AI models don’t resynthesize their training data. They use their training data to determine parameters which enable them to predict a response to an input.
Consider a simple model (too simple to be called AI but really the underlying concepts are very similar) - a linear regression. In linear regression we produce a model which follows a straight line through the “middle” of our training data. We can then use this to predict values outside the range of the original data - albeit will less certainty about the likely error.
In the same way, an LLM can give answers to questions that were never asked in its training data - it’s not taking that data and shuffling it around, it’s synthesising an answer by predicting tokens. Also similarly, it does this less well the further outside the training data you go. Feed them the right gibberish and it doesn’t know how to respond. ChatGPT is very good at dealing with nonsense, but if you’ve ever worked with simpler LLMs you’ll know that typos can throw them off notably… They still respond OK, but things get weirder as they go.
Now it’s certainly true that (at least some) models were trained on CSAM, but it’s also definitely possible that a model that wasn’t could still produce sexual content featuring children. It’s training set need only contain enough disparate elements for it to correctly predict what the prompt is asking for. For example, if the training set contained images of children it will “know” what children look like, and if it contains pornography it will “know” what pornography looks like - conceivably it could mix these two together to produce generated CSAM. It will probably look odd, if I had to guess? Like LLMs struggling with typos, and regression models being unreliable outside their training range, image generation of something totally outside the training set is going to be a bit weird, but it will still work.
None of this is to defend generating AI CSAM, to be clear, just to say that it is possible to generate things that a model hasn’t “seen”.
Not all models use the same training sets, and not all future models would either.
Generating images of humans of different ages doesn’t require having images of that type for humans of all ages.
Like, no one is arguing your link. Some models definitely used training data with that, but your claim that the type of image discussed is “novel” simply isn’t accurate to how these models can combine concepts
Dunno, probably because they didn’t knowingly train it on CSAM - maybe because it’s difficult to prove what actually goes into neural network configuration so it’s unclear how strongly weighted it is… and lastly, maybe because this stuff is so cloaked in obscurity and proprietaryness that nobody is confident how such a case would go.
And don’t understand how generative AI combines existing concepts to synthesize images - it doesn’t have the ability to create novel concepts.
Imagine someone asks you to shoop up some pr0n showing Donald Duck and Darth Vader. You’ve probably never seen that combination in your “training set” (past experience) but it doesn’t exactly take creating novel concepts to fulfill the request. It’s just combining existing ones. Web search on “how stable diffusion works” finds some promising looking articles. I read one a while back and found it understandable. Stable Diffusion was the first of these synthesis programs but the newer ones are just bigger and fancier versions of the same thing.
Of course idk what the big models out there are actually trained on (basically everything they can get, probably not checked too carefully) but just because some combination can be generated in the output doesn’t mean it must have existed in the input. You can test that yourself easily enough, by giving weird and random enough queries.
No, you’re quite right that the combination didn’t need to exist in the input for an output to be generated - this shit is so interesting because you can throw stuff like “A medieval castle but with Iranian architecture with a samurai standing on the ramparts” at it and get something neat out. I’ve leveraged AI image generation for visual D&D references and it’s excellent at combining comprehended concepts… but it can’t innovate a new thing - it excels at mixing things but it isn’t creative or novel. So I don’t disagree with anything you’ve said - but I’d reaffirm that it currently can make CSAM because it’s trained on CSAM and, in my opinion, it would be unable to generate CSAM (at least to the quality level that would decrease demand for CSAM among pedos) without having CSAM in the training set.
it currently can make CSAM because it’s trained on CSAM
That is a non sequitur. I don’t see any reason to believe such a cause and effect relationship. The claim is at least falsifiable in principle though. Remove whatever CSAM found its way into the training set, re-run the training to make a new model, and put the same queries in again. I think you are saying that the new model should be incapable of producing CSAM images, but I’m extremely skeptical, as your medieval castle example shows. If you’re now saying the quality of the images might be subtly different, that’s the no true Scotsman fallacy and I’m not impressed. Synthetic images in general look impressive but not exactly real. So I have no idea how realistic the stuff this person was arrested for was.
It is amazing how Lemmy can usually be such a well informed audience but for some reason when it comes to AI people simply refuse to acknowledge that it was trained on CSAM https://cyber.fsi.stanford.edu/news/investigation-finds-ai-image-generation-models-trained-child-abuse
And don’t understand how generative AI combines existing concepts to synthesize images - it doesn’t have the ability to create novel concepts.
AI models don’t resynthesize their training data. They use their training data to determine parameters which enable them to predict a response to an input.
Consider a simple model (too simple to be called AI but really the underlying concepts are very similar) - a linear regression. In linear regression we produce a model which follows a straight line through the “middle” of our training data. We can then use this to predict values outside the range of the original data - albeit will less certainty about the likely error.
In the same way, an LLM can give answers to questions that were never asked in its training data - it’s not taking that data and shuffling it around, it’s synthesising an answer by predicting tokens. Also similarly, it does this less well the further outside the training data you go. Feed them the right gibberish and it doesn’t know how to respond. ChatGPT is very good at dealing with nonsense, but if you’ve ever worked with simpler LLMs you’ll know that typos can throw them off notably… They still respond OK, but things get weirder as they go.
Now it’s certainly true that (at least some) models were trained on CSAM, but it’s also definitely possible that a model that wasn’t could still produce sexual content featuring children. It’s training set need only contain enough disparate elements for it to correctly predict what the prompt is asking for. For example, if the training set contained images of children it will “know” what children look like, and if it contains pornography it will “know” what pornography looks like - conceivably it could mix these two together to produce generated CSAM. It will probably look odd, if I had to guess? Like LLMs struggling with typos, and regression models being unreliable outside their training range, image generation of something totally outside the training set is going to be a bit weird, but it will still work.
None of this is to defend generating AI CSAM, to be clear, just to say that it is possible to generate things that a model hasn’t “seen”.
Not all models use the same training sets, and not all future models would either.
Generating images of humans of different ages doesn’t require having images of that type for humans of all ages.
Like, no one is arguing your link. Some models definitely used training data with that, but your claim that the type of image discussed is “novel” simply isn’t accurate to how these models can combine concepts
In that case, why haven’t the people who made the AI models been arrested?
Dunno, probably because they didn’t knowingly train it on CSAM - maybe because it’s difficult to prove what actually goes into neural network configuration so it’s unclear how strongly weighted it is… and lastly, maybe because this stuff is so cloaked in obscurity and proprietaryness that nobody is confident how such a case would go.
Imagine someone asks you to shoop up some pr0n showing Donald Duck and Darth Vader. You’ve probably never seen that combination in your “training set” (past experience) but it doesn’t exactly take creating novel concepts to fulfill the request. It’s just combining existing ones. Web search on “how stable diffusion works” finds some promising looking articles. I read one a while back and found it understandable. Stable Diffusion was the first of these synthesis programs but the newer ones are just bigger and fancier versions of the same thing.
Of course idk what the big models out there are actually trained on (basically everything they can get, probably not checked too carefully) but just because some combination can be generated in the output doesn’t mean it must have existed in the input. You can test that yourself easily enough, by giving weird and random enough queries.
No, you’re quite right that the combination didn’t need to exist in the input for an output to be generated - this shit is so interesting because you can throw stuff like “A medieval castle but with Iranian architecture with a samurai standing on the ramparts” at it and get something neat out. I’ve leveraged AI image generation for visual D&D references and it’s excellent at combining comprehended concepts… but it can’t innovate a new thing - it excels at mixing things but it isn’t creative or novel. So I don’t disagree with anything you’ve said - but I’d reaffirm that it currently can make CSAM because it’s trained on CSAM and, in my opinion, it would be unable to generate CSAM (at least to the quality level that would decrease demand for CSAM among pedos) without having CSAM in the training set.
That is a non sequitur. I don’t see any reason to believe such a cause and effect relationship. The claim is at least falsifiable in principle though. Remove whatever CSAM found its way into the training set, re-run the training to make a new model, and put the same queries in again. I think you are saying that the new model should be incapable of producing CSAM images, but I’m extremely skeptical, as your medieval castle example shows. If you’re now saying the quality of the images might be subtly different, that’s the no true Scotsman fallacy and I’m not impressed. Synthetic images in general look impressive but not exactly real. So I have no idea how realistic the stuff this person was arrested for was.