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PaperclipPerfector  1d ago (text post)   5523 thread views

Culture War Roundup for the week of April 20, 2026

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In the last thread, my opinion was that LLMs are missing something essential. And I still think that, but I wouldn't be surprised at all if LLMs required very little theoretical augmentation to reach AGI.

I believe they’re missing good continuous learning.

By definition: with human-level continuous learning, any class of human-solvable problems could be solved by guiding the LLM through examples until it generalizes. After enough generalization, it would be hard to find problems it can’t solve. Granted, “human-level” is doing a lot of heavy lifting, it’s not far from “LLMs are just missing intelligence”.

By observation: the vast majority of LLM failures seem to stem from needing to store everything in context and losing track when it gets too large. The vast majority are stupid mistakes that it seems like, if they were prepended to a small-context prompt, the LLM would not repeat.

The lack of continuous learning seems, to me, one of the biggest weaknesses of LLMs right now, with respect to getting to something that a layman human would recognize as AGI. The one thing I wonder about, though, is that continuous learning is just a speed problem right now. Even if all LLM development were to freeze at this moment, it's a safe bet that, within the next century (and safer-still within the next millennium), we'll have hardware capable of not only running LLMs but also training them at speeds so much faster than now, such that the hours of training using enterprise data servers that went into, say, Mythos, could be done in milliseconds on a cheap phone that someone in the lower-middle class could afford. I'm sure there would be engineering kinks to work out, but I don't see why, once the hardware gets powerful enough, this training couldn't be happening at rates fast enough to be indistinguishable from continuous to a typical human.

And if we do reach that point with LLM software that does this, will it actually be AGI? Will there be bizarre, unexpected and fundamentally impossible-to-predict-right-now issues that arise from such a system? I hope hardware gets fast enough in my lifetime that I can find out.

The word "good" in "good continuous learning" is doing a lot of work. Sample efficiency improves with model size, so it's possible that current models are just too small for good continuous learning, rather than there being some theoretical piece that's missing.

I've always thought this was the best case for a theoretical limit to machine learning as a general technique. Error rates keep growing unless you have a human authority do checkpoint syntheisis. And doing so in precise fashion, we have just reinvented programming.

The interesting part here is that the limit could be much further than practical relevance and context not actually that degraded. Early models already blew everybody's minds because we didn't expect it to work that well.

I still think Wolfram's irreducible complexity argument makes sense. But if it can be good enough we are in the Asimov Robot world where humans have their niche as robot psychologists.

The interesting part here is that the limit could be much further than practical relevance and context not actually that degraded

The limit is far enough that today’s models are useful: for example, they can code and (allegedly) find vulnerabilities in production software.

But I don’t believe today’s architecture can accurately emulate human intelligence, unless the model is retrained very frequently (daily?) on omni-local data (including everyone’s personal details and private codebases), effectively brute forcing continuous learning. Because today’s (consumer) models have been trained on practically the entire internet with the world’s compute, plus synthetic data and tool use, yet still they consistently hallucinate in long complicated tasks that humans after adjustment consistently solve.

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