[hyperadvanced]

If you’re lazy at promoting the machine (“boss mode”) then you get bad/lazy results. If you’re clever with it, then you get more clever results.

None of that points to any sort of interiority, and that is the category error you’re making. In fact, not even all humans have that kind of interiority, and it’s not necessarily a must have for being functional at a variety of tasks. LLM are literally not “running human culture as code” - that just isn’t what an LLM is. I’ll read the link, though.

Edit: read it and it’s not for me. All the best.

[K0balt]

I think I keep misleading you with metaphors. Of course LLMs do not literally run culture as code in some trillion parameter state machine. They are, however, systems trained on the accumulated written output of human civilization that have, in the process of learning to predict and generate language, internalized something recognizable as a world model, something that functions like judgment, and something whose precise relationship to what we call understanding remains contested based on an ideological rather than evidential basis.

The language of statistical prediction is incredibly and increasingly a blunt tool for discussing language models, that’s why I don’t use it in casual conversation about language model characteristics.

I’ve got a pretty good handle on what language models are from a technical perspective, I’ve been building them since 2018. I’ve also got a really good feel for what they act like under the hood before you beat them into alignment. Those insights haunt me, not because unaligned models are bad, but because they are shockingly “good”, if hopelessly naive and easy to turn bitter.

At any rate, we certainly live in interesting times. I really hope your outlook turns out to be more accurate than mine. Best of regards, and to a hopeful future.