Came for: "A computer once beat me at chess, but it was no match for me at kick boxing."

TFA was actually about leaps of intuition, sadly.

One of the experiments I've heard proposed around here is to somehow create an LLM from all text up to 1980 or 1990 and see if it can get back to making itself.

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ben_w an hour ago | parent | next [-]

> One of the experiments I've heard proposed around here is to somehow create an LLM from all text up to 1980 or 1990 and see if it can get back to making itself.

Could be, but preventing leakage from more modern stuff can be challenging.

This was attempted with Victorian public domain content: https://www.estragon.news/mr-chatterbox-or-the-modern-promet...

I can't find the citation right now, but I think people found it was leaking anachronisms? So this probably wasn't as well filtered as the creator had hoped?

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morkalork 39 minutes ago | parent [-]

Progress followed improvements in hardware, would you have to give access to modern hardware in the experiment for it to use? How much could it infer from it?

	▲	ben_w 37 minutes ago \| parent [-]
		> would you have to give access to modern hardware in the experiment for it to use? At a minimum, yes. IIRC, the sum total of all compute manufactured over history only reached the minimum needed to train an OK LMM in the mid 00s. > How much could it infer from it? Only way to find out is to try.

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elar_verole 2 hours ago | parent | prev | next [-]

I think this can't work because an LLM needs too much data, and before the internet there probably just wasn't enough to get close to what we have now

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jvanderbot 2 hours ago | parent | next [-]

Even simpler: Can GPT-2 anticipate and build Gwen/Deepseek? I think the answer is almost trivially "no", so I wonder what changed?

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ben_w an hour ago | parent [-]

Lots of things changed, GPT-2 is small (1.5e9) and is also a base model, so it is only doing next-token/autocomplete rather than prompt-response like even the first ChatGPT-3.5 was doing.

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jebarker an hour ago | parent [-]

Just for the sake of clarity: all LLMs up to today are still only doing next-token/autocomplete. The training process got additional stages to shape the model weights, but standalone LLMs are still deployed essentially identically.

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ben_w 40 minutes ago | parent [-]

If you gave GPT-2 a question and ended with a "?", it might answer, but also it might write several more questions in a similar category.

IMO, the mechanism isn't the important thing, the behaviour is. If you look at the step-by-step, we are also looking for the next word or motor action (and for whoever is about to suggest that we humans plan ahead, Transformer-based LLMs have been shown to also do this); as this is not a useful description of what it means to be a living brain, I'd say it's also not a useful description of what makes everything post-InstructGPT different from what came before.

	▲	jebarker 38 minutes ago \| parent [-]
		I agree completely - behaviorally the models have changed drastically due to RLHF, RLVR and now maybe even more so due to agentic harnesses. But the mechanism of prediction hasn’t changed, that was all I was clarifying.

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inigyou an hour ago | parent | prev | next [-]

Why couldn't an LLM, if it was smart enough, generate and consume its own data?

I know the answer: because it leads to model collapse. But why is that? Wouldn't a smart model not collapse? It's seeming like they keep getting smarter because we keep pouring more of our own knowledge into them, not because they are actually getting smarter. And yes, sometimes a dumb but persistent bruteforcer can make new discoveries.

	▲	Garlef an hour ago \| parent \| next [-]
		> if it was smart enough and i think this is exactly the crux; the really big models need really big datasets and current gen LLMs get a lot of training data beyond "all books + all of the internet" the objection is then that producing this additional data would already confound it with pre "virtual cutoff date" knowledge (since the training data probably implies mathematical and SWE concepts that were developed post "virtual cutoff date")
	▲	smusamashah an hour ago \| parent \| prev [-]
		If it is smart enough to generate data it can consume to train itself better, it is already smart enough to not need to do that.

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hackernudes an hour ago | parent | prev [-]

Maybe we can synthesize large amounts of limited information. I thought that new training data is mostly synthetic anyway.

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Normal_gaussian 2 hours ago | parent | prev | next [-]

The curious case here is how much of a description do we give it of itself? That would almost certainly dominate success rates.

My feeling is that a prompt would have to provide a vague description of a program that meaningfully passes something like a Turing test, an API to conform to, an expectation of novel construction (no 'ifs all the way down'), and then a requirement to search broadly and pursue promising ideas and not get hung up on the philosophy. Anything more precise feels like it would corrupt the test, but as it is that description feels doomed to loop before even trying the interesting parts.

	▲	ModernMech 2 hours ago \| parent [-]
		I wonder if we could just tell it to invent itself without any description and see if it can I introspect enough through its own interface to figure out what it is.

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Tade0 2 hours ago | parent | prev | next [-]

Is that how chessboxing was invented? Genuinely asking.

	▲	the_af an hour ago \| parent [-]
		Nope. Chessboxing was the invention of comics book artist Enki Bilal (and he's credited with this in Wikipedia). I first saw it in his Nikopol trilogy. Because life is weird, it then became a real thing. It's unrelated to computers playing chess. It predates Kasparov's first defeat by Deep Blue. I don't remember any mention of computers being good at chess in the trilogy, either. Or any computers, for that matter.

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throwaway314155 2 hours ago | parent | prev [-]

Article was plenty interesting to me.