@_Dave__White_@OpenAI ex: the best compression we have for 3D models is overtraining a model on one data sample, and the weights serve as your compression vector
@_Dave__White_@OpenAI embedding vectors are a compression of sorts, dependent on model architecture. There are plenty of examples though where the state of the art on compression is not through an embedding
@iamgingertrash also if you match f(x) cheaper than the original creation, you could potentially RLHF to f(x) + 1 with a lower total cost (seems like shaky ground but I wouldn't outright dismiss it)
@krishnanrohit the point is that solving those error manifolds speaks roughly nothing to interpretability / explainability. Regardless on your views of Yudkowsky, this point stands. And there is no clear path as of yet towards alignment (if anything, deeper models are harder to constrain)
@lsukernik will add the caveat though that outsiders often have naive perspectives on companies, industries etc. You can even be in the *same industry* and have a very poor understanding of what customer needs and company solutions *actually* look like
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Select text, right click (or keyboard shortcut), & a pop-up window appears for quick commands.🪄✨
Summarize, translate, reformat, debug -- without switching apps.
Meet Lookie⬇️