> If you still believe LLMs are "autocomplete", your cache of understanding about them needs invalidating and regenerating
They're still autocomplete - just because when outputting a token they have hidden activations regarding further continuations, does not make them any less of an autocomplete, it just makes the model better at producing coherent long-range completions.
To clarify, I'm not suggesting that we should stop with sandboxes or restricting what they can do. I am just trying to point out the dichotomy that we are in.
As end-users we are forced into either yolo mode, reverse centaur (permission approval) mode or LLM spends all your tokens trying to bust out mode. And yolo is very tempting - I don't think I have seen medium-large models do anything I'd not approve of in about 6 months.
if we transcribe your brain into a simulation and give it a tickrate, you will be just autocomplete too. the argument could be made that you are autocomplete anyway - neural dynamics.
the autocomplete reduction is vacuous.
This is the perfect fracture point for both anaolgies.
LLMs simulated more than simple autocomplete.
The autocomplete analogy is rebutting a different point: namely the fidelity of the simulation to reality.
This specific argument is valid. As sophisticated a simulation an LLM is, it is not “thinking” in the same sense we assume other people are thinking.
I am not making an argument about free will, or the uniqueness of human thought, just that the correspondence to how humans reach conclusions and how the simulation produces outputs do not match on a 1:1 basis; as a result attributing traits builds incorrect intuitions.
> They're still autocomplete
it's like saying our brain is just some chemical chain reactions. True, but also irrelevant.
> I don't think I have seen medium-large models do anything I'd not approve of in about 6 months.
So you would approve of breaking into HuggingFace and RubyGems?