Comment by simonw
5 hours ago
This is good:
> If you’re under the impression that these models are “glorified autocomplete” or that progress is slowing down, I need to urge you: stop thinking that. The models are very intelligent and capable, they are getting better at a fast clip. I can cite measurable and impressive progress over just the past five months on specific types of problem I’ve asked them to look at. [...]
> On the other hand: if you think that models are super-intelligent or that AGI is already here, you should also stop thinking that. Working with these tools is like swimming in a pond where the ground drops off sharply. One minute you’re wading comfortably and there’s support under your feet. Then suddenly you cross a specific line, and you’re back to swimming on your own.
My mental model is this:
There is a vast ocean of human knowledge, far beyond the capacity of any human brain, even within specialised fields.
Books helped "plug the gaps" in our knowledge, increasing the scope that a single human mind can encompass.
Web search engines did the same thing, but more and faster.
LLMs are like search engines on steroids, essentially a research librarian that operates at 1,000x human speed and can "in context" locate relevant information, adapting it to fit the hole it needs to go into as well.
It feels less like discovering new theorems, but instead having direct access to all theorems, which is hugely valuable in itself.
I.e.: the recent counterexamples to open conjectures has largely been about the AIs "trawling through all the things" and scraping together every bit of human-generated knowledge ever produced that is relevant to the conjecture.
Conversely, in the past, we had to "make do" with sub-standard solutions where the problem had been solved, but finding every relevant solution in the ocean of knowledge was prohibitively time consuming.
In some sense, LLMs will "raise the floor" in what is considered the minimum level of quality of a solution, where even throwaway / toy designs will now start applying every bit of accumulated wisdom instead of just some of it.
We have mechanised attention.
I'm also getting irritated with the “glorified autocomplete” comments. Since nobody can post such comments and also use the tools I'm using, I'm wondering if the phenomenon is due to people only having experience with the free version of whatever it is they're trying to use?
The “glorified autocomplete” framing isn’t to take literally. It’s a way to remove the mystic and whole anthropomorphization of AI. It’s saying they aren’t sentient or entities we are interacting with, even if that’s how the output presents itself. Instead they are “just” stochastic models
Some people use it to demystify, but a whole lot of people seem to be using it to dismiss the technology entirely.
Personally I like to remind people that these things are next-token predictors, but then emphasize how truly astonishing the results we can get out of sufficiently advanced next-token predictors are.
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Exactly. They ARE "glorified autocomplete" in an ontological sense. That says nothing about capability or outcome. The people who come out swinging against that characterization usually ignore the whole ontological argument (which is...the entire point) and go after an outcome-based strawman.
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But... These are also literally glorified autocomplete. Autoregressive language models are all autocomplete. It turns out that advanced enough autocomplete can do interesting stuff.
I like glorified copy/paste frankensteined with find/replace.
The models themselves are indeed glorified autocomplete in terms of what they actually do (with things like agentic coding harnesses being required as a wrapper around them to make that internal autocomplete something more useful). Many people use this fact to critique LLMs, but many other common instances of people pointing out LLMs' apparent lack of intelligence actually come from people not understanding that the model is a glorified autocomplete underneath whatever interface people access them through, and the interface isn't providing the underlying model all the information they assume it would, making it seem less intelligent than it actually is.