Posts in category: Deepmind

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.Our latest paper introduces details of the new model and the probability density distillation technique we developed to allow the system to work in a massively parallel computing environment.The original WaveNet model used autoregressive connections to synthesise the waveform one sample at a time, with each new sample conditioned on the previous samples. While this produces high-quality audio with up to 24,000 samples per second, this sequential generation is too slow for production environments.Read More

[Editors note: this is the first in a series of blog posts about what weve learned about working in healthcare. Its both exceptionally hard and exceptionally important to get right, and we hope that by sharing our experiences well help other health innovators along the way]In our design studio, we have Indi Youngs 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.Over the course of hundreds of hours of shadowing, interviews and workshops with nurses, doctors and patients, weve been privileged to learn a lot about some of the problems they all face – and were still learning a ton every day. We are constantly impressed by the skill and care that clinicians across the NHS deliver every day, and this is the primary motivation for our team to ensure that these people get the tools they need to appropriately support them in their quest to help patients.Read More

Were excited to announce that weve agreed a five year partnership with Yeovil District Hospital NHS Foundation Trust. Well 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.This will be our fourth Streams partnership, following on from our work with Taunton and Somerset NHS Foundation Trust, Imperial College Healthcare NHS Trust and the Royal Free London NHS Foundation Trust.Read More

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.However, for some problems this human knowledge may be too expensive, too unreliable or simply unavailable. As a result, a long-standing ambition of AI research is to bypass this step, creating algorithms that achieve superhuman performance in the most challenging domains with no human input. In our most recent paper, published in the journal Nature, we demonstrate a significant step towards this goal.Read More

(French translation below)Three months ago we announced the opening of DeepMinds 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.Opening a second office is a natural next step for us in Canada, a country that is globally recognised as a leader in artificial intelligence research. We have always had strong links with the thriving research community in Canada and Montreal, where large companies, startups, incubators and government come together with ground-breaking teams, such as those at the Montreal Institute for Learning Algorithms (MILA) and McGill University.We are delighted that DeepMind Montreal will be led by one of the pioneers of this community,Doina Precup, Associate Professor in the School of Computer Science at McGill, Senior Fellow of the Canadian Institute for Advanced Research, and a member of MILA. Doinas expertise is in reinforcement learning – one of DeepMinds specialities – which is critical for areas such as reasoning and planning.In her new position, Doina will continue to focus on fundamental research at McGill, MILA, and DeepMind.Read More

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. But over the last 12 months we have worked hard to significantly improve both the speed and quality of our model and today we are proud to announce that an updated version of WaveNet is being used to generate the Google Assistant voices for US English and Japanese across all platforms.Using the new WaveNet model results in a range of more natural sounding voices for the Assistant.Read More

At DeepMind, were proud of the role weve 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.As history attests, technological innovation in itself is no guarantee of broader social progress. The development of AI creates important and complex questions. Its impact on societyand on all our livesis not something that should be left to chance. Beneficial outcomes and protections against harms must be actively fought for and built-in from the beginning. But in a field as complex as AI, this is easier said than done.As scientists developing AI technologies, we have a responsibility to conduct and support open research and investigation into the wider implications of our work. At DeepMind, we start from the premise that all AI applications should remain under meaningful human control, and be used for socially beneficial purposes. Understanding what this means in practice requires rigorous scientific inquiry into the most sensitive challenges we face.Read More

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.In our new paper, in Nature Neuroscience, we apply a neuroscience lens to a longstanding mathematical theory from machine learning to provide new insights into the nature of learning and memory. Specifically, wepropose that the area of the brain known as the hippocampus offers a unique solution to this problem by compactly summarising future events using what we call a predictive map.The hippocampus has traditionally been thought to only represent an animals current state, particularly in spatial tasks, such as navigating a maze. This view gained significant traction with thediscovery of place cells in the rodent hippocampus, which fire selectively when the animal is in specific locations. While this theory accounts for many neurophysiological findings, it does not fully explain why the hippocampus is also involved in other functions, such as memory, relational reasoning, and decision making.Read More

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.Testing our agents in games that are not specifically designed for AI research, and where humans play well, is crucial to benchmark agent performance. That is why we, along with our partner Blizzard Entertainment, are excited to announce the release of SC2LE, a set of tools that we hope will accelerate AI research in the real-time strategy game StarCraft II. The SC2LE release includes:A Machine Learning API developedby Blizzard that gives researchers and developers hooks into the game. This includes the release of tools for Linux for the first time.Adataset of anonymised game replays, which will increase from 65k to more than half a million in the coming weeks.An open source version of DeepMinds toolset, PySC2, to allow researchers to easily use Blizzards feature-layer API with their agents.A series of simple RL mini-games to allow researchers to test the performance of agents on specific tasks.A joint paperthat outlines the environment, and reports initial baseline results on the mini-games, supervised learning from replays, and the full 1v1 ladder game against the built-in AI.Read More

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.Read More