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Panel Discussion: Open Questions in Theory of Learning

Recorded:

Nov 12, 2024

Uploaded:

November 19, 2024

Part of

All Captioned Videos, Brains, Minds and Machines Seminar Series

CBMM Speaker(s):

Tomaso Poggio, Ila Fiete, Haim Sompolinsky

Speaker(s):

Philip Isola, MIT, Eran Malach, Harvard

In a society that is confronting the new age of AI in which LLMs begin to display aspects of human intelligence, understanding the fundamental theory of deep learning and applying it to real systems is a compelling and urgent need. This panel will...

Normalization models of attention

Recorded:

Nov 13, 2024

Uploaded:

November 19, 2024

Part of

All Captioned Videos, Computational Tutorials

Speaker(s):

Rachel Denison, Boston University

Attention is a cognitive process that allows us to prioritize the sensory information that is most relevant for our behavioral goals. In a successful class of computational models of attention, attention biases neural responses through its...

Identifying Subgroups in Biomedical Datasets using Data Attribution

Recorded:

Oct 9, 2024

Uploaded:

October 28, 2024

Part of

All Captioned Videos, Computational Tutorials

Speaker(s):

Djuna von Maydell, MIT

Understanding how training data influences model predictions ("data attribution") is an active area of machine learning research. In this tutorial, we will introduce a data attribution method (datamodels: https://gradientscience.org/datamodels-1/)...

Learning to Reason, Insights from Language Modeling

Recorded:

Oct 1, 2024

Uploaded:

October 21, 2024

Part of

All Captioned Videos, Brains, Minds and Machines Seminar Series

CBMM Speaker(s):

Noah Goodman

Noah Goodman, Stanford University

Tomaso Poggio autobiography, produced by NotebookLM

Recorded:

Oct 10, 2024

Uploaded:

October 10, 2024

Part of

All Captioned Videos, Scientific Interviews

Conveying Tasks to Computers: How Machine Learning Can Help

Recorded:

Sep 10, 2024

Uploaded:

September 26, 2024

Part of

All Captioned Videos, Brains, Minds and Machines Seminar Series

Speaker(s):

Michael Littman, Brown University

Abstract: It is immensely empowering to delegate information processing work to machines and have them carry out difficult tasks on our behalf. But programming computers is hard. The traditional approach to this problem is to try to fix people: They...