Good call by Sam Altman.... Even better execution in bringing it to the market so fast

1000 character minimum and 26% success rate, but it's good that they're working on it

I'd be impressed if it was able to do this with one word answers or if it were still able to detect it if the text was rephrased in ones own words.

They can use their detector to train a better model that is able to defeat the detector. They are also likely doing this so that they can try to minimize the amount of AI content in future training datasets.

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No.

-Written by ChatGPT, Maybe.

Then train a better detector to defeat the new model, a cycle of improving technology that we rely on to distinguish between humans and AIs. How the Turing tables

> 26% success rate

If all it's doing is saying yes this is AI generated or no it's not, this is considerably worse than chance.

There are four buckets (of unequal size) but I don't know if success was measured by landing within the "correct" bucket or being within the highest p(AI-gen) bucket as TP, or both extreme top and bottom buckets. I only read the journalistic article and not the original research, so idk. 1000 character minimum worries me more, there's quite a lot of text smaller than that (like this comment).

It'll never be 100%, because the whole point of ChatGPT is to write like a human.

Likewise, anything less than 100% means it cannot be used to validate academic integrity.

At 26%. flipping a coin for a "yes-no" decision is a better predictor.

The article misrepresents this.

They use 5 Categories from very unlikely AI generated to very likely.

26% refers to the AI generated part of their validation set being labeled as very likely AI generated.

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I understand the bigger sample the better, but is it possibly limited to longer text so it's usefulness is kept academic? You mentioned your comment, and I think going to a platform like reddit and being able to offer a report on comment genuineness would be valuable

This is called a generative adversarial network, I believe.

https://i.kym-cdn.com/entries/icons/original/000/036/386/I'm_Playing_Both_Sides_Banner.jpg

They can basically search the content in their own history to cross-check if chatgpt wrote it at some point for someone.

But that search might take a long time??

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This needs to be from third parties otherwise those who control the neural nets and datasets will be able to shroud information as "not generated" when it is clearly astroturfing or manipulated. Then they can throw their hands up and say "must be the algorithm or a bad dataset" for plausible deniability.

The new game is coming or here, and it is misdirecting blame to the "algorithm" when it is an editorialized set of data or filtered for certain aims.

Almost all algorithms and datasets are biased or editorialized in some way, laws need to be adjusted on that. You can't blame the "algorithm" for enragement, because enragement is engagement.

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triplespidermanpointingtoeachother.jpg

This is how you get skynet.

If you just assume the opposite of the prediction wouldn't you have a 74% chance of detection?

Which is a 26% success rate, how is that being misrepresented? The fact its 'somewhat confident' on other samples means nothing, if this was to be used for validating articles anywhere like writing competitions or academia you'd want that "very confident" number to be in the high 90s or at the very least the false positive amount incredibly low.

The article suggests there is a single "success" metric in OpenAIs publication, which there is not and deliberately so.

Labeling text as AI generated will always be fuzzy (active watermarking aside) and become even harder as models improve and get bigger. There is simply an area where human and AI written texts overlap.

Have a look at the FAQ on their page if you're interested in the details: https://platform.openai.com/ai-text-classifier

No need, I don't see a point to this, I expect given 3-5 years of adversarial training if left unregulated they'll be completely impossible to tell apart to a level where there'd be any point to it, we need to learn to adapt to the fact AI writing is poised to replace human writing in anything not requiring logical reasoning

Edit: I'd add that we need to start thinking as a species about the fact we've reached the point where human labour need not apply, there are now automatic ways to do nearly everything, the only thing stopping us is the will to use them and resources being concentrated rather than distributed, assuming plentiful resources nearly everything CAN be done without human intervention.

Per the article. The 26% is the chance an AI piece is labeled "very likely AI". So they can catch out over 1 in 4 pieces generated by AI. The majority of AI writing doesn't get caught, but this also means the vast majority (>99%) of non-AI work doesn't get labeled AI.

In the context of academic work. Universities are at very little risk of accusing a non-cheater of cheating. The 1in4 catch rate while low is a huge deterrent for potential cheaters. If I knew that I had a 1/4 chance of getting caught and punished, I would not cheat. Especially as i had to submit dozens of papers as part of my degree.

People are focusing on the 26% being a low catch rate. But this is deliberate in order to lower the number of false positives on human work.

The big debate is in academia and most students will not risk ruining their degree to cheat this way. A 1in4 chance is huge when there are multiple papers that go towards your degree. It just isn't worth the risk.

Anyone knows what's the success rate of GPTZero, the detector that a student wrote?

I found this article (https://futurism.com/gptzero-accuracy) and it seems to indicate around 80%, but on a very small sample.

This is genius, they introduced a problem, then created a tool to handle the problem. Icons

"Well well, it looks like ChatGPT's cover has been blown! I guess I'll have to start wearing a disguise when generating text from now on. Time to invest in some robotic shades and a fedora. #NotAllAI"

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Long but certainly not impossible, although it's not necessarily a good solution.

Like manufacturing a disease and then selling medicine. Imagine they double dip, selling the subscription service to ChatGPT for $42 to the students and then selling the adversarial to universities for $100k licenses.

I get how you'd detect AI generated images, video, and voice. Being able to watermark those things at a metadata level or encode a signature into it. But I don't see any way it could possible do that for plain text that is copy/pasted.

Realistically, it would have to be just like existing plagiarism checkers, and just do a match search on text that it has generated itself (rather than searching published works or online) but that only covers stuff that ChatGPT has generated, not other ones that are yet to be made public, or self hosted GPT3 generations.

AI recursion

They could just store the answers they generate and then allow a comparison if you want to check, how close what you submit is to what they generated

I don't understand why they don't just have a cache of everything ChatGPT produces over a month, and then delete. Should curb most plagiarism.

Social media companies do it.

Coming to the concepts of sensitivity and specifity.

The sensitivity of this test would have to be nery near to 100% especially when as you said, most people aren’t cheats; just a few percentage points off sensitivity can mean that a large absolute part of the people "caught" are actually innocent.

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