Why is AI hard and Physics simple?
High Energy Physics - Theory
2021-04-09 v1 Artificial Intelligence
Machine Learning
History and Philosophy of Physics
Machine Learning
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.
Cite
@article{arxiv.2104.00008,
title = {Why is AI hard and Physics simple?},
author = {Daniel A. Roberts},
journal= {arXiv preprint arXiv:2104.00008},
year = {2021}
}
Comments
written for a special issue of Machine Learning: Science and Technology as an invited perspective piece