AI

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Artificial intelligence (AI) is intelligence demonstrated by machines, unlike the natural intelligence displayed by humans and animals, which involves consciousness and emotionality. The distinction between the former and the latter categories is often revealed by the acronym chosen.

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Netflix Uses Seemingly AI-Manipulated Images in True Crime Doc (futurism.com)

submitted 11 months ago by [email protected] to c/[email protected]

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[–] [email protected] 9 points 11 months ago* (last edited 11 months ago) (3 children)

I’ve said this since day one - we need a reliable way to identify AI generated content

If we fail to separate the two, or create safeguards like this, we’re in a lot more trouble than the destruction of the job market would be. And that’s saying something.

“Put it back in the box” isn’t a solution.

Banning the technology isn’t a solution.

We must face it for what it is, put our heads together, and create the solution.

Like we always have.

[–] [email protected] 14 points 11 months ago* (last edited 11 months ago)

If you ever create a reliable tool to identify AI images, you automatically provide learning data for AI to generate images that get past the AI detection.

[–] [email protected] 11 points 11 months ago

Unfortunately, an arms race has begun.

Said tool could be used to train new ai to avoid it.

[–] [email protected] 1 points 11 months ago (1 children)

You don't understand that tech; when making an AI model, you do code both a generator of whatever it is you want to make, as well as a "detector" which tells you whether or not the result is convincing.

Then you change the genertor slightly based of the results of the "detector"

You do that a few million times and then you have a correct AI model, the quality of which is dependant on both the quantity of training and the "detector".

If someone comes up with a really strong "detector", they will do work as intended for a few days/weeks, and then AIs will come on the market which will be able to fool the detector

[–] [email protected] 1 points 11 months ago* (last edited 11 months ago) (1 children)

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.

[–] [email protected] 2 points 11 months ago (1 children)

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)).

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

[–] [email protected] 3 points 11 months ago (1 children)

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

[–] [email protected] 1 points 11 months ago

No worries, I appreciate your apology