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- cross-posted to:
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Google rolled out AI overviews across the United States this month, exposing its flagship product to the hallucinations of large language models.
No, hallucination is a really good term. It can be super confident and seemingly correct but still completely made up.
It is, but it isnt applicable in at least the glue-pizza situation as the probable source comment has been found on reddit.
A better use of the term might be how when you try to get Bing’s image creator to make “Battletech” art, you just mostly get really obvious Warhammer 40k Space Marines and occasionally Iron Maiden album art.
That’s not hallucinations (in particular), that’s concept bleed. Try the following:
…and hear them answer “milk”. “White, cold, drink, cow” are all wired to “milk” in our heads logic comes later. It’s quite a bit harder to trick humans with this than AIs because we do have the capacity to double-check but if you simply want to bend an answer, not have it be completely nonsensical, it’s quite easy.
Also your 40k or Iron Maiden result might very well still be Battletech. E.g. when it comes to image composition. Another explanation would be low resolution in the prompt encoding, that’d be similar to boomers calling your PS5 a Nintendo. Most likely though it has only seen two or three Battletech images (face it, it’s not that popular in comparison) and thought “eh looks like a Nintendo that’s where I’ll store it”, Humans and current-gen AI are different in principle in that regard as we can come up with encoding strategies, they can’t. Something something T3 systems and need for exponential amounts of data.
You just described entirety of reddit and last I checked we didn’t call that hallucinating
That is just being WRONG.
It’s a really bad term because it’s usually associated with a mind, and LLMs are nothing of the sort.
So is bullshitting. More so, only human minds can bullshit.
We anthropomorphize machines all the time, it’s fine.
I’d prefer we’d start calling all genai output hallucinations again. It used to be like 10 years ago, but somewhere along the line marketing decided hallucinated truths aren’t “hallucinations”.
And a bull’s anus.
It’s fucking not, amd I’m not changing my mind about it.
Anthropomorphication is hard to avoid in AI.
Many worthy things are difficult.
But is anthropomorphism of AI particularly worrying?
It is when the people tends to give more credence to entities that appear sentient and to have agency.
I think delusion might be a better word. You can hallucinate and know it’s not real
My experience with certain chemicals suggests this is true.
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for it to “hallucinate” things, it would have to believe in what it’s saying. ai is unable to think - so it cannot hallucinate
Hallucination is a technical term. Nothing to do with thinking. The scientific community could have chosen another term to describe the issue but hallucination explains really well what’s happening.
huh, i kinda assumed it was a term made up/taken by journalists mostly, are there actual research papers on this using that term?
Yup. Loads of them! https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=hallucinations+llm&btnG=
It used to mean all generated output though. Calling only mistakes hallucinations is new, definitely because of hype.
So how do you prove it can’t think? Or that you actually can?
because it’s a text generation machine…? i mean, i wouldn’t say i can prove it, but i don’t think anyone can prove it’s capable of thinking, much less of reasoning
like, it can string together a coherent sentence thanks to well crafted equations, sure, but i wouldn’t qualify that as “thinking”, though i guess the definition of “thinking” is debatable
It’s an interesting question. I am inclined to believe that the faster it gets at running those equations, over and over and over, reanalysing is data and responses as it goes, that that ultimately leads to some kind of evolution. You know, Vger style.
It can tell you how to stack things on top of each other the best way to get a high tower. Etc.
Those are not random sentences. If you can not define thinking in a way this machine fails at, then stop saying it does not think.
A parrot can be trained to tell you how to stack things on top of each other the best way to get a high tower.
This is just an electronic parrot, millions of times faster to train than the biological parrot, specialized in repetition alone (can’t really do anything else a parrot can) and which has been trained on billions of texts.
You’re confusing one specific form in which humans externally express cogniscence with the actual cogniscence itself: just because intelligence can produce some forms of textual communication doesn’t mean that the relationship holds in the opposite direction and such forms of textual communication require intelligence, or if you will, just because you can photograph a real pizza to get a picture of a pizza doesn’t mean a picture of a pizza is actually of a real pizza and not something with glue to make it look like it has stringy melted cheese.
Again, it is absolutely capable to come up with it’s own logical stuff, hence my example. Stop saying it just copies existing stuff, that is simply wrong.
interesting, in my experience, it’s only been good at repeating things, and failing on unexpected inputs - it’s able to answer pretty accurately if a small number is even or odd, but not if it’s a large number, which indicates it’s not reasoning but parroting answers to me
do you have example prompts where it showed clear logical reasoning?
Examples showing that it comes up with it’s own solutions to an answer? Just ask it something that could not have been on the Internet before. Professor talking about AGI in GPT 4
Personal examples would be to code python to solve a 2D thermal heat flux problem given some context and constraints.
Sure, whatever.
Amazing reply, given the context.