Similar idea has occurred to me. In a lot of ways, 'traditional' rules-based AI has more 'understanding' than a LLM can have. Because those rules are fixed by

Similar idea has occurred to me. In a lot of ways, 'traditional' rules-based AI has more 'understanding' than a LLM can have. Because those rules are fixed by humans to the machine's inputs, and they create a relationship between the software's model and reality, and usually in human-understandable terms (a database entry on a specific person with their facts listed, for example). That doesn't really exist in a LLM. It's entirely self-referential to itself. Which is why it's not possible for it to explain its reasoning, or give a (real) source for a fact. There is no fixed anchor, no reference point, to validate the LLM's output with.