Yoshua Bengio

University of Montréal

 

Bridging the gap between brains, cognition and deep learning
We start by reviewing connectionist ideas from three decades ago which have fuelled a revolution in artificial intelligence with the rise of deep learning methods. We also discuss the new ideas from deep learning, including a discussion of the newly acquired theoretical understanding of the advantages brought by jointly optimizing a deep architecture. Finally, we summarize some of the recent work aimed at bridging the remaining gap between deep learning and neuroscience, including approaches to implement functional equivalents to backpropagation in a more biologically plausible way, as well as ongoing work connecting language, cognition, reinforcement learning and the learning of abstract representations.