Speaker/s: Profs. Jim DiCarlo, Tomaso A Poggio, and Joshua Tenenbaum

Date/Time: Sept. 29, 2002, 4pm

•  We all believe that if we want to understand how our brain computes intelligence, we need a synergistic combination of the science of brains and the engineering of machines.

•  We all agree that science and engineering are both equally important and should be equally deep and rigorous.

•   Beyond these shared beliefs — which are the soul of CBMM — there are of course many open questions where each one of us may hold different opinions that would be fun to discuss.

1.  Is studying brains a top priority for AI? Do engineers need neuroscience? Current models for visual object categorization and synthetic text generation are thriving without new input from neuroscience, for example.

2.  What aspects of neuroscience are likely to improve AI?

3.  We have had difficulty developing neural network models of symbolic intelligence, intuitive physics, and intuitive psychology, for example. Are prospects better on the science side (real neurons and networks in experiments and models) or engineering (abstract formulations)?

4.  Will theoretical understanding of deep learning translate to a theoretical understanding of human intelligence?

This panel discussion will be hosted remotely via Zoom.