Abstract: Learning is one  of the hallmarks  of human intelligence. It marks a level  of flexibility and adaptation to new information that no artificial model has achieved at this point. This remarkable ability to learn makes it possible to accomplish a multitude  of cognitive tasks without requiring a multitude  of information from any single task. As a new BCS Fellow in Computation, I will describe emergent phenomena that occur during  learning for  neural  network models. First, I will discuss how learning well-defined tasks can lead to the emergence of structured representations complementary to the original task. This emergent structure appears at multiple levels within these models. From semantic factors of variation occurring in the hidden units of an autoencoder to physical structure appearing at the sensory input of an attention model, learning seems to influence all parts of a model. Then I will introduce current and future work that aims to endow neural networks with greater flexibility and adaptation in learning over types of data more akin to what naturally intelligent models experience in the real world.

This research meeting will be hosted remotely via Zoom.

Zoom Webinar link: https://mit.zoom.us/j/92359755680?pwd=STNKU2x0S0RXSGthMXhtcmNndEgrUT09 

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