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PaperclipPerfector  4d ago (text post)   15395 thread views

Culture War Roundup for the week of July 6, 2026

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But LLMs are getting freakishly good at things they haven't been specifically trained on. Their intelligence does generalize.

Such as?

Perhaps we only need to RL them in a few more domains to clinch the rest of generalized superintelligence. E.g. you can have them pilot robots and put them in virtual environments and RL fast them there, or real environments like an academy (a warehouse) a bit less fast.

There's been impressive seeming advances in robotics, though I'm not keeping up too closely. I don't see the connection between operating a warehouse and superintelligence though. Certainly the humans operating the warehouse are not superintelligent.

But LLMs are getting freakishly good at things they haven't been specifically trained on. Their intelligence does generalize.

Such as?

This already seems like such a skeptic's lens that any example I provide will be dismissed as "but it was in the training data lolol".

It's hilarious that I'm apparently a skeptic despite saying right off the bat that I expect transformational impact on much of white collar work.

But that's the crux of it. One main thesis of why LLMs work as well they do is indeed that the inferences were in the training data.

I ask you then. If it wasn't, then where does it come from?

And if it was, then the question becomes: how much of intelligence is encoded is all recorded human language, and that's not something anybody knows.

We don't even really know if humans can encode more than they can fathom.

The thing is that with such a loose definition of "in the training data", the hypothesis that AIs will only be able to do what's in the training data is not reassuring against doom. Persuasive propaganda is in the training data. Mass murder is in the training data. Deadly diseases are in the training data. World wars are in the training data. Doing all those things hundreds of times faster and cheaper than humans, like the current set of programming and science tasks where AI doing them faster and cheaper is being dismissed as uninteresting because it was all in the training data, would be more than enough to largely end humanity.

The exact outputs usually aren't in the training data. Although similar outputs are, you can take any human idea and decompose it into similar older ideas and maybe an infinitesimal amount of chance. That doesn't mean AI will reach human-level intelligence, but makes it impossible to disprove.

My steelman of @sarker is: yeah LLMs are cool but the real advances come from RL which is narrow and special and difficult to do in non-easily verifiable contexts. General superintelligence is therefore not coming soon.

My counter is something like: just from pre training alone we see huge leaps towards general intelligence and some glimmers of superintelligence. LLMs even in GPT4 era are surprisingly good at chess despite no specific training in chess, for example.

We may not need RL across every possible domain to get general superintelligence, just poking at enough diverse points in the frontier may solve the whole.

And there's lots of room to poke at it through RL approaches: revisiting the DeepMind stuff for example, build a bot that can kick ass at every video game with the same training set. Including building a robot hand that can operate a controller and robot eye that sees what's going on by watching the TV. (Despite all of the hype DeepMind was nowhere close to any of this). I have a hard time believing that nailing that narrow seeming RL problem can't generalize widely.

I meant to use "warehouse" to de-hand wave "an academy". Like just put robots in a big space far away from people and give them diverse tasks to train on. I did not mean to literally imply we'd put them to work in a warehouse and simulate them.

The aim is not directly "build better box stacking robots", it's "we're reaching limits on what we can teach by training on words/code/math so maybe we can get the rest of the way there by doing enough different real world tasks and just from having robots amble about in an environment that we unlock general intelligence".

Training on words on the internet has limits so next lets train agents embodied in spaces, virtual and physical.

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