Yes, I like to separate, such as they can be separated, the tasks of 'parsing' and 'thinking' when it comes to AI. In the 80s (and really, all the way back to t

Yes, I like to separate, such as they can be separated, the tasks of 'parsing' and 'thinking' when it comes to AI. In the 80s (and really, all the way back to the 50s when we started trying computational AI), the tasks that we were attempting to solve were in the `thinking` category; solving tasks at a high level of abstraction with complex ontologies. The revolution in Deep Learning has been almost entirely on the parsing side of things. The conversion of unstructured data from sensors (an array of pixels, say) into semi-structured semantic units in the beginnings of an ontology. This makes sense, because some of the greatest success has come from modeling a part of the brain that we understand pretty well; the occipital lobe (specifically, V1), which is responsible for parsing the input from the eyes and feeding the resulting information in to the rest of the brain. This is the motivation behind the layered, columned CNN architecture. We've seen some success performing some tasks that exist in the (quite large) grey space between parsing and thinking (which are probably platonic ideals anyway, more than they are real things), but I get the feeling we're reaching the limit of how far we can reach into the grey starting from the left side of this continuum. So I don't think it's a waste of time at all to consider the deep, impressive work from the 80s and the 60s in Philosophy and logic and GOFAI.