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
Because AIs are (partly) trained by making AI detectors. If an AI can be distinguished from a natural intelligence, it’s not good enough at emulating intelligence. If an AI detector can reliably distinguish AI from humans, the AI companies will use that detector to train their next AI.
I’m not sure I’m following your argument here - you keep switching between talking about AI and AI detectors. Each of the below are just numbered according to the order of your prior responses as sentences:
Can you provide any articles or blog posts from AI companies for this or point me in the right direction?
Agreed
Right…
I’m having trouble finding your support for your claim
See Generative Adversarial Network (GAN). Basically, making new AI detectors will always be harder than beating current ones. AI detectors have to somehow find a new “tell”, the target AI need only train itself on the output of the detector to figure out how to trick it.
give the output a score based on how much it looks like real human text
adjust the parameters slightly to improve the score
repeat
Step #2 is also exactly what an “AI detector” does. If someone is able to write code that reliably distinguishes between AI and human text, then AI developers would plug it in to that training step in order to improve their AI.
In other words, if some theoretical machine perfectly “knows” the difference between generated and human text, then the same machine can also be used to make text that is indistinguishable from human text.
Exactly right, I mentioned this in a comment elsewhere but basically we can’t have our cake and eat it too.
We can’t have a perfect NL impersonator that can also be detected as not NL. (Best case, obviously things arent perfect for any AI model so technically detecting those mistakes could be used to help identify perhaps, but who’s to say what the FP rate would look like!)
Ultimately the cat is out of the bag and I’m not quite sure there is anything we can do now. Ultimately some smart fingerprinting solution would be ideal but I just don’t know how feasible that would remain.
Edit: source: I took a few 600 level ai classes in college and have made several of my own of varying types and what not
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.
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
Because AIs are (partly) trained by making AI detectors. If an AI can be distinguished from a natural intelligence, it’s not good enough at emulating intelligence. If an AI detector can reliably distinguish AI from humans, the AI companies will use that detector to train their next AI.
I’m not sure I’m following your argument here - you keep switching between talking about AI and AI detectors. Each of the below are just numbered according to the order of your prior responses as sentences:
I’m having trouble finding your support for your claim
See Generative Adversarial Network (GAN). Basically, making new AI detectors will always be harder than beating current ones. AI detectors have to somehow find a new “tell”, the target AI need only train itself on the output of the detector to figure out how to trick it.
ChatGPT isn’t a GAN network.
At a very high level, training is something like:
Step #2 is also exactly what an “AI detector” does. If someone is able to write code that reliably distinguishes between AI and human text, then AI developers would plug it in to that training step in order to improve their AI.
In other words, if some theoretical machine perfectly “knows” the difference between generated and human text, then the same machine can also be used to make text that is indistinguishable from human text.
Exactly right, I mentioned this in a comment elsewhere but basically we can’t have our cake and eat it too.
We can’t have a perfect NL impersonator that can also be detected as not NL. (Best case, obviously things arent perfect for any AI model so technically detecting those mistakes could be used to help identify perhaps, but who’s to say what the FP rate would look like!)
Ultimately the cat is out of the bag and I’m not quite sure there is anything we can do now. Ultimately some smart fingerprinting solution would be ideal but I just don’t know how feasible that would remain.
Edit: source: I took a few 600 level ai classes in college and have made several of my own of varying types and what not
deleted by creator
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