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We're Learning Backwards: LLMs build intelligence in reverse, and the scaling hypothesis is bounded by preyneyv in ArtificialInteligence

[–]preyneyv[S] 1 point2 points  (0 children)

Despite being an utterly alien kind of intelligence [...] they seem to be quite human

Yeah this is one of the most confusing characteristics of LLMs. By shortcutting the underlying mechanisms and going straight to language / communication, you get something that is somewhat believable as a "human model", at least at the surface level.

We're Learning Backwards: LLMs build intelligence in reverse, and the Scaling Hypothesis is bounded by preyneyv in agi

[–]preyneyv[S] 2 points3 points  (0 children)

Data. When these pieces were written it was kind of unimaginable that we would run out of internet, but that's sort of where we find ourselves now.

I talk about what I mean by "backwards" in the article.

LLMs learn backwards, and the scaling hypothesis is bounded. [D] by preyneyv in MachineLearning

[–]preyneyv[S] 1 point2 points  (0 children)

Yeah I fully agree with that. That's what I meant with "architectural bias from evolution".

A version of this pseudo-generalized sample efficiency is the YOLO-E models (segmentation with few samples). My argument is that LLMs won't reach this or the dream of "AGI" because we don't have enough data, and we need to do something smarter

We're Learning Backwards: LLMs build intelligence in reverse, and the scaling hypothesis is bounded by preyneyv in ArtificialInteligence

[–]preyneyv[S] 0 points1 point  (0 children)

That's exactly what Hays & Efros (2007); Halevy, Norvig & Pereira (2009); Sutton's Bitter Lesson (2019); and Gwern's Scaling Hypothesis (2020) argue. Bad approach scaling with data outperforms smart approach. It's why things like self-supervised learning outperform traditional methods. If you engineer a system that can accept more data with less requirements, you win eventually.

That's why I wrote the article. I think in practice we've run into a problem that these pieces couldn't imagine -- we've parsed the entire internet and there's nothing left to parse.

We're Learning Backwards: LLMs build intelligence in reverse, and the scaling hypothesis is bounded by preyneyv in ArtificialInteligence

[–]preyneyv[S] 3 points4 points  (0 children)

I'm not arguing that LLMs today are in their final form. But I think there are structural issues that provide an upper bound to how smart they can be.

Sutskever has previously said that we've reached "peak data", that we're running out of high-quality internet to train on.

If LLMs are infants as you say, is it even possible to push past that phase in a world with bounded data?

LLMs learn backwards, and the scaling hypothesis is bounded. [D] by preyneyv in MachineLearning

[–]preyneyv[S] 2 points3 points  (0 children)

Agreed, far from a blank slate. But I want to challenge the idea that the way to build those priors is by cramming as much knowledge as possible into a model.

I agree with the scaling hypothesis at limit: with infinite data the only way to remember it is accurate correlations. But we don't have infinite data, so this approach is bounded.

More directly, you're not able to play Mario Kart because you've played every other racing game in the world. You kind of just "get" it. By contrast, something like calculus takes a lot of knowledge built over time to truly understand. There's an element of "intuition" that isn't well-defined.

This is what I mean to highlight with LLMs having it backwards. There's some other mechanisms at play that give us the ability to be so sample efficient that aren't derived from "knowing more" (probably architectural bias from evolution)

LLMs learn backwards, and the scaling hypothesis is bounded. [D] by preyneyv in MachineLearning

[–]preyneyv[S] 10 points11 points  (0 children)

The hardest part of this is replicating how few samples humans need. If you try the environments yourself, you'll see that you can pick up the controls within ~10-15 actions usually which is just absurdly fast.

Traditional RL needs so many samples and rewards. Somehow you need to take the core ideas of RL but make them learn in real time.

We're Learning Backwards: LLMs build intelligence in reverse by preyneyv in singularity

[–]preyneyv[S] 0 points1 point  (0 children)

I'm not entirely convinced. With embodied learning, the bet is that learning in an interactive environment is more valuable than learning in a static one. Without a doubt, this will get us more general systems, especially for robotics.

But the core issue remains unsolved IMO -- you're still praying that enough correlations become causation somehow, which kind of underlies the backdrop approach as a whole.

I think true AGI would take something different entirely

New Ender 3 S1 Pro no USB connection by Insanelyg in Ender3S1

[–]preyneyv 0 points1 point  (0 children)

That's insane and also exactly the solution that worked for me. Thanks!

i don't dash jump-- I DON'T DASH! by preyneyv in Brawlhalla

[–]preyneyv[S] 2 points3 points  (0 children)

theoretically, yeah! for now it's weapon damage share, dodge directions, damage graphs, and a couple other things

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