Disregarding the negative comments here, I found the article to be very much in line with my experience as a scientist in a different field working with deep le

Disregarding the negative comments here, I found the article to be very much in line with my experience as a scientist in a different field working with deep learning for solving practical problems (reducing compute needed for physics, PDE constrained inverse problems) for a few years. I am not a deep learning detractor, nor am I a fan boy. Increasingly we have found that using hybrid methods: deep learning to extract parameters from large amount of data but then using the said parameters in a physics based method task executor guards against nonsense results. Small number (and dimensionality ) of the deep learning extracted parameters help with reasoning on "is deep learning still working as expected with this data" and continuing from there is usually safe. Even when the whole workflow is deep learning based, we have had to clearly reason out the domain, range and form for activation functions for critical layers in the DL network to make it work. No amount of throw in a huge resnet, transformer, FNO, chimera would do the trick without conscious thought on what the network is supposed to do. I would argue a lot of useful deep learning in hitherto un explored applications will need to have the symbolic manipulations encoded in the structure of the network.