in terms of communication utility, it’s also a very accurate term.
when WE hallucinate, it’s because our internal predictive models are flying off the rails filling in the blanks based on assumptions rather than referencing concrete sensory information and generating results that conflict with reality.
when AIs hallucinate, it’s due to its predictive model generating results that do not align with reality because it instead flew off the rails presuming what was calculated to be likely to exist rather than referencing positively certain information.
it’s the same song, but played on a different instrument.
when WE hallucinate, it’s because our internal predictive models are flying off the rails filling in the blanks based on assumptions rather than referencing concrete sensory information and generating results that conflict with reality.
Is it really? You make it sound like this is a proven fact.
Thanks for the unprompted mansplanation bro, but I was specifically refering to the comment that replied “JuSt lIkE hUmAn BrAin”, to “they generate data based on other data”
That’s crazy, because they weren’t even talking about keyboard autofill, so why’d you even bring that up? How can you imply my comment is irrelevant when it’s a direct response to your initial irrelevant comment?
Nice hijacking of the term mansplaining, btw. Super cool of you.
Fine, I’ll play along, chew it up for you, since you’ve been so helpful and mansplained that a keyboard is different than LLM:
My comment was responding to anthropomorphization of software. Someone said it’s not human because it just generates output based on input. Someone else said “just like human brain”, I said yes, but also just like a keyboard, alluding to the false equivalence.
Oh man, I’m excited for you. Today is the day you learn words can have two meanings! Wait until you see what the rest of the dictionary contains. It is crazy! But not actually crazy, because dictionaries don’t have brains.
Which is okay. I learn new things every day. I just find funny the fact that the other commenter is so fixated on the idea of “it can’t be real because I never heard of it.”
An anthropomorphic model of the software, wherein you can articulate things like “the software is making up packages”, or “the software mistakenly thinks these packages ought to exist”, is the right level of abstraction for usefully reasoning about software like this. Using that model, you can make predictions about what will happen when you run the software, and you can take actions that will lead to the outcomes you want occurring more often when you run the software.
If you try to explain what is going on without these concepts, you’re left saying something like “the wrong token is being sampled because the probability of the right one is too low because of several thousand neural network weights being slightly off of where they would have to be to make the right one come out consistently”. Which is true, but not useful.
The anthropomorphic approach suggests stuff like “yell at the software in all caps to only use python packages that really exist”, and that sort of approach has been found to be effective in practice.
Can we fucking stop anthropomorphising software?
“Hallucinate” is the standard term used to explain the GenAI models coming up with untrue statements
in terms of communication utility, it’s also a very accurate term.
when WE hallucinate, it’s because our internal predictive models are flying off the rails filling in the blanks based on assumptions rather than referencing concrete sensory information and generating results that conflict with reality.
when AIs hallucinate, it’s due to its predictive model generating results that do not align with reality because it instead flew off the rails presuming what was calculated to be likely to exist rather than referencing positively certain information.
it’s the same song, but played on a different instrument.
Is it really? You make it sound like this is a proven fact.
I believe that’s where the scientific community is moving towards, based on watching this Kyle Hill video.
i mean, idk about the assumptions part of it, but if you asked a psych or a philosopher, im sure they would agree.
Or they would disagree and have about 3 pages worth of thoughts to immediately exclaim otherwise they would feel uneasy about their statement.
Better than one of those pesky unproven facts
I think a more accurate term would be confabulate based on your explanation.
you know what, i like that! I like that a lot!
They don’t come up with any statements, they generate data extrapolating other data.
So just like human brains?
Main difference is that human brains usually try to verify their extrapolations. The good ones anyway. Although some end up in flat earth territory.
How many, percentually, do you think are critical to input?
Yes, my keyboard autofill is just like your brain, but I think it’s a bit “smarter” , as it doesn’t generate bad faith arguments.
Your Markov chain based keyboard prediction is a few tens of billions of parameters behind state of the art LLMs, but pop off queen…
Thanks for the unprompted mansplanation bro, but I was specifically refering to the comment that replied “JuSt lIkE hUmAn BrAin”, to “they generate data based on other data”
That’s crazy, because they weren’t even talking about keyboard autofill, so why’d you even bring that up? How can you imply my comment is irrelevant when it’s a direct response to your initial irrelevant comment?
Nice hijacking of the term mansplaining, btw. Super cool of you.
Oh my god, we’ve got a sealion here.
Fine, I’ll play along, chew it up for you, since you’ve been so helpful and mansplained that a keyboard is different than LLM:
My comment was responding to anthropomorphization of software. Someone said it’s not human because it just generates output based on input. Someone else said “just like human brain”, I said yes, but also just like a keyboard, alluding to the false equivalence.
Clearer?
What standard is that? I’d like a reference.
https://en.m.wikipedia.org/wiki/Hallucination_(artificial_intelligence)
It’s as much as “Hallucination” as Tesla’s Autopilot is an Autopilot
https://en.m.wikipedia.org/wiki/Tesla_Autopilot
I don’t propagate techbro “AI” bullshit peddled by companies trying to make a quick buck
Also, in the world of science and technology a “Standard” means something. Something that’s not a link to a wikipedia page.
It’s still anthropomorphising software and it’s fucking cringe.
Oh man, I’m excited for you. Today is the day you learn words can have two meanings! Wait until you see what the rest of the dictionary contains. It is crazy! But not actually crazy, because dictionaries don’t have brains.
Wow, clever. Did you literally hallucinate this yourself or did you ask your LLM girlfriend for help?
And by literally, I mean figuratively.
You’re gonna be real pissed to find out that computer bugs aren’t literal bugs
Well, until a moth gets into your relays, anyhow.
Although they did start out that way-
https://education.nationalgeographic.org/resource/worlds-first-computer-bug/
I know it’s a big word, but surely you can google what anthropomorphization is? Don’t “ask” LLM, those things output garbage. Just google it.
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Where have you been in the last two years, brah?
I’m a different person, but it’s the first time I’ve heard the term used. /shrug
Which is okay. I learn new things every day. I just find funny the fact that the other commenter is so fixated on the idea of “it can’t be real because I never heard of it.”
Not under the sole of fake hype.
My boy, who hurt you?
No?
An anthropomorphic model of the software, wherein you can articulate things like “the software is making up packages”, or “the software mistakenly thinks these packages ought to exist”, is the right level of abstraction for usefully reasoning about software like this. Using that model, you can make predictions about what will happen when you run the software, and you can take actions that will lead to the outcomes you want occurring more often when you run the software.
If you try to explain what is going on without these concepts, you’re left saying something like “the wrong token is being sampled because the probability of the right one is too low because of several thousand neural network weights being slightly off of where they would have to be to make the right one come out consistently”. Which is true, but not useful.
The anthropomorphic approach suggests stuff like “yell at the software in all caps to only use python packages that really exist”, and that sort of approach has been found to be effective in practice.
Woops sorry mate, too late.