We believe that AI has the potential for transformative, positive impact in the world. Fulfilling this potential is not only dependent on the quality of the algorithms being engineered and the data they use, but on the level of public engagement, transparency, and ethical discussion that takes place around them.Its precisely because AI has the potential to have such a major positive impact on the world, that we believe its critical that we build new models of open collaboration and accountability around it. Thats why we at DeepMind are really proud to have worked with Amazon, Google, Facebook, IBM and Microsoft, to form a non-profit organisation that aims to create a forum for open discussion around the benefits and challenges of developing and applying cutting edge AI. Together, we hope to advance public understanding of AI and formulate best practices on some of the most important and challenging ethical issues in the field.Read More
Putting patients at the heart of DeepMind Health
From the outset, weve wanted DeepMind Health to be a truly collaborative effort. Too much hospital IT has been developed from a top-down perspective, often repurposing technology built for completely different sectors thousands of miles away from the NHS frontline. The result: tools that remain out-of-date and imperfectly suited to clinical use, contributing to a patient safety challenge where more than 1 in 10 patients suffer harm during an in-patient stay.We think its possible to transform this through bringing some of the worlds most advanced technology to the NHS. But for this to have any chance of meaningful impact, we know it must have the input of patients and clinicians at its heart.Yesterday we took a step towards that goal by hosting our first open patient and public forum in London, with over 130 patients, carers and members of the public coming to our offices and many more watching on our livestream.Read More
WaveNet: A generative model for raw audio
This post presents WaveNet, a deep generative model of raw audio waveforms. We show that WaveNets are able to generate speech which mimics any human voice and which sounds more natural than the best existing Text-to-Speech systems, reducing the gap with human performance by over 50%. We also demonstrate that the same network can be used to synthesize other audio signals such as music, and present some striking samples of automatically generated piano pieces.Read More