They wouldn't be able to generate it had there been none in the training data, so I assume the labelling and verification systems you talk about aren't very good.
That's not accurate. The systems are designed to generate previously unseen concepts or images by combining known concepts.
It's why it can give you an image of a pony using a hangglider, despite never having seen that. It knows what ponies look like, and it knows what hanggliding looks like, so it can find a way to put both into the image. Where it doesn't know, it will make stuff up from what it does know, often requiring potentially very detailed user explanation to describe how a horse would fit in a hangglider, or that it shouldn't have a little person sticking out of it's back.
I think it would just create adults naked with children's faces unless it actually had CSAM... Which it probably does have.
Again, that's not how it works.
Could you hypothetically describe csam without describing an adult with a child's head, or specifying that it's a naked child?
That's what a person trying to generate csam would need to do, because it doesn't have those concepts.
If you just asked it directly, like I said "horse flying a hangglider" before, you would get what you describe because it's using the only "naked" it knows.
You would need to specifically ask it to demphasize adult characteristics and emphasize child characteristics.
That doesn't mean that it was trained on that content.
For context from the article:
The DOJ alleged that evidence from his laptop showed that Anderegg "used extremely specific and explicit prompts to create these images," including "specific 'negative' prompts—that is, prompts that direct the GenAI model on what not to include in generated content—to avoid creating images that depict adults."
They wouldn't be able to generate it had there been none in the training data, so I assume the labelling and verification systems you talk about aren't very good.
That's not accurate. The systems are designed to generate previously unseen concepts or images by combining known concepts.
It's why it can give you an image of a pony using a hangglider, despite never having seen that. It knows what ponies look like, and it knows what hanggliding looks like, so it can find a way to put both into the image. Where it doesn't know, it will make stuff up from what it does know, often requiring potentially very detailed user explanation to describe how a horse would fit in a hangglider, or that it shouldn't have a little person sticking out of it's back.
I think it would just create adults naked with children's faces unless it actually had CSAM... Which it probably does have.
Again, that's not how it works.
Could you hypothetically describe csam without describing an adult with a child's head, or specifying that it's a naked child?
That's what a person trying to generate csam would need to do, because it doesn't have those concepts.
If you just asked it directly, like I said "horse flying a hangglider" before, you would get what you describe because it's using the only "naked" it knows.
You would need to specifically ask it to demphasize adult characteristics and emphasize child characteristics.
That doesn't mean that it was trained on that content.
For context from the article: