Royal Society to investigate potential impacts and applications of machine learning in the UK20 November 2015
The Royal Society, the UK’s national academy of science, is to start a policy project on how machine learning, the powerful technology that allows machines to learn from data and self-improve, might impact on UK society. Machine learning underpins many of the services people rely on every day, including internet search engines, email filters to sort out spam, websites that make personalised recommendations and many of the applications we use on our phones.
With many more potential applications in the pipeline, future developments in machine learning could power the UK economy and help solve big societal issues. The Royal Society aims to increase awareness and demonstrate the potential of machine learning among policymakers, academia, industry and the wider public and highlight the opportunities and challenges it presents.
Professor Peter Donnelly FRS, Director of the Wellcome Trust Centre for Human Genetics at Oxford University and chair of the Royal Society’s working group on machine learning, said:
“Many of us don’t realise just how many of our daily interactions involve machine learning, and what a powerful impact it has. Just on our phones, machine learning underpins the voice recognition software that allows us to call home in a few words, or dictate messages, the friends Facebook suggests we add, mapping apps, and Google search.
“Looking forward, machine learning could improve disease diagnoses and personalised treatments, and power driverless vehicles that could revolutionise transport systems. As well as increasing awareness of the potential applications of the technology, we want to raise the level of public debate and identify key scientific and technical challenges and the social, ethical and legal issues raised by machine learning.”
The project will focus on current and near term (5-10 years) applications of machine learning. A number of public events will be held and the project will have a variety of outputs, including a policy report.
This Royal Society project will be led by a Working Group involving:
- Professor Margaret Boden FBA OBE - Research Professor of Cognitive Science, University of Sussex
- Professor Roger Brownsword - Professor of Law, King's College London and Bournemouth University
- Professor Peter Donnelly FMedSci FRS - Director of the Wellcome Trust Centre for Human Genetics and Professor of Statistical Science, University of Oxford (Chair)
- Professor Marcus du Sautoy OBE - Charles Simonyi Professor for the Public Understanding of Science and Professor of Mathematics, University of Oxford
- Professor Zoubin Ghahramani FRS - Professor of Information Engineering, University of Cambridge
- Dr Nathan Griffiths - Royal Society Industry Fellow, Associate Professor of Computer Science, University of Warwick
- Dr Demis Hassabis FRSA – Dr Demis Hassabis, Vice President of Engineering at Google DeepMind, leading Google’s general AI projects, London
- Dr Sabine Hauert - Co-founder and President of the Robohub.org website, and Lecturer in Robotics, University of Bristol
- Dr Hermann Hauser KBE CBE FRS FREng FInstP - Entrepreneur, venture capitalist, and non-executive director of several companies including XMOS, Cambridge
- Professor Nick Jennings FREng - Chief Scientific Advisor to the UK Government for National Security, Regius Professor of Computer Science, Southampton University
- Professor Mirella Lapata - Professor in the School of Informatics, University of Edinburgh
- Professor Neil Lawrence - Professor of Machine Learning, University of Sheffield
- Professor Sofia Olhede - Professor of Statistics, Honorary Professor of Computer Science, University College London
- Professor Yee Whye Teh - Professor of Statistical Machine Learning, University of Oxford
- Professor Dame Janet Thornton FMedSci FRS - Director Emeritus of EMBL-EBI (European Bioinformatics Institute) and Senior Scientist, Cambridge
Evidence gathering sessions and public events will be held over the course of the project. A call for evidence opens for submissions today (20 November 2015). For more information about submitting evidence, please visit the project page.