This paper makes me wonder whether, in a very fuzzy sense, we could give #LLMs access to some similarly crystallised analog of emotion or emotional valence, below the level of language
OpenAi as a mega-organism might be intelligent, but the LLMs definitely are not.
The "compressed capture of semantic relationships" is a new thing we don't have a word for.
"Tokens" don't have to be words, or woofs...
[1] It still doesn’t make much sense to view this as a statistical process; dogs can generalize far better than transformers, as perhaps best seen with seeing-eye dogs. I believe dogs’ powers of causal reasoning exceed what is possible from mere surface statistics: e.g. they innately understand object permanence as puppies, whereas transformers still don’t understand it after viewing thousands of dogs’ lifetimes of experience.
But then going the other way, from statistics to a causal model, is just an inverse problem -- just like, say, going from a set of noisy magnetic field measurements at the boundary of a container to a pattern of electric current flow inside a volume, or going from planet positions to orbit shapes and periods to an inverse square law of gravity. Generating a compressed inverse model from surface statistics is exactly the sort of thing that deep learning has proven to be very good at. And by now we've seen no shortage of evidence that LLMs and other deep networks contain stateful world models, which is exactly what you'd expect, because for all their parameters, they aren't nearly big enough to contain an infinitesimal fraction of the statistics they were trained on.
So I think it's overly dismissive to regard LLMs as mere surface statistics.
It's literally what they are though.
Yes those probabilities embed human knowledge but that doesn't mean that the LLM itself is intelligent. It's why every LLM today fails at anything that isn't centred around rote learning.
You can predict better and better with simply more knowledge i.e. data.
Seemed easy, and I thought harmless, tho maybe not