Reading the minds of deep learning AI systems

As expectations soar in the exploding field of artificial intelligence (AI), a small but growing group of researchers is buckling down on a fundamental problem: understanding how increasingly complex computer programs actually work.

One of those researchers is Devinder Kumar, a doctoral candidate in systems design engineering at the University of Waterloo who gave a keynote address on his work recently at the prestigious AI Toronto conference.

Together with colleagues at the Vision and Image Processing (VIP) Lab, Kumar is developing software technology called CLEAR (for ‘class-enhanced attentive response’) that would track backwards from the decisions made by deep-learning AI systems to analyze and ultimately explain them.

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