An ill advised undertaking IMO: Any attempt to build AI generated content detection systems is flawed, since the outputs of such a system may be used to train an even better fake data generator. This leads to an equilibrium state of digital uncertainty: nothing in the digital realm can be deemed as real anymore - only as digital.

AI detectors can't work in theory, but Pangram works well in practice. This is because the AI companies are not trying to fool Pangram. They have no incentive to; in fact the incentive goes the opposite way. They want to watermark their outputs to leave no doubt as to whether something was generated by their AI.

there's some truth to what one says but after hearing how Pangram works I'm pretty impressed at the methodology

I think the founder once said something like "well you can try to fight our detection and do a bunch of stuff, you just might find yourself with a pile of extremely garbled text that is less than useless".

How is that a downside exactly? If we can’t tell then we are no worse off.

It will create false trust in their score - giving people a false sense of certainty.

Adding to this, the most motivated actors to get around it or find an edge case, e.g. in politics, will use it to say "Look, of course the scene depicted in this compromising image actually happened, the detector said so".

> the outputs of such a system may be used to train an even better fake data generator.

[citation needed] - my understanding of how entropy works in real photos vs. synthetic images (AI or not) is that this is extremely difficult without severe degradation of image quality.

At least on gemini generated images vs real ones, it works surprisingly well.

Taking a photo of an AI generated image on my grubby laptop screen with my phone, moire and all, still got detected. So the signal it's using is pretty robust.

Interesting, it detected NovelAI stuff now. Looks like one can easily defeat it by having pixel-level modifications to make AI images to have characteristics of digital brush strokes and reducing "noise" in output. At least in my case for anime stuff.

Can’t these detectors be used in the training loop of models?

People have tried it, it doesn't work.

Which people? I'm no AI researcher but I believe basically all GANs employ a discriminator which basically to me it sounds like this is.

Modern diffusion models aren't trained like GANs. GANs failed to scale and generalize. Diffusion models do sometimes use Variational Auto-encoders (VAEs), which are a similar structure but which functions differently.

I don't know about Pangram's viability as a discriminator but I can tell you as someone who has recreationally trained GANs that they're really really finicky to train and training fails in all sorts of inscrutable ways. It wouldn't surprise me if even though in theory you could do a GAN-style form of training with Pangram, in practice it might actually be very difficult.

I'm very skeptical of AI detection and watermarking for text (especially short passages), but AI detection for images and videos seems much more plausible (for now at least).

I would love if Meta licensed this tech to use for everything on Facebook, Instagram, and Whatsapp. Including ads!

You should look at the data. Look at the actual measurements on the accuracy of Pangram's V4 detection model. Try to fool it. It's not easy at all.

It’s often inaccurate with short text. For example it has flagged text I wrote by hand with no AI input or editing.

But I agree it’s good for longer text of 1000+ words.

Why are you so skeptical of text detection?

Do you believe it generally doesn't work or do you believe that if you work at it you can evade them and therefore there's no point to it?

People often shit on AI detectors but Pangram's text AI detection has been very good, IME. Not perfect, but good enough to be useful in most circumstances.

My trouble with Pangram is that the calibration is totally off. It can tell you if there is a drop or an ocean of AI in the text you submit. The "X% of AI" message doesn't really mean much. You can test this yourself: Slowly add generated text to a human write paragraph and you'll see it hit "100% AI" quite quickly.

It analyzes in segments, and the percentage is of the segments, not of the words.

Yes but the segment sizes are huge and any amount of LLm generated sequences within that block cause all of it to be flagged as AI.

Good?

I don't really know why you would expect a "X% chance of containing AI" number to rise slowly or smoothly alongside the percentage of text that has a 100% chance of containing AI.

Because it isn't an "X% chance". It's an "X% of words".

What’s the use case for these things?

A few likely use cases,

1. Social media moderation. You want to limit the proliferation of this stuff because it annoys users.

2. Trades where authenticity matters, like journalism, law, education. You have to weed out the crap and people will try to deceive you.

eCommerce - you may want to reduce fraudulent listings, e.g. used goods, housing, collectibles/auctions...

Awesome research like this restores my faith in humanity.

just compress it? lossy should fool detection

It doesn't