We work closely with Google and other experts to find ways for our advances to benefit society.
So far, our systems have shown how they can save energy, identify eye disease, accelerate science, and improve Google products used across the world.
Understanding protein folding
Proteins are complex molecules that are essential to life. Each has its own unique 3D shape that determines how it works and what it does.
Knowing how proteins fold could help scientists understand the biological processes of every living thing. To accelerate progress, we created AlphaFold, a system which accurately predicts the shape of proteins. This research has enormous potential in every field of biology, from helping tackle disease and quickly finding new medicines to unlocking the mysteries of how life itself works.
Identifying eye disease faster
We partnered with Moorfields Eye Hospital to develop faster ways of identifying, and better ways of understanding, common eye diseases from routine scans.
Over 100 million people are affected by diabetic retinopathy or age-related macular degeneration. These conditions can cause permanent sight loss unless they’re treated quickly. The results, which were published in Nature Medicine, showed that our AI system could recommend patient referrals as accurately as world-leading expert doctors for over 50 sight-threatening eye diseases. More recently, we showed that our system can predict whether a patient will develop a more severe form of age-related macular degeneration months before it happens–paving the way for future research in sight-loss prevention.
Saving energy at Google scale
Google's data centres contain thousands of servers that power services including Google Search, Gmail, and YouTube.
It’s essential to keep these servers cool, but this takes a lot of electricity. Even minor improvements would significantly reduce energy usage and CO2 emissions. By building an AI system that manages data centre cooling more efficiently, we helped save around 30% of the energy needed. We also designed it with safety and reliability in mind, so the AI system works within strict constraints.
Improving Google products
Working with Google, we’ve applied our cutting-edge research to products and infrastructure used by billions of people.
Our voice synthesis technology WaveNet is in the hands of people who use Google Assistant and Google Cloud Platform around the world. And our systems have helped improve mobile phone battery use and screen brightness for millions of people using the Android Pie operating system.
These real-world projects build on our breakthroughs in fundamental AI research, published in journals like Nature, Science, and more.
WaveNet: A generative model for raw audio
WaveNet generates realistic human-sounding speech that reduced the gap between computer and human performance by over 50%, when it was first introduced. It has now powers the voice of the Google Assistant.
Giving doctors a headstart on acute kidney injury
Our technology is helping doctors diagnose acute kidney injury (AKI) up to 48-hours earlier than current methods. With early detection, patients get better preventative care, avoiding invasive procedures, and reducing costs.
More accurately identifying breast cancer
We worked with Google Health, Northwestern University, Cancer Research UK and Royal Surrey County Hospital to develop an AI system that can better identify breast cancer in X-rays across populations and systems.
AlphaStar plays StarCraft II at Grandmaster level
AlphaStar is the first AI to reach the top league of StarCraft II without any restrictions. Understanding the potential and limitations of open-ended learning like this is a critcial step towards creating robust systems for real-world domains.
AlphaZero: Shedding new light on chess, shogi, and Go
AlphaZero learned to play three famously complex games, becoming the strongest player in history for each. Learning entirely from scratch, it developed its own distinctive style that continues to inspire human grandmasters.
DQN: Human-level control of Atari games
A great challenge in AI is building flexible systems that can take on a wide range of tasks. Our Deep Q-Network (DQN) made progress on this goal when it learned how to play 49 different Atari games using only raw pixels and the score as inputs.
A neural network with dynamic memory
The differentiable neural computer (DNC) can use its external memory to answer questions about complex structured data, such as stories, family trees, or a map of the London Underground.
AlphaGo defeats Lee Sedol in the game of Go
While becoming the first computer program to defeat a professional human Go player, AlphaGo taught the world new knowledge about perhaps the most studied and contemplated game in history.
GQN: Neural scene representation and rendering
The Generative Query Network (GQN) allows computers to learn about a generated scene purely from observation, much like how infants learn to understand the world.