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[–] 22 points 7 months ago* (8 children)

Why did AI say it wasn't real

Because the LLMs can't even see the picture. They're text-only. Those images are first processed by a separate vision model, and whatever question the LLM is answering is based on the description the vision model gave it - not by actually looking at the picture itself.

Just another example of an uneducated user misusing the system and then complaining that it doesn't work for a task it was never intended for.

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  • [–] 17 points 7 months ago* (last edited 7 months ago) (7 children)

    That’s not how multimodal models work. The image is transformed into a vector embedding, just like the text is.

    https://www.ionio.ai/blog/a-comprehensive-guide-to-multimodal-llms-and-how-they-work

    They “see” it just like they “understand” the text. Both Grok and Gemini are cited in the article and both are multimodal in this way. They get it wrong because AIs are prone to hallucinate when they’re at the limits of their knowledge.

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  • [–] 1 point 7 months ago* (6 children)

    That's what I was saying.

    The image gets processed by a separate image encoder (or vision model), which turns it into a vector embedding. The LLM itself never sees the raw pixels - it only works with that embedding. So when it judges whether an image is real or AI-generated, it's reasoning based on a compressed representation, not by directly "looking" at the picture.

    From your link:

    There are different encoders for each modality type, such as text encoder or audio encoder.

    Each modality encoder is designed to extract specific information from its corresponding input data. For example, the text encoder might extract semantic representations from text, while the image encoder extracts visual features from images.

    So the language model that's answering back to you never saw the image - the image encoder did and it then described it back to the language model.

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  • [–] 2 points 7 months ago* (last edited 7 months ago) (5 children)

    That’s clearly not what you were saying, and I’m still pretty sure you don’t understand. You said an image model gave the text model a description of the image. That is false. The image encoder, just like the text encoder gives the LLM a vector embedding.

    Because the LLMs can't even see the picture. They're text-only. Those images are first processed by a separate vision model, and whatever question the LLM is answering is based on the description the vision model gave it - not by actually looking at the picture itself. Just another example of an uneducated user misusing the system and then complaining that it doesn't work for a task it was never intended for.

    Emphasis added.

    An image encoder is in no way an image model that turns an image into text like you were saying:

    https://en.wikipedia.org/wiki/Vision_transformer

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  • [–] 1 point 7 months ago (4 children)

    You're right on the terminology - it's an image encoder that produces a vector embedding, not a literal text description. I should have been more precise with the wording.

    However, my main point still stands: the LLM itself never sees the raw image. It only receives a compressed embedding created by the vision encoder.

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  • [–] 1 point 7 months ago* (last edited 7 months ago) (3 children)

    So what? It never sees the text you type either. You called it “text-only”, but that’s not true in any way for multimodal models. In the strictest sense, transformer models are vector embedding only. The model itself can only work with a series of vectors, so everything you give it needs to be turned into vectors, even text.

    https://en.wikipedia.org/wiki/Transformer_(deep_learning)

    What vectors you give it is entirely dependent on the application and how it was trained, and pretty much anything can be turned into a vector embedding. Anything with semantic meaning at least (otherwise you can’t make an embedding). The attention mechanism can work on literally any series of vectors with semantic meaning.

    Saying that the model isn’t “seeing” the image just because the image was transformed into a compressed set of vectors is akin to saying a human isn’t “seeing” an image when they’re looking at a JPEG (since a JPEG is a compressed set of cosine exponents).

    Why not just admit that you were wrong?

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  • [–] 1 point 7 months ago* (2 children)

    Why not just admit that you were wrong?

    Because I'm not convinced that I am. My layman's explanation might not get every technical detail exactly right, but I think the core point is factual.

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  • [–] 1 point 7 months ago* (last edited 7 months ago) (1 child)

    Since you really don’t believe me, here is an experiment to show how accurately a vector embedding represents the real image:

    Original:

    Output from the LLM:

    Clearly, what the LLM is seeing is an incredibly detailed and accurate representation of the real image. Practically the same quality as a JPEG. (Not quite, as you can see in the garbled text on the tag, but nearly the same quality.)

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  • [–] 2 points 7 months ago

    Okay, after spending way too much time researching this, I've come to the conclusion that my original statement was wrong - and I finally also got my chosen AI assistant to agree with me.

    My original claim: “The AI said the picture was fake because it can’t even see the picture. It only got a description from a vision model.”

    My revised explanation: “It got it wrong because the image embedding it received didn’t contain the relevant information to make that distinction.”

    So yes. While it's true that the LLM can't see the picture in the human sense of vision, that's not what I was talking about. It can "see" the image well enough to tell the difference. It simply doesn't know what to look for. It's not really the LLM's fault - it's because the image encoder wasn't trained on enough pictures labeled as AI-generated.

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  • 18 comments in c/world@lemmy.world view that post
    [–] 27 points 8 months ago (1 child)

    It's incredible that the masses have embraced so fully a technology that makes them dumber, as well as the internet itself. It's as if, in the 90s, we decided that we should embrace chain emails as the way forward, rather than embracing Wikipedia.

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  • [–] 4 points 7 months ago

    While growing up I used tech to make myself more intelligent, I wanted to understand how it worked and functioned. Least on a software level I'm a programmer not hardware (though may do some small projects to see at some point).

    I always wonder why we fell so far, like from growing up I needed to figure out computers and ensure I could use them to their fullest. These days appears most people of a similar age don't care cause it works.

    While I see that point. it hasn't worked that long and at this point we seem to have people not knowing where to save a word document. That's from 3rd party stories but seems believable. So we went from some of the most curious people understanding their tools to people going 'meh it works' without understanding a damn thing.

    It only worked decently cause the operators understood it and we put in our time. Shit did I just get a reason for Matrix operators name? Like a dual meaning besides the phone one. Huh never thought of that but back in the day operators had some answer usually....

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  • [–] 32 points 8 months ago (1 child)

    Ask Gemini, the AI service powered by Google

    How about we don't rely on AI to say if something is AI?

    How about we take the time to train our actual brains to spot the difference?

    Like, that's the silver lining of dlss 5 for me. Theyre just slapping that stupid AI filter and getting a bunch of blowback, but there was a very brief window where people kept using the same filter on normal pictures.

    Not only is that shit useless, it's functionally dangerous because that blurs the lines. It picks up all the hallmarks of AI, giving false positives that an image isn't real.

    I'm hoping the dlss 5 blowback will kill the AI upscaling of random modern images. And people actually start using their fucking brains.

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  • [–] 19 points 8 months ago*

    I mean yeah thats the entire point of the article. All the slop machines were wrong and actual manual research was required to find the truth.

    The cemetery image, it turns out, is authentic. Researchers have cross referenced the photo of the site with satellite images that confirm its location, and it can be cross-referenced again with dozens more images taken of the same site from slightly different angles, and again with video footage – none of which experts say show signs of tampering or digital manipulation. The “factchecks” by Gemini and Grok are just one example of a tidal wave of AI-generated slop – hallucinated facts, nonsense analysis and faked images – that are engulfing coverage of the Iran war. Experts say it is wasting investigative time and risks atrocities being denied – as well as heralding alarming weaknesses as people increasingly rely on AI summaries for news and information.

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  • [–] 11 points 8 months ago

    Yeah I dont trust ai for any reason

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  • [–] 9 points 7 months ago (2 children)

    we asked AI about the AI generated image to check if it was AI generated

    we asked AI about the image to check to see if it was AI generated

    we asked AI about the image to verify claims of the context

    all of these have one fatal flaw that completely misses the point.

    we asked AI

    1000003242

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  • [–] 8 points 7 months ago (1 child)

    Yes, that flaw is exactly what they're pointing out. They're saying, "we asked AI a question we knew the answer to and it was wrong."

    Leaving out the context is bad.

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  • [–] 7 points 7 months ago (3 children)

    Does it matter? It happened, and has been widely confirmed.

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  • [–] 10 points 8 months ago
    [–] -3 points 7 months ago (4 children)

    I just tested it, gemini says:

    What it depicts: The photo shows an aerial view of newly dug graves at a cemetery in Minab, Iran. These graves were prepared in early March 2026 for the victims of the February 28, 2026, airstrike on the Shajareh Tayyebeh Primary School.

    Ironic that an article about ai misinformation is itself misinformation. Maybe it was written by ai?

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  • [–] 16 points 7 months ago (2 children)

    You can give Gemini the exact same prompt and context 100 different times and you might get 95 very similar responses and 5 wildly different responses.

    I don't understand why people think a random text generator can ever be relied on for truth. It has no concept of truth. It is a random text generator. A pretty consistent one, but still fucking random. It has no intelligence. It is not intelligent. Stop acting like it is. Its conclusions are meaningless. They do not contain actual meaning. They are random.

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  • [–] 1 point 7 months ago (1 child)

    You can force it to return the same answer each time. There’s a setting when you call the “temperature”, and it’s basically “how much leeway do you want to give it for ‘creativity’”. But then it’s more apparent when it has no clue, so they change the temperature to allow for different answers and combine that with RAG to get better overall answers.

    It’s still a fancy autocomplete, but the “why” can be interesting

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  • [–] 3 points 7 months ago

    I agree, they're an extremely interesting technology. But laypeople are not going to understand why they're interesting no matter how carefully you phrase it, I'm not trying to convince people who understand what they are that they're not interesting and that they don't have real potential and real applications.

    I am trying to convince laypeople that they're being misled (for profit) into believing these things are intelligent, can do things humans can do, and are capable of making decisions. I would rather have laypeople believing these are stupid atrocities against humanity (which is, in the current situation, closer to the truth) than I would bother trying to explain to them why it is still an interesting technology. If it ends up being completely banned (ha, fat chance) I'm not going to cry for it. I would rather have humanity protected from this vile, dishonest, and dangerous schemes they are using this technology for, even if it comes at the cost of ever being able to use this technology for good. My interest in it does not outweigh the harm that people are choosing to do with it.

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  • 1 comment in c/technology@beehaw.org view that post
    [–] 43 points 7 months ago

    saved you a click, the image is real:

    The cemetery image, it turns out, is authentic. Researchers have cross referenced the photo of the site with satellite images that confirm its location, and it can be cross-referenced again with dozens more images taken of the same site from slightly different angles, and again with video footage – none of which experts say show signs of tampering or digital manipulation. The “factchecks” by Gemini and Grok are just one example of a tidal wave of AI-generated slop – hallucinated facts, nonsense analysis and faked images – that are engulfing coverage of the Iran war. Experts say it is wasting investigative time and risks atrocities being denied – as well as heralding alarming weaknesses as people increasingly rely on AI summaries for news and information.

    The story is about how LLMs are unreliable at fact-checking, misleading people into thinking the image was fake.

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