Google apologizes for “missing the mark” after Gemini generated racially diverse Nazis
www.theverge.com
It acknowledged ‘inaccuracies’ in historical prompts.

Google apologizes for ‘missing the mark’ after Gemini generated racially diverse Nazis::Google says it’s aware of historically inaccurate results for its Gemini AI image generator, following criticism that it depicted historically white groups as people of color.

  • @FooBarringtonEnglish
    -5•9 months ago

    I can ask AI models specific questions about knowledge it has, which it can correctly reply to. Excel sheets can’t do that.

    That’s not to say the knowledge is perfect - but we know that AI models contain partial world models. How do you differentiate that from “cognizance”?

    • @rambarooEnglish
      8•9 months ago

      Omg give me a break with this complete nonsense. LLMs are not an intelligence. They are language processors. They do not “think” about anything and don’t have any level of self awareness that implies cognizance. A cognizant ai would have recognized that the Nazis it was creating looked historically inaccurate, based on its training data. But guess what, it didn’t do that because it’s fundamentally incapable of thinking about anything.

      So sick of reading this amateurish bullshit on social media.

      • @[email protected]English
        2•9 months ago

        This gets the question…how do we think? Are we not just language (and other inputs as well) processors? I’m not sure the answer is “no.”

        I also listened to an interesting podcast, I believe it was this American life or some other npr one, about whether ai has intelligence. To avoid the just “compressed knowledge” they came up with questions that the ai almost certainly would not have found in the web. Early ai models were clearly just predicting the next word, and the example was asking it to stack a list of objects. And it just said to stack them one on top of another, in a way that would no way be stable.

        However when they asked a new model to do the same, with the stipulation that it explain it’s reasoning, it stacked the objects in a way that would likely be stable. Even noting that the nail on top should be placed on the head so it doesn’t roll around, and laying eggs down in a grid between a book and a plank of wood so they wouldn’t roll out.

        Another experiment they did was take a language model and asked it to use some obscure programming language to draw a picture of a unicorn. Now this is a language model, not trained on any images.

        And you know what it did? It produced a picture of a unicorn. Just in rough shapes, but even when they moved the horn and flipped it around, it was able to put it back. Without even ever seeing a unicorn, or anything even, it was able to draw a picture of one.

        I don’t think the answer is as simple and clear as you want it to be. And the fact that it “fucked up” on a vague prompt doesn’t really prove anything. Even humans do stupid shit like this if they learn something incorrectly.

      • @FooBarringtonEnglish
        -5•9 months ago

        A cognizant ai would have recognized that the Nazis it was creating looked historically inaccurate, based on its training data.

        Do you understand that the model is specifically prompted to create “historically inaccurate looking Nazis”? Models aren’t supposed to inject their own guidelines and rules, they simply produce output for your input. If you tell it to produce black Hitler it will produce a black Hitler. Do you expect the model to instead produce white Hitler?