Raw LLM Responses

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Hello there, fellow ML human! Something I'd also like to add is that the calculation of that "huge database of numbers" is essentially done by reducing a piece of artwork to a collection of pixel values and a few labels ("girl", "anime", etc.), metaphorically turning the knobs on a machine to make the output *kind of* look like the original artwork, taking another piece of artwork with the same labels, turning the knobs again so the output *kind of* look like that artwork, but also the first artwork as well, until you'd gone through the entire repertoire of images within a label and have the knobs in a position to give the user an "average" representation of that label. And this is why the output of an AI will always look generic--it is literally trained to give a generic output! If you give a human artist the prompt "pretty girl", they will probably be thinking back to specific girls that they'd had a crush on, dated, or otherwise in their life. Maybe her hair was straight, maybe it's curly; maybe she was thin, maybe she wasn't; maybe she had glasses, braces, or neither; in any case, a hundred different human artists will give you a hundred different interpretations of what a pretty girl is to them But if you ask AI, all one hundred of these girls are the exact same, insofar as it is concerned. And what you will get is an "average" of all of these girls, which may look decent enough, but is bad art, because art is about expressing your unique viewpoint, and all of the uniqueness had been evened out by the nature of the training process.
youtube Viral AI Reaction 2025-03-31T04:3… ♥ 63
Coding Result
DimensionValue
Responsibilityai_itself
Reasoningdeontological
Policyliability
Emotionmixed
Coded at2026-04-27T06:26:44.938723
Raw LLM Response
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