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Child sexual abuse survivor alleges Elon Musk’s AI chatbot used photos of her to generate new illegal images (www.theguardian.com)

submitted 1 week ago by MicroWave@lemmy.world to c/news@lemmy.world

13 comments fedilink hide all child comments

Musk denied he was aware Grok ever produced ‘any naked underage images’

A survivor of child sexual abuse has sued Elon Musk’s artificial intelligence company, alleging that its chatbot used pictures of her abuse to generate new illegal pornographic images that depict her.

“Using real images of Plaintiff and class members, Grok generated child pornography depicting Plaintiff and class members,” states the complaint, which was filed last week in a US district court in California.

Attorneys for the plaintiff, who is listed as Jane Doe in the case to protect her identity, accuse xAI of both generating CSAM of their client and of ingesting child sexual abuse images depicting her into the company’s datasets after new images were publicly posted.

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[–] partofthevoice@lemmy.zip 4 points 6 days ago

It’s more like if you kept the recipe to the photo, rather than the photo itself. The AI model, when trained, is having its “weights” updated — which means they’re tuning a very long set of lists of numbers.

For example, imagine:

(
  [0.028474, 0.274729, …],
  [0.827482, 0.283759, …],
  …*billions of lists
)

The numbers are actually random generated at first. Training can involve tricks like cutting out pieces of an image, then telling an AI to predict what goes in the empty space. Or reducing a photos quality, then telling the AI to increase its quality. The important part is that you distort the image while retaining the original as the answer key.

For each answer, you measure how correct/incorrect the model was. If it’s correct, you don’t do anything. If it’s incorrect, you do mathematical tricks to update those numbers.

Those numbers bias the model, all the way from random gibberish output to coherent output. So they go through the process I mentioned before billions of times, each time with different pictures / distortions, each time ever so slightly nudging those numbers until the model spits out coherent outputs.

Those numbers wind up being something like a recipe. Like if you’d stored the exact pixel configuration to a photo, but didn’t actually have the photo. Except this recipe tries to measure the general semantic relationships between words and images, such that it can generate images when given prompts. This recipe is less deterministic, but nonetheless it’s tainted with CP shit.