Although I agree that statistical / ML methods will continue to conquer hurdle after hurdle in NLP, I wouldn't say that the article is total nonsense. The fact

Although I agree that statistical / ML methods will continue to conquer hurdle after hurdle in NLP, I wouldn't say that the article is total nonsense. The fact that NLP models trained purely on text data do not have access to the context available to a human interlocutor (years of visual, auditory, and other sense experience, along with all the higher-level mental hierarchies built from those experiences) is clearly a major handicap. I'm not positive that makes text-only NLP techniques inherently limited, but it has to be acknowledged. As far as I'm aware, we're still some years off from having NLP models capable of incorporating this sort of information. If I had to guess, I'd say that the first NLP model truly able to pass the Turing Test will be trained on a multitude of data source - textual, visual, and audio. It will have a moderately sophisticated internal representation of "the world", though I doubt that model will be very amenable to human understanding. I'm not even sure what I really mean when I say a model of the world, because I don't think this quasi-intelligent NLP actor will necessarily have an understanding of the physical world in the way humans or animals do, especially when it's been trained with the purpose of demonstrating language understanding.