Posts in category: Deepmind

Were 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.1 in 75 men and 1 in 150 women will be diagnosed with oral cancer during their lifetime, and oral cavity cancer has risen by 92% since the 1970s.Read More

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.This post introduces some of our latest research in progressing the capabilities and training procedures of neural networks called Decoupled Neural Interfaces using Synthetic Gradients. This work gives us a way to allow neural networks to communicate, to learn to send messages between themselves, in a decoupled, scalable manner paving the way for multiple neural networks to communicate with each other or improving the long term temporal dependency of recurrent networks. This is achieved by using a model to approximate error gradients, rather than by computing error gradients explicitly with backpropagation. The rest of this post assumes some familiarity with neural networks and how to train them. If youre new to this area we highly recommend Nando de Freitas lecture series on Youtube on deep learning and neural networks.Neural networks and the problem of lockingIf you consider any layer or module in a neural network, it can only be updated once all the subsequent modules of the network have been executed, and gradients have been backpropagated to it.Read More

From smartphone assistants to image recognition and translation, machine learning already helps us in our everyday lives. But it can also help us to tackle some of the worlds most challenging physical problems – such as energy consumption. Large-scale commercial and industrial systems like data centres consume a lot of energy, and while much has been done to stem the growth of energy use, there remains a lot more to do given the worlds increasing need for computing power.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. Compared to five years ago, we now get around 3.5 times the computing power out of the same amount of energy, and we continue to make many improvements each year.Major breakthroughs, however, are few and far between – which is why we are excited to share that by applying DeepMinds machine learning to our own Google data centres, weve managed to reduce the amount of energy we use for cooling by up to 40 percent. In any large scale energy-consuming environment, this would be a huge improvement.Read More

We founded DeepMind to make the world a better place by developing technologies that help address some of society’s toughest challenges.So were excited to announce our first medical research project with an NHS Trust.Well be working with Moorfields Eye Hospital NHS Foundation Trust, one of the worlds leading eye hospitals with a 200 year track record in clinical care, research and education.This collaboration came about when Pearse Keane, a consultant ophthalmologist at Moorfields, contacted DeepMind to explore how we could work together on two specific conditions that cause sight loss: diabetic retinopathy and age-related macular degeneration (AMD). Together, these affect more than 625,000 people in the UK and over 100 million people worldwide.Read More

Humans excel at solving a wide variety of challenging problems, from low-level motor control through to high-level cognitive tasks. Our goal at DeepMind is to create artificial agents that can achieve a similar level of performance and generality. Like a human, our agents learn for themselves to achieve successful strategies that lead to the greatest long-term rewards. 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 vision, without any hand-engineered features or domain heuristics. This is achieved by deep learning of neural networks. At DeepMind we have pioneered the combination of these approaches – deep reinforcement learning – to create the first artificial agents to achieve human-level performance across many challenging domains.Our agents must continually make value judgements so as to select good actions over bad. This knowledge is represented by a Q-network that estimates the total reward that an agent can expect to receive after taking a particular action. Two years ago we introducedthe first widely successful algorithm for deep reinforcement learning. The key idea was to use deep neural networks to represent the Q-network, and to train this Q-network to predict total reward.Read More

We founded DeepMind to solve intelligence and use it to make the world a better place by developing technologies that help address some of society’s toughest challenges. It was clear to us that we should focus on healthcare because its an area where we believe we can make a real difference to peoples 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 clinicians get the tools and support they need to continue providing world-class care.Frontline nurses, doctors and other healthcare professionals who spend their days treating patients know better than anyone what’s needed to provide outstanding care. We at DeepMind Health aim to support clinicians by providing the technical expertise needed to build and scale technologies that help them provide the best possible care to their patients.The FutureWhile projects like Hark and AKI detection are in their early stages, the problems they solve are fundamental to the NHS. The hope is that these tools can help shift more resources away from reaction and towards better prevention.Read More