Mike Osborne is the Dyson Associate Professor in Machine Learning at the University of Oxford. Back in 2013, he co-authored a highly-cited paper (with economist Carl Frey) that used machine learning to estimate that 47% of US jobs are at risk of being automatable through advances in artificial intelligence and robotics. This work was born of Mike’s interests in the practical use of machine learning to enable automation, while ensuring that such advances are made in sympathy with societal needs.
Mike’s technical expertise in Bayesian optimisation and probabilistic numerics underpins recent advances in automated and interpretable machine learning pipelines. His algorithms have been deployed in industrial and scientific applications ranging from battery monitoring, pigeon navigation and self-driving cars. As co-director of the Oxford Martin Programme on Technology and Employment, his research on the future of work has resulted in both sustained coverage in almost all major media venues (e.g. his being interviewed on BBC Newsnight, a cover feature in the Economist) and policy impact (including presenting oral evidence to the House of Commons Science and Technology Committee).