LLMs are parameter based representations of linguistic representations of the world. Relative to robot predictive control problems, they are low dimensional and static. They are batch trained using supervised learning and are not designed to manage real time shifts in the external world or the reward space. They work because they operate in abstract, rule governed spaces like language and mathematics. They are ill suited to predictive control tasks. They are the IBM 360s of AI. Even so, they are astonishing achievements.
LeCun is right to say that continuous self supervised (hierarchical) learning is the next frontier, and that means we need world models. I'm not sure that JEPA is the right tool to get us past that frontier, but at the moment there are not a lot of alternatives on the table.
LeCun is right to say that continuous self supervised (hierarchical) learning is the next frontier, and that means we need world models. I'm not sure that JEPA is the right tool to get us past that frontier, but at the moment there are not a lot of alternatives on the table.