You could argue all the building blocks are forms of curve fits, but that isn't a terribly useful statement even if true. If you can fit a curve to the desired behavior of any function, or composition of functions (which is a function) then you can solve any problem you can express the desired behavior of. Including the expressing of desired behavior for some other class if problems. Saying it is just curve fitting is like saying something is just math. The entirety of reality is just math.

By that logic, anything that is predictive is curve fitting, including entire academic fields like physics and climatology. You could say that all automation is curve fitting. I don’t think there’s much to be gained by being that reductive.

From a technical standpoint, it’s not correct analogy either, because it assumes you have a curve to fit. What curve is language? What’s curve is images? No answer, because there isn’t one. Deep learning is about modeling complex behaviors, not curve fitting. Images and language for instance are based in social and cultural patterns and not intrinsic curves to be fit.

At best, it’s an imprecise statement. But I’d disagree entirely.