[trhway]

>It's an empirical science because the mathematical tools we possess are not sufficient to describe the phenomena we observe and make predictions under one unified theory.

To me the deep learning is actually itself a [long-awaited] tool (which has well established, and simple at that, math underneath - gradient based optimization, vector space representation and compression) to make a good progress toward mathematical foundations of the empirical science of cognition.

In the 90-ies there were works showing that for example Gabors in the first layer of the biological visual cortex are optimal for the feature based image recognition that we have. And as it happens in the DL visual NNs the convolution kernels in the first layers also converge to the Gabor-like. I see [signs of] similar convergence in the other layers (and all those semantically meaningful vector operations in the embedding space in LLMs are also very telling). Proving optimality or similar is much harder there, yet to me those "repeatable controlled experiments" (i.e. stable convergence) provide strong indication that it will be the case (as something does drive that convergence, and when there is such a drive in dynamic systems, you naturally end asymptotically up ("attracted") near something either fixed or periodic), and that would be a (or even "the") math foundation for understanding of cognition (dis-convergence from the real biological cognition, ie. emergence of completely different, yet comparable, type of cognition would also be great, if not even the much greater result) .