This seminar is part of the Oxford Martin School Hilary Term seminar series: Blurring the lines: the changing dynamics between man and machine

Speakers:

• Dr Carl Frey, James Martin Fellow, Oxford Martin Programme on the Impacts of Future Technology
• Dr Michael Osborne, University Lecturer in Machine Learning, University of Oxford

Will you one day lose your job to a robot, or even an algorithm? Dr Carl Frey and Dr Michael Osborne's recent working paper, 'The Future of Employment: How susceptible are jobs to computerisation?', found that nearly half of US jobs could be susceptible to computerisation over the next two decades. So as technology races ahead, will low-skilled workers need to retrain in order to remain part of the workforce?

About the speakers

Dr Carl Frey is a James Martin Fellow with the Oxford Martin Programme on the Impacts of Future Technology.

His research interests include the transition of industrial nations to knowledge-driven economies; subsequent challenges in terms of economic growth and the efficiency of financial markets. In particular his focus is on regulatory implications of asymmetric information in financial markets; technology change and impacts on labour markets and income inequality; intellectual property rights, investment and economic growth.

Dr Michael A. Osborne is a University Lecturer in Machine Learning and Fellow of Exeter College at the University of Oxford. He co-leads the Machine Learning Research Group, a sub-group of the Robotics Research Group in the Department of Engineering Science.

He designs intelligent algorithms capable of making sense of complex big data. His work in non-parametric data analytics has been successfully applied in diverse and challenging contexts. These contexts range from astrostatistics, where his probabilistic algorithms have aided the detection of planets in distant solar systems, to zoology, where his work has helped to clarify how pigeons are able to navigate such extraordinary distances. Most recently, he has addressed key societal challenges, analysing how intelligent algorithms might soon substitute for human workers, and predicting the resulting impact on employment.