Professor Michael A. Osborne

Michael A. Osborne, Ph.D. is a Machine Learning Researcher, Cofounder of Mind Foundry, and Professor of Machine Learning at the Machine Learning Research Group of the Information Engineering in the Department of Engineering Science at the University of Oxford. He is also the Director of the EPSRC Centre for Doctoral Training in Autonomous Intelligent Machines and Systems and Official Fellow of Exeter College at the University of Oxford.

Within machine learning, Mike has particular expertise in Gaussian processes, active learning, Bayesian optimization, and Bayesian quadrature, and is a Cofounder of the emerging field of probabilistic numerics.

He has applied machine learning to scientific and engineering problems in fields as diverse as exoplanet search, finance, crystallography, the study of pigeon navigation, and autonomous robotics, enabling self-driving cars to determine when their maps may have changed due to roadworks. He is also interested in how new technologies, particularly machine learning, are changing the nature of work.

His interest in Bayesian models and numeric algorithms, offer a framework for AI that is transparent, performant, and safe. He aims to apply the probabilistic numeric framework to the identification and communication of computational errors within composite AI systems. Probabilistic numerical methods offer the promise of monitoring assumptions in running computations, yielding a monitoring regime that can safely interrupt algorithms overwhelmed by their task’s complexity. This approach will allow AI systems to monitor the extent to which their own internal model matches external data, and to respond appropriately cautiously. Read Why do you care about AI Existential Safety?

Mike is also the Cofounder and scientific advisor of the Oxford spin-out company Mind Foundry Ltd, alongside Professor Stephen Roberts, which has grown to sixty employees. It enables anyone, of any technical ability, to solve their business problems using machine learning. Mind Foundry recognizes machine learning’s role is to augment human intelligence, not to automate it away.

As co-director of the Oxford Martin Programme on Technology and Employment, his work on the societal impacts of machine learning and robotics has been cited over 10,000 times and 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 UK House of Commons Science and Technology Committee. Read The future of employment: How susceptible are jobs to computerisation? and The future of skills: Employment in 2030.

Mike earned his Ph.D. in Machine Learning in 2010 from the University of Oxford with the thesis Bayesian Gaussian processes for sequential prediction, optimisation, and quadrature. He earned his Bachelor’s Degree of Engineering (BEng) in Mechanical Engineering with First Class Honours in 2005 at the University of Western Australia, and was awarded the BHP Billiton Iron Prize in Engineering, for the highest weighted average mark in the final year.

He also earned his Bachelor’s Degree of Science in Pure Mathematics and Physics with First Class Honours in 2002 from the University of Western Australia and in 2001 earned the Blakers Prize in Mathematics for the best student in the final year at UWA.

He took a year-in-research internship at the Institute of Materials and Engineering Sciences, at the Australian Nuclear Science and Technology Organization in 2004 where he was responsible for laboratory experiments investigating the photocatalytic properties of various materials.

Between 2009 and 2012, Mike was Postdoctoral Research Assistant in the Machine Learning Research Group at the University of Oxford. He was working on the project Control and Management of autonomous mobile sensors until 2011. He developed efficient Gaussian process inferences and optimization for intelligent sensor networks. Read Gaussian processes for time series prediction. He continued with the project Human-agent collectives: from foundations to applications until 2012 for which he developed scalable inference techniques for applications related to future energy networks.

He was Associate Professor in Machine Learning from 2012 in the Department of Engineering Science at the University of Oxford, until 2019, when he became Full Professor.

Since his appointment in 2012, Mike has been awarded grants totaling £10.6M as either Principal Investigator or Co-Investigator.

In 2018 he was awarded by the University of Oxford the Mathematical, Physical, and Life Sciences Impact Award, for contributing “to the political and social understanding and debate of how computerisation might affect jobs in the future” and in the same year was Highly Commended in the Early Career category of the University of Oxford’s Vice-Chancellor Innovation Awards, for “The Future of Employment: How Susceptible Are Jobs to Computerisation?” project.

Mike has been an Advisor at the Institute for the Future of Work and at Exawizards Inc. since 2018. Since 2017, he has been Associate Fellow at the Leverhulme Centre for the Future of Intelligence.

Previously, he was a Member of the Working Group on AI, Labor and the Economy of the Partnership on AI in 2018, a Commissioner for the Independent Parliamentary Commission on the Future of Work in 2017, and was a part of the Core Group for scoping of Royal Society policy project on Machine Learning.

Mike is a Member of the AI Safety Community Professors at the Future of Life Institute.

In 2012, he was also Associate Fellow of the UK Higher Education Academy and from 2013 until 2016 was a member of the Steering Group of the EPSRC Computational Statistics and Machine Learning Network.

Mike’s work has a great Policy Impact and his work has attracted broad and sustained media coverage from BBC Newsnight, the Economist, the Financial Times, the Guardian, the New York Times, and the Independent, amongst other venues. Read Robots can set us free and reverse decline, says Labour’s Tom Watson and Why governments have overestimated the economic returns of higher education.

Read China Won’t Win the Race for AI Dominance and Artificial intelligence holds huge promise — and peril. Let’s choose the right path.

Watch Automation: Last Week Tonight with John Oliver (HBO), Algorithms among us: machine learning and society, and Interview with Michael A. Osborne at the “digitising europe” summit. Watch Replaced by robots? The challenges and opportunities of automation for the workforce.

Read AI interview: Michael Osborne, professor of machine learning.

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