But symbolic reasoning mostly worked, did it not? However, its Achilles heel was that for it to be useful, it's necessary to distill a lot of domain knowledge

But symbolic reasoning mostly worked, did it not? However, its Achilles heel was that for it to be useful, it's necessary to distill a lot of domain knowledge into a format that can be processed by an expert system. That means, writing 10s upon thousands of rows of "if then then that". Machine learning is different in that it is more amenable to distilling those rules from the data automatically. It is successful where symbolic reasoning failed because it can go from the raw data. A good portion of machine learning research is in new ways to preprocess and format data into a structure that can be further consumed by linear algebra, which turns out to be a lot easier and practical than figure out a huge database of sensible first order predicate logic statements. If ML techniques can be used to feed symbolic systems, the latter would show promise again, which is already happening in recent trends in causal inference and graph networks. The marriage of these two fields is inevitable, and has already started.