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.

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.

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.

At a very high level, training is something like:

• generate some output
• 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.

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.

It’s not even about diffusion models. Adversarial networks are basically obsolete