The Science of Deep Learning



Organized by:  David Donoho, Maithra Raghu, Ali Rahimi, Ben Recht and Matan Gavish

Artificial neural networks have re-emerged as a powerful concept for designing state-of-the-art algorithms in machine learning and artificial intelligence. Across a variety of fields, these architectures seem to outperform time-honored machine learning methods. Interestingly, our understanding of why and when these methods work remains limited. At the same time, an increasing number of mission-critical systems depend on deep neural networks, from autonomous vehicles to social media platforms that influence political discourse. Scientists are also beginning to rely more on deep learning as a knowledge discovery tool as research becomes ever more data driven.

This interdisciplinary meeting will begin with talks that survey the state of affairs in deep learning in academia and industry, the projected developments in the coming years, and the broader implications on science and society. The colloquium will then cover two timely, interleaved topics: First, what can deep learning do for science? What disciplines already integrating deep learning, and what lies ahead for scientists using deep learning? Second, what can science do for deep learning? What insights can deep learning gain from scientists who study complex systems (e.g. in Physics, Chemistry and the Life Sciences)? Can experimental techniques be used to study the nature of artificial deep neural networks? Can familiar principles that emerge in natural complex systems help us understand deep neural networks?





  • Where

  • National Academy of Sciences
    2101 Constitution Avenue, NW
    Washington, District of Columbia 20418

Registration Fee

Public Registration $300.00 payable by Visa or MasterCard online. (No American Express) 

Elected NAS/NAE/NAM members - $0.00 Registration Fee 

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