One general question about intelligent systems is whether they will be dominated by "generative" models which explain data as a sequence of transformations, or by black-box machines that are trained on data at ever greater scale. In perception systems 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”. Exciting progress has already been made on integrating the two approaches. It is also fascinating to speculate what other new paradigms in learning might transform the speed at which artificial perception can develop.
Watch Professor Andrew Blake's 2017 Lovelace lecture
More about Prof Andrew Blake
Andrew Blake is an engineer whose innovative work on image analysis has helped make it possible for computers to react to the world around them, based on the visual data they receive.
His research has focused particularly on the accurate tracking of motion and the reconstruction of visible surfaces. Amongst his contributions to the field, he is perhaps best known for the development of the condensation algorithm that allowed computers to interpret complex visual motion in real time.
At Microsoft Research Cambridge, Andrew was also part of the team that put machine intelligence into the company's Kinect controller - a revolutionary gaming system capable of following instructions dictated by the body movements of its users.
He is the recipient of the Silver Medal and of the MacRobert Gold Medal of the Royal Academy of Engineering, of which he is a Fellow. He is also a Fellow of the Royal Society, and has won the prestigious Mountbatten Medal from the Institution of Engineering and Technology.