> we need to be able to extend it to do things like reasoning, learning causality, and exploring the world in order to learn and acquire information. Having re

> we need to be able to extend it to do things like reasoning, learning causality, and exploring the world in order to learn and acquire information. Having recently read the Book of Why by Judea Pearl, I found particularly interesting his remarks on causality. Pearl’s approach is based on causality being a testable assumption that requires domain expertise to be expressed. Having done that, it provides powerful techiques to address causal questions from data. Any efforts towards a general AI capable of causal reasoning should instead be able to create causal assumption from experience and “reasoning”. I have not seen around (have not looked much) discussion on how to combine general purpose AI approaches such as DL with domain specific approaches such as the Causal Inference techniques described by Pearl. Anyone has references to share?