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by jandrewrogers
289 days ago
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This understates the complexity of the problem. I have built a career modeling/learning entity behavior in the physical world at scale. Language is almost a trivial case by comparison. Even the existence of most relationships in the physical world can only be inferred, never mind dimensionality. The correlations are often weak unless you are able to work with data sets that far exceed the entire corpus of all human text, and sometimes not even then. Language has relatively unambiguous structure that simply isn't the norm in real space-time data models. In some cases we can't unambiguously resolve causality and temporal ordering in the physical world. Human brains aren't fussed by this. There is a powerful litmus test for things "AI" can do. Theoretically, indexing and learning are equivalent problems. There are many practical data models for which no scalable indexing algorithm exists in literature. This has an almost perfect overlap with data models that current AI tech is demonstrably incapable of learning. A company with novel AI tech that can learn a hard data model can demonstrate a zero-knowledge proof of capability by qualitatively improving indexing performance of said data models at scale. Synthetic "world models" so thoroughly nerf the computer science problem that they won't translate to anything real. |
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In terms of "world building", it makes sense for the "world" to not be dreamed up by an AI, but to have hard deterministic limits to bump up against in training.
I guess what I mean is that humans in the world constantly face a lot of conditions that can lead to undefined behavior as well, but 99% of the time not falling on your face is good enough to get you a job washing dishes.