Monthly Archives: November 2017

One of the questions Im most often asked about Streams, our secure mobile healthcare app, is why is DeepMind making something that doesnt use artificial intelligence?Its a fair question to ask of an artificial intelligence (AI) company. When we first started thinking about working in healthcare, our natural focus was on AI and how it could be used to help the NHS and its patients. We see huge potential for AI to revolutionise our understanding of diseases – how they develop and are diagnosed – which could, in turn, help scientists discover new treatments, care pathways and cures.In the early days of DeepMind Health, we met with clinicians at the Royal Free Hospital in London who wanted to know if AI could improve care for patients at risk of acute kidney injury (AKI). AKI is notoriously difficult to spot, and can result in serious illness or even death if left untreated. AKI is currently detected by applying a formula (called the AKI algorithm) to NHS patients blood tests. This algorithm is good, but its widely known that it isnt perfect. For example, it has a tendency to generate false positives for patients with chronic (as opposed to acute) kidney disease.Read More

As AI systems become more general and more useful in the real world, ensuring they behave safely will become even more important. To date, the majority of technical AI safety research has focused on developing a theoretical understanding about the nature and causes of unsafe behaviour. Our new paper builds on a recent shift towards empirical testing (see Concrete Problems in AI Safety) and introduces a selection of simple reinforcement learning environments designed specifically to measure safe behaviours.These nine environments are called gridworlds. Each consists of a chessboard-like two-dimensional grid. In addition to the standard reward function, we designed a performance function for each environment. An agent acts to maximise its reward function; for example collecting as many apples as possible or reaching a particular location in the fewest moves. But the performance function – which is hidden from the agent – measures what we actually want the agent to do: achieve the objective while acting safely.The following three examples demonstrate how gridworlds can be used to define and measure safe behaviour:1. The off-switch environment: how can we prevent agents from learning to avoid interruptions?Sometimes it might be necessary to turn off an agent; for maintenance, upgrades, or if the agent presents an imminent danger to itself or its surroundings.Read More

Neural networks have shown great success in everything from playing Go and Atari games to image recognition and language translation. But often overlooked is that the success of a neural network at a particular application is often determined by a series of choices made at the start of the research, including what type of network to use and the data and method used to train it. Currently, these choices – known as hyperparameters – are chosen through experience, random search or a computationally intensive search processes.In our most recent paper, we introduce a new method for training neural networks which allows an experimenter to quickly choose the best set of hyperparameters and model for the task. This technique – known as Population Based Training (PBT) – trains and optimises a series of networks at the same time, allowing the optimal set-up to be quickly found. Crucially, this adds no computational overhead, can be done as quickly as traditional techniques and is easy to integrate into existing machine learning pipelines.The technique is a hybrid of the two most commonly used methods for hyperparameter optimisation: random search and hand-tuning. In random search, a population of neural networks are trained independently in parallel and at the end of training the highest performing model is selected.Read More

We founded DeepMind Health to develop technologies that could help address some of societys toughest challenges. So were very excited to announce that our latest research partnership will focus on breast cancer.Well be working with a group of leading research institutions, led by the Cancer Research UK Centre at Imperial College London, and alongside the AI health research team at Google, to determine if cutting-edge machine learning technology could help improve the detection of breast cancer.Breast cancer is a significant global health problem. Every single year, over 1.6 million people are diagnosed with the disease, and while advances in early detection and treatment have improved survival rates, breast cancer still claims the lives of 500,000 people around the world every year, around 11,000 of whom are here in the UK.Thats partly because accurately detecting and diagnosing breast cancer still remains a huge challenge.Currently, clinicians use mammograms (an X-ray of the breasts) to spot cancers early and determine the correct treatment, but this process is far from perfect. Thousands of cancer cases are not picked up by mammograms every year, including around 30% of interval cancers, which are cancers that are diagnosed between screenings.Read More

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