| ▲ | chongli a day ago | |
A little bit disappointed that there was no mention of the Frame Problem [1], a major challenge with world models. The issue arises when you're building an AI agent with the ability to move through and act in the real world, updating its world model as it does so. The challenge comes from the problem of finding a set of axioms that tell you how to make predictions about what changes a particular action will cause in the world. Naively, we might suppose that the laws of physics would be suitable axioms but this immediately turns out to be computationally intractable. So then we're stuck trying to find a set of heuristics, as alluded to in the article. Without being a neuroscientist, I think it's likely that at least some of the axioms of our own world models (as human beings) are built into the structure of our brains, rather than being knowledge that we learn as we grow up. We know, for example, that our visual systems have a great deal of built-in assumptions about the way light works and how objects appear under different lighting conditions, a fact revealed to us by optical illusions such as the checker shadow illusion [2]. Building a complete set of heuristics such as this does not sound impossible, just somewhat obscure and unexplored as an engineering problem, and does not seem to be related whatsoever to currently popular means of building and training AI models. | ||