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snooggums ,
@snooggums@midwest.social avatar

Hallucinating is a fancy term for BEING WRONG.

Unreliable bullshit generator is still unreliable. Imagine that!

doodledup , (edited )

AI doesn’t know what’s wrong or correct. It hallucinates every answer. It’s up to the supervisor to determine whether it’s wrong or correct.

Mathematically verifying the correctness of these algorithms is a hard problem. It’s intentional and the trade-off for the incredible efficiency.

Besides, it can only “know” what it has been trained on. It shouldn’t be suprising that it cannot answer about the Trump shooting. Anyone who thinks otherwise simply doesn’t know how to use these models.

snooggums ,
@snooggums@midwest.social avatar

It is impossible to mathematically determine if something is correct. Literally impossible.

At best the most popular answer, even if it is narrowed down to reliable sources, is what it can spit out. Even that isn’t the same thing is consensus, because AI is not intelligent.

If the ‘supervisor’ has to determine if it is right and wrong, what is the point of AI as a source of knowledge?

doodledup , (edited )

It is impossible to mathematically determine if something is correct. Literally impossible.

No, you’re wrong. You can indeed prove the correctness of a neural network. You can also prove the correctness of many things. It’s the most integral part of mathematics and computer-science.

For example a very simple proof: with the conjecture that an even number is 2k of a number k, then you can prove that the addition of two even numbers is again an even number (and that prove is definite): 2a+2b=2(a+b), since a+b=k for some k.

Obviously, proving more complex mathematical problems like AI is more involved. But that’s why we have scientists that work on that.

At best the most popular answer, even if it is narrowed down to reliable sources, is what it can spit out. Even that isn’t the same thing is consensus, because AI is not intelligent.

That is correct. But it’s not a limitation. It’s by design. It’s the tradeoff for the efficiency of the models. It’s like lossy JPG compression. You accept some artifacts but in return you get much smaller images and much faster loading times.

But there are indeed "AI"s and neural networks that have been proven correct. This is mostly applied to safety critical applications like airplane collision avoidance systems or DAS. But a language model is not safety critical; so we take full advantage.

If the ‘supervisor’ has to determine if it is right and wrong, what is the point of AI as a source of knowledge?

You’re completely misunderstanding the whole thing. The only reason why it’s so incredibly good in many applications is because it’s bad in others. It’s intentionally designed that way. There are exact algorithms and there approximation algorithms. The latter tend to be much more efficient and usable in practice.

snooggums ,
@snooggums@midwest.social avatar

The only reason why it’s so incredibly good in many applications is because it’s bad in others. It’s intentionally designed that way.

lolwut

doodledup ,

It’s designed in a ways that’ll make it inherently incorrect. Even on a physical basis (due to numeric issues). It’s not a problem of the algorithm because it has been designed that way. The problem is that you don’t know how to correctly use it.

I can’t explain it any differently without getting overly technical. You wouldn’t understand it anyways, judging by your comment “lolwut”. If you want to learn how LLMs work specifically, there are plenty of ressources on the internet.

uranibaba ,

Can you recommend any for resource to start with? (If I can be picky, then something I can consume after a whole day of being a patent because there is no energy for much else.)

Cethin ,

You can prove some things are correct, like math problems (assuming the axioms they are based on are also correct).

You can’t prove that things like events having happened are correct. That’s even a philosophical issue with human memory. We can’t prove anything in the past actually happened. We can hope that our memory of events is accurate and reliable and work from there, but it can’t actually be proven. In theory everything before could have just been implanted into our minds. This is incredibly unlikely (as well as not useful at best), but it can’t be ruled out.

If we could prove events in the past are true we wouldn’t have so many pseudo-historians making up crazy things about the pyramids, or whatever else. We can collect evidence and make inferences, but we can’t prove it because it is no longer happening. There’s a chance that we miss something or some information can’t be recovered.

LLMs are algorithms that use large amounts of data to identify correlations. You can tune them to give more unique answers or more consistent answers (and other conditions) but they aren’t intelligent. They are, at best, correlation finders. If you give it bad data (internet conversations) or incomplete data then it at best will (usually confidently) give back bad information. People who don’t understand how they work assume they’re actually intelligent and can do more than this. This is dangerous and should be dispelled quickly, or they believe any garbage it spits out, like the example from this post.

otp ,

This sounds like an overly pedantic view of “prove”

conciselyverbose ,

It’s not pedantic. You can mathematically prove math.

You can’t mathematically/algorithmically prove an event happened or did not happen.

randon31415 ,

The shooting happened after the end of the training date. Like asking windows 95 clippy about 9/11 and it saying it didn’t happen.

Evotech ,

Clippy being a 9/11 conspiracy theorist is now canon

catloaf ,

Kaplan noted that AI chatbots “are not always reliable when it comes to breaking news or returning information in real time,” because “the responses generated by large language models that power these chatbots are based on the data on which they were trained, which can at times understandably create some issues when AI is asked about rapidly developing real-time topics that occur after they were trained.”

If you’re expecting a glorified autocomplete to know about things it doesn’t have in its training data, you’re an idiot.

Catoblepas ,

There are definitely idiots, but these idiots don’t get their ideas of how the world works out of thin air. These AI chatbot companies push the cartoon reality that this is a smart robot that knows things hard in their advertisements, and to learn otherwise you have to either listen to smart people or read a lot of text.

brucethemoose ,

Some services will use glorified RAG to put more current info in the context.

But yeah, if it’s just the raw model, I’m not sure what they were expecting.

Telorand ,

Sir, are you telling me AI isn’t a panacea for conveying facts? /s

Empricorn ,

Yeah, the average person is the idiot here, for something they never asked for, and for something they see no value in. Companies threw billions of dollars at this emerging technology. Many things like Google Search have hallucinating, error-prone AI forced into the main product that is impossible to opt-out or use the (working) legacy version now…

doodledup ,

Nobody is forcing you to use it.

I’m using it and I see great value in it. And if there are people that see value in a product then it’s worth the investment.

nondescripthandle ,

Is it wrong to root this on simply because I hate that shitbag?

intensely_human ,

Hatred is a path to the dark side.

As evidenced by you now rooting for misinformation.

intensely_human ,

Does the AI consistently say that, no matter who asks?

Because if so, that’s not a hallucination.

Papanca ,

Does this AI work with real time info?

expatriado ,

maybe Meta AI is into something

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