Comment on Amazon builds AI model to optimize packaging

• Syntha@sh.itjust.works ⁨2⁩ ⁨months⁩ ago

  AI is not prone to hallucinations, LLMs are. I doubt Amazon is building a chatbot to optimise packaging.

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  • Ashelyn@lemmy.blahaj.zone ⁨2⁩ ⁨months⁩ ago

    I mean, AI is used in fraud detection pretty often; when it hits a false positive (which happens frequently on a population-level basis), is that not a hallucination of some sort? Obviously LLMs can go off the rails much further because it’s readable text, but any machine learning model will occasionally spit out really bad guesses almost any person could have done better with. (To be fair, humans are highly capable of really bad guesses too).

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    • Womble@lemmy.world ⁨2⁩ ⁨months⁩ ago

      No, false positives and false negatives are not hallucinations. Otherwise things like a blood test not involving any ml would also be halucinating which removes all meaning from the term.

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      • Ashelyn@lemmy.blahaj.zone ⁨2⁩ ⁨months⁩ ago

        That’s fair. I think fundamentally a false positive/negative isn’t that much different. Pretty much all tests—especially those dealing with real world conditions—are heuristic, as are all LLMs by necessity of the design. Hallucination is a pretty specific term given to AI as an attempt to assign agency to a system that doesn’t actually have any (by implying it’s crazy and making stuff up instead of a black box with deterministic inputs and outputs spitting out something factually wrong but with a similar format to what is trained on). I feel like the nature of any tool where “you can’t trust this to be entirely accurate” should have an umbrella term that encompasses both types of providing inaccurate info under certain conditions.

        I suppose the difference is that AI is a lot more likely to randomly go off, whereas a blood test is likelier to provide repeated false positives for the same person with their unique biology? There’s also the fact that most medical tests represent a true/false dichotomy or lookup table, whereas an LLM is given the entire bounds of language.

        Would an AI clustering algorithm (say, K-means for instance) giving an inaccurate diagnosis be a false positive/negative or a hallucination? These models can be programmed on a sliding scale and I feel like there’s definitely an area where the line could get pretty blurry.

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  • Llewellyn@lemm.ee ⁨2⁩ ⁨months⁩ ago

    What do you consider to be an AI?
    And do you consider any of the existing systems to be the one?

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    • Syntha@sh.itjust.works ⁨2⁩ ⁨months⁩ ago

      When I use “AI” I’m using computer science terminology. Artificial intelligence is a subfield of CS, in that sense, any model that comes of that field is, by definition, AI.

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      • Llewellyn@lemm.ee ⁨2⁩ ⁨months⁩ ago

        Then it’s strange, that you are separating AI and LLM, because LLM is a type of artificial intelligence.

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  • polygon6121@lemmy.world ⁨2⁩ ⁨months⁩ ago

    AI in general is definitely prone to hallucinations. It is more commonly seen in LLMs because it is more widely used by the public. It is definitely a problem with all AI

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    • Syntha@sh.itjust.works ⁨2⁩ ⁨months⁩ ago

      Besides generative AI, what models can hallucinate?

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      • polygon6121@lemmy.world ⁨2⁩ ⁨months⁩ ago

        Text to video, automated driving, object detection, language translations. I might be misusing the term, you could argue that the word is describing what LLMs commonly does and that is where the term is derived from. You can also argue that AI is sometimes correct and the human have issues identifying the correct answer. But In my mind it is much the same just different applications. A car completely missing a firetruck approaching or a LLM just spewing out wrong statements is the same to me.

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