Posts in category: Deepmind

With just three stones on the board, it was clear that this was going to be no ordinary game of Go. Chinese Go Grandmaster and world number one Ke Jie departed from his typical style of play and opened with a 3:3 point strategy – a highly unusual approach aimed at quickly claiming corner territory at the start of the game. The placement is rare amongst Go players, but its a favoured position of our program AlphaGo. Ke Jie was playing it at its own game.Ke Jies thoughtful positioning of that single black stone was a fitting motif for the opening match of The Future of Go Summit in Wuzhen, China, an event dedicated to exploring the truth of this beautiful and ancient game. Over the last five days we have been honoured to witness games of the highest calibre.Read More

Just over a year ago, we saw a major milestone in the field of artificial intelligence: DeepMinds AlphaGo took on and defeated one of the worlds 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.Read More

One of the great promises of AI is its potential to help us unearth new knowledge in complex domains. Weve 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.Read More

Its now nearly a year since DeepMind made the decision to switch the entire research organisation to using TensorFlow (TF). Its 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 weve 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, and were excited to announce that today we are open sourcing it. We call this framework Sonnet.Read More

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.For this reason, we at DeepMind are enthusiastic supporters of Distill, a new independent, web-based medium for clear and open – demystified – machine learning research, comprising a journal, prizesrecognising outstanding work,and tools to create interactive essays.Read More

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.Read More

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, theyre unearthing opportunities to change how they work today and identifying exciting ideas for the future.Data can only benefit society if it has societys trust and confidence, and here we all face a challenge. Now that you can use data for so many more purposes, people arent just asking about whos holding information and whether its being kept securely they also want greater assurances about precisely what is being done with it.In that context, auditability becomes an increasingly important virtue. Any well-built digital tool will already log how it uses data, and be able to show and justify those logs if challenged. But the more powerful and secure we can make that audit process, the easier it becomes to establish real confidence about how data is being used in practice.Imagine a service that could give mathematical assurance about what is happening with each individual piece of personal data, without possibility of falsification or omission. Imagine the ability for the inner workings of that system to be checked in real-time, to ensure that data is only being used as it should be.Read More

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.The first version of Streams has now been deployed at the Royal Free and were delighted that the early feedback from nurses, doctors and patients has so far been really positive. Some of the nurses using Streams at the hospital estimate that the app is saving them up to two hours per day, giving them more time to spend with patients in need. And were starting to hear the first stories of patients whose conditions were picked up and acted on faster thanks to Streams alerts.Patients likeAfia Ahmed, who was seen more quickly thanks to the instant alerts. You can read more about the deployment and some of the early positive signs over on the Royal Frees website.Read More

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.Read More

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.Read More