Comment on We have to stop ignoring AI’s hallucination problem

ALostInquirer@lemm.ee ⁨1⁩ ⁨month⁩ ago

• Danksy@lemmy.world ⁨1⁩ ⁨month⁩ ago

  It’s not a bug, it’s a natural consequence of the methodology. A language model won’t always be correct when it doesn’t know what it is saying.

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  • vrighter@discuss.tchncs.de ⁨1⁩ ⁨month⁩ ago

    it never knows what it’s saying

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    • TheDarksteel94@sopuli.xyz ⁨1⁩ ⁨month⁩ ago

      Oh, at some point it will lol

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    • Danksy@lemmy.world ⁨1⁩ ⁨month⁩ ago

      That was what I was trying to say, I can see that the wording is ambiguous.

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  • ALostInquirer@lemm.ee ⁨1⁩ ⁨month⁩ ago

    Yeah, on further thought and as I mention in other replies, my thoughts on this are shifting toward the real bug of this being how it’s marketed in many cases (as a digital assistant/research aid) and in turn used, or attempted to be used (as it’s marketed).

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• machinin@lemmy.world ⁨1⁩ ⁨month⁩ ago

  …wikipedia.org/…/Hallucination_(artificial_intell…

  The term “hallucinations” originally came from computer researchers working with image producing AI systems. I think you might be hallucinating yourself 😉

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  • ALostInquirer@lemm.ee ⁨1⁩ ⁨month⁩ ago

    Fun part is, that article cites a paper mentioning misgivings with the terminology: AI Hallucinations: A Misnomer Worth Clarifying. So at the very least I’m not alone on this.

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• xthexder@l.sw0.com ⁨1⁩ ⁨month⁩ ago

  Because hallucinations pretty much exactly describes what’s happening? All of your suggested terms are less descriptive of what the issue is.

  The definition of hallucination:

  A hallucination is a perception in the absence of an external stimulus.

  In the case of generative AI, it’s generating output that doesn’t match it’s training data “stimulus”. Or in other words, false statements, or “facts” that don’t exist in reality.

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  • ALostInquirer@lemm.ee ⁨1⁩ ⁨month⁩ ago

    perception

    This is the problem I take with this, there’s no perception in this software. It’s faulty, misapplied software when one tries to employ it for generating reliable, factual summaries and responses.

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    • xthexder@l.sw0.com ⁨1⁩ ⁨month⁩ ago

      I have adopted philosophy that human brains might not be as special as we’ve thought, and that the untrained behavior emerging from LLMs and image generators is so similar to human behaviors that I can’t help but think of it as an underdeveloped and handicapped mind.

      I hypothesis that a human brain, who’s only perception of the world is the training data force fed to it by a computer, would have all the same problems the LLMs do right now.

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• blazeknave@lemmy.world ⁨1⁩ ⁨month⁩ ago

  Ty. As soon as I saw the headline, I knew I wouldn’t be finding value in the article.

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  • ALostInquirer@lemm.ee ⁨1⁩ ⁨month⁩ ago

    It’s not a bad article, honestly, I’m just tired of journalists and academics echoing the language of businesses and their marketing. “Hallucinations” aren’t accurate for this form of AI. These are sophisticated generative text tools, and in my opinion lack any qualities that justify all this fluff terminology personifying them.

    Also frankly, I think students have one of the better applications for large-language model AIs than many adults, even those trying to deploy them. Students are using them to do their homework, to generate their papers, exactly one of the basic points of them. Too many adults are acting like these tools should be used in their present form as research aids, but the entire generative basis of them undermines their reliability for this. It’s trying to use the wrong tool for the job.

    You don’t want any of the generative capacities of a large-language model AI for research help, you’d instead want whatever text-processing it may be able to do to assemble and provide accurate output.

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