[unshavedyak]

Depends on what the topic of understanding is. In this case it's actually token relationships, right? It does know that very, very well. And there's a lot (.. potentially, hah) that we can do with token relationships.

By itself it's unlikely to ever be knowledge of course.. i see it more akin to NLP than knowledge. Which is to say, a general purpose language parsing tool which we can hand the result to something else. A conversational API, if you will, but we'll still need layers to actually run logic. To know math if you will.

Disclaimer: I know very little on the subject. Pure speculation.

[Sharlin]

The question is what happens when you go multimodal (which these things can do) and GPT(N+1) learns the associations between words and images/video, as well as the relationships between successive frames of video, at what point does it become unreasonable to claim that it doesn't "understand" something? How good at general-purpose predicting does an AI have to be in order for people to accept that it obviously has an internal model of things and is capable of abstractions?

(Assuming that this happens, of course. Diminishing returns could make scaling infeasible past some point, for instance.)

[SpicyLemonZest]

The question is how sure we should be that the kind of knowledge you and I have is fundamentally different than token relationships.

[btown]

And additionally, whether our memory and long term learning - and even our goal-choosing - is fundamentally different from an indexed storage of strings of tokens that can be brought back into short-term context when “triggered” by their embedding-similarity to the current context.

[unshavedyak]

I definitely have that question too. I view us as big LLMs.

But, even if we drop that interesting edge case i suspect we can make something very useful with the primitive that LLMs offer.. in the calculator example. ChainLang and co seem a really interesting tool for LLMs.