“Intelligence” has technical meaning, as it must if we want to have any clarity in discussions about it. It basically boils down to being able to exploit structure in a problem or problem domain to efficiently solve problems. The “G” and AGI just means that it is unconstrained by problem domain, but the “intelligence” remains the same: problem solving.

Can ChatGPT solve problems? It is trivial to see that it can. Ask it to sort a list of numbers, or debug a piece of segfaulting code. You and I both know that it can do that, without being explicitly trained or modified to handle that problem, other than the prompt/context (which itself natural language that can express any problem, hence generality).

What you are sneaking into this discussion is the notion of human-equivalence. Is GPT smarter than you? Or smarter than some average human?

I don’t think the answer to this is as clear-cut. I’ve been using LLMs on my work daily for a year now, and I have seen incredible moments of brilliance as well as boneheaded failure. There are academic papers being released where AIs are being credited with key insights. So they are definitely not limited to remixing their training set.

The problem with the “AI are just statistical predictors, not real intelligence” argument is what happens when you turn it around and analyze your own neurons. You will find that to the best of our models, you are also just a statistical prediction machine. Different architecture, but not fundamentally different in class from an LLM. And indeed, a lot of psychological mistakes and biases start making sense when you analyze them from the perspective of a human being like an LLM.

But again, you need to define “real intelligence” because no, it is not at all obvious what that phrase means when you use it. The technical definitions of intelligence that have been used in the past, have been met by LLMs and other AI architectures.