Thanks for the link to the J.Pearl interview , it 's very interesting. There are many counterpoints that are not examined though: - There's nothing wrong with

Thanks for the link to the J.Pearl interview , it 's very interesting. There are many counterpoints that are not examined though: - There's nothing wrong with curve fitting per se. NNs fit hundreds of curves in parallel and many of them may contain cues about the causal structure of the data. - Deep learning has become part of reinforcement learning, which is trying to learn a causal structure. The primary determinant of causality is the temporal order of cause and effect. The question is , do humans use other hints apart from time for causal inference? - There is also not much evidence from neuroscience that wet brains are causality-inferrence machines, most of the evidence is that they are decision-making machines. Humans are also pretty bad at inferring causality when it's not obvious, but we re pretty good at associations/patterns. - Reasoning (conscious) is often considered to act on a meta-level, which observes the internal action of the human brain itself and vocalizes what it sees. What the brain sees at this level is not the external world, but the representation, and we don't have evidence there is a model of the world in there (except perhaps temporary maps of space that exist in hippocampus). Assuming this is true, it s not impossible that current methods can be extended so that self-explaining an NN ends up being causal reasoning the more important question is whether any of these methods can lead to the remarkable ability of brains to generate extremely intricate and improbable causal chains. can we get a CNN to start from a photo of maxwell's equations and output the theory of relativity? who knows