Yesterday I tried to google "can the Halifax Wanderers still make the CPL playoffs?"
So obviously what appears right at the top is the AI summary, which told me "they've already secured their #4 position and made the playoffs". I knew this wasn't true, and I guess I could have just scrolled down a bit further and found my answer but now I was curious.
So I said "that's not true, they're still #5, what I want to know is _could they still make the playoffs_"
It says they've got an upcoming game against Ottawa, and if they win their chances are good. That game has already taken place, so I correct it again and finally I get a reasonable answer.
My question is: what's the point of the AI in the search engine if it itself isn't going to use the search engine first before answering? Like, I can't wrap my head around that. The answer is on the same page as its hallucination. It could have done a cursory look around before first hallucinating something completely false, and when corrected the first time giving me outdated information. It's meant to be A SEARCH ENGINE!
This is similar to how, not too long ago, LLM's had extreme difficulty counting the number of letters in some words. LLM's don't "think" or "reason" in the normal definition of those terms. They can do some pretty amazing things, but still screw up basic things like telling you something that is obviously wrong and contradicts the top search results.
LLM's, in their present stage of development, are sort of like a crack-addled idiot savant. Sometimes they are obviously insane, and sometimes they seem quite cogent, but you must never trust them implicitly. This may be why they are so difficult to constrain. You could give them something equivalent to the laws of robotics, but following laws requires thought processes they simply don't have.
I'm actually sort of amazed Google doesn't make people accept some kind of butt-covering EULA and post disclaimers about the inaccuracy of results before even showing you their AI's output. Are they not being sued over this kind of thing?
This is true but a sufficiently smart LLM (run in a harness like opencode, no special MCP, no customization done whatsoever) will quickly turn out a basic 1 to 2 page sized python script to do the math. They can't do the math with any guarantee of accuracy with their own internal reasoning since it's a language model.
But, for example, if you ask deepseek v4 flash 0731 to produce a python script to calculate the distance or azimuth directions between two points on an oblate spheroid using the vincenty and haversine geodetic formulas, it'll turn out the factually accurate vincenty and haversine formulas which has a perfect 100% correlation with what is hard coded into human-written GIS software. These things are clearly in its training data set from whatever whole-internet-crawl/scrape built the training set.
Heck, just for fun I asked a reasonably smart LLM to re-implement the Karney formula (which is considerably more complex than Vincenty), just in case I ever had a need to calculate the distance between two points down to the nanometer, and it did it: <a href="https://www.google.com/search?&q=karney+formula+geodetic+" rel="nofollow">https://www.google.com/search?&q=karney+formula+geodetic+
You still have to be skeptical of its results and capable of understanding if it's gone off on a hallucinatory path, but saying LLMs can't do math isn't really a hundred percent accurate anymore. More precisely it's that they can't do the math internally but they're quite capable of producing the tool that does the math. And often producing a basic one-off tool that does the math takes less than a few seconds, then it runs it, and will spit back the results.
Deepseek v4 flash 0731 (a somewhat randomly chosen example) isn't even particularly sophisticated, large, or capable compared to a GLM5.3 size model or Kimi K3 size thing.
> Heck, just for fun I asked a reasonably smart LLM to ...
LLMs are neither smart nor stupid. They are statistical token generators whose results are dependent upon their training data set and involve a degree of randomness.
> You still have to be skeptical of its results and capable of understanding if it's gone off on a hallucinatory path ...
Again, LLMs do not "hallucinate." They are statistical token generators whose results are dependent upon their training data set and involve a degree of randomness.
Nothing more.
See also anthropomorphism[0].
> More precisely it's that [LLMs] can't do the math internally but they're quite capable of producing the tool that does the math.
This still falls under the purvey of statistical token generation. To wit, given enough variations of:
bc -e '1 + 2'
bc -e '41 + 1'
...
LLMs can identify the addition expression in "What is 4 + 1?" and then emit a `'bc "4 + 1"'` command to produce a response. This is not "doing" or "understanding" math.
It is pattern recognition, a task in which ANNs[1] excel.
During conversation, we are statistical token generators whose results are dependent upon our training set. Seriously, write that definition out rigorously. It encompasses virtually everything. It is totally meaningless. So to say "nothing more" is effectively also a tautology.
This argument was asinine in 2024. It is insane to be saying these things in 2026. Where have you been? What have you been looking at? How many articles explaining why the "statistical parrot" analogy fails have you missed? How much mental gymnastics do you have to do to explain how a modern LLM can solve novel math problems that fall really far outside of its training set?
It absolutely understands how to do math, by whatever reasonable definition you want to provide to the word "understand". For example, the identification of the addition expression is understanding, and no, it does not do tool calling for basic arithmetic any more than humans might. Isolation of individual concepts in intermediate layers can already be demonstrated, or else transfer learning wouldn't possibly work. Nobody is saying that LLMs are humans. But we need labels for some of the things that we observe and dismissing them because "statistical" is laughable.
Look at the proof of this: <a href="https://github.com/anthropics/formal-math/blob/795efb86f191735c5481675763537cfb4ff37e55/percolation/summary.pdf" rel="nofollow">https://github.com/anthropics/formal-math/blob/795efb86f1917... . Forget the Lean, look at the underlying argument construction. At the very least, this is continuing from an argument that was hinted at in the literature in 2024, but these proceedings were difficult enough that humans were not able to do them within two years. Do you attribute this to the harness alone? If so, that's a pretty sophisticated bit of engineering, I would say! Probabilities are far too small to argue infinite monkey theorem.
If there was even a shred of a reasonable argument that LLMs were incapable of concept extraction and manipulation, I and my colleagues would be all over it. We would relish in it. It would bring us comfort. It is unbelievable that people think they can spew whatever basic garbage they think of as a gotcha, and think that minds all over the world haven't already considered that. This is like climate denial at this point.
> During conversation, we are statistical token generators whose results are dependent upon our training set. Seriously, write that definition out rigorously.
If you do not see a difference between humans conversing (known consciousness as defined by humans) and the output of an LLM (known algorithms as defined by humans), I don't know what to say.
Yes, the numerical point counter at the bottom of the popular video game Dark Souls. I doubt it was the soul anyone was expecting, but they do, in fact, exist.
Hugsbox · · focus · HN ↗
So obviously what appears right at the top is the AI summary, which told me "they've already secured their #4 position and made the playoffs". I knew this wasn't true, and I guess I could have just scrolled down a bit further and found my answer but now I was curious.
So I said "that's not true, they're still #5, what I want to know is _could they still make the playoffs_"
It says they've got an upcoming game against Ottawa, and if they win their chances are good. That game has already taken place, so I correct it again and finally I get a reasonable answer.
My question is: what's the point of the AI in the search engine if it itself isn't going to use the search engine first before answering? Like, I can't wrap my head around that. The answer is on the same page as its hallucination. It could have done a cursory look around before first hallucinating something completely false, and when corrected the first time giving me outdated information. It's meant to be A SEARCH ENGINE!
beloch · · focus · HN ↗
LLM's, in their present stage of development, are sort of like a crack-addled idiot savant. Sometimes they are obviously insane, and sometimes they seem quite cogent, but you must never trust them implicitly. This may be why they are so difficult to constrain. You could give them something equivalent to the laws of robotics, but following laws requires thought processes they simply don't have.
I'm actually sort of amazed Google doesn't make people accept some kind of butt-covering EULA and post disclaimers about the inaccuracy of results before even showing you their AI's output. Are they not being sued over this kind of thing?
VCFundedGenYer · · focus · HN ↗
walrus01 · · focus · HN ↗
But, for example, if you ask deepseek v4 flash 0731 to produce a python script to calculate the distance or azimuth directions between two points on an oblate spheroid using the vincenty and haversine geodetic formulas, it'll turn out the factually accurate vincenty and haversine formulas which has a perfect 100% correlation with what is hard coded into human-written GIS software. These things are clearly in its training data set from whatever whole-internet-crawl/scrape built the training set.
Heck, just for fun I asked a reasonably smart LLM to re-implement the Karney formula (which is considerably more complex than Vincenty), just in case I ever had a need to calculate the distance between two points down to the nanometer, and it did it: <a href="https://www.google.com/search?&q=karney+formula+geodetic+" rel="nofollow">https://www.google.com/search?&q=karney+formula+geodetic+
reference: <a href="https://github.com/pbrod/karney" rel="nofollow">https://github.com/pbrod/karney
You still have to be skeptical of its results and capable of understanding if it's gone off on a hallucinatory path, but saying LLMs can't do math isn't really a hundred percent accurate anymore. More precisely it's that they can't do the math internally but they're quite capable of producing the tool that does the math. And often producing a basic one-off tool that does the math takes less than a few seconds, then it runs it, and will spit back the results.
Deepseek v4 flash 0731 (a somewhat randomly chosen example) isn't even particularly sophisticated, large, or capable compared to a GLM5.3 size model or Kimi K3 size thing.
AdieuToLogic · · focus · HN ↗
LLMs are neither smart nor stupid. They are statistical token generators whose results are dependent upon their training data set and involve a degree of randomness.
> You still have to be skeptical of its results and capable of understanding if it's gone off on a hallucinatory path ...
Again, LLMs do not "hallucinate." They are statistical token generators whose results are dependent upon their training data set and involve a degree of randomness.
Nothing more.
See also anthropomorphism[0].
> More precisely it's that [LLMs] can't do the math internally but they're quite capable of producing the tool that does the math.
This still falls under the purvey of statistical token generation. To wit, given enough variations of:
LLMs can identify the addition expression in "What is 4 + 1?" and then emit a `'bc "4 + 1"'` command to produce a response. This is not "doing" or "understanding" math.It is pattern recognition, a task in which ANNs[1] excel.
0 - <a href="https://en.wikipedia.org/wiki/Anthropomorphism" rel="nofollow">https://en.wikipedia.org/wiki/Anthropomorphism
1 - <a href="https://en.wikipedia.org/wiki/Neural_network_(machine_learning)" rel="nofollow">https://en.wikipedia.org/wiki/Neural_network_(machine_learni...
hodgehog11 · · focus · HN ↗
This argument was asinine in 2024. It is insane to be saying these things in 2026. Where have you been? What have you been looking at? How many articles explaining why the "statistical parrot" analogy fails have you missed? How much mental gymnastics do you have to do to explain how a modern LLM can solve novel math problems that fall really far outside of its training set?
It absolutely understands how to do math, by whatever reasonable definition you want to provide to the word "understand". For example, the identification of the addition expression is understanding, and no, it does not do tool calling for basic arithmetic any more than humans might. Isolation of individual concepts in intermediate layers can already be demonstrated, or else transfer learning wouldn't possibly work. Nobody is saying that LLMs are humans. But we need labels for some of the things that we observe and dismissing them because "statistical" is laughable.
Look at the proof of this: <a href="https://github.com/anthropics/formal-math/blob/795efb86f191735c5481675763537cfb4ff37e55/percolation/summary.pdf" rel="nofollow">https://github.com/anthropics/formal-math/blob/795efb86f1917... . Forget the Lean, look at the underlying argument construction. At the very least, this is continuing from an argument that was hinted at in the literature in 2024, but these proceedings were difficult enough that humans were not able to do them within two years. Do you attribute this to the harness alone? If so, that's a pretty sophisticated bit of engineering, I would say! Probabilities are far too small to argue infinite monkey theorem.
If there was even a shred of a reasonable argument that LLMs were incapable of concept extraction and manipulation, I and my colleagues would be all over it. We would relish in it. It would bring us comfort. It is unbelievable that people think they can spew whatever basic garbage they think of as a gotcha, and think that minds all over the world haven't already considered that. This is like climate denial at this point.
AdieuToLogic · · focus · HN ↗
If you do not see a difference between humans conversing (known consciousness as defined by humans) and the output of an LLM (known algorithms as defined by humans), I don't know what to say.
hardbass · · focus · HN ↗
latentsea · · focus · HN ↗
butlike · · focus · HN ↗
diseasedyak · · focus · HN ↗