[Kim_Bruning]

This being HN, I get to be pedantic ;-).

Humans are not stochastic, they're obviously chaotic[1]. Which is to say: not parrots at all.

Some of the modern models I've seen also seem to be chaotic too though, so that's interesting [2]. I'm going to assume LLMs probably exhibit the same properties.

[1] https://en.wikipedia.org/wiki/Chaos_theory (Chaotic systems sometimes seem to be stochastic, but they're actually much stranger and more interesting!)

[2] I've been messing with stable diffusion to get a feel for (and/or avoid) tipping points: that is to say, points in latent space where the model becomes very sensitive to small changes in initial parameters. You can find instances fairly quickly even by hand by doing bisect search.

[freejazz]

>[2]. I'm going to assume LLMs probably exhibit the same properties.

That's quite an assumption to make.

[Kim_Bruning]

They use very similar technology, so it's not a large leap.

[freejazz]

LOL this isn't serious, is it? you can't point to a lack of understanding in the human brain as a basis for equating the logic a machine performs when it is also unclear. that's just rhetorically fallacious as a first step.

[Kim_Bruning]

If you carefully read my previous statement, I said that LLMs use similar technology to tools such as Stable Diffusion (which I have been playing with recently), not necessarily similar technology to the human brain.

[freejazz]

My bad