I see a lot of hype around GPT models I have yet to see a use case besides convincing nonsense generator, kind of like those deep net toys generating strange ar

I see a lot of hype around GPT models I have yet to see a use case besides convincing nonsense generator, kind of like those deep net toys generating strange art, but now via language. “Colorless green ideas sleep furiously” version 3. NLP research has been stuck in syntax for 70 years, but how about semantics? I mean yeah you gather bazillions of sentences and able to predict the most plausible and grammatical sequence of words that would follow particular prompt by doing a whole lot of trial and error training on massive gpu farms. But it’s just iteration on the least ambitious NLP work in the 70s and 80s. I’d argue that distributional semantics as in these popular vector space models is still solving the syntax problem. The word ‘semantics’ there is a misnomer. Are there any attempts to add actual real world meaning, causality and some ‘common sense’ representation to these models, concept graph or something, try to make them less ‘dumb’ for the lack of better word, like in you know, actual ‘AI’, that has a concept of apples and oranges and a concept of people who eat them or throw in a trash bin when fruits start to rot or draw them on paper in kindergarten or use ‘apples vs. oranges’ as a rhetorical device for telling things apart etc., etc. It seems like the field is stuck optimizing for some artificial toy benchmarks instead, making more convincing but ultimately stupid chat bots. And I mean ‘stupid’ not in derogatory sense but as a formal definition of their capabilities, as opposed to understanding things like a child would.