All this ML/AI talk makes me wonder if there could be an angle for something like "discovery of principles by debugging".

Imagine, for example, training a neural net to classify random numbers into prime and non-prime.

If such a model succeeds at the task, how would one understand what the basic "theory/higher level function" is which the model "discovered/learned"?

I am definitely gonna try this, just for fun.