Machines that learn: big data or explanatory models?
A leading question about machines that learn is whether they will turn out to depend more on probabilistic models that explain the data, or on networks that react to data and are trained on data at ever greater scale? In machine vision systems, for instance, this boils down to the comparative roles of two paradigms: analysis-by-synthesis versus empirical recognisers. Each approach has its strengths, and empirical recognisers especially have made great strides in performance in the last few years, through deep learning. One can speculate about how deeply the two approaches may eventually be integrated, and on the progress that has already been made with such integration.
Andrew Blake took up his current post as Institute Director of The Alan Turing Institute in October 2015. He was previously a Microsoft Distinguished Scientist and the Laboratory Director of Microsoft Research Cambridge, UK. Prior to joining Microsoft, Andrew trained in mathematics and electrical engineering in Cambridge, UK, and studied for a doctorate in artificial intelligence at the University of Edinburgh. He was an academic for 18 years, in Edinburgh and latterly on the faculty in Engineering at Oxford University, where he was a pioneer in the development of the theory and algorithms that can make it possible for computers to behave as seeing machines. He has published several books including “Visual Reconstruction” with A. Zisserman (MIT press), “Active Vision” with A. Yuille (MIT Press), and “Active Contours” with M. Isard (Springer-Verlag).
He won the prize of the European Conference on Computer Vision twice, with R. Cipolla in 1992 and with M. Isard in 1996, and was awarded the IEEE David Marr Prize (jointly with K. Toyama) in 2001. In 2006 the Royal Academy of Engineering awarded him its Silver Medal and in 2007 the Institution of Engineering and Technology presented him with the Mountbatten Gold Medal (previously awarded to computer pioneers Maurice Wilkes and Tim Berners-Lee, amongst others). In 2011, he and colleagues at Microsoft Research received the Royal Academy of Engineering MacRobert Gold Medal for the machine learning recognition capability of the Microsoft Kinect 3D human motion-capture system.
He was elected Fellow of the Royal Academy of Engineering in 1998, Fellow of the IEEE in 2008, and Fellow of the Royal Society in 2005. In 2010, Andrew was elected to the Council of the Royal Society and in 2012 was appointed to the Council of the EPSRC. He has received honorary Doctorates from the University of Edinburgh and the University of Sheffield.