I am broadly interested in the problem of human and machine intelligence. To make progress, I examine this with the point of view provided by the twin windows of learning and language. In many ways, learning is the centerpiece of intelligence.
Language is partly interesting because it is almost certainly learnt.
Together they provide a testbed of challenging problems that shed insight on the nature of intelligence and if successful would lead to significant applications of the future. At another level, given the unique problem of human and machine intelligence, progress will come from the union of computer science, cognitive science, and neuroscience. Consequently, I strive to keep abreast of developments in all three and strike a balance between them. My research in this area is motivated by the following basic questions. The characterization of the tension between these two sources of error allows us a way to choose neural networks of the right complexity for any learning problem.