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> In my time using models, I have used them to produce samples that were well outside the training distribution No you haven’t, because it’s not yet possible.
by dahart 2y ago
> In my time using models, I have used them to produce samples that were well outside the training distribution
No you haven’t, because it’s not yet possible. Either you didn’t see the training data, or you don’t know how neural networks work, or you’re anthropomorphizing and drinking AI kool aid.
- FeepingCreature 2y agoOr you're wrong. I mean, I'm just saying. I know what I saw. You're asserting I didn't see what I thought I did, because it's a problem for your argument. That's not very convincing though. edit: Let me clarify, obviously the LLMs/diffusion models I've used can only apply the patterns that they have happened to have learnt. However, due to the high level of abstraction these systems are capable of, the concept of a "pattern" can be very abstract, and even include such things as "recognizing a pattern in the input" as an abstract pattern itself. As such, at the least the output is not constrained to some sort of trivial interpolation of the training set, but a deep nonlinear function of the training set, that has even partially fed back on itself. Again, arguably, just like us.