Earlier quoted context omitted.
You say that, but if I'm confused about something and think hard about it, I think in language. If you blinded me, paralyzed me, deafened me and desensitized my olfactions, I could still think, but what I would be doing is feeding one language thought into another. It's not so much different from "text" imho.
yes, but for you all of that text is associated with ideas. The word "dog" has an associated object. For a machine like GPT-4, the word "dog" has no meaning or object, but it does have an associated likelihood for adjacent words. The words themselves aren't the intelligence, the ideas behind them are.
There was a recent podcast with Sean Carroll interviewing Raphaël Millière where they go into this topic and some of the research on it. Two examples I can remember are: 1) DALL-E had subject-specific, domain-specific neurons, 2) language models' representations of color terms encoded the geometry of the underlying color space, e.g. vectors in RGB space.
https://www.preposterousuniverse.com/podcast/2023/03/20/230-...
I don't think we should be too quick to assume how these models work. There's a lot that even the researchers don't know and these are empirical questions that can be studied.