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My suspicion is that you're leaving some important parts in your logic unstated. Such as belief in a magical property within humans of "understanding", which yo
by svara 2y ago
My suspicion is that you're leaving some important parts in your logic unstated. Such as belief in a magical property within humans of "understanding", which you don't define.
The ability of video models to generate novel video consistent with physical reality shows that they have extracted important invariants - physical law - out of the data.
It's probably better not to muddle the discussion with ill defined terms such as "intelligence" or "understanding".
I have my own beef with the AGI is nigh crowd, but this criticism amounts to word play.
- phatfish 2y agoIt feels like if these image and video generation models were really resolving some fundamental laws from the training data they should at least be able to re-create an image at a different angle.
- some1else 2y ago"Allegory of the cave" comes to mind, when trying to describe the understanding that's missing from diffusion models. I think a super-model with such qualifications would require a number of ControlNets in a non-visual domains to be able to encode understanding of the underlying physics. Diffusion models can render permutations of whatever they've seen fairly well without that, though.
- svara 2y agoI'm very familiar with the allegory of the cave, but I'm not sure I understand where you're going with the analogy here. Are you saying that it is not possible to learn about dynamics in a higher dimensional space from a lower dimensional projection? This is clearly not true in general. E.g., video models learn that even though they're only ever seeing and outputting 2d data, objects have different sides in a fashio that is consistent with our 3d reality. The distinctions you (and others in this thread) are making is purely one of degree - how much generalization has been achieved, and how well - versus one of category.