by tapping 30 years of computer vision research

Building a responsible approach to data collection with the Partnership on AI...

Meet Sylvia Christie, our education partnerships manager who’s played a leading role in expanding our scholarship programme, which is marking its five-year anniversary.

Developing a vaccine that could save hundreds of thousands of lives

Perception – the process of experiencing the world through senses – is a significant part of intelligence. And building agents with human-level perceptual understanding of the world is a central but challenging task, which is becoming increasingly important in robotics, self-driving cars, personal assistants, medical imaging, and more. So today, we’re introducing the Perception Test, a multimodal benchmark using real-world videos to help evaluate the perception capabilities of a model.

As we build increasingly advanced artificial intelligence (AI) systems, we want to make sure they don’t pursue undesired goals. Such behaviour in an AI agent is often the result of specification gaming – exploiting a poor choice of what they are rewarded for. In our latest paper, we explore a more subtle mechanism by which AI systems may unintentionally learn to pursue undesired goals: goal misgeneralisation (GMG). GMG occurs when a system's capabilities generalise successfully but its goal does not generalise as desired, so the system competently pursues the wrong goal. Crucially, in contrast to specification gaming, GMG can occur even when the AI system is trained with a correct specification.

In our paper, published today in Nature, we introduce AlphaTensor, the first artificial intelligence (AI) system for discovering novel, efficient, and provably correct algorithms for fundamental tasks such as matrix multiplication. This sheds light on a 50-year-old open question in mathematics about finding the fastest way to multiply two matrices. This paper is a stepping stone in DeepMind’s mission to advance science and unlock the most fundamental problems using AI. Our system, AlphaTensor, builds upon AlphaZero, an agent that has shown superhuman performance on board games, like chess, Go and shogi, and this work shows the journey of AlphaZero from playing games to tackling unsolved mathematical problems for the first time.

Detecting signs of disease before bones start to break

Helping uncover how protein mutations cause diseases and disorders

We’re partnering with six education charities and social enterprises in the United Kingdom (UK) to co-create a bespoke education programme to help tackle the gaps in STEM education and boost existing programmes.

Creating a tool to study extinct species from 50,000 years ago

In our latest paper, we introduce Sparrow – a dialogue agent that’s useful and reduces the risk of unsafe and inappropriate answers. Our agent is designed to talk with a user, answer questions, and search the internet using Google when it’s helpful to look up evidence to inform its responses.

Predictions that pave the way to new treatments

Our Operating Principles have come to define both our commitment to prioritising widespread benefit, as well as the areas of research and applications we refuse to pursue. These principles have been at the heart of our decision making since DeepMind was founded, and continue to be refined as the AI landscape changes and grows. They are designed for our role as a research-driven science company and consistent with Google’s AI principles.

Colin, CBO at DeepMind, discusses collaborations with Alphabet and how we integrate ethics, accountability, and safety into everything we do.

Former intern turned intern manager, Richard Everett, describes his journey to DeepMind, sharing tips and advice for aspiring DeepMinders. The 2023 internship applications will open on the 16th September, please visit https://dpmd.ai/internshipsatdeepmind for more information.

Our new paper, In conversation with AI: aligning language models with human values, explores a different approach, asking what successful communication between humans and an artificial conversational agent might look like and what values should guide conversation in these contexts.

Using human and animal motions to teach robots to dribble a ball, and simulated humanoid characters to carry boxes and play football

We came across Zindi – a dedicated partner with complementary goals – who are the largest community of African data scientists and host competitions that focus on solving Africa’s most pressing problems. Our Science team’s Diversity, Equity, and Inclusion (DE&I) team worked with Zindi to identify a scientific challenge that could help advance conservation efforts and grow involvement in AI. Inspired by Zindi’s bounding box turtle challenge, we landed on a project with the potential for real impact: turtle facial recognition.

We want to build safe, aligned artificial general intelligence (AGI) systems that pursue the intended goals of its designers. Causal influence diagrams (CIDs) are a way to model decision-making situations that allow us to reason about agent incentives. By relating training setups to the incentives that shape agent behaviour, CIDs help illuminate potential risks before training an agent and can inspire better agent designs. But how do we know when a CID is an accurate model of a training setup?

Meet Edgar Duéñez-Guzmán, a research engineer on our Multi-Agent Research team who’s drawing on knowledge of game theory, computer science, and social evolution to get AI agents working better together.

Accelerating the search for life saving leishmaniasis treatments

Looking into a protein’s past to unlock the mysteries of life itself

New insights into immunity to help protect the world’s flora

Big data that leads to discoveries that benefit everyone

Researchers are designing more effective drugs than ever before

Helping plastics become 100% recyclable

Piecing together one of the largest molecular structures in human cells

Unlocking a decade of data in minutes to help beat antibiotic resistance

Today, in partnership with EMBL’s European Bioinformatics Institute (EMBL-EBI), we’re now releasing predicted structures for nearly all catalogued proteins known to science, which will expand the AlphaFold DB by over 200x - from nearly 1 million structures to over 200 million structures - with the potential to dramatically increase our understanding of biology.

When we announced AlphaFold 2 last December, it was hailed as a solution to the 50-year old protein folding problem. Last week, we published the scientific paper and source code explaining how we created this highly innovative system, and today we’re sharing high-quality predictions for the shape of every single protein in the human body, as well as for the proteins of 20 additional organisms that scientists rely on for their research.

We recently caught up with Petar Veličković, a research scientist at DeepMind. Along with his co-authors, Petar is presenting his paper The CLRS Algorithmic Reasoning Benchmark at ICML 2022 in Baltimore, Maryland, USA.

We develop Perceiver AR, an autoregressive, modality-agnostic architecture which uses cross-attention to map long-range inputs to a small number of latents while also maintaining end-to-end causal masking. Perceiver AR can directly attend to over a hundred thousand tokens, enabling practical long-context density estimation without the need for hand-crafted sparsity patterns or memory mechanisms.

Starting this weekend, the thirty-ninth International Conference on Machine Learning (ICML 2022) is meeting from 17-23 July, 2022 at the Baltimore Convention Center in Maryland, USA, and will be running as a hybrid event. Researchers working across artificial intelligence, data science, machine vision, computational biology, speech recognition, and more are presenting and publishing their cutting-edge work in machine learning.

Despite significant effort, current AI systems pale in their understanding of intuitive physics, in comparison to even very young children. In the present work, we address this AI problem, specifically by drawing on the field of developmental psychology.

In our recent paper, published in Nature Human Behaviour, we provide a proof-of-concept demonstration that deep reinforcement learning (RL) can be used to find economic policies that people will vote for by majority in a simple game. The paper thus addresses a key challenge in AI research - how to train AI systems that align with human values.

Avishkar Bhoopchand, a research engineer on the Game Theory and Multi-agent team, shares his journey to DeepMind and how he’s working to raise the profile of deep learning across Africa.

We present BYOL-Explore, a conceptually simple yet general approach for curiosity-driven exploration in visually-complex environments. BYOL-Explore learns a world representation, the world dynamics, and an exploration policy all-together by optimizing a single prediction loss in the latent space with no additional auxiliary objective. We show that BYOL-Explore is effective in DM-HARD-8, a challenging partially-observable continuous-action hard-exploration benchmark with visually-rich 3-D environments.

According to empirical evidence from prior works, utility degradation in DP-SGD becomes more severe on larger neural network models – including the ones regularly used to achieve the best performance on challenging image classification benchmarks. Our work investigates this phenomenon and proposes a series of simple modifications to both the training procedure and model architecture, yielding a significant improvement on the accuracy of DP training on standard image classification benchmarks.

Today we caught up with Gemma Jennings, a product manager on the Applied team, who led a session on vision language models at the AI Summit, one of the world’s largest AI events for business.