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High Energy Physics - Theory

arXiv:2104.00008 (hep-th)
[Submitted on 31 Mar 2021]

Title:Why is AI hard and Physics simple?

Authors:Daniel A. Roberts
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    Abstract: We discuss why AI is hard and why physics is simple. We
    discuss how physical intuition and the approach of theoretical
    physics can be brought to bear on the field of artificial
    intelligence and specifically machine learning. We suggest that
    the underlying project of machine learning and the underlying
    project of physics are strongly coupled through the principle of
    sparsity, and we call upon theoretical physicists to work on AI
    as physicists. As a first step in that direction, we discuss an
    upcoming book on the principles of deep learning theory that
    attempts to realize this approach.

Comments:      written for a special issue of Machine Learning:
               Science and Technology as an invited perspective piece
               High Energy Physics - Theory (hep-th); Artificial
Subjects:      Intelligence (cs.AI); Machine Learning (cs.LG);
               History and Philosophy of Physics (physics.hist-ph);
               Machine Learning (stat.ML)
Report number: MIT-CTP/5269
Cite as:       arXiv:2104.00008 [hep-th]
               (or arXiv:2104.00008v1 [hep-th] for this version)

Submission history

From: Dan Roberts [view email]
[v1] Wed, 31 Mar 2021 18:00:01 UTC (61 KB)
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