> Because the reality is, for NLP and anything related to CV, deep learning has consistently wildly outperformed every other approach. Oh yes sure because you

> Because the reality is, for NLP and anything related to CV, deep learning has consistently wildly outperformed every other approach. Oh yes sure because you have so much erudition you can make such statements? No you do not, I though have erudition in comparison to the HN crowd and there are many important instances where symbolic outperform neural networks in NLP. For example: Sentence disambiguation AKA the task of determining the boundaries of a sentence (where it starts, where it ends). The state of the art is rule based. The task of generating a desired inflection for a given word: the state of the art is rule based. The task of quantifier scope disambiguation: algorithms are on parity with the data driven approach although in this special cases, data sets are lacking and this in fact apply to many areas or NLP, data sets are lacking and for those places, rule based parsing is the SOTA. But for the two first tasks I mentioned, datasets are not lacking, rules engines are just more correct and efficient. Because neural networks are extremely bad at achieving more than 95% accuracy, they often hit a wall because they are only an approximation engine at the end of the day,not an automated code generation of the ideal parser.