Because you’re training a detector on something that is designed to emulate regular languages closest possible, and human speech has so much incredible variability that it’s almost impossible to identify if someone or something has been written by an AI.
You can detect maybe your typical generic chat GPT type outputs, but you can characterize a conversation with chat GPT or any of the other much better local models (privacy and control are aspects which make them better) and after doing that you can get radically human seeming outputs that are totally different from anything chat GPT will output.
In short, given a static block of text it’s going to be nearly impossible to detect if it’s coming from an AI. It’s just too difficult to problem, and if you’re going to solve it it’s going to be immediately obsolete the next time someone fine tunes their own model
Yeah this makes a lot of sense considering the vastness of language and it’s imperfections (English I’m mostly looking at you, ya inbred fuck)
Are there any other detection techniques that you know of? Wb forcing AI models to have a signature that is guaranteed to be indentifiable, permanent, and unique for each tuning produced? It’d have to be not directly noticeable but easy to calculate in order to prevent any “distractions” for the users.
The output is pure text so you would have to hide the signature in the response itself. On top of being useless since most users slightly modify the text after receiving it, it would probably have a negative effect on the quality. It’s also insanely complicated to train that kind of behavior into an llm.
Your implementation of my concept might be useless, but that doesn’t mean the concept is.
One possible solution would be to look at how responses are structured, letter frequencies, etc. The flexibility/ambiguous nature natural language is that you can word things in many many different ways which allows for some creative meta techniques to accomplish a fingerprint.
Cats out of the bag my friend. Just like the nuke, the ideas are always out there. Once it’s been discovered and shared that’s that.
We can huff and puff and come up with all the cute little laws we want but the fact of the matter is we know the recipe now. All we can do is dive deeper into the technology to understand it even better, make new findings and adapt as we always do.
Not sure if you’re disagreeing or agreeing with me. What I mean is, if a LLM’s output is in practice indistinguishable from human output, fingerprinting some popular services just creates a false sense of security, since we know malicious agents will for sure not fingerprint it.
Isn’t it just better to let humanity accept that a LLM’s output is identical to a person’s and always be skeptical?
forcing AI models to have a signature that is guaranteed to be indentifiable, permanent, and unique for each tuning produced
Either AI remains entirely in the hands of fucks like open AI or this is impossible and easily removed. AI should be a free common use tool, not an extension of corporate control.
It’s no different than owning your computer. Something is absolutely a central and productivity boosting is artificial intelligence should not be kept in the hands of the few.
The only way that it could be is through government intervention, you don’t need an anarchist to be against an open AI monopoly.
Gans are part of the NN toolbox, like cnns and rnns and such.
Basically all commercial algorithms (not just nns, everything) are what I like to call “hybrid” methods, which means keep throwing different tools at it until things work well enough.
It doesn’t matter. Even the training process makes it pretty much impossible to tell these things apart.
And if we do find a way to distinguish, we’ll immediately incorporate that into the model design in a GAN like manner, and we’ll soon be unable to distinguish again.
Which is why hardcoded fingerprints/identifications are required to identify the individual as a speaker rather than as an AI vs Human. Which is what we’re ultimately agreeing on here outside of the pedantics of the article and scientific findings:
Trying to find the model who is supposed to be human as an AI is counter intuitive. They’re direct opposites if one works, both can’t be exist in this implementation.
The hard part will obviously be making sure that such a “fingerprint” wouldn’t be removable which will take some wild math and out of the box thinking I’m sure.
they never did, they never will.
Why tho or are you trying to be vague on purpose
Because you’re training a detector on something that is designed to emulate regular languages closest possible, and human speech has so much incredible variability that it’s almost impossible to identify if someone or something has been written by an AI.
You can detect maybe your typical generic chat GPT type outputs, but you can characterize a conversation with chat GPT or any of the other much better local models (privacy and control are aspects which make them better) and after doing that you can get radically human seeming outputs that are totally different from anything chat GPT will output.
In short, given a static block of text it’s going to be nearly impossible to detect if it’s coming from an AI. It’s just too difficult to problem, and if you’re going to solve it it’s going to be immediately obsolete the next time someone fine tunes their own model
Yeah this makes a lot of sense considering the vastness of language and it’s imperfections (English I’m mostly looking at you, ya inbred fuck)
Are there any other detection techniques that you know of? Wb forcing AI models to have a signature that is guaranteed to be indentifiable, permanent, and unique for each tuning produced? It’d have to be not directly noticeable but easy to calculate in order to prevent any “distractions” for the users.
The output is pure text so you would have to hide the signature in the response itself. On top of being useless since most users slightly modify the text after receiving it, it would probably have a negative effect on the quality. It’s also insanely complicated to train that kind of behavior into an llm.
Your implementation of my concept might be useless, but that doesn’t mean the concept is.
One possible solution would be to look at how responses are structured, letter frequencies, etc. The flexibility/ambiguous nature natural language is that you can word things in many many different ways which allows for some creative meta techniques to accomplish a fingerprint.
The idea itself is valid, but wouldn’t that just make it more dangerous when malicious agents use the technology without fingerprinting?
Cats out of the bag my friend. Just like the nuke, the ideas are always out there. Once it’s been discovered and shared that’s that.
We can huff and puff and come up with all the cute little laws we want but the fact of the matter is we know the recipe now. All we can do is dive deeper into the technology to understand it even better, make new findings and adapt as we always do.
Not sure if you’re disagreeing or agreeing with me. What I mean is, if a LLM’s output is in practice indistinguishable from human output, fingerprinting some popular services just creates a false sense of security, since we know malicious agents will for sure not fingerprint it.
Isn’t it just better to let humanity accept that a LLM’s output is identical to a person’s and always be skeptical?
Either AI remains entirely in the hands of fucks like open AI or this is impossible and easily removed. AI should be a free common use tool, not an extension of corporate control.
Owning the means of AI production huh? I guess anarchists will win after all.
It’s no different than owning your computer. Something is absolutely a central and productivity boosting is artificial intelligence should not be kept in the hands of the few.
The only way that it could be is through government intervention, you don’t need an anarchist to be against an open AI monopoly.
deleted by creator
Agreed, such power should belong to everyone or has yet to be discovered. Even Oppenheimer knew, once the cats out of the bag…
Because generative Neural Networks always have some random noise. Read more about it here
Isn’t that article about GANs?
Isn’t GPT not a GAN?
It almost certainly has some gan-like pieces.
Gans are part of the NN toolbox, like cnns and rnns and such.
Basically all commercial algorithms (not just nns, everything) are what I like to call “hybrid” methods, which means keep throwing different tools at it until things work well enough.
The findings were for GAN models, not GAN like components though.
It doesn’t matter. Even the training process makes it pretty much impossible to tell these things apart.
And if we do find a way to distinguish, we’ll immediately incorporate that into the model design in a GAN like manner, and we’ll soon be unable to distinguish again.
Which is why hardcoded fingerprints/identifications are required to identify the individual as a speaker rather than as an AI vs Human. Which is what we’re ultimately agreeing on here outside of the pedantics of the article and scientific findings:
Trying to find the model who is supposed to be human as an AI is counter intuitive. They’re direct opposites if one works, both can’t be exist in this implementation.
The hard part will obviously be making sure that such a “fingerprint” wouldn’t be removable which will take some wild math and out of the box thinking I’m sure.
Tough problem!
It’s not even about diffusion models. Adversarial networks are basically obsolete