Well, yes and no ... Any language model alone isn't going to solve natural language understanding, but some future ML full-brain model surely will achieve AGI

Well, yes and no ... Any language model alone isn't going to solve natural language understanding, but some future ML full-brain model surely will achieve AGI and therefore language understanding as part of that. What's missing from a pure language model, is any grounding in reality and ability to interact with the world to test and extend its knowledge beyond what is derivable from the corpus it was trained on. It's level of understanding is ultimately limited by the content of the training corpus, regardless of how massive that may be. Something like GPT-3 is really just a statistical twist on Doug Lenat's Cyc; it's understanding is always going to be limited by it's own fundamental nature. Yes, one deals with language, and one with facts, but ultimately both are just large, fixed, self-referential bodies of data. Cyc really is a great analogy, and for some reason it took decades for Lenat et al to eventually realize that regardless of how much fixed data you added to it, it was never going to be enough. A closed black box can never know what's outside of the box, although it may gamely try to tell you if you ask it. These modern language models, GPT-3, etc, have certainly been a bit of an eye opener, and can perform some impressive feats (question answering, etc), but one shouldn't be tempted to believe that if scaled up sufficiently they'll eventually somehow transcend their own nature and become more than a language model... a one-trick pony capable of generating plausible continuations of whatever you seed it with.