2021/03/31 by Daniel A. Roberts, Roberts, Daniel A. · 4 voices · 1 citation
Computer Science · Mathematics · Physics and Astronomy · Psychology · #Artificial intelligence #Cognitive science #Computational Physics and Python Applications #Computer science #Epistemology #Gaussian Processes and Bayesian Inference #Intuition #Philosophy #Physics #Psychology #Simple (philosophy) #Statistical Mechanics and Entropy #Theoretical physics #cs.AI #cs.LG #hep-th #physics.hist-ph #stat.ML
paper · pdf · doi:10.48550/arxiv.2104.00008
published in arXiv (Cornell University) (Cornell University) · written for a special issue of Machine Learning: Science and Technology as an invited perspective piece
arxiv created 2021/03/31 · openalex publication_date 2021/03/31 · arxiv updated 2021/04/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
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.