> This is because conceptual association is not statistical association. In such texts the conceptual association is "standing for", "opposing", "suffering from", "in need of". Not "likely to occur with".
The better GPT gets, the wronger you will probably be. Why a machine wouldn't be able to abstract conceptual associations from a statistical framework?
Conceptual associations are not in text. The frequency with which words co-occur says nothing about why they co-occur.
Deep Learning systems merely interpolate across all their training data: ie., they remember every since document they have been shown and merely generate a document which is close to a subset of their previous inputs.
This seems meaningful only because they've stored (a compressed representation of) billions of documents.
There is something, frankly, psychotic in thinking the text these systems generate is meaningful. Run GPT twice on the same input, and the text it generates across runs contradicts itself.
To think these systems are talking to you is to "read a telephone directory as-if it had hidden messages planted by the CIA".
GPT if it says, "I like new york" does not mean that. It hasn't been to new york, and doesnt know what new york is. It has no intention to communicate with you; it has no intentions. It has nothing it wants to say. It isn't saying anything.
It's a trick. An illusion. It's replying fragments of a history of people actually talking to each other. It's a fancy tape recorder. It has never been in the world those people were talking about, and when it repeats their words, it isn't using them to say anything.
None of what you say is incompatible with GPT being able to understand these concepts a few generations down the line.
I mean, your central point is that GPT could not possibly understand these concepts because it only perceived them from text, not real life, but... that's kind of true of people too?
I can make observations and guesses about New York, even though I've never been in the US in my life. I can try to understand the hardships faced by minorities, even though I have never suffered from race or gender-based discrimination.
A huge part of everything we know about the world around us comes from information we got from Wikipedia, or TV shows, or Youtube. It's information GPT could be trained on.
You can always make a philosophical argument that even GPT-6 won't "really" what it's saying, but we have yet to see what the upper bound of GPT is given enough computing power. I'd expect most non-philosophical predictions about it can't do to be falsified within a few years.
The better GPT gets, the wronger you will probably be. Why a machine wouldn't be able to abstract conceptual associations from a statistical framework?