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I asked Google "how many golf balls can fit in a Boeing 737 cabin" last week. The "AI" answer helpfully broke the solution into 4 stages; 1) A Boeing 737 cabin

I asked Google "how many golf balls can fit in a Boeing 737 cabin" last week. The "AI" answer helpfully broke the solution into 4 stages; 1) A Boeing 737 cabin is about 3000 cubic metres [wrong, about 4x2x40 ~ 300 cubic metres] 2) A golf ball is about 0.000004 cubic metres [wrong, it's about 40cc = 0.00004 cubic metres] 3) 3000 / 0.000004 = 750,000 [wrong, it's 750,000,000] 4) We have to make an adjustment because seats etc. take up room, and we can't pack perfectly. So perhaps 1,500,000 to 2,000,000 golf balls final answer [wrong, you should have been reducing the number!] So 1) 2) and 3) were out by 1,1 and 3 orders of magnitude respectively (the errors partially cancelled out) and 4) was nonsensical. This little experiment made my skeptical about the state of the art of AI. I have seen much AI output which is extraordinary it's funny how one serious fail can impact my point of view so dramatically.

Sunspark 2y ago
It's fascinating to me when you tell one that you'd like to see translated passages of work from authors who never have written or translated the item in question, especially if they passed away before the piece was written. The AI will create something for you and tell you it was them.
- prawn 2y ago
  "That's impossible because..." "Good point! Blah blah blah..." Absolutely shameless!
senordevnyc 2y ago
Just tried with o3-mini-high and it came up with something pretty reasonable: https://chatgpt.com/share/67f35ae9-5ce4-800c-ba39-6288cb4685f0 https://chatgpt.com/share/67f35ae9-5ce4-800c-ba39-6288cb4685...
- CamperBob2 2y ago
  It's just the usual HN sport: ask a low-end, obsolete or unspecified model, get a bad answer, brag about how you "proved" AI is pointless hype, collect karma. Edit: Then again, maybe they have a point, going by an answer I just got from Google's best current model ( https://g.co/gemini/share/374ac006497d https://g.co/gemini/share/374ac006497d ) I haven't seen anything that ridiculous from a leading-edge model for a year or more.
greenmartian 2y ago
Weird thing is, in Google AI Studio all their models—from the state-of-the-art Gemini 2.5Pro, to the lightweight Gemma 2—gave a roughly correct answer. Most even recognised the packing efficiency of spheres. But Google search gave me the exact same slop you mentioned. So whatever Search is using, they must be using their crappiest, cheapest model. It's nowhere near state of the art.
- aurareturn 2y ago
  Makes sense that search has a small, fast, dumb model designed to summarize and not to solve problems. Nearly 14 billion Google searches per day. Way too much compute needed to use a bigger model.
  - fire_lake 2y ago
    Massive search overlap though - and some questions (like the golf ball puzzle) can be cached for a long time.
    - summerlight 2y ago
      AFAIK they got 15% of unseen queries everyday, so it might be not very simple to design an effective cache layer on that. Semantic-aware clustering of natural language queries and projecting them into a cache-able low rank dimension is a non-trivial problem. Of course, LLM can effectively solve that, but then what's the point of using cache when you need LLM for clustering queries...
      - fire_lake 2y ago
        Not a search engineer, but wouldn’t a cache lookup to a previous LLM result be faster than a conventional free text search over the indexed websites? Seems like this could save money whilst delivering better results?
        summerlight 2y ago
aezart 2y ago
> I have seen much AI output which is extraordinary it's funny how one serious fail can impact my point of view so dramatically. I feel the same way. It's like discovering for the first time that magicians aren't doing "real" magic, just sleight of hand and psychological tricks. From that point on, it's impossible to be convinced that a future trick is real magic, no matter how impressive it seems. You know it's fake even if you don't know how it works.
- katsura 2y ago
  To be fair, I love that magicians can pull tricks on me even though I know it is fake.
- bambax 2y ago
  I think there is a big divide here. Every adult on earth knows magic is "fake", but some can still be amazed and entertained by it, while others find it utterly boring because it's fake, and the only possible (mildly) interesting thing about it is to try to figure out what the trick is. I'm in the second camp but find it kind of sad and often envy the people who can stay entertained even though they know better.
  - nucleogenesis 2y ago
    Idk I don’t think of it as fake - it’s creative fiction paired with sometimes highly skilled performance. I’ve learned a lot about how magic tricks work and I still love seeing performers do effects because it takes so much talent to, say, hold and hide 10 coins in your hands while showing them as empty or to shuffle a deck of cards 5x and have the audience cut it only to pull 4 aces off the top.
  - toddmorey 2y ago
    I think the problem-solving / want-to-be-engineer side of my brain lights up in that "how did he do that??" way. To me that's the fun of it... I immediately try to engineer my own solutions to what I just saw happen. So I guess I'm the first camp, but find trying to figure out the trick hugely interesting.
  - 2y ago
throwawaymaths 2y ago
I've seen humans make exactly these sorts of mistakes?
- toddmorey 2y ago
  As another commenter mentioned, LLMs tend to make these bad mistakes with enormous confidence. And because they represent SOTA technology (and can at times deliver incredible results), they have extra credence. More than even filling the gaps in knowledge / skills, would be a huge advancement in AI for it to admit when it doesn't know the answer or is just wildly guessing.
tim333 2y ago
A lot of humans are similarly good at some stuff and bad at other things. Looking up the math ability of the average American this is given as an example for the median (from https://www.wyliecomm.com/2021/11/whats-the-latest-u-s-numeracy-rate/ https://www.wyliecomm.com/2021/11/whats-the-latest-u-s-numer...): >Review a motor vehicle logbook with columns for dates of trip, odometer readings and distance traveled; then calculate trip expenses at 35 cents a mile plus $40 a day. Which is ok but easier than golf balls in a 747 and hugely easier than USAMO. Another question you could try from the easy math end is: Someone calculated the tariff rate for a country as (trade deficit)/(total imports from the country). Explain why this is wrong.
swader999 2y ago
It'll get it right next time because they'll hoover up the parent post.
CivBase 2y ago
I just asked my company-approved AI chatbot the same question. It got the golf ball volume right (0.00004068 cubic meters), but it still overestimated the cabin volume at 1000 cubic meters. It's final calculation was reasonably accurate at 24,582,115 golf balls - even though 1000 ÷ 0.00004068 = 24,582,104. Maybe it was using more significant figures for the golf ball size than it showed in its answer? It didn't acknowledge other items in the cabin (like seats) reducing its volume, but it did at least acknowlesge inefficiencies in packing spherical objects and suggested the actual number would be "somewhat lower", though it did not offer an estimate. When I pressed it for an estimate, it used a packing density of 74% and gave an estimate of 18,191,766 golf balls. That's one more than the calculation should have produced, but arguably insignificant in context. Next I asked it to account for fixtures in the cabin such as seats. It estimated a 30% reduction in cabin volume and redid the calculations with a cabin volume of 700 cubic meters. These calculations were much less accurate. It told me 700 ÷ 0.00004068 = 17,201,480 (off by ~6k). And it told me 17,201,480 × 0.74 was 12,728,096 (off by ~1k). I told it the calculations were wrong and to try again, but it produced the same numbers. Then I gave it the correct answer for 700 ÷ 0.00004068. It told me I was correct and redid the last calculation correctly using the value I provided. Of all the things for an AI chatbot which can supposedly "reason" to fail at, I didn't expect it to be basic arithmetic. The one I used was closer, but it was still off by a lot at times despite the calculations being simple multiplication and division. Even if might not matter in the context of filling an air plane cabin with golf balls, it does not inspire trust for more serious questions.
- billforsternz 2y ago
  > It's final calculation was reasonably accurate at 24,582,115 golf balls - even though 1000 ÷ 0.00004068 = 24,582,104. Maybe it was using more significant figures for the golf ball size than it showed in its answer? 1000 ÷ 0.00004068 = 25,000,000. I think this is an important point that's increasingly widely misunderstood. All those extra digits you show are just meaningless noise and should be ruthlessly eliminated. If 1000 cubic metres in this context really meant 1000.000 cubic metres, then by all means show maybe the four digits of precision you get from the golf ball (but I am more inclined to think 1000 cubic metres is actually the roughest of rough approximations, with just one digit of precision). In other words, I don't fault the AI for mismatching one set of meaninglessly precise digits for another, but I do fault it for using meaninglessly precise digits in the first place.
aoeusnth1 2y ago
2.5 pro nails each of these calculations. I don’t agree with Google’s decision to use a weak model in its search queries, but you can’t say progress on LLMs in bullshit as evidenced by a weak model no one thinks is close to SOTA.
raxxorraxor 2y ago
This reminds me of Google quick answers we had for a time in search. It is quite funny if you live outside the US, because it very often got the units or numbers wrong because of different decimal delimiters. No wonder Trump isn't afraid to put taxes against Canada. Who could take a 3.8 sqare miles country seriously?

Sunspark 2y ago

It's fascinating to me when you tell one that you'd like to see translated passages of work from authors who never have written or translated the item in question, especially if they passed away before the piece was written. The AI will create something for you and tell you it was them.

prawn 2y ago
"That's impossible because..." "Good point! Blah blah blah..." Absolutely shameless!

senordevnyc 2y ago

Just tried with o3-mini-high and it came up with something pretty reasonable: https://chatgpt.com/share/67f35ae9-5ce4-800c-ba39-6288cb4685f0 https://chatgpt.com/share/67f35ae9-5ce4-800c-ba39-6288cb4685...

CamperBob2 2y ago
It's just the usual HN sport: ask a low-end, obsolete or unspecified model, get a bad answer, brag about how you "proved" AI is pointless hype, collect karma. Edit: Then again, maybe they have a point, going by an answer I just got from Google's best current model ( https://g.co/gemini/share/374ac006497d https://g.co/gemini/share/374ac006497d ) I haven't seen anything that ridiculous from a leading-edge model for a year or more.

greenmartian 2y ago

Weird thing is, in Google AI Studio all their models—from the state-of-the-art Gemini 2.5Pro, to the lightweight Gemma 2—gave a roughly correct answer. Most even recognised the packing efficiency of spheres. But Google search gave me the exact same slop you mentioned. So whatever Search is using, they must be using their crappiest, cheapest model. It's nowhere near state of the art.

aurareturn 2y ago
Makes sense that search has a small, fast, dumb model designed to summarize and not to solve problems. Nearly 14 billion Google searches per day. Way too much compute needed to use a bigger model.
- fire_lake 2y ago
  Massive search overlap though - and some questions (like the golf ball puzzle) can be cached for a long time.
  - summerlight 2y ago
    AFAIK they got 15% of unseen queries everyday, so it might be not very simple to design an effective cache layer on that. Semantic-aware clustering of natural language queries and projecting them into a cache-able low rank dimension is a non-trivial problem. Of course, LLM can effectively solve that, but then what's the point of using cache when you need LLM for clustering queries...
    - fire_lake 2y ago
      Not a search engineer, but wouldn’t a cache lookup to a previous LLM result be faster than a conventional free text search over the indexed websites? Seems like this could save money whilst delivering better results?
      - summerlight 2y ago

aezart 2y ago

> I have seen much AI output which is extraordinary it's funny how one serious fail can impact my point of view so dramatically. I feel the same way. It's like discovering for the first time that magicians aren't doing "real" magic, just sleight of hand and psychological tricks. From that point on, it's impossible to be convinced that a future trick is real magic, no matter how impressive it seems. You know it's fake even if you don't know how it works.

katsura 2y ago
To be fair, I love that magicians can pull tricks on me even though I know it is fake.
bambax 2y ago
I think there is a big divide here. Every adult on earth knows magic is "fake", but some can still be amazed and entertained by it, while others find it utterly boring because it's fake, and the only possible (mildly) interesting thing about it is to try to figure out what the trick is. I'm in the second camp but find it kind of sad and often envy the people who can stay entertained even though they know better.
- nucleogenesis 2y ago
  Idk I don’t think of it as fake - it’s creative fiction paired with sometimes highly skilled performance. I’ve learned a lot about how magic tricks work and I still love seeing performers do effects because it takes so much talent to, say, hold and hide 10 coins in your hands while showing them as empty or to shuffle a deck of cards 5x and have the audience cut it only to pull 4 aces off the top.
- toddmorey 2y ago
  I think the problem-solving / want-to-be-engineer side of my brain lights up in that "how did he do that??" way. To me that's the fun of it... I immediately try to engineer my own solutions to what I just saw happen. So I guess I'm the first camp, but find trying to figure out the trick hugely interesting.
- 2y ago

throwawaymaths 2y ago

I've seen humans make exactly these sorts of mistakes?

toddmorey 2y ago
As another commenter mentioned, LLMs tend to make these bad mistakes with enormous confidence. And because they represent SOTA technology (and can at times deliver incredible results), they have extra credence. More than even filling the gaps in knowledge / skills, would be a huge advancement in AI for it to admit when it doesn't know the answer or is just wildly guessing.

tim333 2y ago

A lot of humans are similarly good at some stuff and bad at other things. Looking up the math ability of the average American this is given as an example for the median (from https://www.wyliecomm.com/2021/11/whats-the-latest-u-s-numeracy-rate/ https://www.wyliecomm.com/2021/11/whats-the-latest-u-s-numer...): >Review a motor vehicle logbook with columns for dates of trip, odometer readings and distance traveled; then calculate trip expenses at 35 cents a mile plus $40 a day. Which is ok but easier than golf balls in a 747 and hugely easier than USAMO. Another question you could try from the easy math end is: Someone calculated the tariff rate for a country as (trade deficit)/(total imports from the country). Explain why this is wrong.

swader999 2y ago

It'll get it right next time because they'll hoover up the parent post.

CivBase 2y ago

I just asked my company-approved AI chatbot the same question. It got the golf ball volume right (0.00004068 cubic meters), but it still overestimated the cabin volume at 1000 cubic meters. It's final calculation was reasonably accurate at 24,582,115 golf balls - even though 1000 ÷ 0.00004068 = 24,582,104. Maybe it was using more significant figures for the golf ball size than it showed in its answer? It didn't acknowledge other items in the cabin (like seats) reducing its volume, but it did at least acknowlesge inefficiencies in packing spherical objects and suggested the actual number would be "somewhat lower", though it did not offer an estimate. When I pressed it for an estimate, it used a packing density of 74% and gave an estimate of 18,191,766 golf balls. That's one more than the calculation should have produced, but arguably insignificant in context. Next I asked it to account for fixtures in the cabin such as seats. It estimated a 30% reduction in cabin volume and redid the calculations with a cabin volume of 700 cubic meters. These calculations were much less accurate. It told me 700 ÷ 0.00004068 = 17,201,480 (off by ~6k). And it told me 17,201,480 × 0.74 was 12,728,096 (off by ~1k). I told it the calculations were wrong and to try again, but it produced the same numbers. Then I gave it the correct answer for 700 ÷ 0.00004068. It told me I was correct and redid the last calculation correctly using the value I provided. Of all the things for an AI chatbot which can supposedly "reason" to fail at, I didn't expect it to be basic arithmetic. The one I used was closer, but it was still off by a lot at times despite the calculations being simple multiplication and division. Even if might not matter in the context of filling an air plane cabin with golf balls, it does not inspire trust for more serious questions.

billforsternz 2y ago
> It's final calculation was reasonably accurate at 24,582,115 golf balls - even though 1000 ÷ 0.00004068 = 24,582,104. Maybe it was using more significant figures for the golf ball size than it showed in its answer? 1000 ÷ 0.00004068 = 25,000,000. I think this is an important point that's increasingly widely misunderstood. All those extra digits you show are just meaningless noise and should be ruthlessly eliminated. If 1000 cubic metres in this context really meant 1000.000 cubic metres, then by all means show maybe the four digits of precision you get from the golf ball (but I am more inclined to think 1000 cubic metres is actually the roughest of rough approximations, with just one digit of precision). In other words, I don't fault the AI for mismatching one set of meaninglessly precise digits for another, but I do fault it for using meaninglessly precise digits in the first place.

aoeusnth1 2y ago

2.5 pro nails each of these calculations. I don’t agree with Google’s decision to use a weak model in its search queries, but you can’t say progress on LLMs in bullshit as evidenced by a weak model no one thinks is close to SOTA.

raxxorraxor 2y ago

This reminds me of Google quick answers we had for a time in search. It is quite funny if you live outside the US, because it very often got the units or numbers wrong because of different decimal delimiters. No wonder Trump isn't afraid to put taxes against Canada. Who could take a 3.8 sqare miles country seriously?