What fascinates me most about these LLMs and other machine-learning technology is how it is driven by the availability of GPUs. Essentially, everything has to be carefully encoded into - differentiable - linear algebra operations for practical reasons.
On the other hand, there are modeling languages (Modelica being a prime example) in search of a principled target calculus. So for me the obvious question is: Should one strive to develop a modeling language that compiles to GPU- and learning-compatible operations? Would that help to improve the models as well as understand them?