> 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.
BoppreH 8 hours ago [-]
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.
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.
slim 46 minutes ago [-]
some of the metaphors make no sense
To be fair some famous rappers are guilty of this, too
ACCount39 9 minutes ago [-]
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.
huflungdung 31 minutes ago [-]
[dead]
Terr_ 5 hours ago [-]
One might say a calculator is just another example of "spiky intelligence", merely spikier.
exe34 4 hours ago [-]
How often does the calculator get something wrong?
teamonkey 1 hours ago [-]
This is a real deep rabbit hole FYI
famouswaffles 5 hours ago [-]
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.
Terr_ 4 hours ago [-]
My point (using irony) is that "spiky intelligence" is a somewhat hollow phrase, something that sounds like it could be objective but really it's just a flavor on top of "I'll know it when I see it."
Take anything "intelligent", alter it to be spikier and spikier, and eventually *poof* somehow the intelligence vanishes. You can do the same with the phrases "flawed intelligence" or "specialized intelligence."
> Calculators [have] no intelligence. [...] I mean there's no adaptability
To short-circuit a long discussion, I submit that "adaptability" will (once the Scooby Doo gang catches it) turn out to be "intelligence" in a tautological mask, both equally undefinable except in relation to one-another.
Something will be intelligent because you perceive adaptability, and it'll be adaptable because you infer intelligence. If it doesn't seem adaptable, it can't be intelligent, and if you don't want it to be intelligent, it won't have "real" adaptability.
> A Chess bot has [adaptability] (in its narrow domain of Chess). A calculator does not.
My calculator solves equations with unknown variables, what makes that insufficiently adaptable? What determines the cutoff-point?
famouswaffles 3 hours ago [-]
>My (ironic) point is that "spiky intelligence" seems rather unhelpful because the "intelligence" part still operates under the rules of "I just know it when I see it."
All intelligence is 'spiky' or 'jagged' or whatever, even human intelligence. We evolved in certain environments and situations and sometimes there's a mismatch and it causes all sorts of wonky things. We just call them funny names like optical illusions and cognitive biases. But it's the same thing. I agree there's no point in call LLMs a 'spiky' intelligence, but for probably the oppoosite reasons as you.
>My calculator solves equations with unknown variables, what makes that insufficiently adaptable? What determines the cutoff-point?
A chess engine can be dropped into a board position it has never encounterd and search over possible continuations, evaluating and selecting actions based on the state it finds itself in.
A calculator solving x+3=7 is doing something quite different. The fact that x can take arbtrary values doesn't make the calculator adaptive; it just means the fixed procedure operates over a range of inputs. Every problem a calculator can solve was effectively anticipated when it was built, and anything outside that grammar produces an error with no partial credit. A chess engine can face positions nobody enumerated, in situations nobody could even dream of and still produce sensible moves.
The core of being intelligent is being able to make decisions independently. That's why we hire smart people - to make better decisions. You can't make decisions if everything is spelt out for you. And naturally, if you can't make decisions, you can't adapt.
neuroticnews25 1 hours ago [-]
>A chess engine can be dropped into a board position it has never encounterd and search over possible continuations, evaluating and selecting actions based on the state it finds itself in.
Sounds pretty similar to a calculator with a numerical root-finding algorithm, if you only substitute board position it has never encounterd with a polynomial it has never encountered.
>The core of being intelligent is being able to make decisions independently
What does it mean for a deterministic algorithm to make decisions?
famouswaffles 1 hours ago [-]
>Sounds pretty similar to a calculator with a numerical root-finding algorithm, if you only substitute board position it has never encounterd with a polynomial it has never encountered.
>What does it mean for a deterministic algorithm to make decisions?
Newton-Raphson isn't choosing among possible actions. Given (x_n), its next step is mechanically specified by the update rule: compute the derivative, take the tangent intercept, repeat. The intermediate result changes the next input, but that's not by-itself decision making.
A chess engine, again, does something different. From a position, there are many legal actions it could take. It considers alternatives, estimates their consequences according to some objective, and selects one. The engine has to work out which available move best advances its objective.
You can make both algorithms determinstic, but determinism isn't the distinction i'm drawing. 'Decision' here doesn't mean some metaphysical excercise of free will. It's more about selecting an action from alternatives based on an evaluation of their expected consequences. Determinism is orthogonal to decision making. Deterministic doesn't mean predictable, nor does it make its choices any less it own computation.
neuroticnews25 45 minutes ago [-]
What about Minimax algorithm playing Tic-Tac-Toe? Is it inteligent? Is it inteligent we if we reduce the search depth so the right decision is not obvious?
famouswaffles 15 minutes ago [-]
A tic-tac-toe minimax algorithm makes choices but with exhaustive search so it really doesn't have to form a judgement about an unresolved situation or decide what is likely to work. Not much of a decision if you're not exercising any judgememt.
Exhaustive search is impossible in chess, so again, chess engines do something different. A chess engine has to stop well before terminal positions and make judgements about positions it cannot fully resolve.
Reducing the search depth would make it more interesting because it too has to evaluate unresolved positions. But then the interesting part becomes the evaluation function is. For tic tac toe, it's going to be very easy to be written in such a manner where most of the judgement is supplied by the designer and not the system.
4 hours ago [-]
drekipus 4 hours ago [-]
They adapt to the buttons you press. Thus, intelligent and capable of feeling pain.
busssard 2 hours ago [-]
any suffiently complex calculator is indifferentiable to intelligence
theteapot 4 hours ago [-]
Thermostats are smarter than calculators :thinking_face:
Waterluvian 9 hours ago [-]
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.
ZeroGravitas 3 hours ago [-]
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.
meowface 8 hours ago [-]
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)
xmprt 8 hours ago [-]
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...
mattjoyce 1 hours ago [-]
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.
morpheos137 9 hours ago [-]
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?
nl 4 hours ago [-]
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.
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.
kfarr 6 hours ago [-]
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!
NiloCK 8 hours ago [-]
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.
NewJazz 9 hours ago [-]
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.
Isamu 8 hours ago [-]
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.
NewJazz 7 hours ago [-]
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.
exe34 4 hours ago [-]
Palaeolithic natural selection did the training.
bkovacev 5 hours ago [-]
Unfortunately, there are reports that they have “dumbed” down Opus 5.5 already.
Which reports? There are lots of people watching model quality now, so it seems like there would be clear evidence if it happened already.
Neil44 18 minutes ago [-]
I didn't realize that Dodos only lived on Mauritius. I thought they were everywhere for some reason. I guess little meat footballs that can't fly were never going to colonize the planet.
early_exit 9 hours ago [-]
Good read! How many pages of text were in scope? I'm not sure if the 1615+1629 pages were the total or just a subagent.
If they were the total I would say it was arguably more impressive the author was able to narrow it down to just 3000 pages than it was to find the dodo mention amongst those!
mkl 8 hours ago [-]
Those are years, not page counts, right? The article mentions "millions of records", but I'm not sure how big a record can be.
jamienk 7 hours ago [-]
My dad died and had many many notebooks of his journals with very hard-to-read handwriting. Is it worth the effort to scan all of these so I can feed them in and go to work. Seems like so much minutia is out there, ready to be meta-understood.
komali2 7 hours ago [-]
I set up an OCR flow using local models on all my many tens of journals stretching back the last 30 years.
I would say it's about 80% accurate, which means it's missing enough key words to make a lot of it uselessly unintelligible. I can easily compare the images against text I turn up in a grep which is nice if I'm looking for something.
Allegedly Claude set up a system for retraining for my handwriting, but it would require me to manually revise several hundred pages by hand so I don't think I'll ever do it.
> 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.
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
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.
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.
Take anything "intelligent", alter it to be spikier and spikier, and eventually *poof* somehow the intelligence vanishes. You can do the same with the phrases "flawed intelligence" or "specialized intelligence."
> Calculators [have] no intelligence. [...] I mean there's no adaptability
To short-circuit a long discussion, I submit that "adaptability" will (once the Scooby Doo gang catches it) turn out to be "intelligence" in a tautological mask, both equally undefinable except in relation to one-another.
Something will be intelligent because you perceive adaptability, and it'll be adaptable because you infer intelligence. If it doesn't seem adaptable, it can't be intelligent, and if you don't want it to be intelligent, it won't have "real" adaptability.
> A Chess bot has [adaptability] (in its narrow domain of Chess). A calculator does not.
My calculator solves equations with unknown variables, what makes that insufficiently adaptable? What determines the cutoff-point?
All intelligence is 'spiky' or 'jagged' or whatever, even human intelligence. We evolved in certain environments and situations and sometimes there's a mismatch and it causes all sorts of wonky things. We just call them funny names like optical illusions and cognitive biases. But it's the same thing. I agree there's no point in call LLMs a 'spiky' intelligence, but for probably the oppoosite reasons as you.
>My calculator solves equations with unknown variables, what makes that insufficiently adaptable? What determines the cutoff-point?
A chess engine can be dropped into a board position it has never encounterd and search over possible continuations, evaluating and selecting actions based on the state it finds itself in.
A calculator solving x+3=7 is doing something quite different. The fact that x can take arbtrary values doesn't make the calculator adaptive; it just means the fixed procedure operates over a range of inputs. Every problem a calculator can solve was effectively anticipated when it was built, and anything outside that grammar produces an error with no partial credit. A chess engine can face positions nobody enumerated, in situations nobody could even dream of and still produce sensible moves.
The core of being intelligent is being able to make decisions independently. That's why we hire smart people - to make better decisions. You can't make decisions if everything is spelt out for you. And naturally, if you can't make decisions, you can't adapt.
Sounds pretty similar to a calculator with a numerical root-finding algorithm, if you only substitute board position it has never encounterd with a polynomial it has never encountered.
>The core of being intelligent is being able to make decisions independently
What does it mean for a deterministic algorithm to make decisions?
>What does it mean for a deterministic algorithm to make decisions?
Newton-Raphson isn't choosing among possible actions. Given (x_n), its next step is mechanically specified by the update rule: compute the derivative, take the tangent intercept, repeat. The intermediate result changes the next input, but that's not by-itself decision making.
A chess engine, again, does something different. From a position, there are many legal actions it could take. It considers alternatives, estimates their consequences according to some objective, and selects one. The engine has to work out which available move best advances its objective.
You can make both algorithms determinstic, but determinism isn't the distinction i'm drawing. 'Decision' here doesn't mean some metaphysical excercise of free will. It's more about selecting an action from alternatives based on an evaluation of their expected consequences. Determinism is orthogonal to decision making. Deterministic doesn't mean predictable, nor does it make its choices any less it own computation.
Exhaustive search is impossible in chess, so again, chess engines do something different. A chess engine has to stop well before terminal positions and make judgements about positions it cannot fully resolve.
Reducing the search depth would make it more interesting because it too has to evaluate unresolved positions. But then the interesting part becomes the evaluation function is. For tic tac toe, it's going to be very easy to be written in such a manner where most of the judgement is supplied by the designer and not the system.
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.
(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)
However I can image a part, see it in my mind, rotate it, place it in context, and have an intuition about it.
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
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.
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.
Maybe the model isn't intelligence in any form, except perhaps as an imperfect reflection of the intelligence of its training data.
If they were the total I would say it was arguably more impressive the author was able to narrow it down to just 3000 pages than it was to find the dodo mention amongst those!
I would say it's about 80% accurate, which means it's missing enough key words to make a lot of it uselessly unintelligible. I can easily compare the images against text I turn up in a grep which is nice if I'm looking for something.
Allegedly Claude set up a system for retraining for my handwriting, but it would require me to manually revise several hundred pages by hand so I don't think I'll ever do it.
https://github.com/508-dev/journal-ocr