• sebi@lemmy.world
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      1 year ago

      Because generative Neural Networks always have some random noise. Read more about it here

        • PetDinosaurs@lemmy.world
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          1 year ago

          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.

            • PetDinosaurs@lemmy.world
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              1 year ago

              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.

              • stevedidWHAT@lemmy.world
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                1 year ago

                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!