[lyind]

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.

[aardvark179]

ML explainability is a whole research area.

[lyind]

Thank you! I never looked into that subject before.

[lyind]

The interesting question is not if the answer (42) is correct or not, but how to understand how to reach that solution.