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A recent paper by Davies et al (2021) describes how deep learning (DL)technology was used to find plausible hypotheses that have led to two originalmathematical results: one in knot theory, one in representation theory. I arguehere that the significance and novelty of this application of DL technology tomathematics is significantly overstated in the paper under review and has beenwildly overstated in some of the accounts in the popular science press. In theknot theory result, the role of DL was small, and a conventional statisticalanalysis would probably have sufficed. In the representation theory result, therole of DL is much larger; however, it is not very different in kind from whathas been done in experimental mathematics for decades. Moreover, it is notclear whether the distinctive features of DL that make it useful here willapply across a wide range of mathematical problems. Finally, I argue that theDL here "guides human intuition" is unhelpful and misleading; what the DL doesprimarily does is to mark many possible conjectures as false and a few othersas possibly worthy of study.  Certainly the representation theory result represents an original andinteresting application of DL to mathematical research, but its largersignificance is uncertain.

Deep Learning and Mathematical Intuition: A Review of (Davies et al. 2021)