I just crashed a Zoom conference in which Noam Chomsky was the discussant. (What I have to say will be heavily paraphrased: I wasn’t taking notes.) One back-and-forth stuck with me. Someone asked Noam what people interested in language and cognition ought to study, other than linguistics itself. He mentioned various biological systems, and said however, that they probably shouldn’t bother to study neural networks, since they have very little in common with intelligent biological systems (despite their branding as “neural” and “brain-inspired”). He stated that he is grateful for Zoom closed captions  (he has some hearing loss), but that one should not conflate that with language understanding. He said, similarly, that he’s grateful for snow plows, but one shouldn’t confuse such a useful technology with theories of the physical world.

For myself, I think they’re not uninteresting devices, and that linguists are uniquely situated to evaluate them—adversarily, I hope—as models of language. I also think they can be viewed as powerful black boxes for studying the limits of domain-general pattern learning. Sometimes we actually want to ask whether certain linguistic information is actually present in the input, and some of my work (e.g., Gorman et al. 2019) looks at that in some detail. But I do share some intuition that they are not likely to greatly expand our understanding of human language overall.

References

Gorman, K., McCarthy, A. D., Cotterell, R., Vylomova, E., Silfverberg, M., and Markowska, M. Weird inflects but OK: making sense of morphological generation errors. In Proceedings of the 23rd Conference on Computational Natural Language Learning, pages 140-151.