> Can you elaborate please and maybe point to some external support for such a bold claim?

SOTA LLMs?

If you think about what, say, a chair or electricity or love are, or what it means for something to be something, etc., I believe you'll quickly realize that words and concepts don't have well-defined meanings. Rather, we define things in terms of other things, which themselves are defined in terms of other things, and so on. There's no atomic meaning, the meaning is in the relationships between the thought and other thoughts.

And that is exactly what those models capture. They're trained by consuming a large amount of text - but not random text, real text - and they end up positioning tokens as points in high-dimensional space. As you increase the number of dimensions, there's eventually enough of them that any relationship between any two tokens (or groups; grouping concepts out of tokens is just another relationship) can be encoded in the latent space as proximity along some vector.

You end up with real computational artifact that's implementing the idea of defining concepts only in terms of other concepts. Now, between LLMs and the ability to identify and apply arbitrary concepts with vector math, I believe that's as close to the idea of "understanding" as we've ever come.

That does sound a bit like Peircian semiotic so I’m with you so far as the general concept of meaning being a sort of iterative construct.

Where I don’t follow is how a bitmap approximation captures that in a semiotic way. As far as I can tell the semiosis still is all occurring in the human observer of the machine’s output. The mathematics still hasn’t captured the interpretant so far as I can see.

Regardless of my possible incomprehension, I appreciate your elucidation. Thanks!