Just got schooled by an AI.
According to Wiktionary:
(UK) IPA(key): /ˈstɹɔːb(ə)ɹi/
(US) IPA(key): /ˈstɹɔˌbɛɹi/
…there are indeed only two /ɹ/ in strawberry.
So much for dissing on AIs for not being able to count.
Did you ask how many /ɹ/ there are, or how many r there are? It can’t count, then it went and tried to justify its moronic behavior, and manipulated you into believing its “logic”.
Why do you ass-u-me that?
So much for dissing on AIs for not being able to count.
no, I’m still going to do that.
Nobody’s stopping you. I’m going to reassess and double check my assumptions instead… and ask the AI to explain itself.
wait you’re serious? this isn’t satire???
What were your assumptions to say that?
I shudder to think how much electricity got wasted so you could get fooled by an LLM into believing nonsense. Let alone the equally-unnecessary followup questions.
Also, the LLM is just Yes Manning. OP gave it the ‘rr’ counts as a single ‘r’ answer with a very loaded question
I asked it how many X’s there are in the word Bordeaux it told me there are none.
I asked it how many times X is pronounced in Bordeaux it told me the x in Bordeaux isn’t pronounced with the word ending in an “o” sound.
I asked it how many “o” there are in Bordeaux it told me there are no o in Bordeaux.
So, is it counting the sounds made in the word? Or is it counting the letters? Or is it doing none of the above and just giving a probabilistic output based on an existing corpus of language, without any thought or concepts.
Yes, no, both… and all other interpretations… all at once.
With any ambiguity in a prompt, it assumes a “blend” of all the possible interpretations, then responds using them all over the place.
In the case of “Bordeaux”:
It’s pronounced “bor-DOH”, with the emphasis on the second syllable and a silent “x.”
So… depending on how you squint: there is no “o”, no “x”, only a “bor” and a “doh”, with a “silent x”, and ending in an “oh like o”.
Perfectly “logical” 🤷
Wrong maths, you say?
Anyway. You didn’t ask the number of times the phoneme /ɹ/ appears in the spoken word, so by context you’re talking about the written word, and the letter ⟨r⟩. And the bot interpreted it as such, note it answers
here, let me show you: s-t-r-a-w-b-e-r-r-y
instead of specifying the phonemes.
By the way, all explanation past the «are you counting the “rr” as a single r?» is babble.
Wrong maths, you say?
Yes. If I want to know what 1+2 equals, and I throw a dice, there’s a chance I will get the correct answer. If I do, that doesn’t mean it knows how to do Maths. Also, notice where it said “Here’s the calculation”, it didn’t actually show you the calculation? e.g. long multiplication, or even grouping, or the way the Chinese do it. Even a broken clock is right twice a day. Even if AI manages to randomly get a correct answer here and there, it still doesn’t know how to do Maths (which includes not knowing how to count to begin with)
What’s interesting IMO is that it got the first two and the last two digits right; and this seems rather consistent across attempts with big numbers. It doesn’t “know” how to multiply numbers, but it’s “trying” to output an answer that looks correct.
In other words, it’s “bullshitting” - showing disregard to truth value, but trying to convince you.
Those are all the smallest models, and you don’t seem to have reasoning mode, or external tooling, enabled?
LLM ≠ AI system
It’s been known for some time, that LLMs do “vibe math”. Internally, they try to come up with an answer that “feels” right… which makes it pretty impressive for them to come anywhere close, within a ±10% error margin.
Ask people to tell you what a right answer could be, give them 1 second to answer… see how many come that close to the right one.
A chatbot/AI system on the other hand, will come up with some Python code to do the calculation, then run it. Still can go wrong, but it’s way less likely.
all explanation past the «are you counting the “rr” as a single r?» is babble
Not so sure about that. It treats r as a word, since it wasn’t specified as “r” or single letter. Then it interpretes it as… whatever. Is it the letter, phoneme, font, the programming language R… since it wasn’t specified, it assumes “whatever, or a mix of”.
It failed at detecting the ambiguity and communicating it spontaneously, but corrected once that became part of the conversation.
It’s like, in your examples… what do you mean by “by”? “3 by 6” is 36… you meant to “multiply 36”? That’s nonsense… 🤷
[special pleading] Those are all the smallest models
[sarcasm] Yeah, because if you randomly throw more bricks in a construction site, the bigger pile of debris will look more like a house, right. [/sarcasm]
and you don’t seem to have reasoning [SIC] mode, or external tooling, enabled?
Those are the chatbots available through DDG. I just found it amusing enough to share, given
- The logic procedure to be followed (multiplication) is rather simple, and well documented across the internet, thus certainly present in their corpora.
- The result is easy to judge: it’s either correct or incorrect.
- All answers are incorrect and different from each other.
Small note regarding “reasoning”: just like “hallucination” and anything they say about semantics, it’s a red herring that obfuscates what is really happening.
At the end of the day it’s simply weighting the next token based on the previous tokens + prompt, and optionally calling some external tool. It is not really reasoning; what’s doing is not too different in spirit from Markov chains, except more complex.
[no true Scotsman] LLM ≠ AI system
If large “language” models don’t count as “AI systems”, then what you shared in the OP does not either. You can’t eat your cake and have it too.
It’s been known for fome time, that LLMs do “vibe math”.
I.e. they’re unable to perform actual maths.
[moving goalposts] Internally, they try to come up with an answer that “feels” right…
It doesn’t matter if the answer “feels” right (whatever this means). The answer is incorrect.
which makes it pretty impressive for them to come anywhere close, within a ±10% error margin.
No, the fact they are unable to perform a simple logical procedure is not “impressive”. Specially not when outputting the “approximation” as if it was the true value; note how none of the models outputted anything remotely similar to “the result is close to
$number” or “the result is approximately$number”.[arbitrary restriction + whataboutism] Ask people to tell you what a right answer could be, give them 1 second to answer… see how many come that close to the right one.
None of the prompts had a time limit. You’re making shit up.
Also. Sure, humans brainfart all the time; that does not magically mean that those systems are smart or doing some 4D chess as your OP implies.
A chatbot/AI system on the other hand, will come up with some Python code to do the calculation, then run it. Still can go wrong, but it’s way less likely.
I.e. it would need to use some external tool, since it’s unable to handle logic by itself, as exemplified by maths.
all explanation past the «are you counting the “rr” as a single r?» is babble
Not so sure about that. It treats r as a word, since it wasn’t specified as “r” or single letter. Then it interpretes it as… whatever. Is it the letter, phoneme,
The output is clearly handling it as letters. It hyphenates the letters to highlight them, it mentions “digram” (i.e. a sequence of two graphemes), so goes on. And in no moment is referring to anything that can be understood as associated with sounds, phonemes. And it’s claiming there’s an ⟨r⟩ «in the middle of the “rr” combination».
font, the programming language R…
There’s no context whatsoever to justify any of those interpretations.
since it wasn’t specified, it assumes “whatever, or a mix of”.
If this was a human being, it would not be an assumption. Assumption is that sort of shit you make up from nowhere; here context dictates the reading of “r” as “the letter ⟨r⟩”.
However since this is a bot it isn’t even assuming. Just like a boulder doesn’t “assume” you want it to roll down; it simply reacts to an external stimulus.
It failed at detecting the ambiguity and communicating it spontaneously, but corrected once that became part of the conversation.
There’s no ambiguity in the initial prompt. And no, it did not correct what it says; the last reply is still babble, you don’t count ⟨rr⟩ in English as a single letter.
It’s like, in your examples… what do you mean by “by”? “3 by 6” is 36… you meant to “multiply 36”? That’s nonsense… 🤷
I’d rather not answer this one because, if I did, I’d be pissing on Beehaw’s core values.
I’d rather not answer this one because, if I did, I’d be pissing on Beehaw’s core values.
I feel like you already did, and I won’t be responding in kind. Good day, to you.
Oh, for fuck’s sake … another land war in Asia?
Also ignoring the fact it said one r was in the middle of the word
There is a middle ground between “blindly rejecting” and “blindly believing” whatever an AI says.
LLMs use tokens. The answer is “correct, in its own way”, one just needs to explore why and how much. Turns out, that can also lead to insights.
It is not correct in any way, though. Unless you count a way you gave it to justify it’s wrong answer, but that is just it being a Yes Man to keep you engaged.
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It is correct in an “ambiguous multi-dimensional” sense
That’s a lot of words to say it’s wrong.
The question is incredibly straightforward, and again the “reason” it gave is one you provided in the clarifying question itself. There is no reasoning going on, because it can’t understand the question (or reason for that matter).
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Ah cool, you’ve resorted to being a jerk. Have fun wasting water and electricity overthinking wrong answers from chatbots.
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