Scientifically Principled
We use humble algorithms that preserve transparency without any black boxes.
We use humble, scientifically principled algorithms that preserve transparency without any black boxes.
We make tools that help people achieve their goals efficiently and effectively.
We keep you in the loop with tools that let you embed your unique knowledge into the solution.
Professor Stephen Roberts is the Royal Academy of Engineering / Man Group Professor of Machine Learning at the University of Oxford. He has published extensively, having over 350 publications which have accrued more than 27,000 citations and 22 awards, including two medals.
Stephen’s interests lie in the theory and methodology of machine learning for large-scale real-world problems, especially those in which noise and uncertainty abound. He has successfully applied these approaches to a wide range of problem domains including astronomy, biology, finance, engineering, control, sensor networks, and system monitoring.
Michael A Osborne is a Professor of Machine Learning at the University of Oxford. His work in Machine Learning has been successfully applied in diverse 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.
Mike also has deep interests in the practical use of machine learning to enable automation, while ensuring that such advances are made in sympathy with societal needs. His work on the significance of machine learning and robotics to future labour markets is highly cited and has impacted government policy and been featured in major media outlets.
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