Abstract

ABSTRACT

Robots and computers are inexorably developing the skills to work in ways once considered quintessentially human. Think about self-driving cars, or Amazon’s product recommendations, or Google Translate. The real question is not whether these technologies will have employment implications, but rather: how much, and to whom. We provide some answers to these questions, using machine learning techniques to analyse data from both the US and UK. In particular, we find that 47% of current US employment is at high risk of computerisation by 2030.

About the Speaker

Michael A Osborne (DPhil Oxon) is an expert in the development of machine intelligence in sympathy with societal needs. His work on robust and scalable inference algorithms in Machine Learning has been successfully applied in diverse and challenging contexts, from aiding the detection of planets in distant solar systems to enabling self-driving cars to determine when their maps may have changed due to roadworks. Dr Osborne also has deep interests in the broader societal consequences of machine learning and robotics, and has analysed how intelligent algorithms might soon substitute for human workers.

Dr Osborne is an Associate Professor in Machine Learning, a co-director of the Oxford Martin programme on Technology and Employment, an Official Fellow of Exeter College, and a Faculty Member of the Oxford-Man Institute for Quantitative Finance, all at the University of Oxford.

He is co-author (with Carl Frey) of The Future of Employment: How susceptible are jobs to computerisation?