Abstract: About fifty years ago, holography was proposed as a model of associative memory. Associative memories with similar properties were soon after implemented as simple networks of threshold neurons by Willshaw and Longuet-Higgins. It turns out that the recurrent Willshaw networks were very similar to today's deep nets. Thinking about deep learning in terms of associative networks memories a more realistic and sober perspective on the promises of deep learning and on its role in eventually understanding human intelligence.

This panel discussion will be hosted remotely via Zoom.

Zoom link: https://mit.zoom.us/j/99556310473