All arguments presented show that FORMAL methods won't work for a general NLP. ALL of the problems discussed in this post are solved to some extent by machine

All arguments presented show that FORMAL methods won't work for a general NLP. ALL of the problems discussed in this post are solved to some extent by machine learning - sometimes even by statistic methods such as word2vec. GPT-3 goes much further. Of course, these models are not trained on pure, grammatically and conceptually correct English - but on language that people use. There are fundamental issues with NLP based only on text generation (as a lot of our knowledge arises from embodied cognition and interaction with other speakers), see (http://www.abigailsee.com/2017/08/30/four-deep-learning-trends-from-acl-2017-part-1.html http://www.abigailsee.com/2017/08/30/four-deep-learning-tren...): > while word embeddings capture certain conceptual features such as “is edible”, and “is a tool”, they do not tend to capture perceptual features such as “is chewy” and “is curved” – potentially because the latter are not easily inferred from distributional semantics alone. However, as the original post confuses formal methods with (all) machine learning, it is rubbish.