[gabipurcaru]

As usual, journalists trying to explain what scientists do and misrepresenting the facts.

The paper mentions accuracy i.e. (true positives + true negatives) / total examples. And it's actually 100% accurate i.e. there are no false positives _or_ false negatives.

But the big caveats are:

1. this was tested only on 180 examples, which is a very very small dataset to draw conclusions on, and

2. this is obviously an adversarial space so any classifier will be obsolete with the next training run

I'm bearish on any attempt to distinguish real content vs. AI generated content (on any medium, text, image or anything else). This is an adversarial game and the AIs can incorporate your fancy algorithm to fool you better. In the end these projects only end up improving the AI models in terms of realism.

[marginalia_nu]

> 1. this was tested only on 180 examples, which is a very very small dataset to draw conclusions on, and

If you have 180 samples, and a >99% accuracy (meaning a single misprediction), that is a statistically significant conclusion with a p-value of 99.994%.