If AI has the means to generate inappropriate material, then that means the developers have allowed it to train from inappropriate material.
That’s not how generative AI works. It’s capable of creating images that include novel elements that weren’t in the training set.
Go ahead and ask one to generate a bonkers image description that doesn’t exist in its training data and there’s a good chance it’ll be able to make one for you. The classic example is an “avocado chair”, which an early image generator was able to produce many plausible images of despite only having been trained on images of avocados and chairs. It understood the two general concepts and was able to figure out how to meld them into a common depiction.
The trainers didn’t train the image generator on images of Mr. Bean hugging Pennywise, and yet it’s able to generate images of Mr. Bean hugging Pennywise. Yet you insist that it can’t generate inappropriate images without having been specifically trained on inappropriate images? Why is that suddenly different?
First, you need to figure out exactly what it is that the “blame” is for.
If the problem is the abuse of children, well, none of that actually happened in this case so there’s no blame to begin with.
If the problem is possession of CSAM, then that’s on the guy who generated them since they didn’t exist at any point before then. The trainers wouldn’t have needed to have any of that in the training set so if you want to blame them you’re going to need to do a completely separate investigation into that, the ability of the AI to generate images like that doesn’t prove anything.
If the problem is the creation of CSAM, then again, it’s the guy who generated them.
If it’s the provision of general-purpose art tools that were later used to create CSAM, then sure, the AI trainers are in trouble. As are the camera makers and the pencil makers, as I mentioned sarcastically in my first comment.
AI only knows what has gone through it’s training data, both from the developers and the end users.
Yes, and as I’ve said repeatedly, it’s able to synthesize novel images from the things it has learned.
If you train an AI with pictures of green cars and pictures of red apples, it’ll be able to figure out how to generate images of red cars and green apples for you.
It’s possible to legally photograph young people. Completely ordinary legal photographs of young people exist, from which an AI can learn the concept of what a young person looks like.
Is an image of a child inappropriate? Fully clothed, nothing going on.
Is the image of an adult engaging in sexual activity inappropriate?
Based on those two concepts, it can generate inappropriate child sexual imagery.
You may have done OCR work a while ago, but that is not the same type of machine learning that goes into typical generative AI systems in the modern world. It very much seems as though you are profoundly misunderstanding how this technology operates if you think it can’t generate a novel combination of previously trained concepts without a prior example.
3,226 suspected images out of 5.8 billion. About 0.00006%. And probably mislabeled to boot, or it would have been caught earlier. I doubt it had any significant impact on the model’s capabilities.
That’s not how generative AI works. It’s capable of creating images that include novel elements that weren’t in the training set.
Go ahead and ask one to generate a bonkers image description that doesn’t exist in its training data and there’s a good chance it’ll be able to make one for you. The classic example is an “avocado chair”, which an early image generator was able to produce many plausible images of despite only having been trained on images of avocados and chairs. It understood the two general concepts and was able to figure out how to meld them into a common depiction.
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The trainers didn’t train the image generator on images of Mr. Bean hugging Pennywise, and yet it’s able to generate images of Mr. Bean hugging Pennywise. Yet you insist that it can’t generate inappropriate images without having been specifically trained on inappropriate images? Why is that suddenly different?
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First, you need to figure out exactly what it is that the “blame” is for.
If the problem is the abuse of children, well, none of that actually happened in this case so there’s no blame to begin with.
If the problem is possession of CSAM, then that’s on the guy who generated them since they didn’t exist at any point before then. The trainers wouldn’t have needed to have any of that in the training set so if you want to blame them you’re going to need to do a completely separate investigation into that, the ability of the AI to generate images like that doesn’t prove anything.
If the problem is the creation of CSAM, then again, it’s the guy who generated them.
If it’s the provision of general-purpose art tools that were later used to create CSAM, then sure, the AI trainers are in trouble. As are the camera makers and the pencil makers, as I mentioned sarcastically in my first comment.
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Ha.
Yes, and as I’ve said repeatedly, it’s able to synthesize novel images from the things it has learned.
If you train an AI with pictures of green cars and pictures of red apples, it’ll be able to figure out how to generate images of red cars and green apples for you.
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It’s possible to legally photograph young people. Completely ordinary legal photographs of young people exist, from which an AI can learn the concept of what a young person looks like.
Is an image of a child inappropriate? Fully clothed, nothing going on.
Is the image of an adult engaging in sexual activity inappropriate?
Based on those two concepts, it can generate inappropriate child sexual imagery.
You may have done OCR work a while ago, but that is not the same type of machine learning that goes into typical generative AI systems in the modern world. It very much seems as though you are profoundly misunderstanding how this technology operates if you think it can’t generate a novel combination of previously trained concepts without a prior example.
The trainers taught it what Mr. Bean looks like and what Pennywise looks like - it took those concepts and combined them to create your image. To make CSAM it was, unfortunately, trained on CSAM https://cyber.fsi.stanford.edu/news/investigation-finds-ai-image-generation-models-trained-child-abuse
3,226 suspected images out of 5.8 billion. About 0.00006%. And probably mislabeled to boot, or it would have been caught earlier. I doubt it had any significant impact on the model’s capabilities.