At the scale/complexity GPT operates at, how is that different from "understanding"? It seems like you've just rephrased it but with more words.
> in which case you could just use machine translation as your example to show that some computational model is capable of understanding, and leave ChatGPT out of it.
It is trivial to tie these machines up in knots, blatantly contradicting themselves from one sentence to the next. If that is understanding then it is an understanding utterly foreign to any human, and a form of understanding that looks a whole lot like bullshit.
Google Translate works in a comparable way, and nobody suggests that it is sentient.
Frankly, the argument that "GPT operates at a scale that means it must be sentient" is begging the question.
And again, this magical word "just". "Just highly correlated", "just probabilities". Putting the word "just" in front of something doesn't mean you've explained it.
This is a related, but fundamentally different thing to the point I replied to in your original comment. You asked:
> I don't see how it can [apply training across languages] unless it really has some kind of understanding.
I provided a potential explanation that is in line with how we think GPT works, and which doesn't require it to have understanding. You may feel that GPT is complex enough that this process itself models understanding - but I disagree, and I think that's begging the question because it falls back on a fact (GPT is highly complex) that is independent of the above problem (how GPT applied training across languages.)
I am not compelled by the translation example to believe beyond doubt that GPT actually models and understands abstract concepts. I don't think the fact that its training works across languages is any proof that it parses that training in an abstract way, or that it forms abstract links between the same ideas in different languages, or indeed that it has any notions of language at all.
Once you understand long division, you can do it on infinite numbers without ever having seen the specific numbers. You can get this full understanding just from a handful of examples, no need for terabytes of them.
No matter how many examples of long division examples you fed to statistical model like GPT, there will always be infinite amount of numbers you can tell it where it will give the wrong answer*, unless you cheated and actually hard coded the understanding into the model.
If it cannot understand long division just from few examples it cannot ever understand it. The very reason it needs ridiculous amounts of data is precisely because it cannot understand. If you think it understands you simply aren't trying very hard to confirm otherwise.
* in a way that reveals there is no understanding of long division, obviously a human would also give wrong answer after being awake 100 hours writing numbers on paper