While this comment is getting a handful of downvotes (probably for its sarcastic tone), I do want to add something here: Personally, I think the best way to learn is by doing, and there are a lot of really great tutorials on things you can do with deep learning (yes, you can find them by doing a google search), however I found that I was really taxing my laptop trying to do some of the tutorials for instance from sentdex... BUT it turns out that Google has a research platform you can use for FREE that gives you access to GPUs and TPUs specifically for the purposes of doing ML tasks... So check out that channel, and then check out https://colab.research.google.com/ for a great platform to start putting your code together!

Even more fascinating would be if such a test could be developed, if it is then further possible to train an AI to be able to pass the test. As with all questions of these nature, the real end-game is like the Turing Test. If the AI can be trained so well that no (blind) test can differentiate between the AI and a Human, what are the implications of that?

So, I AM a programmer, and I've got, lets say, a bit more than basic knowledge of machine learning... We'll leave it at that, but suffice it to say I find recent models, especially stable diffusion and GPT, remarkable. I also think its interesting to wonder about how one might differentiate AI from any other abstract type of art...

So a while back, I wrote a script (actually, I wrote several of them, but I digress) that tests certain kinds of data sets for compliance with Benfords Law in a few different ways... For almost any arbitrary set of binary coded data, I can examine the bit values for compliance, but for things like ascii text, it is interesting to also look at the specific ascii coded values (so for instance, the leading letter "A" might appear roughly twice as often as the letter "B" or "E", depending on how you want to encapsulate the law, but the idea being that the statistical appearance of the model should be roughly the same for all real world data, and it would show anomalies if that data was artificially tampered with). For things like graphics, I can enumerate pixel/color values, and sure enough, the same pattern holds true. For instance, if you take a picture with a DSLR camera, the raw data encoded by that picture will comply with Benfords Law. If that picture has been touched up after the fact, for instance in Photoshop or GIMP, it is less likely to comply with Benfords Law.

You might wonder how this is useful in analyzing AI data, and I don't have a [coherent] answer for you yet, but I have a hypothesis, which is basically that when looked at the right way, thoeretically, AI data should be differentiable from Human created data by virtue of the fact that one will adhere to Benfords Law more often than the other... How, I don't entirely know. The funny thing about that theory is that Human data is typically less compliant with the rule, it is typically natural, ordered data which is more compliant. I'm still working out how this rule might be applied in such a way that makes it easier to detect a difference, but I'm curious whether in the end that will show Humans to be more compliant or AI to be more compliant with the rule. Maybe it won't be able to detect the difference. Anyway, its a side project that I'll probably dedicate some time to when I'm not up to my eyeballs in other things.