Posts in category: Deepmind

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.Now were excited to start working on the next research challenge whether we can help clinicians predict eye diseases before symptoms set in.There are two types of age-related macular degeneration (AMD), one of the most common blinding eye diseases, with 170 million sufferers worldwide. The dry form is relatively common among those over 65, and often only causes mild sight loss. However, about 15% of patients with dry AMD go on to develop the more serious form of the disease wet AMD which can cause permanent, blinding sight loss.Currently, ophthalmologists diagnose wet AMD by analysing highly detailed 3D scans of the back of the eye, called OCT scans.Read More

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.A typical deep reinforcement learning agent consists of a large number of interacting components: at the very least, these include the environment and some deep network representing values or policies, but they often also include components such as a learned model of the environment, pseudo-reward functions or a replay system.These parts tend to interact in subtle ways (often not well-documented in papers, as highlighted by Henderson and colleagues), thus making it difficult to identify bugs in such large computational graphs. A recent blog post by OpenAI highlighted this issue by analysing some of the most popular open-source implementations of reinforcement learning agents and finding that six out of 10 had subtle bugs found by a community member and confirmed by the author.One approach to addressing this issue, and helping those in the research community attempting to reproduce results from papers, is through open-sourcing complete agent implementations.Read More

Japanese version followsSix 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.Breast cancer is a huge global health problem. Around the world, over 1.6 million people are diagnosed with the disease every single year, and 500,000 lose their life to it partly because accurately detecting and diagnosing breast cancer still remains a huge challenge.Working alongside leading breast cancer experts, clinicians and academics in the UK, weve been exploring whether machine learning (a form of AI) could help address this issue.Today, were delighted to announce that this project is expanding internationally, with The Jikei University Hospital, one of Japans foremost medical institutions, joining the collaborationas part of a wider five year partnership they have signed with DeepMind Health.For the purposes of this research, they will be working with us to analyse historic, de-identified mammograms from around 30,000 women taken at the hospital between 2007 and 2018.Read More

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 findingsalso show that our system can complete this process in a fraction of the time.Speeding up the segmentation processMore than half a million people are diagnosed each year with cancers of the head and neck worldwide. Radiotherapy is a key part of treatment, but clinical staff have to plan meticulously so that healthy tissue doesnt get damaged by radiation: a process which involves radiographers, oncologists and/or dosimetrists manually outlining the areas of anatomy that need radiotherapy, and those areas that should be avoided.Although our work is still at an early stage, we hope it could one day reduce the waiting time between diagnosis and treatment, which could potentially improve outcomes for cancer patients.Read More

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 likeDQN 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 crucial. However, so far, this has proven to be a significant challenge.One reason is that there are often differences in the reward scales our reinforcement learning agents use to judge success, leading them to focus on tasks where the reward is arbitrarilyhigher. For example, in the Atari game Pong, the agent receives a reward of either -1, 0, or +1 per step. In contrast, an agent playing Ms. Pac-Man can obtain hundreds or thousands of points in a single step. Even if the size of individual rewards is comparable, the frequency of rewards can change over time as the agent gets better.Read More

Many of societys 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.In 2016, we jointly developed an AI-powered recommendation system to improve the energy efficiency of Googles already highly-optimised data centres. Our thinking was simple: even minor improvements would provide significant energy savings and reduce CO2 emissions to help combat climate change.Now were taking this system to the next level: instead of human-implemented recommendations, our AI system is directly controlling data centre cooling, while remaining under the expert supervision of our data centre operators. This first-of-its-kind cloud-based control system is now safely delivering energy savings in multiple Google data centres.Read More

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.The results, published online inNature Medicine(open access full text, see end of blog), show that our AI system can quickly interpret eye scans from routine clinical practice with unprecedented accuracy. It can correctly recommend how patients should be referred for treatment for over 50 sight-threatening eye diseases as accurately as world-leading expert doctors.These are early results, but they show that our system could handle the wide variety of patients found in routine clinical practice. In the long term, we hope this will help doctors quickly prioritise patients who need urgent treatment which could ultimately save sight.A more streamlined processCurrently, eyecare professionals use optical coherence tomography (OCT) scans to help diagnose eye conditions. These 3D images provide a detailed map of the back of the eye, but they are hard to read and need expert analysis to interpret.The time it takes to analyse these scans, combined with the sheer number of scans that healthcare professionals have to go through (over 1,000 a day at Moorfields alone), can lead to lengthy delays between scan and treatment even when someone needs urgent care.Read More

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.In Look, Listen, and Learn and Objects that Sound (to appear at ECCV 2018), we explore this observation by asking: what can be learnt by looking at and listening to a large number of unlabelled videos? By constructing an audio-visual correspondence learning task that enables visual and audio networks to be jointly trained from scratch, we demonstrate that:the networks are able to learn useful semantic concepts;the two modalities can be used to search one another (e.g. to answer the question, Which sound fits well with this image?); andthe object making the sound can be localised.Limitations of previous cross-modal learning approachesLearning from multiple modalities is not new; historically, researchers have largely focused on image-text or audio-vision pairings.Read More

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.Read More

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!Efficient Neural Audio SynthesisAuthors: Nal Kalchbrenner, Erich Elsen, Karen Simonyan, Seb Nouri, Norman Casagrande, Edward Lockhart, Sander Dieleman, Aaron van den Oord, Koray KavukcuogluSequential models achieve state-of-the-art results in audio, visual and textual domains with respect to both estimating the data distribution and generating desired samples. Efficient sampling for this class of models at the cost of little to no loss in quality has however remained an elusive task. With a focus on text-to-speech synthesis, we show that compact recurrent architectures, a remarkably high degree of weight sparsification and a novel reordering of the variables greatly reduce sampling latency while maintaining high audio fidelity. We first describe a compact single-layer recurrent neural network, the WaveRNN, with a novel dual softmax layer that matches the quality of the state-of-the-art WaveNet model.Read More