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cursory look around before first hallucinating something completely false, and when corrected the first time giving me outdated information.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.
by that reasoning then neither are there smart or stupid designs, questions, answers, or any of the millions of things that were described as smart or stupid, that did not possess any brain to actually be smart or stupid long before LLMs showed up.
The analogical process implied in many common English usages means that describing an LLM as smart or stupid is perfectly reasonable.
I'll just note here that sure, there are people who go around thinking that LLMs are actually endowed with the capacity to reason, but generally I find the people who think this do not know what an LLM and will just use the name "ChatGPT"
What would it take for you to say that an LLM can reason?
The completions they provide are generally internally consistent. We're at the point where they can produce proofs that eluded human mathematicians for centuries. VLMs and self driving cars can handle ambiguity and run safely in a variety of situations.
If it looks like a duck, walks like a duck, and quacks like a duck maybe it just makes sense to call it a duck and put off the philosophy for when it might make a difference.
I mean, the same way I know I have no soul, or that there is no heaven or hell - they are just silly concepts. Or how I know my calculator isn't reasoning when it gives me an answer - LLMs just go through a set of steps iterating through their training data until they spew something that looks about right. Obviously you can feed the output of the machine back into itself so it looks like its reasoning with itself - very good show. LLMs are inherintely incapable of reasoning or thought, it should really be obvious.
Eh, it's not obvious to me. A lot of DL NNs generalize well, meaning that they learn whatever the underlying pattern to the data is, and then can accurately reproduce answers that are outside of the training set. (And we can verify this with mechanistic interpretability). They learn and "understand" the pattern, not just the training data.
So it is not clear to me that LLMs are fundamentally incapable of also generalizing broadly and learning to reason. "Reasoning", here, would be deriving the underlying pattern of how concepts logically relate to each other in the abstract, and applying that pattern as needed to reach new conclusions.
Can you explain your thinking here? I.e., why LLMs cannot generalize with regards to abstract deduction.
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...
bryanrasmussen · · focus · HN ↗
by that reasoning then neither are there smart or stupid designs, questions, answers, or any of the millions of things that were described as smart or stupid, that did not possess any brain to actually be smart or stupid long before LLMs showed up.
The analogical process implied in many common English usages means that describing an LLM as smart or stupid is perfectly reasonable.
bryanrasmussen · · focus · HN ↗
SR2Z · · focus · HN ↗
The completions they provide are generally internally consistent. We're at the point where they can produce proofs that eluded human mathematicians for centuries. VLMs and self driving cars can handle ambiguity and run safely in a variety of situations.
If it looks like a duck, walks like a duck, and quacks like a duck maybe it just makes sense to call it a duck and put off the philosophy for when it might make a difference.
gambiting · · focus · HN ↗
Nothing, because LLMs can't reason and never will. It would have to be a completely different kind of technology altogether.
someonebaggy · · focus · HN ↗
gambiting · · focus · HN ↗
Windchaser · · focus · HN ↗
Eh, it's not obvious to me. A lot of DL NNs generalize well, meaning that they learn whatever the underlying pattern to the data is, and then can accurately reproduce answers that are outside of the training set. (And we can verify this with mechanistic interpretability). They learn and "understand" the pattern, not just the training data.
So it is not clear to me that LLMs are fundamentally incapable of also generalizing broadly and learning to reason. "Reasoning", here, would be deriving the underlying pattern of how concepts logically relate to each other in the abstract, and applying that pattern as needed to reach new conclusions.
Can you explain your thinking here? I.e., why LLMs cannot generalize with regards to abstract deduction.