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I presented the idea as a thought experiment, not as an actual proposal.
No, you presented it as a conceptual proof that LLMs will never get better. All it takes is one innovation that addresses your concern about recycled data to make it invalid. All arguments about intelligence are necessarily a bit wishy-washy, mind you, so I'm not saying your thought experiment is useless.
I think if you really want to argue that LLMs have an inherent cap on their capability, you should address their actual algorithm rather than how they're trained. However much we rejigger them with CoT thinking and non-text data sources, they're fundamentally not designed for anything more than next-token prediction. It should be a source of constant surprise that they do so well on such a wide variety of non-creative-writing tasks (look at early SSC posts about GPT3's output to see this surprise evolve in real time). You could argue that if LLMs end up hitting a soft or hard limit, that's really just the "surprise" petering out, that we really can't just take a glorified text completer and keep pumping neurons into it until it's a genius.
I don't personally believe this will happen, but hey, I don't think anyone really knows for sure.
Umm, no. In fact I totally think that LLMs will get better.
I presented it as a thought experiment to show that LLMs seem to be missing some essential attribute possessed by human brains.
Yes, of course. Well, perhaps more than one innovation. But yes, if LLMs are missing something important; and we create LLMs 2.0 which include that important thing (or those important things), then yeah, we'll have AGI.
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