Earlier quoted context omitted.
Isn't this essemtially the approach to image recognition etc. that failed for ages until we brute forced it with bigger and deeper matrices? It seems sensible to extract features and reason about things the way a human would, but it turns out its easier to scale pattern matching purely done by computer.
This is Sutton's Bitter Lesson : https://www.cs.utexas.edu/~eunsol/courses/data/bitter_lesson...
The second general point to be learned from the bitter lesson is that the actual contents of minds are tremendously, irredeemably complex; we should stop trying to find simple ways to think about the contents of minds, such as simple ways to think about space, objects, multiple agents, or symmetries. All these are part of the arbitrary, intrinsically-complex, outside world. They are not what should be built in, as their complexity is endless; instead we should build in only the meta-methods that can find and capture this arbitrary complexity. Essential to these methods is that they can find good approximations, but the search for them should be by our methods, not by us. We want AI agents that can discover like we can, not which contain what we have discovered. Building in our discoveries only makes it harder to see how the discovering process can be done."