REVIEW 1 cited by
Why is AI hard and Physics simple?
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original 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.
Forward citations
Cited by 1 Pith paper
-
Criticality analysis of nuclear binding energy neural networks
On a two-input nuclear binding energy network, the paper validates ANNFT predictions for variance, kurtosis, and an optimal depth-to-width ratio r*=0.034 under SGD, while adaptive optimizers obscure criticality.
Discussion (0). Continue with ORCID to comment.