Episode 32: Jamie Simon, UC Berkeley: On theoretical principles for how neural networks learn and generalize

Jamie Simon is a 4th year Ph.D. student at UC Berkeley advised by Mike DeWeese, and also a Research Fellow with us at Generally Intelligent. He uses tools from theoretical physics to build fundamental understanding of deep neural networks so they can be designed from first-principles. In this episode, we discuss reverse engineering kernels, the conservation of learnability during training, infinite-width neural networks, and much more.

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We started Generally Intelligent because we believe that software with human-level intelligence will have a transformative impact on the world. We’re dedicated to ensuring that that impact is a positive one.  

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