Gemini (@gemini)
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See what 5 builders are making with Gemini Omni
Gemini Omni makes creating videos as easy as having a conversation. Here’s how five people use it to edit videos and visualize ideas.
AI model achieves breakthrough in forecasting cyclones
WeatherNext enables accurate cyclone forecasts that can give an extra day of warning. Now we are open sourcing the model.
How Gemini plans such detailed vacation itineraries for you
Learn how Gemini provides personalized recommendations and creates custom travel itineraries when you ask it to plan a trip.
Simplify your morning with this vibe-coded schedule app.
Tired of starting your morning staring at bright screens and stressful notifications? Raph, a creative technologist at Google, built a custom app called Glanceboard with…
Find out what’s new in the Gemini app in July's Gemini Drop.
Gemini Drops is our regular monthly update on how to get the most out of the Gemini app.
Gemini Robotics 2 brings whole body intelligence to robots
From feet to fingertips — we are teaching robots intelligent whole-body control, fine dexterity, and teamwork to complete a broad range of complex tasks.
Gemini Spark now integrates with Chrome
An overview of the latest Gemini Spark updates, including new Chrome web browsing capabilities.
Gemini for macOS adds new natural language capabilities
A look at how the Gemini app for macOS now lets you speak naturally to get clean transcriptions, edits, and summaries just using your voice.
How Gemini Flash agents are helping a Michigan dairy farmer
See how Paul Windemuller, a Michigan dairy farmer, is changing the way he works by using AI agents built with Gemini 3.6 Flash.
Here’s how to ask Gemini Live for help with anything you see.
Have you ever struggled to describe something you’re looking at? Whether it’s a complex manual, a blinking error code, or a unique object, sometimes you just need an exp…
Introducing Gemini 3.5 Flash Cyber
Google introduces Gemini 3.5 Flash Cyber to help defenders find, validate, and patch software vulnerabilities quickly and efficiently.
Introducing Gemini 3.6 Flash, 3.5 Flash-Lite, and 3.5 Flash Cyber
We’re introducing new Gemini models, including Gemini 3.6 Flash, 3.5 Flash-Lite and 3.5 Flash Cyber.
5 ways to build a side hustle with Gemini
Launching a side business? Use Gemini to design your side hustle, conduct market research and automate logistics.
Google DeepMind and Isomorphic Labs approach to bioresilience
Google DeepMind and Isomorphic Labs approach to bioresilience, using AI models to support prevention, detection and response.
How Gemini is speaking the language of Southeast Asia
Gemini is taking off across Southeast Asia, thanks to its local language fluency and the region’s mobile-first population.
AI in Indian Education: Atal Innovation Mission and Google launch ATL Saathi
Atal Innovation Mission launches ATL Saathi, a Gemini powered AI assistant empowering India's educators to nurture the next generation of innovators.
Here’s how to make study notebooks in the Gemini app.
Studying for a test, but not sure where to start? Study notebooks, a new feature in the Gemini app, can help you get organized and learn more efficiently.Think of study …
3 ways this coffee shop is growing with Gemini
Small businesses like coffee shops can use Gemini to save time on graphic design, email marketing and sales forecasting.
Start building with Nano Banana 2 Lite and Gemini Omni Flash
Scale your ideas with Nano Banana 2 Lite, our fastest, most cost-efficient Gemini Image model, and Gemini Omni Flash for high-quality video and conversational editing.
Gemini Spark updates: macOS launch, connected apps and more
The latest Gemini Spark updates brings Spark to the macOS app, connects with your favorite apps and tracks topics in real time.
The Gemini app is bringing personalized image creation to more users.
Personal Intelligence makes the Gemini app feel tailored to you. With your permission, it pulls from Google tools like Gmail, Google Photos, YouTube and Search to provid…
Here's how Gemini can help you avoid jetlag.
If you’ve got a faraway trip coming up, the Gemini app can help you avoid jetlag so you can make the most of your visit.Once you’ve given Gemini permission to access you…
5 ways to learn with study notebooks in the Gemini app
Study notebooks is a new space in the Gemini app that serves as an interactive learning tool tailored to any student's goals.
Try these 3 Google AI tools to help find your next job.
Use Google AI tools — like Career Dreamer, NotebookLM and Gemini Live — for resumes, cover letters, interview prep and more.
5 ways Google parents are using Gemini
How Gemini helps with homework, meal planning and more, so parents have time to focus on the good stuff.
Introducing computer use in Gemini 3.5 Flash
A look at the built-in computer use tool in Gemini 3.5 Flash.
Securing internal systems against increasingly capable and imperfectly aligned AI
Discover our AI Control Roadmap: a defense-in-depth system to securely manage advanced, potentially misaligned AI agents.
Unlocking UK house-building with AI-accelerated planning
Google DeepMind is working alongside the UK government to co-develop an AI-powered prototype to help cut application decision times by 50%.
Google DeepMind and partners announce multi-agent safety research funding call.
Google DeepMind and partners are announcing a new technical research funding call of up to $10M for researchers worldwide to strengthen multi-agent safety.
Save time and grow your business with new Gemini tools
An overview of new features in the Gemini app designed specifically to support businesses and entrepreneurs.
Gemini’s guided learning: results from a randomized controlled trial in Sierra Leone
Google DeepMind shares results from a randomized controlled trial in Sierra Leone, measuring the impact of AI in education on student learning and engagement.
Fluid, natural voice translation with Gemini 3.5 Live Translate
Gemini 3.5 Live Translate brings near real-time, natural speech translation to Google AI Studio, Google Translate and Google Meet.
9 demos of Gemini Omni and Gemini 3.5 in action
Watch 9 videos showing the capabilities of Gemini Omni and Gemini 3.5, announced at Google I/O 2026.
Google DeepMind & Singapore: National AI partnership
Google DeepMind and Singapore partner to apply frontier AI to address challenges across health, education, sustainability and more through the National Partnerships for AI initiative.
Gemini 3.5: frontier intelligence with action
At Google I/O we released Gemini 3.5, our latest series of models combining frontier intelligence with action.
Introducing Gemini Omni
Introducing Gemini Omni, which allows you to create anything from any input and edit naturally using conversational language.
The Gemini app becomes more agentic, delivering proactive, 24/7 help
A look at how the Gemini app is becoming more agentic, delivering proactive, 24/7 help.
Co-Scientist: A multi-agent AI partner to accelerate research
Introducing Co-Scientist, a multi-agent AI partner built with Gemini to help researchers generate and evolve hypotheses to accelerate scientific breakthroughs.
AI breakthrough: WeatherNext predicts Hurricane Melissa
Discover how our WeatherNext AI model helps the National Hurricane Center predict Hurricane Melissa's Category 5 landfall in Jamaica
Shaping the future of AI interaction by reimagining the mouse pointer
Google DeepMind is transforming the mouse pointer into a context-aware AI partner. Move beyond the friction of traditional prompting with intuitive AI collaboration in Chrome and beyond.
Digitize your paper notes with Gemini.
Gemini can turn hundreds of pages of notes into study guides or flashcards — or organize a semester’s worth of learning.
AlphaEvolve: Gemini-powered coding agent scaling impact across fields
Discover how AlphaEvolve optimizes algorithms for genomics, quantum physics, global infrastructure, and more to accelerate scientific progress and solve real-world challenges.
AI co-clinician: researching the path toward AI-augmented care
Google DeepMind is researching the path toward an AI co-clinician that could work under physician authority to assist doctors and patients, enabling new models for AI-augmented care.
You can now easily generate files in Gemini.
Move from a brainstorm to a polished document, sheet or PDF with a single prompt in Gemini.
Google DeepMind and Korea Partner to Accelerate Scientific Discovery
Google DeepMind partners with Korea's MSIT to establish an AI Campus to help accelerate scientific breakthroughs, support local talent, and advance AI safety research
Find out what’s new in the Gemini app in April's Gemini Drop.
Gemini Drops is our regular monthly update on how to get the most out of the Gemini app.
8 Gemini tips for organizing your space (and life)
Organize your home and digital space with Gemini. Use AI-powered tips for cleaning schedules, inbox decluttering, seasonal chores.
Decoupled DiLoCo: Resilient, Distributed AI Training at Scale
Google’s new distributed architecture keeps AI training runs on track across distant data centers, with exceptional efficiency – even when hardware fails.
Google DeepMind partners with global consultancies to accelerate enterprise AI adoption.
Google DeepMind is partnering with leading consultancies to bridge the AI adoption gap and drive agentic transformation with frontier models and expert research.
Gemini Embedding 2 is now generally available.
We’re announcing the general availability of Gemini Embedding 2 via the Gemini API and Vertex AI.
Deep Research Max: a step change for autonomous research agents
Introducing Deep Research and Deep Research Max, the next generation of Google’s autonomous research agents.
New ways to create personalized images in the Gemini app
Nano Banana 2 now uses your personal context and Google Photos to create images that reflect your unique life.
Gemini 3.1 Flash TTS: the next generation of expressive AI speech
Gemini 3.1 Flash TTS is now available across Google products.
The Gemini app is now on Mac
Google is bringing the Gemini app to macOS as a native desktop experience.
Gemini Robotics ER 1.6: Enhanced Embodied Reasoning
Gemini Robotics ER 1.6 upgrades spatial reasoning and multi-view understanding, unlocking new capabilities like instrument reading for autonomous robots.
The Gemini app can now generate interactive simulations and models.
Gemini can now transform your questions and complex topics into custom and interactive visualizations.
6 ways Gemini can help you find your next home
Moving soon? See how Gemini can help you analyze neighborhoods, summarize inspection reports, visualize renovations and streamline your home search.
Try notebooks in Gemini to easily keep track of projects
Notebooks in Gemini give you a project base that connects the Gemini app with our AI-powered research partner, NotebookLM, for an easy workflow.
Find out what’s new in the Gemini app in March's Gemini Drop.
Gemini Drops is our regular monthly update on how to get the most out of the Gemini app.
Protecting People from Harmful Manipulation
Google DeepMind releases new findings and an evaluation framework to measure AI's potential for harmful manipulation in areas like finance and health, with the goal of enhancing AI safety.
Gemini 3.1 Flash Live: Making audio AI more natural and reliable
Gemini 3.1 Flash Live is now available across Google products.
Make the switch: Bring your AI memories and chat history to Gemini
The Gemini app just made it easier to switch from another AI chat app, without starting from scratch.
AlphaGo at 10: How AI Innovation Is Paving the Path to AGI
Ten years since AlphaGo, we explore how its search and learning methods are catalyzing scientific discovery and paving a path to AGI.
Gemini Embedding 2: Our first natively multimodal embedding model
An overview of Gemini Embedding 2, our first fully multimodal embedding model that maps text, images, video, audio and documents into a single space.
Gemini 3.1 Flash-Lite: Built for intelligence at scale
Gemini 3.1 Flash-Lite is our fastest and most cost-efficient Gemini 3 series model yet.
Find out what’s new in the Gemini app in February's Gemini Drop.
Gemini Drops is our regular monthly update on how to get the most out of the Gemini app.
Create personalized “Year of the Fire Horse” musical tracks with Gemini
Celebrate the Year of the Fire Horse with personalized musical greetings created directly in the Gemini app.
Let Gemini handle your multi-step daily tasks on Android.
Launching soon for Pixel 10 and Samsung Galaxy S26 Series, you can offload multi-step tasks to Gemini.
6 tips for prompting Lyria 3 in the Gemini app
Learn more about Lyria 3, Google DeepMind’s newest generative AI that can help you create your own tunes.
Gemini 3.1 Pro: A smarter model for your most complex tasks
3.1 Pro is designed for tasks where a simple answer isn’t enough.
A new way to express yourself: Gemini can now create music
Lyria 3 is now available in the Gemini app. Create custom, high-quality 30-second tracks from text and images.
Google DeepMind Partnerships in India: scaling AI in science and education
Google DeepMind announces new AI partnerships in India to advance scientific research, empower students with interactive Gemini-powered learning, and support national goals in agriculture and renewable energy.
Gemini 3 Deep Think: Advancing science, research and engineering
We’re releasing a major upgrade to Gemini 3 Deep Think, our specialized reasoning mode.
Gemini Deep Think: Redefining the Future of Scientific Research
Gemini Deep Think is accelerating discovery in maths, physics, and computer science by acting as a powerful scientific companion for researchers.
10 ways to plan your 2026 budget with Gemini
Learn how to use simple Gemini prompts to create a 2026 budget, find hidden savings and organize your spending.
Find out what’s new in the Gemini app in January's Gemini Drop.
Gemini Drops is our regular monthly update on how to get the most out of the Gemini app.
In our latest podcast, hear how the “Smoke Jumpers” team brings Gemini to billions of people.
Bringing Gemini to billions of users requires a massive, coordinated infrastructure effort. In the latest episode of the Google AI: Release Notes podcast, host Logan Kil…
D4RT: Unified, Fast 4D Scene Reconstruction & Tracking
Meet D4RT, a unified AI model for 4D scene reconstruction and tracking.
How Nano Banana got its name
We’re peeling back the origin story of Nano Banana, one of Google DeepMind’s most popular models.
Gemini introduces Personal Intelligence
Personal Intelligence connects the Gemini app to your Google apps to provide more personalized suggestions.
13 of the best Nano Banana trends from 2025
From pet figurines to isometric images, here are some of our favorite Nano Banana trends of the year.
Here’s where you can use Nano Banana Pro
Learn more about Nano Banana Pro, Google’s state-of-the-art image generation and editing model, and where you can use it now.
Gemma Scope 2: Helping the AI Safety Community Deepen Understanding of Complex Language Model Behavior
Announcing Gemma Scope 2, a comprehensive, open suite of interpretability tools for the entire Gemma 3 family to accelerate AI safety research.
Find out what’s new in the Gemini app in December's Gemini Drop.
Gemini Drops is our regular monthly update on how to get the most out of the Gemini app.
10 Gemini prompts to help you keep your New Year's resolutions
Turn your New Year's resolutions into a reality. Here are 10 prompts you can use with Gemini to get a head start on your goals.
Google DeepMind & DOE Partner on Genesis: AI for Science
Google DeepMind supports U.S. Department of Energy on Genesis: a national mission to accelerate innovation and scientific discovery.
Gemini 3 Flash: frontier intelligence built for speed
Gemini 3 Flash offers frontier intelligence built for speed at a fraction of the cost.
Gemini 3 Flash comes to the Gemini app
Introducing Gemini 3 Flash, a major upgrade to your everyday AI. It delivers next-generation intelligence at lightning speeds.For too long, AI forced a choice: big model…
Bring your research to life with integrated visual reports from Gemini Deep Research.
We’re enhancing Gemini Deep Research to help you visualize complex information instantly. Now available to Google AI Ultra subscribers, Deep Research can go beyond text …
Improved Gemini audio models for powerful voice interactions
An upgraded Gemini 2.5 Native Audio model across Google products and live speech translation in the Google Translate app.
Deepening AI Safety Research with UK AI Security Institute (AISI)
Google DeepMind and the UK AI Security Institute (AISI) strengthen collaboration through a new research partnership, focusing on critical safety research areas like monitoring AI reasoning and evaluating social and economic impacts.
Our partnership with the UK government
We're collaborating with the UK government to accelerate progress in science, education, national security and more.
FACTS Benchmark Suite: a new way to systematically evaluate LLMs factuality
The FACTS Benchmark Suite provides a systematic evaluation of Large Language Models (LLMs) factuality across three areas: Parametric, Search, and Multimodal reasoning.
A Googler explains how to “meta prompt” for incredible Veo videos
Google DeepMind UX Engineer Anna Bortsova asks Gemini to draft detailed prompts for her videos — and the results are show-stopping.
15 examples of what Gemini 3 can do
Learn how Gemini 3, Google’s latest model, can help you learn anything, build anything and plan anything.
How AlphaFold is helping scientists engineer more heat-tolerant crops
Scientists are using AlphaFold to strengthen a vital photosynthesis enzyme (GLYK), paving the way for more resilient, heat-tolerant crops that can adapt to a warming climate and help secure food production for future generations.
Gemini 3 Deep Think is now available in the Gemini app.
Today, we’re rolling out Gemini 3 Deep Think mode to Google AI Ultra subscribers in the Gemini app. This new mode delivers a meaningful improvement in reasoning capabili…
AlphaFold: Five Years of Impact
Explore five years of AlphaFold’s impact on biology. Learn how this Nobel Prize-winning AI is accelerating scientific discovery globally
AlphaFold Reveals a Key Protein Behind Heart Disease
Discover how scientists used AlphaFold to map the protein behind heart disease and how this breakthrough could transform treatment.
Breeding Healthier Honeybees With AlphaFold
Learn how AlphaFold is helping scientists protect honeybees and speeding up breeding programs for healthier hives.
Find out what’s new in the Gemini app in November's Gemini Drop.
Gemini Drops are our regular update on what’s new in the Gemini app. Here's a look at the latest features this month:Gemini 3 brings upgraded smarts and new capabilities…
48 tips and prompts for holiday planning, travel and more
Learn more about ways to use Google tools like Gemini, Google Photos, Search and more to get things done over the holidays.
7 tips to get the most out of Nano Banana Pro
Here are some tips for writing more effective prompts for image generation and editing in Gemini using Nano Banana Pro.
Google DeepMind opens Singapore research lab for Asia-Pacific AI.
Google DeepMind opens a new research lab in Singapore to accelerate the development of frontier AI across the Asia-Pacific region through research, talent, and strategic collaborations.
A new era of intelligence with Gemini 3
Today we’re releasing Gemini 3 – our most intelligent model that helps you bring any idea to life.
Introducing Gemini 3
Gemini 3, our most intelligent model, combines all of Gemini’s capabilities together so you can bring any idea to life.
Gemini 3 brings upgraded smarts and new capabilities to the Gemini app
Today we’re unveiling a major update for the Gemini app, and it all starts with Gemini 3.
SIMA 2: A Gemini-Powered AI Agent for 3D Virtual Worlds
Introducing SIMA 2, the next milestone in our research creating general and helpful AI agents. By integrating the advanced capabilities of our Gemini models, SIMA is evolving from an instruction-follower into an interactive gaming companion.
5 ways to have more natural conversations with Gemini
Here’s how Gemini Live can help you have a more natural conversation with new updates.
Teaching AI to See the World More Like Humans Do
Aligning AI vision models with human knowledge, improves their robustness and ability to generalize.
Three ways Google scientists use AI to better understand nature
Discover how AI models are helping scientists better understand our biosphere, from predicting deforestation to mapping species and listening to wildlife.
Gemini Deep Research can now connect to your Gmail, Docs, Drive and even Chat.
Learn more about how Google Workspace apps now work with Gemini’s Deep Research tool.
Find out what’s new in the Gemini app in October's Gemini Drop.
Gemini Drops is our new monthly update on how to get the most out of the Gemini app.
Try these tips and prompts for spooky AI images
Learn how to use the Gemini app’s tools — like Veo 3, Nano Banana and more — to create spooky Halloween visuals.
6 ways I spent a busy weekend with Gemini on Pixel Watch 4
Learn more about Gemini on Wear OS features like Raise to Talk and Personal Context, all accessible on Google Pixel Watch 4.
Google DeepMind is bringing AI to the next generation of fusion energy
We’re announcing our research partnership with Commonwealth Fusion Systems (CFS) to bring clean, safe, limitless fusion energy closer to reality with our advanced AI systems. This partnership builds on our groundbreaking work using AI to successfully control a plasma.
Bringing the best AI to university students in Europe, the Middle East, and Africa, at no cost
Google is bringing the best of its AI tools to university students 18+, at no cost, including Gemini 2.5 Pro, the world’s leading model for learning.
Introducing CodeMender: an AI agent for code security
Using advanced AI to fix critical software vulnerabilities
4 tips for using Nano Banana to create amazing images
Get the scoop on how to use Nano Banana, the Gemini app's viral new image generation and editing model from Google DeepMind.
The Global AI Film Award is now accepting applications
The Global AI Film Award is now accepting applications from creators using Google AI tools, with the winner awarded a prize of USD 1 million by the 1 Billion Followers S…
Gemini Robotics 1.5 brings AI agents into the physical world
We’re powering an era of physical agents — enabling robots to perceive, plan, think, use tools and act to better solve complex, multi-step tasks.
How Gemini's Guided Learning can help you study more effectively
Guided Learning with Gemini is a new feature from Google that offers people a personalized, interactive and effective way to learn.
Google DeepMind strengthens the Frontier Safety Framework
Today, we’re publishing the third iteration of our Frontier Safety Framework (FSF) — our most comprehensive approach yet to identifying and mitigating severe risks from advanced AI models. This update builds on ongoing collaboration with experts across industry, academia and government. We’ve also incorporated lessons learned from implementing previous versions and evolving best practices in frontier AI safety.
Find out what’s new in the Gemini app in September’s Gemini Drop.
Gemini Drops is our new monthly update on how to get the most out of the Gemini app.
3 ways to use photo-to-video in Gemini
Here’s how I’ve been using Gemini’s photo-to-video tool as a multimedia storyteller, plus some tips for making your own videos.
You can now share your custom Gems in the Gemini app.
You can now share your custom Gems in the Gemini app.
Discovering new solutions to century-old problems in fluid dynamics
Our new method could help mathematicians leverage AI techniques to tackle long-standing challenges in mathematics, physics and engineering.
Gemini achieves gold-medal level at the International Collegiate Programming Contest World Finals
Gemini 2.5 Deep Think achieves breakthrough performance at the world’s most prestigious computer programming competition, demonstrating a profound leap in abstract problem solving.
10 examples of Nano Banana, our new native image editing in the Gemini app
A new Google DeepMind image editing model, fondly known as Nano Banana, is now in the Gemini app, giving you more creative control to blend and edit photos.
Using AI to perceive the universe in greater depth
Using AI to perceive the universe in greater depth
New Gemini app tools to help students in Europe, the Middle East and Africa
Try these new tools to learn, study and understand complex topics even better.
Tips for getting the best image generation and editing in the Gemini app
Here are some tips for writing more effective prompts for image generation and editing in Gemini.
Image editing in Gemini just got a major upgrade
Transform images in amazing new ways with updated native image editing in the Gemini app.
5 new things Gemini can do on Pixel
Learn more about new Gemini features and model updates that make Pixel phones even better.
Gemini Live: A more helpful, natural and visual assistant
We're making major upgrades to Gemini Live so it’s more expressive, more visually aware and more deeply integrated with your Google apps.
Catch up on the newest features in August’s Gemini Drop.
Gemini Drops is our new monthly update on how to get the most out of the Gemini app.
Gemini adds Temporary Chats and new personalization features
Today, we are updating the Gemini app so that it learns about your preferences the more you use it.
How AI is helping advance the science of bioacoustics to save endangered species
Our new Perch model helps conservationists analyze audio faster to protect endangered species, from Hawaiian honeycreepers to coral reefs.
Bringing the best of AI to college students for free
We’re committing $1 billion for AI training and resources and rolling out our most advanced AI tools for learning to students for free.
New Gemini app tools to help students learn, understand and study even better
Try these new tools to learn, study and understand complex topics even better.
Create personal illustrated storybooks in the Gemini app.
Simply describe any story, and Gemini instantly generates a unique 10-page book with custom art and audio.
Genie 3: A new frontier for world models
Today we are announcing Genie 3, a general purpose world model that can generate an unprecedented diversity of interactive environments. Given a text prompt, Genie 3 can generate dynamic worlds that you can navigate in real time at 24 frames per second, retaining consistency for a few minutes at a resolution of 720p.
Try Deep Think in the Gemini app
Deep Think utilizes extended, parallel thinking and novel reinforcement learning techniques for significantly improved problem-solving.
AlphaEarth Foundations helps map our planet in unprecedented detail
New AI model integrates petabytes of Earth observation data to generate a unified data representation that revolutionizes global mapping and monitoring
Aeneas transforms how historians connect the past
Introducing the first model for contextualizing ancient inscriptions, designed to help historians better interpret, attribute and restore fragmentary texts.
Advanced version of Gemini with Deep Think officially achieves gold-medal standard at the International Mathematical Olympiad
The International Mathematical Olympiad (“IMO”) is the world’s most prestigious competition for young mathematicians, and has been held annually since 1959. Each country taking part is represented by six elite, pre-university mathematicians who compete to solve six exceptionally difficult problems in algebra, combinatorics, geometry, and number theory.
Exploring the context of online images with Backstory
New experimental AI tool helps people explore the context and origin of images seen online.
Find out the latest Gemini updates with Gemini Drops.
Gemini Drops is our new monthly update on how to get the most out of the Gemini app.
Turn your photos into videos in Gemini
You can now transform your photos into eight-second videos using Veo 3.
Hear a podcast discussion about Gemini’s multimodal capabilities.
The latest episode of the Google AI: Release Notes podcast focuses on how Gemini was built from the ground up as a multimodal model — meaning a model that works with tex…
AlphaGenome: AI for better understanding the genome
Introducing a new, unifying DNA sequence model that advances regulatory variant-effect prediction and promises to shed new light on genome function — now available via API.
Gemini Robotics On-Device brings AI to local robotic devices
We’re introducing an efficient, on-device robotics model with general-purpose dexterity and fast task adaptation.
We’re expanding our Gemini 2.5 family of models
Gemini 2.5 Flash and Pro are now generally available, and we’re introducing 2.5 Flash-Lite, our most cost-efficient and fastest 2.5 model yet.
How we're supporting better tropical cyclone prediction with AI
We’re launching Weather Lab, featuring our experimental cyclone predictions, and we’re partnering with the U.S. National Hurricane Center to support their forecasts and warnings this cyclone season.
Plan ahead with scheduled actions in the Gemini app.
As we shared at I/O, the Gemini app is getting more personal, proactive and powerful. Starting today, we're rolling out scheduled actions in the Gemini app, a new featur…
Try the latest Gemini 2.5 Pro before general availability.
We’re introducing an upgraded preview of Gemini 2.5 Pro, our most intelligent model yet.
We’re bringing Veo 3 to more countries, and to more users on the Gemini mobile app.
We’re thrilled by the response to Veo 3. The Google AI Ultra plan grants the highest access to Veo 3 and later today we’re launching it in the UK. The Ultra plan is now …
Gemini gets more personal, proactive and powerful
The Gemini app is getting major new updates, from Veo 3 and Imagen 4 to Deep Research and Canvas.
Advancing Gemini's security safeguards
We’ve made Gemini 2.5 our most secure model family to date.
AlphaEvolve: A Gemini-powered coding agent for designing advanced algorithms
New AI agent evolves algorithms for math and practical applications in computing by combining the creativity of large language models with automated evaluators
Build rich, interactive web apps with an updated Gemini 2.5 Pro
Our updated version of Gemini 2.5 Pro has improved capabilities for coding.
Upload and edit your images directly in the Gemini app
We’re rolling out the ability to easily modify both your AI creations and images you upload from your phone or computer.
Music AI Sandbox, now with new features and broader access
Google has long collaborated with musicians, producers, and artists in the research and development of music AI tools. Ever since launching the Magenta project, in 2016, we’ve been exploring how AI can enhance creativity — sparking inspiration, facilitating exploration and enabling new forms of expression, always hand-in-hand with the music community.
Developers can now start building with Gemini 2.5 Flash.
Developers can start building with Gemini 2.5 Flash, our fast, cost-efficient thinking model now in preview in the Gemini API in Google AI Studio and Vertex AI.
College students in the U.S. are now eligible for the best of Google AI — and 2 TB storage — for free
Students can get the Google One AI Premium plan for free for 15 months, which includes Gemini Advanced, NotebookLM Plus and more.
Generate videos in Gemini and Whisk with Veo 2
You can now generate videos in Gemini, powered by Veo 2.
Deep Research is now available on Gemini 2.5 Pro Experimental.
Gemini Advanced subscribers can now use Deep Research with Gemini 2.5 Pro Experimental, the world’s most capable AI model according to industry reasoning benchmarks and …
5 ways to use Gemini Live with camera and screen sharing
Gemini Live with video and screen sharing is now rolling out to Android mobile devices.
Start building with Gemini 2.5 Pro.
We’ve seen incredible developer enthusiasm and early adoption of Gemini 2.5 Pro, and we’ve been listening to your feedback. To make this powerful model available to more…
4 ways media and entertainment companies are using Gemini
When Gemini helps with tedious tasks, teams can focus on creativity.
Building secure AGI: Evaluating emerging cyber security capabilities of advanced AI
Our framework enables cybersecurity experts to identify which defenses are necessary—and how to prioritize them
Taking a responsible path to AGI
We’re exploring the frontiers of AGI, prioritizing technical safety, proactive risk assessment, and collaboration with the AI community.
How we built the new family of Gemini Robotics models
Robots powered by Gemini Robotics models can learn complex actions like preparing salads and even folding an origami fox.
6 tips to get the most out of Gemini Deep Research
Learn more about using Google’s Deep Research, an AI assistant that helps you explore complex topics and delivers detailed reports.
New ways to collaborate and get creative with Gemini
Check out the Gemini app’s latest features, like Canvas and Audio Overview.
The Assistant experience on mobile is upgrading to Gemini
Over the coming months, we’ll be upgrading users on mobile devices from Google Assistant to Gemini.
Gemini gets personal, with tailored help from your Google apps
Gemini with personalization is a new capability that references your Search history, with your permission.
New Gemini app features, available to try at no cost
We’re making big upgrades to the performance and availability of our most popular Gemini features.
Introducing Gemini Robotics and Gemini Robotics-ER, AI models designed for robots to understand, act and react to the physical world.
Introducing Gemini Robotics and Gemini Robotics-ER, AI models designed for robots to understand, act and react to the physical world.
Updating the Frontier Safety Framework
Our next iteration of the FSF sets out stronger security protocols on the path to AGI
How Gemini co-composed this contemporary classical music piece
Learn how Google Gemini collaborated with German composers to create “The Twin Paradox: A Symphonic Discourse,” first performed by the Munich Symphony Orchestra in Octob…
A more powerful Android assistant with Gemini
New features for the Gemini app on Android announced at Samsung Galaxy Unpacked.
Gemini 2.0: Our latest, most capable AI model yet
See how Gemini 2.0 and our research prototypes work — and how they’ll help make our Google products more helpful.
FACTS Grounding: A new benchmark for evaluating the factuality of large language models
Our comprehensive benchmark and online leaderboard offer a much-needed measure of how accurately LLMs ground their responses in provided source material and avoid hallucinations
Try Deep Research and our new experimental model in Gemini, your AI assistant
Try Deep Research, a new research assistant in Gemini Advanced, and a chat optimized version of Gemini 2.0 Flash Experimental.
Take our pop quiz for Gemini’s first anniversary
Google’s AI model, Gemini, was launched with Gemini 1.0 one year ago. Take this pop quiz to see how well you’ve been paying attention!
Google DeepMind at NeurIPS 2024
Advancing adaptive AI agents, empowering 3D scene creation, and innovating LLM training for a smarter, safer future
GenCast predicts weather and the risks of extreme conditions with state-of-the-art accuracy
New AI model advances the prediction of weather uncertainties and risks, delivering faster, more accurate forecasts up to 15 days ahead
Genie 2: A large-scale foundation world model
Generating unlimited diverse training environments for future general agents
5 ways I'm handling the holidays with Gemini
Learn more about how you can use Google’s Gemini app for holiday help.
The Gemini app is now available on iPhone
The standalone Gemini app is now available for iPhone users in the iOS App Store.
Pushing the frontiers of audio generation
Our pioneering speech generation technologies are helping people around the world interact with more natural, conversational and intuitive digital assistants and AI tools.
New generative AI tools open the doors of music creation
Our latest AI music technologies are now available in MusicFX DJ, Music AI Sandbox and YouTube Shorts
Demis Hassabis & John Jumper awarded Nobel Prize in Chemistry
Co-founder and CEO of Google DeepMind and Isomorphic Labs Sir Demis Hassabis, and Google DeepMind Senior Research Scientist Dr. John Jumper were co-awarded the 2024 Nobel Prize in Chemistry. The award recognizes their work developing AlphaFold, a groundbreaking AI system that predicts the 3D structure of proteins from their amino acid sequences.
New in Gemini: Gemini Live and connected Google apps in more languages
We are expanding Gemini Live and extensions to over 40 languages so you can chat with Gemini in your preferred language.
How AlphaChip transformed computer chip design
Our AI method has accelerated and optimized chip design, and its superhuman chip layouts are used in hardware around the world. AlphaChip was one of the first reinforcement learning approaches used to solve a real-world engineering problem. It generates superhuman or comparable chip layouts in hours, rather than weeks or months of human effort, and its layouts are used in chips all over the world, from data centers to mobile phones.
Empowering YouTube creators with generative AI
New video generation technology in YouTube Shorts will help millions of people realize their creative vision
Our latest advances in robot dexterity
Two new AI systems, ALOHA Unleashed and DemoStart, help robots learn to perform complex tasks that require dexterous movement
5 tips on getting started with Gems, your custom AI experts
Learn how to get started with Gems, customized versions of Gemini.
AlphaProteo generates novel proteins for biology and health research
New AI system designs proteins that successfully bind to target molecules, with potential for advancing drug design, disease understanding and more.
5 ways Gemini can help students study smarter
These new Gemini features help students learn new material and study smarter.
New in Gemini: Custom Gems and improved image generation with Imagen 3
New updates for Gemini include custom Gems and improved image generation with Imagen 3.
FermiNet: Quantum physics and chemistry from first principles
In August 2024, we published the next phase of our work in Science. Our research proposes a solution to one of the most difficult challenges in computational quantum chemistry: understanding how molecules transition to and from excited states when stimulated.
Gemini makes your mobile device a powerful AI assistant
At Made by Google, we shared how Gemini is evolving to provide AI-powered assistance that will be infinitely more helpful.
Mapping the misuse of generative AI
New research analyzes the misuse of multimodal generative AI today, in order to help build safer and more responsible technologies
Gemma Scope: helping the safety community shed light on the inner workings of language models
Announcing a comprehensive, open suite of sparse autoencoders for language model interpretability.
Gemini’s big upgrade: Faster responses with 1.5 Flash, expanded access and more
We’re bringing 1.5 Flash to Gemini, launching a new related content feature and expanding our Gemini for Teens experience.
AI achieves silver-medal standard solving International Mathematical Olympiad problems
Breakthrough models AlphaProof and AlphaGeometry 2 solve advanced reasoning problems in mathematics
Google DeepMind at ICML 2024
Teams from across Google DeepMind will present more than 80 research papers exploring AGI, the challenges of scaling and the future of multimodal generative AI.
Generating audio for video
Video-to-audio research uses video pixels and text prompts to generate rich soundtracks
Looking ahead to the AI Seoul Summit
How summits in Seoul, France and beyond can galvanize international cooperation on frontier AI safety
Introducing the Frontier Safety Framework
Our approach to analyzing and mitigating future risks posed by advanced AI models
Get more done with Gemini: Try 1.5 Pro and more intelligent features
Gemini Advanced subscribers will get access to Gemini 1.5 Pro, a 1 million token context window and more personalized features.
Watermarking AI-generated text and video with SynthID
Announcing our novel watermarking method for AI-generated text and video, and how we’re bringing SynthID to key Google products
Google DeepMind at ICLR 2024
Developing next-gen AI agents, exploring new modalities, and pioneering foundational learning
The ethics of advanced AI assistants
Exploring the promise and risks of a future with more capable AI
TacticAI: an AI assistant for football tactics
As part of our multi-year collaboration with Liverpool FC, we develop a full AI system that can advise coaches on corner kicks
A generalist AI agent for 3D virtual environments
Introducing SIMA, a Scalable Instructable Multiworld Agent
Gemini image generation got it wrong. We'll do better.
An explanation of how the issues with Gemini’s image generation of people happened, and what we’re doing to fix it.
Bard becomes Gemini: Try Ultra 1.0 and a new mobile app today
Bard is now known as Gemini, with a mobile app and an Advanced experience that gives you access to our most capable AI model, Ultra 1.0.
Bard’s latest updates: Access Gemini Pro globally and generate images
We’re expanding Gemini Pro in Bard to all supported languages and bringing text-to-image generation into Bard.
AlphaGeometry: An Olympiad-level AI system for geometry
Our AI system surpasses the state-of-the-art approach for geometry problems, advancing AI reasoning in mathematics
Shaping the future of advanced robotics
AutoRT, SARA-RT, and RT-Trajectory build on our historic Robotics Transformers work to help robots make decisions faster, and better understand and navigate their environments.
Images altered to trick machine vision can influence humans too
In a series of experiments published in Nature Communications, we found evidence that human judgments are indeed systematically influenced by adversarial perturbations.
2023: A Year of Groundbreaking Advances in AI and Computing
This has been a year of incredible progress in the field of Artificial Intelligence (AI) research and its practical applications.
FunSearch: Making new discoveries in mathematical sciences using Large Language Models
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.
Google DeepMind at NeurIPS 2023
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.
Bard gets its biggest upgrade yet with Gemini
We’re starting to bring Gemini’s advanced capabilities into Bard.
Millions of new materials discovered with deep learning
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.
Transforming the future of music creation
Announcing our most advanced music generation model and two new AI experiments, designed to open a new playground for creativity
How we’ve created a helpful and responsible Bard experience for teens
We’re responsibly expanding access to Bard to teens to help them find inspiration, learn new things and solve everyday problems.
Empowering the next generation for an AI-enabled world
Today, Google DeepMind and the Raspberry Pi Foundation are expanding access to the Experience AI program. This comprehensive introductory course is designed for educators to teach 11-14 year old students foundational AI knowledge through responsible and interactive lesson materials, activities, and video tutorials. The materials, created with educational experts, align with proven learning and development practices. Now, we’re broadening the reach of the Experience AI program on a global scale, aiming to empower more students for an AI-enabled world.
GraphCast: AI model for faster and more accurate global weather forecasting
Our state-of-the-art model delivers 10-day weather predictions at unprecedented accuracy in under one minute
A glimpse of the next generation of AlphaFold
Progress update: Our latest AlphaFold model shows significantly improved accuracy and expands coverage beyond proteins to other biological molecules, including ligands.
Evaluating social and ethical risks from generative AI
Introducing a context-based framework for comprehensively evaluating the social and ethical risks of AI systems
Scaling up learning across many different robot types
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?
Bard can now connect to your Google apps and services
Bard gets its most capable model yet, along with new and expanded features.
A catalogue of genetic mutations to help pinpoint the cause of diseases
New AI tool classifies the effects of 71 million ‘missense’ mutations Uncovering the root causes of disease is one of the greatest challenges in human genetics. With millions of possible mutations and limited experimental data, it’s largely still a mystery which ones could give rise to disease. This knowledge is crucial to faster diagnosis and developing life-saving treatments.
Identifying AI-generated images with SynthID
Today, in partnership with Google Cloud, we’re beta launching SynthID, a new tool for watermarking and identifying AI-generated images. It’s being released to a limited number of Vertex AI customers using Imagen, one of our latest text-to-image models that uses input text to create photorealistic images. This technology embeds a digital watermark directly into the pixels of an image, making it imperceptible to the human eye, but detectable for identification. While generative AI can unlock huge creative potential, it also presents new risks, like creators spreading false information — both int
10 helpful ways to use Bard
Check out 10 ways Bard can help you get things done, from brainstorming ideas to planning trip itineraries.
RT-2: New model translates vision and language into action
Introducing Robotic Transformer 2 (RT-2), 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, while retaining web-scale capabilities. This work builds upon Robotic Transformer 1 (RT-1), a model trained on multi-task demonstrations which can learn combinations of tasks and objects seen in the robotic data. RT-2 shows improved generalisation capabilities and semantic and visual understanding, beyond the robotic data it was exposed to. This includes interpreting new commands and re
Using AI to fight climate change
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? The effects of climate change on Earth’s ecosystems are incredibly complex, and as part of our effort to use AI for solving some of the world’s most challenging problems, here are some of the ways we’re working to advance our understanding, optimise existing systems, and accelerate breakthrough science of climate and its effects.
Google DeepMind’s latest research at ICML 2023
Google DeepMind researchers are presenting more than 80 new papers at the 40th International Conference on Machine Learning (ICML 2023), taking place 23-29 July in Honolulu, Hawai'i.
Developing reliable AI tools for healthcare
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.
Bard’s latest update: more features, languages and countries
Today we're expanding Bard to new languages and countries, and launching new features to help you get creative and boost your productivity.
Exploring institutions for global AI governance
New white paper investigates models and functions of international institutions that could help manage opportunities and mitigate risks of advanced AI. Growing awareness of the global impact of advanced artificial intelligence (AI) has inspired public discussions about the need for international governance structures to help manage opportunities and mitigate risks involved. Many discussions have drawn on analogies with the ICAO (International Civil Aviation Organization) in civil aviation; CERN (European Organization for Nuclear Research) in particle physics; IAEA (International Atomic Energy
RoboCat: A self-improving robotic agent
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.
YouTube: Enhancing the user experience
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: Driving digital transformation
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.
MuZero, AlphaZero, and AlphaDev: Optimizing computer systems
How MuZero, AlphaZero, and AlphaDev are optimizing the computing ecosystem that powers our world of devices.
AlphaDev discovers faster sorting algorithms
In our paper published today in Nature, we introduce AlphaDev, an artificial intelligence (AI) system that uses reinforcement learning to discover enhanced computer science algorithms – surpassing those honed by scientists and engineers over decades.
An early warning system for novel AI risks
AI researchers already use a range of evaluation benchmarks to identify unwanted behaviours in AI systems, such as AI systems making misleading statements, biased decisions, or repeating copyrighted content. Now, as the AI community builds and deploys increasingly powerful AI, we must expand the evaluation portfolio to include the possibility of extreme risks from general-purpose AI models that have strong skills in manipulation, deception, cyber-offense, or other dangerous capabilities.
DeepMind’s latest research at ICLR 2023
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.
How can we build human values into AI?
As artificial intelligence (AI) becomes more powerful and more deeply integrated into our lives, the questions of how it is used and deployed are all the more important. What values guide AI? Whose values are they? And how are they selected?
Announcing Google DeepMind
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.
Competitive programming with AlphaCode
Solving novel problems and setting a new milestone in competitive programming.
AI for the board game Diplomacy
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.
Mastering Stratego, the classic game of imperfect information
Game-playing artificial intelligence (AI) systems have advanced to a new frontier. Stratego, the classic board game that’s more complex than chess and Go, and craftier than poker, has now been mastered. Published in Science, we present DeepNash, an AI agent that learned the game from scratch to a human expert level by playing against itself.
DeepMind’s latest research at NeurIPS 2022
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.
Building interactive agents in video game worlds
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 st
Benchmarking the next generation of never-ending learners
Our new paper, NEVIS’22: A Stream of 100 Tasks Sampled From 30 Years of Computer Vision Research, proposes a playground to study the question of efficient knowledge transfer in a controlled and reproducible setting. The Never-Ending Visual classification Stream (NEVIS’22) is a benchmark stream in addition to an evaluation protocol, a set of initial baselines, and an open-source codebase. This package provides an opportunity for researchers to explore how models can continually build on their knowledge to learn future tasks more efficiently.
Best practices for data enrichment
At DeepMind, our goal is to make sure everything we do meets the highest standards of safety and ethics, in line with our Operating Principles. One of the most important places this starts with is how we collect our data. In the past 12 months, we’ve collaborated with Partnership on AI (PAI) to carefully consider these challenges, and have co-developed standardised best practices and processes for responsible human data collection.
Stopping malaria in its tracks
When biochemist Matthew Higgins established his research group in 2006, he had malaria firmly in his sights. The mosquito-borne disease is second only to tuberculosis in terms of its devastating global impact. Malaria killed an estimated 627,000 people in 2020, mostly children under five, and almost half of the world’s population is within its reach, though Africa is by far the hardest hit. Symptoms of infection can begin with just a fever and a headache, making it easily missed or misdiagnosed – and therefore left untreated.
Measuring perception in AI models
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.
How undesired goals can arise with correct rewards
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
Discovering novel algorithms with AlphaTensor
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
Fighting osteoporosis before it starts
Right now, medicine is too dependent on radiographic imaging techniques for diagnosing osteoporosis. It can be a debilitating disease that develops slowly over several years, weakening bones and rendering them dangerously fragile. Such diagnostic radiographic tools have their own limitations, detecting osteoporosis only once it has already developed. This means that it is already too late to properly control it.
Understanding the faulty proteins linked to cancer and autism
Being a structural biologist in the age of AlphaFold is like the early days of gold mining. Before this technology, everyone was doing painstaking work to find individual gold nuggets, cleaning them and looking at them one by one. Then, all of a sudden, a gold mine appeared. We couldn’t believe our luck.
Building safer dialogue agents
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.
Solving the mystery of how an ancient bird went extinct
Could burn marks on ancient eggshells explain the disappearance of the giant flightless bird Genyornis newtoni? This ostrich-sized “thunderbird”, dubbed “the demon-duck of doom” for its huge head, disappeared from Australia’s fossil record about 50,000 years ago. The discovery of burned eggshells led scientists, including a team of scientists led by Gifford Miller at the University of Colorado Boulder, to propose that their extinction was caused by early humans eating their eggs.
Targeting early-onset Parkinson’s with AI
It was a source of hard-earned satisfaction after what had often felt like an uphill battle. David Komander and his colleagues had finally published the long-sought structure of PINK1. Mutations in the gene that encodes this protein cause early-onset Parkinson’s, a neurodegenerative disease with a wide range of progressive symptoms – particularly body tremors and difficulty in moving. But when other scientific teams published their own structures for the same protein, it became clear that something was amiss.
How our principles helped define AlphaFold’s release
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.
Maximising the impact of our breakthroughs
Colin, CBO at DeepMind, discusses collaborations with Alphabet and how we integrate ethics, accountability, and safety into everything we do.
In conversation with AI: building better language models
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.
From motor control to embodied intelligence
Using human and animal motions to teach robots to dribble a ball, and simulated humanoid characters to carry boxes and play football
Advancing conservation with AI-based facial recognition of turtles
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.
Discovering when an agent is present in a system
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?
Accelerating the race against antibiotic resistance
Most people who have access to a modern healthcare system would not consider a disease like bubonic plague to be a threat. Such bacterial infections are usually dealt with easily through modern antibiotics. Yet antibiotic resistance, where bacteria evolve the ability to defeat these drugs, is quickly becoming a global problem. With new antibiotics thin on the ground, scientists like Marcelo Sousa and Megan Mitchell at the University of Colorado Boulder are looking at a different approach: targeting the resistance mechanism itself.
Advancing discovery of better drugs and medicine
With the help of AlphaFold, researchers are designing more effective drugs like never before. Karen Akinsanya is President of R&D, Therapeutics, at Schrödinger in New York City. She shares her AlphaFold story.
AlphaFold reveals the structure of the protein universe
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.
AlphaFold transforms biology for millions around the world
Big data in biology leads to discoveries that can benefit humankind. That’s the core belief at EMBL’s European Bioinformatics Institute (EMBL-EBI), and what its scientists have proven to be true ever since its founding at the Wellcome Genome Campus in Hinxton, UK, in 1994.
AlphaFold unlocks one of the greatest puzzles in biology
When Pietro Fontana joined the Wu Lab at Harvard Medical School and Boston Children’s Hospital in May 2019, he had before him what has been called one of the world’s hardest, giant jigsaw puzzles. It was the task of piecing together a model of the nuclear pore complex, one of the largest molecular machines in human cells.
Creating plastic-eating enzymes that could save us from pollution
The world produces about 400 million tonnes of plastic waste each year. Much of it ends up in landfills, and a significant portion is polluting the world’s oceans. Yet even when plastic is recycled, the process degrades the material, limiting its future recyclability.
The race to cure a billion people from a deadly parasitic disease
Globally, about a billion people are at risk of leishmaniasis and each year there are 50-90,000 new cases of visceral leishmaniasis, the majority in children. While medical treatments vary by region, most are lengthy and come with significant side effects.
Tracing the evolution of proteins back to the origin of life
Looking hundreds of millions of years into a protein’s past with AlphaFold to learn about the beginnings of life itself. Pedro Beltrao is a geneticist at ETH Zurich in Switzerland. He shares his AlphaFold story.
Putting the power of AlphaFold into the world’s hands
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.
Perceiver AR: general-purpose, long-context autoregressive generation
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.
DeepMind’s latest research at ICML 2022
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.
Intuitive physics learning in a deep-learning model inspired by developmental psychology
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.
Human-centred mechanism design with Democratic AI
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.
BYOL-Explore: Exploration with Bootstrapped Prediction
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.
Unlocking High-Accuracy Differentially Private Image Classification through Scale
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.
Bridging DeepMind research with Alphabet products
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.
Evaluating Multimodal Interactive Agents
In this paper, we assess the merits of these existing evaluation metrics and present a novel approach to evaluation called the Standardised Test Suite (STS). The STS uses behavioural scenarios mined from real human interaction data.
Dynamic language understanding: adaptation to new knowledge in parametric and semi-parametric models
To study how semi-parametric QA models and their underlying parametric language models (LMs) adapt to evolving knowledge, we construct a new large-scale dataset, StreamingQA, with human written and generated questions asked on a given date, to be answered from 14 years of time-stamped news articles. We evaluate our models quarterly as they read new articles not seen in pre-training. We show that parametric models can be updated without full retraining, while avoiding catastrophic forgetting.
Kyrgyzstan to King’s Cross: the star baker cooking up code
My day can vary, it really depends on which phase of the project I'm on. Let’s say we want to add a feature to our product – my tasks could range from designing solutions and working with the team to find the best one, to deploying new features into production and doing maintenance. Along the way, I’ll communicate changes to our stakeholders, write docs, code and test solutions, build analytics dashboards, clean-up old code, and fix bugs.
Building a culture of pioneering responsibly
When I joined DeepMind as COO, I did so in large part because I could tell that the founders and team had the same focus on positive social impact. In fact, at DeepMind, we now champion a term that perfectly captures my own values and hopes for integrating technology into people’s daily lives: pioneering responsibly. I believe pioneering responsibly should be a priority for anyone working in tech. But I also recognise that it’s especially important when it comes to powerful, widespread technologies like artificial intelligence. AI is arguably the most impactful technology being developed today
Open-sourcing MuJoCo
In October 2021, we announced that we acquired the MuJoCo physics simulator, and made it freely available for everyone to support research everywhere. We also committed to developing and maintaining MuJoCo as a free, open-source, community-driven project with best-in-class capabilities. Today, we’re thrilled to report that open sourcing is complete and the entire codebase is on GitHub! Here, we explain why MuJoCo is a great platform for open-source collaboration and share a preview of our roadmap going forward.
From LEGO competitions to DeepMind's robotics lab
If you want to be at DeepMind, go for it. Apply, interview, and just try. You might not get it the first time but that doesn’t mean you can’t try again. I never thought DeepMind would accept me, and when they did, I thought it was a mistake. Everyone doubts themselves – I’ve never felt like the smartest person in the room. I’ve often felt the opposite. But I’ve learned that, despite those feelings, I do belong and I do deserve to work at a place like this. And that journey, for me, started with just trying.
Emergent Bartering Behaviour in Multi-Agent Reinforcement Learning
In our recent paper, we explore how populations of deep reinforcement learning (deep RL) agents can learn microeconomic behaviours, such as production, consumption, and trading of goods. We find that artificial agents learn to make economically rational decisions about production, consumption, and prices, and react appropriately to supply and demand changes.
A Generalist Agent
Inspired by progress in large-scale language modelling, we apply a similar approach towards building a single generalist agent beyond the realm of text outputs. The agent, which we refer to as Gato, works as a multi-modal, multi-task, multi-embodiment generalist policy. The same network with the same weights can play Atari, caption images, chat, stack blocks with a real robot arm and much more, deciding based on its context whether to output text, joint torques, button presses, or other tokens.
Active offline policy selection
To make RL more applicable to real-world applications like robotics, we propose using an intelligent evaluation procedure to select the policy for deployment, called active offline policy selection (A-OPS). In A-OPS, we make use of the prerecorded dataset and allow limited interactions with the real environment to boost the selection quality.
Tackling multiple tasks with a single visual language model
We introduce Flamingo, a single visual language model (VLM) that sets a new state of the art in few-shot learning on a wide range of open-ended multimodal tasks.
When a passion for bass and brass help build better tools
We caught up with Kevin Millikin, a software engineer on the DevTools team. He’s in Salt Lake City this week to present at PyCon US, the largest annual gathering for those using and developing the open-source Python programming language.
DeepMind’s latest research at ICLR 2022
Beyond supporting the event as sponsors and regular workshop organisers, our research teams are presenting 29 papers, including 10 collaborations this year. Here’s a brief glimpse into our upcoming oral, spotlight, and poster presentations.
An empirical analysis of compute-optimal large language model training
We ask the question: “What is the optimal model size and number of training tokens for a given compute budget?” To answer this question, we train models of various sizes and with various numbers of tokens, and estimate this trade-off empirically. Our main finding is that the current large language models are far too large for their compute budget and are not being trained on enough data.
GopherCite: Teaching language models to support answers with verified quotes
Language models like Gopher can “hallucinate” facts that appear plausible but are actually fake. Those who are familiar with this problem know to do their own fact-checking, rather than trusting what language models say. Those who are not, may end up believing something that isn’t true. This paper describes GopherCite, a model which aims to address the problem of language model hallucination. GopherCite attempts to back up all of its factual claims with evidence from the web.
Predicting the past with Ithaca
The birth of human writing marked the dawn of History and is crucial to our understanding of past civilisations and the world we live in today. For example, more than 2,500 years ago, the Greeks began writing on stone, pottery, and metal to document everything from leases and laws to calendars and oracles, giving a detailed insight into the Mediterranean region. Unfortunately, it’s an incomplete record. Many of the surviving inscriptions have been damaged over the centuries or moved from their original location. In addition, modern dating techniques, such as radiocarbon dating, cannot be used
Learning Robust Real-Time Cultural Transmission without Human Data
In this work, we use deep reinforcement learning to generate artificial agents capable of test-time cultural transmission. Once trained, our agents can infer and recall navigational knowledge demonstrated by experts. This knowledge transfer happens in real time and generalises across a vast space of previously unseen tasks.
Probing Image-Language Transformers for Verb Understanding
Multimodal Image-Language transformers have achieved impressive results on a variety of tasks that rely on fine-tuning (e.g., visual question answering and image retrieval). We are interested in shedding light on the quality of their pretrained representations--in particular, if these models can distinguish verbs or they only use the nouns in a given sentence. To do so, we collect a dataset of image-sentence pairs consisting of 447 verbs that are either visual or commonly found in the pretraining data (i.e., the Conceptual Captions dataset). We use this dataset to evaluate the pretrained model
Accelerating fusion science through learned plasma control
Successfully controlling the nuclear fusion plasma in a tokamak with deep reinforcement learning
MuZero’s first step from research into the real world
Collaborating with YouTube to optimise video compression in the open source VP9 codec.
Red Teaming Language Models with Language Models
In our recent paper, we show that it is possible to automatically find inputs that elicit harmful text from language models by generating inputs using language models themselves. Our approach provides one tool for finding harmful model behaviours before users are impacted, though we emphasize that it should be viewed as one component alongside many other techniques that will be needed to find harms and mitigate them once found.
DeepMind: The Podcast returns for Season 2
We believe artificial intelligence (AI) is one of the most significant technologies of our age and we want to help people understand its potential and how it’s being created.
Spurious normativity enhances learning of compliance and enforcement behavior in artificial agents
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.
AlphaFold: Using AI for scientific discovery
We’re excited to share DeepMind’s first significant milestone in demonstrating how artificial intelligence research can drive and accelerate new scientific discoveries. With a strongly interdisciplinary approach to our work, DeepMind has brought together experts from the fields of structural biology, physics, and machine learning to apply cutting-edge techniques to predict the 3D structure of a protein based solely on its genetic sequence.
Simulating matter on the quantum scale with AI
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.
Creating Interactive Agents with Imitation Learning
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.
Improving language models by retrieving from trillions of tokens
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”.
Language modelling at scale: Gopher, ethical considerations, and retrieval
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.
Exploring the beauty of pure mathematics in novel ways
More than a century ago, Srinivasa Ramanujan shocked the mathematical world with his extraordinary ability to see remarkable patterns in numbers that no one else could see. The self-taught mathematician from India described his insights as deeply intuitive and spiritual, and patterns often came to him in vivid dreams.
On the Expressivity of Markov Reward
Our main results prove that while reward can express many tasks, there exist instances of each task type that no Markov reward function can capture. We then provide a set of polynomial-time algorithms that construct a reward function which allows an agent to optimize tasks of each of these three types, and correctly determine when no such reward function exists.
Unsupervised deep learning identifies semantic disentanglement in single inferotemporal face patch neurons
Our brain has an amazing ability to process visual information. We can take one glance at a complex scene, and within milliseconds be able to parse it into objects and their attributes, like colour or size, and use this information to describe the scene in simple language. Underlying this seemingly effortless ability is a complex computation performed by our visual cortex, which involves taking millions of neural impulses transmitted from the retina and transforming them into a more meaningful form that can be mapped to the simple language description. In order to fully understand how this pro
Real-world challenges for AGI
When people picture a world with artificial general intelligence (AGI), robots are more likely to come to mind than enabling solutions to society’s most intractable problems. But I believe the latter is much closer to the truth. AI is already enabling huge leaps in tackling fundamental challenges: from solving protein folding to predicting accurate weather patterns, scientists are increasingly using AI to deduce the rules and principles that underpin highly complex real-world domains - ones they might never have discovered unaided. Advances in AGI research will supercharge society’s ability to
Opening up a physics simulator for robotics
When you walk, your feet make contact with the ground. When you write, your fingers make contact with the pen. Physical contacts are what makes interaction with the world possible. Yet, for such a common occurrence, contact is a surprisingly complex phenomenon. Taking place at microscopic scales at the interface of two bodies, contacts can be soft or stiff, bouncy or spongy, slippery or sticky. It’s no wonder our fingertips have four different types of touch-sensors. This subtle complexity makes simulating physical contact — a vital component of robotics research — a tricky task.
Stacking our way to more general robots
Picking up a stick and balancing it atop a log or stacking a pebble on a stone may seem like simple — and quite similar — actions for a person. However, most robots struggle with handling more than one such task at a time. Manipulating a stick requires a different set of behaviours than stacking stones, never mind piling various dishes on top of one another or assembling furniture. Before we can teach robots how to perform these kinds of tasks, they first need to learn how to interact with a far greater range of objects. As part of DeepMind’s mission and as a step toward making more generalisa
Predicting gene expression with AI
When the Human Genome Project succeeded in mapping the DNA sequence of the human genome, the international research community were excited by the opportunity to better understand the genetic instructions that influence human health and development. DNA carries the genetic information that determines everything from eye colour to susceptibility to certain diseases and disorders. The roughly 20,000 sections of DNA in the human body known as genes contain instructions about the amino acid sequence of proteins, which perform numerous essential functions in our cells. Yet these genes make up less t
Nowcasting the next hour of rain
Our lives are dependent on the weather. At any moment in the UK, according to one study, one third of the country has talked about the weather in the past hour, reflecting the importance of weather in daily life. Amongst weather phenomena, rain is especially important because of its influence on our everyday decisions. Should I take an umbrella? How should we route vehicles experiencing heavy rain? What safety measures do we take for outdoor events? Will there be a flood? Our latest research and state-of-the-art model advances the science of Precipitation Nowcasting, which is the prediction of
Is Curiosity All You Need? On the Utility of Emergent Behaviours from Curious Exploration
We argue that merely using curiosity for fast environment exploration or as a bonus reward for a specific task does not harness the full potential of this technique and misses useful skills. Instead, we propose to shift the focus towards retaining the behaviours which emerge during curiosity-based learning. We posit that these self-discovered behaviours serve as valuable skills in an agent’s repertoire to solve related tasks.
Challenges in Detoxifying Language Models
In our paper, we focus on LMs and their propensity to generate toxic language. We study the effectiveness of different methods to mitigate LM toxicity, and their side-effects, and we investigate the reliability and limits of classifier-based automatic toxicity evaluation.
Building architectures that can handle the world’s data
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 soun
Generally capable agents emerge from open-ended play
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, suc
Enabling high-accuracy protein structure prediction at the proteome scale
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.
Melting Pot: an evaluation suite for multi-agent reinforcement learning
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.
Advancing sports analytics through AI research
Creating testing environments to help progress AI research out of the lab and into the real world is immensely challenging. Given AI’s long association with games, it is perhaps no surprise that sports presents an exciting opportunity, offering researchers a testbed in which an AI-enabled system can assist humans in making complex, real-time decisions in a multiagent environment with dozens of dynamic, interacting individuals.
Game theory as an engine for large-scale data analysis
Modern AI systems approach tasks like recognising objects in images and predicting the 3D structure of proteins as a diligent student would prepare for an exam. By training on many example problems, they minimise their mistakes over time until they achieve success. But this is a solitary endeavour and only one of the known forms of learning. Learning also takes place by interacting and playing with others. It’s rare that a single individual can solve extremely complex problems alone. By allowing problem solving to take on these game-like qualities, previous DeepMind efforts have trained AI age
Data, Architecture, or Losses: What Contributes Most to Multimodal Transformer Success?
In this work, we examine what aspects of multimodal transformers – attention, losses, and pretraining data – are important in their success at multimodal pretraining. We find that Multimodal attention, where both language and image transformers attend to each other, is crucial for these models’ success. Models with other types of attention (even with more depth or parameters) fail to achieve comparable results to shallower and smaller models with multimodal attention.
MuZero: Mastering Go, chess, shogi and Atari without rules
In 2016, we introduced AlphaGo, the first artificial intelligence (AI) program to defeat humans at the ancient game of Go. Two years later, its successor - AlphaZero - learned from scratch to master Go, chess and shogi. Now, in a paper in the journal Nature, we describe MuZero, a significant step forward in the pursuit of general-purpose algorithms. MuZero masters Go, chess, shogi and Atari without needing to be told the rules, thanks to its ability to plan winning strategies in unknown environments.
Imitating Interactive Intelligence
We first create a simulated environment, the Playroom, in which virtual robots can engage in a variety of interesting interactions by moving around, manipulating objects, and speaking to each other. The Playroom’s dimensions can be randomised as can its allocation of shelves, furniture, landmarks like windows and doors, and an assortment of children's toys and domestic objects. The diversity of the environment enables interactions involving reasoning about space and object relations, ambiguity of references, containment, construction, support, occlusion, partial observability. We embedded two
Using JAX to accelerate our research
DeepMind engineers accelerate our research by building tools, scaling up algorithms, and creating challenging virtual and physical worlds for training and testing artificial intelligence (AI) systems. As part of this work, we constantly evaluate new machine learning libraries and frameworks.
AlphaFold: a solution to a 50-year-old grand challenge in biology
Proteins are essential to life, supporting practically all its functions. They are large complex molecules, made up of chains of amino acids, and what a protein does largely depends on its unique 3D structure. Figuring out what shapes proteins fold into is known as the “protein-folding problem”, and has stood as a grand challenge in biology for the past 50 years. In a major scientific advance, the latest version of our AI system AlphaFold has been recognised as a solution to this grand challenge by the organisers of the biennial Critical Assessment of protein Structure Prediction (CASP). This
Using Unity to Help Solve Intelligence
We present our use of Unity, a widely recognised and comprehensive game engine, to create more diverse, complex, virtual simulations. We describe the concepts and components developed to simplify the authoring of these environments, intended for use predominantly in the field of reinforcement learning.
Fast reinforcement learning through the composition of behaviours
Imagine if you had to learn how to chop, peel and stir all over again every time you wanted to learn a new recipe. In many machine learning systems, agents often have to learn entirely from scratch when faced with new challenges. It’s clear, however, that people learn more efficiently than this: they can combine abilities previously learned. In the same way that a finite dictionary of words can be reassembled into sentences of near infinite meanings, people repurpose and re-combine skills they already possess in order to tackle novel challenges.
Traffic prediction with advanced Graph Neural Networks
By partnering with Google, DeepMind is able to bring the benefits of AI to billions of people all over the world. From reuniting a speech-impaired user with his original voice, to helping users discover personalised apps, we can apply breakthrough research to immediate real-world problems at a Google scale. Today we’re delighted to share the results of our latest partnership, delivering a truly global impact for the more than one billion people that use Google Maps.
Computational predictions of protein structures associated with COVID-19
The scientific community has galvanised in response to the recent COVID-19 outbreak, building on decades of basic research characterising this virus family. Labs at the forefront of the outbreak response shared genomes of the virus in open access databases, which enabled researchers to rapidly develop tests for this novel pathogen. Other labs have shared experimentally-determined and computationally-predicted structures of some of the viral proteins, and still others have shared epidemiological data. We hope to contribute to the scientific effort using the latest version of our AlphaFold syste
RL Unplugged: Benchmarks for Offline Reinforcement Learning
We propose a benchmark called RL Unplugged to evaluate and compare offline RL methods. RL Unplugged includes data from a diverse range of domains including games (e.g., Atari benchmark) and simulated motor control problems (e.g. DM Control Suite). The datasets include domains that are partially or fully observable, use continuous or discrete actions, and have stochastic vs. deterministic dynamics.
dm_control: Software and Tasks for Continuous Control
The dm_control software package is a collection of Python libraries and task suites for reinforcement learning agents in an articulated-body simulation. A MuJoCo wrapper provides convenient bindings to functions and data structures. The PyMJCF and Composer libraries enable procedural model manipulation and task authoring.
Acme: A new framework for distributed reinforcement learning
Acme is a framework for building readable, efficient, research-oriented RL algorithms. At its core Acme is designed to enable simple descriptions of RL agents that can be run at various scales of execution — including distributed agents. By releasing Acme, our aim is to make the results of various RL algorithms developed in academia and industrial labs easier to reproduce and extend for the machine learning community at large.
Using AI to predict retinal disease progression
Vision loss among the elderly is a major healthcare issue: about one in three people have some vision-reducing disease by the age of 65. Age-related macular degeneration (AMD) is the most common cause of blindness in the developed world. In Europe, approximately 25% of those 60 and older have AMD. The ‘dry’ form is relatively common among people over 65, and usually causes only mild sight loss. However, about 15% of patients with dry AMD go on to develop a more serious form of the disease – exudative AMD, or exAMD – which can result in rapid and permanent loss of sight. Fortunately, there are
Simple Sensor Intentions for Exploration
In this paper we focus on a setting in which goal tasks are defined via simple sparse rewards, and exploration is facilitated via agent-internal auxiliary tasks. We introduce the idea of simple sensor intentions (SSIs) as a generic way to define auxiliary tasks. SSIs reduce the amount of prior knowledge that is required to define suitable rewards. They can further be computed directly from raw sensor streams and thus do not require expensive and possibly brittle state estimation on real systems.
Learning to Segment Actions from Observation and Narration
We apply a generative segmental model of task structure, guided by narration, to action segmentation in video. We focus on unsupervised and weakly-supervised settings where no action labels are known during training. Despite its simplicity, our model performs competitively with previous work on a dataset of naturalistic instructional videos.
Specification gaming: the flip side of AI ingenuity
Specification gaming is a behaviour that satisfies the literal specification of an objective without achieving the intended outcome. We have all had experiences with specification gaming, even if not by this name. Readers may have heard the myth of King Midas and the golden touch, in which the king asks that anything he touches be turned to gold - but soon finds that even food and drink turn to metal in his hands. In the real world, when rewarded for doing well on a homework assignment, a student might copy another student to get the right answers, rather than learning the material - and thus
Towards understanding glasses with graph neural networks
Under a microscope, a pane of window glass doesn’t look like a collection of orderly molecules, as a crystal would, but rather a jumble with no discernable structure. Glass is made by starting with a glowing mixture of high-temperature melted sand and minerals. Once cooled, its viscosity (a measure of the friction in the fluid) increases a trillion-fold, and it becomes a solid, resisting tension from stretching or pulling. Yet the molecules in the glass remain in a seemingly disordered state, much like the original molten liquid – almost as though the disordered liquid state had been flash-fro
Agent57: Outperforming the human Atari benchmark
The Atari57 suite of games is a long-standing benchmark to gauge agent performance across a wide range of tasks. We’ve developed Agent57, the first deep reinforcement learning agent to obtain a score that is above the human baseline on all 57 Atari 2600 games. Agent57 combines an algorithm for efficient exploration with a meta-controller that adapts the exploration and long vs. short-term behaviour of the agent.
Visual Grounding in Video for Unsupervised Word Translation
Our goal is to use visual grounding to improve unsupervised word mapping between languages. The key idea is to establish a common visual representation between two languages by learning embeddings from unpaired instructional videos narrated in the native language.
A new model and dataset for long-range memory
Throughout our lives, we build up memories that are retained over a diverse array of timescales, from minutes to months to years to decades. When reading a book, we can recall characters who were introduced many chapters ago, or in an earlier book in a series, and reason about their motivations and likely actions in the current context. We can even put the book down during a busy week, and pick up from where we left off without forgetting the plotline.
AlphaFold: Using AI for scientific discovery
In our study published in Nature, we demonstrate how artificial intelligence research can drive and accelerate new scientific discoveries. We’ve built a dedicated, interdisciplinary team in hopes of using AI to push basic research forward: bringing together experts from the fields of structural biology, physics, and machine learning to apply cutting-edge techniques to predict the 3D structure of a protein based solely on its genetic sequence.
Dopamine and temporal difference learning: A fruitful relationship between neuroscience and AI
Learning and motivation are driven by internal and external rewards. Many of our day-to-day behaviours are guided by predicting, or anticipating, whether a given action will result in a positive (that is, rewarding) outcome. The study of how organisms learn from experience to correctly anticipate rewards has been a productive research field for well over a century, since Ivan Pavlov's seminal psychological work. In his most famous experiment, dogs were trained to expect food some time after a buzzer sounded. These dogs began salivating as soon as they heard the sound, before the food had arriv
Artificial Intelligence, Values and Alignment
This paper looks at philosophical questions that arise in the context of AI alignment. It defends three propositions. First, normative and technical aspects of the AI alignment problem are interrelated, creating space for productive engagement between people working in both domains. Second, it is important to be clear about the goal of alignment. There are significant differences between AI that aligns with instructions, intentions, revealed preferences, ideal preferences, interests and values. A principle-based approach to AI alignment, which combines these elements in a systematic way, has c
International evaluation of an AI system for breast cancer screening
Screening mammography aims to identify breast cancer before symptoms appear, enabling earlier therapy for more treatable disease. Despite the existence of screening programs worldwide, interpretation of these images suffers from suboptimal rates of false positives and false negatives. Here we present an AI system capable of surpassing a single expert reader in breast cancer prediction performance.
Using WaveNet technology to reunite speech-impaired users with their original voices
As a teenager, Tim Shaw put everything he had into football practice: his dream was to join the NFL. After playing for Penn State in college, his ambitions were finally realised: the Carolina Panthers drafted him at age 23, and he went on to play for the Chicago Bears and Tennessee Titans, where he broke records as a linebacker. After six years in the NFL, on the cusp of greatness, his performance began to falter. He couldn’t tackle like he once had; his arms slid off the pullup bar. At home, he dropped bags of groceries, and his legs began to buckle underneath him. In 2013 Tim was cut from th
Learning human objectives by evaluating hypothetical behaviours
When we train reinforcement learning (RL) agents in the real world, we don’t want them to explore unsafe states, such as driving a mobile robot into a ditch or writing an embarrassing email to one’s boss. Training RL agents in the presence of unsafe states is known as the safe exploration problem. We tackle the hardest version of this problem, in which the agent initially doesn’t know how the environment works or where the unsafe states are. The agent has one source of information: feedback about unsafe states from a human user.
From unlikely start-up to major scientific organisation: Entering our tenth year at DeepMind
Since we started DeepMind nearly 10 years ago, our mission has been to unlock answers to the world’s biggest questions by understanding and recreating intelligence itself.
Advanced machine learning helps Play Store users discover personalised apps
Over the past few years we've applied DeepMind's technology to Google products and infrastructure, with notable successes like reducing the amount of energy needed for cooling data centers, and extending Android battery performance. We're excited to share more about our work in the coming months.
AlphaStar: Grandmaster level in StarCraft II using multi-agent reinforcement learning
AlphaStar is the first AI to reach the top league of a widely popular esport without any game restrictions. This January, a preliminary version of AlphaStar challenged two of the world's top players in StarCraft II, one of the most enduring and popular real-time strategy video games of all time. Since then, we have taken on a much greater challenge: playing the full game at a Grandmaster level under professionally approved conditions.
Restoring ancient text using deep learning: a case study on Greek epigraphy
This work presents PYTHIA, the first ancient text restoration model that recovers missing characters from a damaged text input using deep neural networks. Its architecture is carefully designed to handle longterm context information, and deal efficiently with missing or corrupted character and word representations.
Causal Bayesian Networks: A flexible tool to enable fairer machine learning
Decisions based on machine learning (ML) are potentially advantageous over human decisions, as they do not suffer from the same subjectivity, and can be more accurate and easier to analyse. At the same time, data used to train ML systems often contain human and societal biases that can lead to harmful decisions: extensive evidence in areas such as hiring, criminal justice, surveillance, and healthcare suggests that ML decision systems can treat individuals unfavorably (unfairly) on the basis of characteristics such as race, gender, disabilities, and sexual orientation – referred to as sensitiv
DeepMind’s health team joins Google Health
Over the last three years, DeepMind has built a team to tackle some of healthcare’s most complex problems—developing AI research and mobile tools that are already having a positive impact on patients and care teams. Today, with our healthcare partners, the team is excited to officially join the Google Health family. Under the leadership of Dr. David Feinberg, and alongside other teams at Google, we’ll now be able to tap into global expertise in areas like app development, data security, cloud storage and user design to build products that support care teams and improve patient outcomes.
The Podcast: Episode 8: Demis Hassabis - The interview
In this special extended episode, Hannah Fry meets Demis Hassabis, the CEO and co-founder of DeepMind.
The Podcast: Episode 7: Towards the future
AI researchers around the world are trying to create a general purpose learning system that can learn to solve a broad range of problems without being taught how.
Replay in biological and artificial neural networks
Our waking and sleeping lives are punctuated by fragments of recalled memories: a sudden connection in the shower between seemingly disparate thoughts, or an ill-fated choice decades ago that haunts us as we struggle to fall asleep. By measuring memory retrieval directly in the brain, neuroscientists have noticed something remarkable: spontaneous recollections, measured directly in the brain, often occur as very fast sequences of multiple memories. These so-called 'replay' sequences play out in a fraction of a second–so fast that we're not necessarily aware of the sequence.
Making Efficient Use of Demonstrations to Solve Hard Exploration Problems
This paper introduces R2D3, an agent that makes efficient use of demonstrations to solve hard exploration problems in partially observable environments with highly variable initial conditions. We also introduce a suite of eight tasks that combine these three properties, and show that R2D3 can solve several of the tasks where other state of the art methods (both with and without demonstrations) fail to see even a single successful trajectory after tens of billions of steps of exploration.
The Podcast: Episode 6: AI for everyone
While there is a lot of excitement about AI research, there are also concerns about the way it might be implemented, used and abused.
The Podcast: Episode 5: Out of the lab
The ambition of AI research is to create systems that can help to solve problems in the real world.
The Podcast: Episode 4: AI, Robot
Forget what sci-fi has told you about superintelligent robots that are uncannily human-like; the reality is more prosaic. Inside DeepMind’s robotics laboratory, Hannah explores what researchers call ‘embodied AI’: robot arms that are learning tasks like picking up plastic bricks, which humans find comparatively easy.
The Podcast: Episode 3: Life is like a game
Video games have become a favourite tool for AI researchers to test the abilities of their systems. In this episode, Hannah sits down to play StarCraft II - a challenging video game that requires players to control the onscreen action with as many as 800 clicks a minute.
The Podcast: Episode 2: Go to Zero
In March 2016, more than 200 million people watched AlphaGo become first computer program to defeat a professional human player at the game of Go, a milestone in AI research that was considered to be a decade ahead of its time.
The Podcast: Episode 1: AI and neuroscience - The virtuous circle
What can the human brain teach us about AI? And what can AI teach us about our own intelligence? These questions underpin a lot of AI research.
Welcome to the DeepMind podcast
What’s AI? What can it be used for? Is it safe? And how do I get involved? These are the kinds of questions we often get asked at public events like science festivals, talks and workshops. We love answering them and really value the conversations and thinking they provoke.
Using machine learning to accelerate ecological research
The Serengeti is one of the last remaining sites in the world that hosts an intact community of large mammals. These animals roam over vast swaths of land, some migrating thousands of miles across multiple countries following seasonal rainfall. As human encroachment around the region becomes more intense, these species are forced to alter their behaviours in order to survive. Increasing agriculture, poaching, and climate abnormalities contribute to changes in animal behaviours and population dynamics, but these changes have occurred at spatial and temporal scales which are difficult to monitor
Using AI to give doctors a 48-hour head start on life-threatening illness
Artificial intelligence can now predict one of the leading causes of avoidable patient harm up to two days before it happens, as demonstrated by our latest research published in Nature. Working alongside experts from the US Department of Veterans Affairs (VA), we have developed technology that, in the future, could give doctors a 48-hour head start in treating acute kidney injury (AKI), a condition that is associated with over 100,000 people in the UK every year. These findings come alongside a peer-reviewed service evaluation of Streams, our mobile assistant for clinicians, which shows that p
How evolutionary selection can train more capable self-driving cars
Waymo’s self-driving vehicles employ neural networks to perform many driving tasks, from detecting objects and predicting how others will behave, to planning a car's next moves. Training an individual neural net has traditionally required weeks of fine-tuning and experimentation, as well as enormous amounts of computational power. Now, Waymo, in a research collaboration with DeepMind, has taken inspiration from Darwin’s insights into evolution to make this training more effective and efficient.
Unsupervised learning: The curious pupil
Over the last decade, machine learning has made unprecedented progress in areas as diverse as image recognition, self-driving cars and playing complex games like Go. These successes have been largely realised by training deep neural networks with one of two learning paradigms—supervised learning and reinforcement learning. Both paradigms require training signals to be designed by a human and passed to the computer. In the case of supervised learning, these are the “targets” (such as the correct label for an image); in the case of reinforcement learning, they are the “rewards” for successful be
Capture the Flag: the emergence of complex cooperative agents
Mastering the strategy, tactical understanding, and team play involved in multiplayer video games represents a critical challenge for AI research. In our latest paper, now published in the journal Science, we present new developments in reinforcement learning, resulting in human-level performance in Quake III Arena Capture the Flag. This is a complex, multi-agent environment and one of the canonical 3D first-person multiplayer games. The agents successfully cooperate with both artificial and human teammates, and demonstrate high performance even when trained with reaction times comparable to h
Identifying and eliminating bugs in learned predictive models
Bugs and software have gone hand in hand since the beginning of computer programming. Over time, software developers have established a set of best practices for testing and debugging before deployment, but these practices are not suited for modern deep learning systems. Today, the prevailing practice in machine learning is to train a system on a training data set, and then test it on another set. While this reveals the average-case performance of models, it is also crucial to ensure robustness, or acceptably high performance even in the worst case. In this article, we describe three approache
TF-Replicator: Distributed Machine Learning for Researchers
At DeepMind, the Research Platform Team builds infrastructure to empower and accelerate our AI research. Today, we are excited to share how we developed TF-Replicator, a software library that helps researchers deploy their TensorFlow models on GPUs and Cloud TPUs with minimal effort and no previous experience with distributed systems. TF-Replicator’s programming model has now been open sourced as part of TensorFlow’s tf.distribute.Strategy. This blog post gives an overview of the ideas and technical challenges underlying TF-Replicator. For a more comprehensive description, please read our arXi
Machine learning can boost the value of wind energy
Carbon-free technologies like renewable energy help combat climate change, but many of them have not reached their full potential. Consider wind power: over the past decade, wind farms have become an important source of carbon-free electricity as the cost of turbines has plummeted and adoption has surged. However, the variable nature of wind itself makes it an unpredictable energy source—less useful than one that can reliably deliver power at a set time.
AlphaStar: Mastering the real-time strategy game StarCraft II
Games have been used for decades as an important way to test and evaluate the performance of artificial intelligence systems. As capabilities have increased, the research community has sought games with increasing complexity that capture different elements of intelligence required to solve scientific and real-world problems. In recent years, StarCraft, considered to be one of the most challenging Real-Time Strategy (RTS) games and one of the longest-played esports of all time, has emerged by consensus as a “grand challenge” for AI research.
AlphaZero: Shedding new light on chess, shogi, and Go
In late 2017 we introduced AlphaZero, a single system that taught itself from scratch how to master the games of chess, shogi (Japanese chess), and Go, beating a world-champion program in each case. We were excited by the preliminary results and thrilled to see the response from members of the chess community, who saw in AlphaZero’s games a ground-breaking, highly dynamic and “unconventional” style of play that differed from any chess playing engine that came before it.
Scaling Streams with Google
We’re excited to announce that the team behind Streams - our mobile app that supports doctors and nurses to deliver faster, better care to patients - will be joining Google.
Predicting eye disease with Moorfields Eye Hospital
In August, we announced the first stage of our joint research partnership with Moorfields Eye Hospital, which showed how AI could match world-leading doctors at recommending the correct course of treatment for over 50 eye diseases, and also explain how it arrives at its recommendations.
Open sourcing TRFL: a library of reinforcement learning building blocks
Today we are open sourcing a new library of useful building blocks for writing reinforcement learning (RL) agents in TensorFlow. Named TRFL (pronounced ‘truffle’), it represents a collection of key algorithmic components that we have used internally for a large number of our most successful agents such as DQN, DDPG and the Importance Weighted Actor Learner Architecture.
Expanding our research on breast cancer screening to Japan
Six months ago, we joined a groundbreaking new research partnership led by the Cancer Research UK Imperial Centre at Imperial College London to explore whether AI technology could help clinicians diagnose breast cancers on mammograms quicker and more effectively.
Preserving Outputs Precisely while Adaptively Rescaling Targets
Multi-task learning - allowing a single agent to learn how to solve many different tasks - is a longstanding objective for artificial intelligence research. Recently, there has been a lot of excellent progress, with agents like DQN able to use the same algorithm to learn to play multiple games including Breakout and Pong. These algorithms were used to train individual expert agents for each task. As artificial intelligence research advances to more complex real world domains, building a single general agent - as opposed to multiple expert agents - to learn to perform multiple tasks will be cru
Using AI to plan head and neck cancer treatments
Early results from our partnership with the Radiotherapy Department at University College London Hospitals NHS Foundation Trust suggest that we are well on our way to developing an artificial intelligence (AI) system that can analyse and segment medical scans of head and neck cancer to a similar standard as expert clinicians. This segmentation process is an essential but time-consuming step when planning radiotherapy treatment. The findings also show that our system can complete this process in a fraction of the time.
Safety-first AI for autonomous data centre cooling and industrial control
Many of society’s most pressing problems have grown increasingly complex, so the search for solutions can feel overwhelming. At DeepMind and Google, we believe that if we can use AI as a tool to discover new knowledge, solutions will be easier to reach.
A major milestone for the treatment of eye disease
We are delighted to announce the results of the first phase of our joint research partnership with Moorfields Eye Hospital, which could potentially transform the management of sight-threatening eye disease.
Objects that Sound
Visual and audio events tend to occur together: a musician plucking guitar strings and the resulting melody; a wine glass shattering and the accompanying crash; the roar of a motorcycle as it accelerates. These visual and audio stimuli are concurrent because they share a common cause. Understanding the relationship between visual events and their associated sounds is a fundamental way that we make sense of the world around us.
Measuring abstract reasoning in neural networks
Neural network-based models continue to achieve impressive results on longstanding machine learning problems, but establishing their capacity to reason about abstract concepts has proven difficult. Building on previous efforts to solve this important feature of general-purpose learning systems, our latest paper sets out an approach for measuring abstract reasoning in learning machines, and reveals some important insights about the nature of generalisation itself.
DeepMind papers at ICML 2018
The 2018 International Conference on Machine Learning will take place in Stockholm, Sweden from 10-15 July. For those attending and planning the week ahead, we are sharing a schedule of DeepMind presentations at ICML (you can download a pdf version here). We look forward to the many engaging discussions, ideas, and collaborations that are sure to arise from the conference!
DeepMind Health Response to Independent Reviewers' Report 2018
When we set up DeepMind Health we believed that pioneering technology should be matched with pioneering oversight. That’s why when we launched in February 2016, we did so with an unusual and additional mechanism: a panel of Independent Reviewers, who meet regularly throughout the year to scrutinise our work. This is an innovative approach within tech companies - one that forces us to question not only what we are doing, but how and why we are doing it - and we believe that their robust challenges make us better.
Neural scene representation and rendering
There is more than meets the eye when it comes to how we understand a visual scene: our brains draw on prior knowledge to reason and to make inferences that go far beyond the patterns of light that hit our retinas. For example, when entering a room for the first time, you instantly recognise the items it contains and where they are positioned. If you see three legs of a table, you will infer that there is probably a fourth leg with the same shape and colour hidden from view. Even if you can’t see everything in the room, you’ll likely be able to sketch its layout, or imagine what it looks like
Royal Free London publishes findings of legal audit in use of Streams
Last July, the Information Commissioner concluded an investigation into the use of the Streams app at the Royal Free London NHS Foundation Trust. As part of the investigation the Royal Free signed up to a set of undertakings – one of which was to commission a third party to audit the Royal Free’s current data processing arrangements with DeepMind, to ensure that they fully complied with data protection law and respected the privacy and confidentiality rights of its patients.You can read the full report on the Royal Free’s website here, and the Information Commissioner’s Office’s response here.
Prefrontal cortex as a meta-reinforcement learning system
Recently, AI systems have mastered a range of video-games such as Atari classics Breakout and Pong. But as impressive as this performance is, AI still relies on the equivalent of thousands of hours of gameplay to reach and surpass the performance of human video game players. In contrast, we can usually grasp the basics of a video game we have never played before in a matter of minutes.
Navigating with grid-like representations in artificial agents
Most animals, including humans, are able to flexibly navigate the world they live in – exploring new areas, returning quickly to remembered places, and taking shortcuts. Indeed, these abilities feel so easy and natural that it is not immediately obvious how complex the underlying processes really are. In contrast, spatial navigation remains a substantial challenge for artificial agents whose abilities are far outstripped by those of mammals.
DeepMind, meet Android
We’re delighted to announce a new collaboration between DeepMind for Google and Android, the world’s most popular mobile operating system. Together, we’ve created two new features that will be available to people with devices running Android P later this year
DeepMind papers at ICLR 2018
Between 30 April and 03 May, hundreds of researchers and engineers will gather in Vancouver, Canada, for the Sixth International Conference on Learning Representations.
Our first COO Lila Ibrahim takes DeepMind to the next level
One of the greatest pleasures of coming to work every day at DeepMind is the chance to collaborate with brilliant researchers and engineers from so many different fields and perspectives - with machine learning experts alongside neuroscientists, physicists, mathematicians, roboticists, ethicists and more.
Retour à Paris / A return to Paris
When we set up our headquarters in London in 2010, we wanted to make DeepMind the best possible place to do cutting-edge AI research. We also wanted to help the wider AI community grow - publishing peer-reviewed papers (over 180 to date, and counting!) and sharing our insights to advance the field, supporting our staff to teach at local universities, and working with schools and NGOs to foster the next generation of scientists.
Learning to navigate in cities without a map
How did you learn to navigate the neighborhood of your childhood, to go to a friend’s house, to your school or to the grocery store? Probably without a map and simply by remembering the visual appearance of streets and turns along the way. As you gradually explored your neighborhood, you grew more confident, mastered your whereabouts and learned new and increasingly complex paths. You may have gotten briefly lost, but found your way again thanks to landmarks, or perhaps even by looking to the sun for an impromptu compass.
Learning to write programs that generate images
Through a human’s eyes, the world is much more than just the images reflected in our corneas. For example, when we look at a building and admire the intricacies of its design, we can appreciate the craftsmanship it requires. This ability to interpret objects through the tools that created them gives us a richer understanding of the world and is an important aspect of our intelligence. We would like our systems to create similarly rich representations of the world. For example, when observing an image of a painting we would like them to understand the brush strokes used to create it and not jus
Understanding deep learning through neuron deletion
Deep neural networks are composed of many individual neurons, which combine in complex and counterintuitive ways to solve a wide range of challenging tasks. This complexity grants neural networks their power but also earns them their reputation as confusing and opaque black boxes.
Stop, look and listen to the people you want to help
‘I like to take things slow. Take it slowly and get it right first time,’ one participant said, but was quickly countered by someone else around the table: ‘But I’m impatient, I want to see the benefits now.’ This exchange neatly captures many of the conversations I heard at DeepMind Health’s recent Collaborative Listening Summit. It also represents, in layman’s terms, the debate that tech thinkers and policy-makers are having right now about the future of artificial intelligence.
Learning by playing
Getting children (and adults) to tidy up after themselves can be a challenge, but we face an even greater challenge trying to get our AI agents to do the same. Success depends on the mastery of several core visuo-motor skills: approaching an object, grasping and lifting it, opening a box and putting things inside of it. To make matters more complicated, these skills must be applied in the right sequence.
Researching patient deterioration with the US Department of Veterans Affairs
We’re excited to announce a medical research partnership with the US Department of Veterans Affairs (VA), one of the world’s leading healthcare organisations responsible for providing high-quality care to veterans and their families across the United States.
Scalable agent architecture for distributed training
Deep Reinforcement Learning (DeepRL) has achieved remarkable success in a range of tasks, from continuous control problems in robotics to playing games like Go and Atari. The improvements seen in these domains have so far been limited to individual tasks where a separate agent has been tuned and trained for each task.
Learning explanatory rules from noisy data
Suppose you are playing football. The ball arrives at your feet, and you decide to pass it to the unmarked striker. What seems like one simple action requires two different kinds of thought.
Open-sourcing Psychlab
Consider the simple task of going shopping for your groceries. If you fail to pick-up an item that is on your list, what does it tell us about the functioning of your brain? It might indicate that you have difficulty shifting your attention from object to object while searching for the item on your list. It might indicate a difficulty with remembering the grocery list. Or it could it be something to do with executing both skills simultaneously.
Game-theory insights into asymmetric multi-agent games
As AI systems start to play an increasing role in the real world it is important to understand how different systems will interact with one another.
2017: DeepMind's year in review
In July, the world number one Go player Ke Jie spoke after a streak of 20 wins. It was two months after he had played AlphaGo at the Future of Go Summit in Wuzhen, China.
Collaborating with patients for better outcomes
Working as a doctor in the NHS for over 10 years, I felt that I had developed good understanding of how patients and their families felt when faced with an upsetting diagnosis or important health decision. I had been lucky with my own health, having only spent one night in hospital for what ended up being a false alarm. But when my son was born prematurely two years ago, I had a glimpse into what being on the other side feels like - an experience that has profoundly shaped my thinking today.
DeepMind papers at NIPS 2017
Between 04-09 December, thousands of researchers and experts will gather for the Thirty-first Annual Conference on Neural Information Processing Systems (NIPS) in Long Beach, California.
Why doesn't Streams use AI?
One of the questions I’m most often asked about Streams, our secure mobile healthcare app, is “why is DeepMind making something that doesn’t use artificial intelligence?”
Specifying AI safety problems in simple environments
As AI systems become more general and more useful in the real world, ensuring they behave safely will become even more important. To date, the majority of technical AI safety research has focused on developing a theoretical understanding about the nature and causes of unsafe behaviour. Our new paper builds on a recent shift towards empirical testing (see Concrete Problems in AI Safety) and introduces a selection of simple reinforcement learning environments designed specifically to measure ‘safe behaviours’.
Population based training of neural networks
Neural networks have shown great success in everything from playing Go and Atari games to image recognition and language translation. But often overlooked is that the success of a neural network at a particular application is often determined by a series of choices made at the start of the research, including what type of network to use and the data and method used to train it. Currently, these choices - known as hyperparameters - are chosen through experience, random search or a computationally intensive search processes.
Applying machine learning to mammography screening for breast cancer
We founded DeepMind Health to develop technologies that could help address some of society’s toughest challenges. So we’re very excited to announce that our latest research partnership will focus on breast cancer.
High-fidelity speech synthesis with WaveNet
In October we announced that our state-of-the-art speech synthesis model WaveNet was being used to generate realistic-sounding voices for the Google Assistant globally in Japanese and the US English. This production model - known as parallel WaveNet - is more than 1000 times faster than the original and also capable of creating higher quality audio.
Sharing our insights from designing with clinicians
In our design studio, we have Indi Young’s mantra on the wall as a reminder to “fall in love with the problem, not the solution”. Nowhere is this more true than in health, where there are so many real problems to address, and where introducing theoretically clever but practically flawed software could easily do more harm than good.
Bringing Streams to Yeovil District Hospital NHS Foundation Trust
We’re excited to announce that we’ve agreed a five year partnership with Yeovil District Hospital NHS Foundation Trust. We’ll be providing them with Streams, our secure mobile app that helps nurses and doctors access important clinical information and get the right care to the right patient as quickly as possible.
AlphaGo Zero: Starting from scratch
Artificial intelligence research has made rapid progress in a wide variety of domains from speech recognition and image classification to genomics and drug discovery. In many cases, these are specialist systems that leverage enormous amounts of human expertise and data.
Strengthening our commitment to Canadian research
Three months ago we announced the opening of DeepMind’s first ever international AI research laboratory in Edmonton, Canada. Today, we are thrilled to announce that we are strengthening our commitment to the Canadian AI community with the opening of a DeepMind office in Montreal, in close collaboration with McGill University.
WaveNet launches in the Google Assistant
Just over a year ago we presented WaveNet, a new deep neural network for generating raw audio waveforms that is capable of producing better and more realistic-sounding speech than existing techniques. At that time, the model was a research prototype and was too computationally intensive to work in consumer products.
Why we launched DeepMind Ethics & Society
At DeepMind, we’re proud of the role we’ve played in pushing forward the science of AI, and our track record of exciting breakthroughs and major publications. We believe AI can be of extraordinary benefit to the world, but only if held to the highest ethical standards. Technology is not value neutral, and technologists must take responsibility for the ethical and social impact of their work.
The hippocampus as a predictive map
Think about how you choose a route to work, where to move house, or even which move to make in a game like Go. All of these scenarios require you to estimate the likely future reward of your decision. This is tricky because the number of possible scenarios explodes as one peers farther and farther into the future. Understanding how we do this is a major research question in neuroscience, while building systems that can effectively predict rewards is a major focus in AI research.
DeepMind and Blizzard open StarCraft II as an AI research environment
DeepMind's scientific mission is to push the boundaries of AI by developing systems that can learn to solve complex problems. To do this, we design agents and test their ability in a wide range of environments from the purpose-built DeepMind Lab to established games, such as Atari and Go.
DeepMind papers at ICML 2017 (part one)
The first of our three-part series, which gives brief descriptions of the papers we are presenting at the ICML 2017 Conference in Sydney, Australia.
DeepMind papers at ICML 2017 (part three)
The final part of our three-part series that gives an overview of the papers we are presenting at the ICML 2017 Conference in Sydney, Australia.
DeepMind papers at ICML 2017 (part two)
The second of our three-part series, which gives an overview of the papers we are presenting at the ICML 2017 Conference in Sydney, Australia.
AI and Neuroscience: A virtuous circle
Recent progress in AI has been remarkable. Artificial systems now outperform expert humans at Atari video games, the ancient board game Go, and high-stakes matches of heads-up poker. They can also produce handwriting and speech indistinguishable from those of humans, translate between multiple languages and even reformat your holiday snaps in the style of Van Gogh masterpieces.
Going beyond average for reinforcement learning
Consider the commuter who toils backwards and forwards each day on a train. Most mornings, her train runs on time and she reaches her first meeting relaxed and ready. But she knows that once in awhile the unexpected happens: a mechanical problem, a signal failure, or even just a particularly rainy day. Invariably these hiccups disrupt her pattern, leaving her late and flustered.
Agents that imagine and plan
Imagining the consequences of your actions before you take them is a powerful tool of human cognition. When placing a glass on the edge of a table, for example, we will likely pause to consider how stable it is and whether it might fall. On the basis of that imagined consequence we might readjust the glass to prevent it from falling and breaking. This form of deliberative reasoning is essentially ‘imagination’, it is a distinctly human ability and is a crucial tool in our everyday lives.
Imagine this: Creating new visual concepts by recombining familiar ones
Around two and a half thousand years ago a Mesopotamian trader gathered some clay, wood and reeds and changed humanity forever. Over time, their abacus would allow traders to keep track of goods and reconcile their finances, allowing economics to flourish.
Producing flexible behaviours in simulated environments
The agility and flexibility of a monkey swinging through the trees or a football player dodging opponents and scoring a goal can be breathtaking. Mastering this kind of sophisticated motor control is a hallmark of physical intelligence, and is a crucial part of AI research.
DeepMind expands to Canada with new research office in Edmonton, Alberta
DeepMind has always been a unique hybrid of startup culture and academia, and we’ve been lucky to collaborate with many of the best researchers from around the world. Today we’re thrilled to announce our next phase: the opening of DeepMind’s first ever international AI research office in Edmonton, Canada, in close collaboration with the University of Alberta (UAlberta).
Independent Reviewers release first annual report on DeepMind Health
Today, a panel of Independent Reviewers has published its first annual report into DeepMind Health. As I wrote in the foreword to their report (written, I add, before I’d read it): “We chose people who had specific expertise but also reputations for integrity, who did not hold back, who could be angry and critical… That’s good for us and makes us better.”
The Information Commissioner, the Royal Free, and what we’ve learned
Today, dozens of people in UK hospitals will die preventably from conditions like sepsis and acute kidney injury (AKI) when their warning signs aren't picked up and acted on in time. To help address this, we built the Streams app with clinicians at the Royal Free London NHS Foundation Trust, using mobile technology to automatically review test results for serious issues starting with AKI. If one is found, Streams sends a secure smartphone alert to the right clinician, along with information about previous conditions so they can make an immediate diagnosis.
Interpreting Deep Neural Networks using Cognitive Psychology
Deep neural networks have learnt to do an amazing array of tasks - from recognising and reasoning about objects in images to playing Atari and Go at super-human levels. As these tasks and network architectures become more complex, the solutions that neural networks learn become more difficult to understand.
Enhancing patient safety at Taunton and Somerset NHS Foundation Trust
We’re delighted to announce our first partnership outside of London to help doctors and nurses break new ground in the NHS’s use of digital technology.
Learning through human feedback
We believe that Artificial Intelligence will be one of the most important and widely beneficial scientific advances ever made, helping humanity tackle some of its greatest challenges, from climate change to delivering advanced healthcare. But for AI to deliver on this promise, we know that the technology must be built in a responsible manner and that we must consider all potential challenges and risks.
A neural approach to relational reasoning
Consider the reader who pieces together the evidence in an Agatha Christie novel to predict the culprit of the crime, a child who runs ahead of her ball to prevent it rolling into a stream or even a shopper who compares the relative merits of buying kiwis or mangos at the market.
AlphaGo's next move
With just three stones on the board, it was clear that this was going to be no ordinary game of Go.
Exploring the mysteries of Go with AlphaGo and China's top players
Just over a year ago, we saw a major milestone in the field of artificial intelligence: DeepMind’s AlphaGo took on and defeated one of the world’s top Go players, the legendary Lee Sedol. Even then, we had no idea how this moment would affect the 3,000 year old game of Go and the growing global community of devotees to this beautiful board game.
Innovations of AlphaGo
One of the great promises of AI is its potential to help us unearth new knowledge in complex domains. We’ve already seen exciting glimpses of this, when our algorithms found ways to dramatically improve energy use in data centres - as well as of course with our program AlphaGo.
Open sourcing Sonnet - a new library for constructing neural networks
It’s now nearly a year since DeepMind made the decision to switch the entire research organisation to using TensorFlow (TF). It’s proven to be a good choice - many of our models learn significantly faster, and the built-in features for distributed training have hugely simplified our code. Along the way, we found that the flexibility and adaptiveness of TF lends itself to building higher level frameworks for specific purposes, and we’ve written one for quickly building neural network modules with TF. We are actively developing this codebase, but what we have so far fits our research needs well,
Distill: Communicating the science of machine learning
Like every field of science, the importance of clear communication in machine learning research cannot be over-emphasised: it helps to drive forward the state-of-the art by allowing the research community to share, discuss and build upon new findings.
Enabling Continual Learning in Neural Networks
Computer programs that learn to perform tasks also typically forget them very quickly. We show that the learning rule can be modified so that a program can remember old tasks when learning a new one. This is an important step towards more intelligent programs that are able to learn progressively and adaptively.
Trust, confidence and Verifiable Data Audit
Data can be a powerful force for social progress, helping our most important institutions to improve how they serve their communities. As cities, hospitals, and transport systems find new ways to understand what people need from them, they’re unearthing opportunities to change how they work today and identifying exciting ideas for the future.
A milestone for DeepMind Health and Streams
In November we announced a groundbreaking five year partnership with the Royal Free London to deploy and expand on Streams, our secure clinical app that aims to improve care by getting the right information to the right clinician at the right time.
Understanding Agent Cooperation
We employ deep multi-agent reinforcement learning to model the emergence of cooperation. The new notion of sequential social dilemmas allows us to model how rational agents interact, and arrive at more or less cooperative behaviours depending on the nature of the environment and the agents’ cognitive capacity. The research may enable us to better understand and control the behaviour of complex multi-agent systems such as the economy, traffic, and environmental challenges.
Our collaborations with academia to advance the field of AI
When I was studying in the mid-90s as an undergraduate, there was very little active engagement between the academic communities pushing the boundaries of maths and science, and the industries that many students ended up going into, such as finance. This struck me as a missed opportunity. While private institutions benefited from the technological advances being driven by university researchers, the subsequent breakthroughs they made were rarely shared for mutual benefit between the two.
DeepMind’s work in 2016: a round-up
In a world of fiercely complex, emergent, and hard-to-master systems - from our climate to the diseases we strive to conquer - we believe that intelligent programs will help unearth new scientific knowledge that we can use for social benefit. To achieve this, we believe we’ll need general-purpose learning systems that are capable of developing their own understanding of a problem from scratch, and of using this to identify patterns and breakthroughs that we might otherwise miss. This is the focus of our long-term research mission at DeepMind.
Bringing the best of mobile technology to Imperial College Healthcare NHS Trust
We’re really excited to announce that we’ve agreed a five year partnership with Imperial College Healthcare NHS Trust, helping them make the most of the opportunity for mobile clinical applications to improve care. This is now our second NHS partnership for clinical apps, following a similar partnership we announced last month with the Royal Free London NHS Foundation Trust.
DeepMind Papers @ NIPS (Part 2)
The second blog post in this series, sharing brief descriptions of the papers we are presenting at NIPS 2016 Conference in Barcelona.
Open-sourcing DeepMind Lab
DeepMind's scientific mission is to push the boundaries of AI, developing systems that can learn to solve any complex problem without needing to be taught how.
DeepMind Papers @ NIPS (Part 1)
Over the next three blogposts, we're going to share with you brief descriptions of the papers we are presenting at the NIPS 2016 Conference in Barcelona.
Working with the NHS to build lifesaving technology
We’re very proud to announce a groundbreaking five year partnership with the Royal Free London NHS Foundation Trust.
Reinforcement learning with unsupervised auxiliary tasks
Our primary mission at DeepMind is to push the boundaries of AI, developing programs that can learn to solve any complex problem without needing to be taught how. Our reinforcement learning agents have achieved breakthroughs in Atari 2600 games and the game of Go. Such systems, however, can require a lot of data and a long time to learn so we are always looking for ways to improve our generic learning algorithms.
DeepMind and Blizzard to release StarCraft II as an AI research environment
Today at BlizzCon 2016 in Anaheim, California, we announced our collaboration with Blizzard Entertainment to open up StarCraft II to AI and Machine Learning researchers around the world.
Differentiable neural computers
In a recent study in Nature, we introduce a form of memory-augmented neural network called a differentiable neural computer, and show that it can learn to use its memory to answer questions about complex, structured data, including artificially generated stories, family trees, and even a map of the London Underground. We also show that it can solve a block puzzle game using reinforcement learning.
Announcing the Partnership on AI to Benefit People & Society
We believe that AI has the potential for transformative, positive impact in the world. Fulfilling this potential is not only dependent on the quality of the algorithms being engineered and the data they use, but on the level of public engagement, transparency, and ethical discussion that takes place around them.
Putting patients at the heart of DeepMind Health
From the outset, we’ve wanted DeepMind Health to be a truly collaborative effort. Too much hospital IT has been developed from a top-down perspective, often repurposing technology built for completely different sectors thousands of miles away from the NHS frontline. The result: tools that remain out-of-date and imperfectly suited to clinical use, contributing to a patient safety challenge where more than 1 in 10 patients suffer harm¹ during an in-patient stay.
WaveNet: A generative model for raw audio
This post presents WaveNet, a deep generative model of raw audio waveforms. We show that WaveNets are able to generate speech which mimics any human voice and which sounds more natural than the best existing Text-to-Speech systems, reducing the gap with human performance by over 50%.
Applying machine learning to radiotherapy planning for head & neck cancer
We’re excited to announce a new research partnership with the Radiotherapy Department at University College London Hospitals NHS Foundation Trust, which provides world-leading cancer treatment.
Decoupled Neural Interfaces Using Synthetic Gradients
Neural networks are the workhorse of many of the algorithms developed at DeepMind. For example, AlphaGo uses convolutional neural networks to evaluate board positions in the game of Go and DQN and Deep Reinforcement Learning algorithms use neural networks to choose actions to play at super-human level on video games.
DeepMind AI Reduces Google Data Centre Cooling Bill by 40%
Reducing energy usage has been a major focus for us over the past 10 years: we have built our own super-efficient servers at Google, invented more efficient ways to cool our data centres and invested heavily in green energy sources, with the goal of being powered 100 percent by renewable energy.
Deep Reinforcement Learning
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. This paradigm of learning by trial-and-error, solely from rewards or punishments, is known as reinforcement learning (RL). Also like a human, our agents construct and learn their own knowledge directly from raw inputs, such as v
Announcing DeepMind Health research partnership with Moorfields Eye Hospital
We founded DeepMind to make the world a better place by developing technologies that help address some of society's toughest challenges. So we’re excited to announce our first medical research project with an NHS Trust.
We are very excited to announce the launch of DeepMind Health
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.We're starting in the UK, where the National Health Service is hugely important to our team. The NHS helped bring many of us into the world, and has looked after our loved ones when they've most needed help. We want to see the NHS thrive, and to ensure that its talented cl
