The problem , really, is human cognitive dissonance. We draw false conclusions that competence at some tasks implies competence at another. It’s not a universa

The problem , really, is human cognitive dissonance. We draw false conclusions that competence at some tasks implies competence at another. It’s not a universal human problem, we intuit that a front end loader , just because it can dig really well, is not therefore good at all other tasks. But when it comes down to cognition, our models break down quickly. I suspect this is because our proxies are predicated on a task set that inherently includes the physical world, which at some level connects all tasks and creates links between capabilities that generally pervade our environment. LLMs do not exist in this physical world, and are therefore not within the set of things that can be reasoned about with those proxies. This will probably gradually change with robotics, as the competencies required to exist and function in the physical world will (I postulate) generalize to other tasks in such a way that it more closely matches the pattern that our assumptions are based on. Of course, if we segregate intelligence into isolated modules for motility and cognition, this will not be the case as we will not be taking advantage of that generalization. I think that would be a big mistake, especially in light of the hypotheses that the massive leap in capabilities of LLMs came more from the training on things we weren’t specifically trying to achieve- the bulk of seemingly irrelevant data that unlocked simple language processing into reasoning and world modeling.