LLMs obviously have *some* understanding of what they read and generate. But this understanding is very limited and superficial. Otherwise, they wouldn't confabulate so much and wouldn't make mistakes that are contrary to common sense. I have argued, since at least 2016, that AI systems need to have internal models of the world that would allow them to predict the consequences of their actions, and thereby allow them to reason and plan. Current Auto-Regressive LLMs do not have this ability, nor anything close to it, and hence are nowhere near reaching human-level intelligence. In fact, their complete lack of understanding of the physical world and lack of planning abilities puts them way below cat-level intelligence, never mind human-level. AR-LLMs can accumulate large amounts of textual knowledge (if only approximately) and can retrieve it with appropriate context (if only approximately). More than a cat, certainly. But how is that any 10 year-old can learn to clear up the dinner table and fill up the dishwasher in one shot, whereas we are nowhere near having robots capable of learning this in any amount of time. Obviously, we are still missing something really big to reach human-level AI. I have written where I think AI research should go over the next decade or two to bridge that gap: openreview.net/forum?id=BZ5a… All my talks of the last couple of years have been on "objective driven AI architectures" which are an attempt to bridge that gap while making AI systems controlable, safe, and subservient to humanity. E.g. this one: youtube.com/live/pd0JmT6rYcI…