I don't know which kinds of AIs you've worked on but my description (although using the incorrect terms) is certainly valid. I've described how GANs work, I'm not pulling this out of thin air 🤷♂️
The generative network generates candidates while the discriminative network evaluates them. The contest operates in terms of data distributions. Typically, the generative network learns to map from a latent space to a data distribution of interest, while the discriminative network distinguishes candidates produced by the generator from the true data distribution. The generative network's training objective is to increase the error rate of the discriminative network (i.e., "fool" the discriminator network by producing novel candidates that the discriminator thinks are not synthesized (are part of the true data distribution)).
So yes, whichever method you design which allows the product of an AI to be detected can be used by a discriminative network for a GAN, which defeats the purpose of designing the method to begin with
If trained and written several different kinds of AI including neural nets and LLMs.
This isn’t even close to how LLMs work, let alone how AI works.
You’re literally describing how to overfit model data which is the exact opposite of what you want to do.
Do everyone else a favor next time and don’t try to armchair.
I don't know which kinds of AIs you've worked on but my description (although using the incorrect terms) is certainly valid. I've described how GANs work, I'm not pulling this out of thin air 🤷♂️
Wikipedia
So yes, whichever method you design which allows the product of an AI to be detected can be used by a discriminative network for a GAN, which defeats the purpose of designing the method to begin with
Apologies for the ignorant comment, while GANs have lost popularity in favor of Diffusion models, they’re still used more or less.
Been having a really shit day and I took it out on you - that wasn’t fair
No worries, I appreciate your apology