Comment by nl
8 days ago
> Epistemological weirdness
> They are also notably bad at judging the historical significance of what they find.
I use LLMs for some things that are outside the more common use-cases (in my case 3D design for 3D printing) and one thing I've noticed is that the errors it makes are so completely unlike human errors that they are hard to anticipate.
It will do things like build perfect snap catches but put them so the the pieces they are connecting are rotated 90 degrees from how they should be. It's "dumb" error, but hard to say the model itself if dumb because it does other very hard things so perfectly.
> seven chord groups
This sounds a lot more like Opus 5.0 than Opus 5.5 TBH. I wonder if that was an earlier investigation because 5.5 has improved that kind of language a lot.
AI capabilities are "spiky": they extend far in some dimensions but fall short in others, seemingly at random. See for example the recent "thus spoke compute" musical[1]. It's an absolute banger, the graphics are impressive, and so is the writing. But some of the metaphors make no sense, the text highlights are in the wrong places, and the train animation at 2:35 is running backwards!
A person capable of making the rest of the video would never make those mistakes, but an AI does. Perhaps our intelligence is also spiky, and we're just used to the general shape and variance within humans.
[1] https://www.youtube.com/watch?v=Cq8qO-NjYIg
Part of this is that we implicitly compare AI capabilities to human capabilities, which are also 'spiky'.
You could image aliens coming to earth being shocked that we were able to discover general relativity but can't remember 100 digit numbers in our heads.
I suppose there's no accounting for taste, but to me this is awful. Is it anthropomorphism to experience vicarious embarrassment on behalf of a machine?
To be fair some famous rappers are guilty of this, too
Yeah, it's a very human error type - "go for a line that sounds good on the surface but doesn't actually make sense" is not at all uncommon. Like, humans will definitely go for a metaphor that falls apart mid-sentence even in a live conversation.
Something about the way some AIs are trained to write causes them to go for metaphors aggressively - and they don't always come up with good ones off the cuff. But they don't double back and get rid of the failures.
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One might say a calculator is just another example of "spiky intelligence", merely spikier.
How often does the calculator get something wrong?
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No, one might not say that. Calculators are not regarded as even a Narrow Intelligence because there's no intelligence. And no, not because of the 'humans so special' or 'it's software!' tautology that oft gets repeated in these discussions. I mean there's no adaptability whatsoever. A Chess bot has it (in its narrow domain of Chess). A calculator does not.
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This analogy may be too close to the real thing to work, but it reminds me of a Chinese room type situation where its entire understanding of the world is through messages of text.
You say that’s an error a human couldn’t do, but imagine if the human has never seen or touched the kind of item you were making and relied entirely on text descriptions to build its ontology. Off by 90 seems like such a believable mistake.
Also, not to sound like a naive hypemonger, but: in a decade I'd bet a ton of money the best AI systems will make strange mistakes of this nature at a far, far lower rate than they do today. They will gain a more holistic and more human-like perspective about each task.
(even if it's through some silly means like explicitly talking to themselves like "if I were a human doing this, what [... 5 million tokens in 2 seconds ...]" but also of course if they crack ASI and get something more efficient and intelligent than a human brain by then)
I think of it kind of like how Chess AI make "mistakes" which are unrecognizable to humans but a stronger AI would be able to pick them apart. That's kind of scary...
There's a famous early world map created by Ptolemy from compiling reports of sailors which is amazingly accurate for its time but had the country of Scotland off by 90 degrees.
In general llms are weak with spatial reasoning. This seems to be an unsolved problem. Probably because human language is generally imprecise spatially and humans think about spatial problems in visual terms. I wonder if having an llm make a 3d design in a format an image model could check would result in a better outcome?
Having used them heavily for 3D model understanding since February I can say it's nuanced.
Opus 4.6 and 4.7 were bad, but GPT 5.2 and above were very usable. Opus 4.8 was usable, but the GPT 5.x series was better.
Fable is great.
Opus 5.0 was interesting. It could solve some problems that Sol 5.x couldn't solve (applying a G2 curve on a 3 way corner where one face was a Bezier curve) but you had to be super prescriptive ("only answer the question"/"only do what I tell you and stop when done") or it would go on a hugely involved validation journey that didn't really achieve a lot.
Opus 5.5 is better than that was in that respect.
Sol 6.x is great, and my daily driver for this (I use Opus for coding though)
Astra can solve problems that Sol can't but for some reason on easy stuff makes uglier solutions.
For all models it's very interactive though - we aren't at the "agentic design" phase for most things yet.
Here's a sample of what I've been able to get them to design with me: https://x.com/nlothian/status/2099023496794018067
Is it that LLMs are weak with spatial reasoning (and memory) or is it that we are unusually good at it?
When I need to use a program I seldomly use I'm far more likely to remember where I need to click to open it than the word I need to search for to open it.
Yes I like to think of humans with built-in accelerators for certain tasks -- our visual and spatial reasoning is off the charts presumably because it's a life or death skill!
I made a building and had astra fill out the interior of the bathroom with toilets. It put 6 of them in two rows back to back with no way to reach the second row. Other than climbing over the stalls of the first row I suppose.
So yeah, they are very weak at spatial reasoning.
I think that this is an unsolved problem in the same way that mangled fingers in image generation was an unsolved problem.
Through at least Opus 4, LLMs were practically useless for authoring any sort of coherent procedural closed-curve geometry (I know this with strong confidence because of the little animated guys at https://letterspractice.com).
Opus 5.5 can bang it all out. Possibly a deliberate RL sort of thing or maybe another surprise emergent capability.
There's an interesting comparison in the creative/literary end of llm output too, they're in my experience, dreadful with anatomy. Like, it knows humans have hands, heads, etc, but often times a seemingly limited concept of how anything is connected, or degrees of freedom. (e.g., Why yes, certainly there are many examples of humans rotating their torsos 180º at the hip, seems perfectly cromulent)
I honestly don't know if an image model would help, or if it might analyze the output and go "13 fingers? ship it!" anyway.
I also use llama for 3d modelling (openscad) and have seen some similarly odd arrangement, but also very impressive and very good at part I would find boring or onerous.
However I can image a part, see it in my mind, rotate it, place it in context, and have an intuition about it.
I got Claude to design me a shed the other day. I asked it to make the door hinged in the CAD software I am using, and it did, but parts of the door weren't attached to each other so some were left floating when I set the door open.
It's "dumb" error, but hard to say the model itself if dumb because it does other very hard things so perfectly.
Maybe the model isn't intelligence in any form, except perhaps as an imperfect reflection of the intelligence of its training data.
I agree, there’s the collective intelligence that created all the content used to train the model. The model is a superposition of all that material with RL tuning. Analogously to reading a book, the intelligence you perceive is from the book’s creator.
What about DNA? There are things we do that we never read in a book, maybe never seen someone else do them vefore, but we still do them. Or we still feel a certain way. That doesn't come from "human training data", unless you count the DNA as training data.
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Could this be a feedback loop problem? I gave mine a script to render the object from all sides and it makes it more likely for it to avoid attacking of mistake.
Would you mind sharing your setup for using LLMs to generate stls/prints? I’ve had similar experiences as you describe lol
Unfortunately, there are reports that they have “dumbed” down Opus 5.5 already.
https://github.com/ninjahawk/livenerf
Which reports? There are lots of people watching model quality now, so it seems like there would be clear evidence if it happened already.