I agree with the reasoning of this post, and believe it could be a valuable instrument to advance science.
There does exists scientific forecasting on sites like Manifold market and Hypermind, but those are not monetarily traded as sports betting is.
One problem I see with scientific prediction markets with money, is that it may create poor incentives (as you also discuss in your first foot note).
For example, if a group of scientists are convinced hypothesis A is true, and bet on it in a prediction market, they may publish biased papers supporting their hypothesis.
However, this doesn't seem to be a big problem in other betting markets, so with the right design I don't expect the negative effects to be too big.
Perhaps an advanced game engine could be used to create lots of simulations of piles of money. Like, if 100 3d objects of money are created (like 5 coins, 3 bills with 10 variations each (like folded etc), some fake money and other objects). Then these could be randomly generated into constellations. Further, it would then be possible to make videos instead of pictures, which makes it even harder for AI's to classify. Like, imagine the camera changing angel of a table, and a minimum of two angels are needed to see all bills.I don't think the photos/videos needs to be super realistic, we can add different types of distortions to make it harder for the AI to find patterns.
'identify humans using some kind of physical smart card system requiring frequent or continuous re-authentication via biometric sensors'
This is a really fascinating concept. Maybe the captcha could work in a way like "make a cricle with your index finger" or some other strange movement, and the chip would use that data to somehow verify that the action was done. If no motion is required I guess you could simply store the data outputted at one point and reuse it? Or the hacker using their own smart chip to authenticate them without them actually having to do something...Deepfakes are still detectable using AI, especially if you do complicated motions like putting your hand on your face, or talk (which also gives us sound to work with).
This idea is really brilliant I think, quite promising that it could work. It requires the image AI to understand the entire image, it is hard to divide it up into one frame per bill/coin. And it can't use the intelligence of LLM models easily.
To aid the user, on the side there could be a clear picture of each coin and their worth, that we we could even have made up coins, that could further trick the AI.
All this could be combined with traditional image obfucation techniques (like making them distorted.
I'm not entirely sure how to generate images of money efficiently, Dall-E couldn't really do it well in the test I ran. Stable diffusion probably would do better though.If we create a few thousand real world images of money though, they might be possible to combine and obfuscate and delete parts of them in order to make several million different images. Like one bill could be taken from one image, and then a bill from another image could be placed on top of it etc.
I get what you mean, if an AI can do things as well as the human, why block it?I'm not really sure how that would apply in most cases however. For example bot swarms on social media platforms is a problem that has received a lot of attention lately. Of course, solving a captcha is not as deterring as charging let's say 8 usd per month, but I still think captchas could be useful in a bot deterring strategy.Is this a useful problem work on? I understand that for most people it probably isn't, but personally I find it fun, and it might even be possible to start a SAAS business to make money that could be spent on useful things (although this seems unlikely).
Please correct me if I misunderstand you.We have to first train the model that generates the image from the captcha, before we can provide any captcha, meaning that the hacker can train their discriminator on images generated by our model.But even if this was not the case, generating is a more difficult task that evaluating. I'm pretty sure a small clip model that is two years old can detects hands generated by stable diffusion (probably even without any fine tuning), which is a more modern and larger model.What happens when you train using GANs, is that eventually progress stagnates, even if you keep the discriminator and generator "balanced" (train whichever is doing worse until the other is worse). The models then continually change to trick/not be tricked by the other models. So the limit in making better generators is not that we can't make discriminators that can't detect them.
While it is hard for AI to generate very real looking hands, it is a significantly easier task for AI to classify if hands are real or AI generated.But perhaps it's possible to make extra distortions somehow that makes it harder for both AI and humans to determine which are real...
I think "video reasoning" could be an interesting approach as you say.Like if there are 10 frames and no single frame shows a tennis racket, but if you play them real fast, a human could infer there being a tennis racket because part of the racket is in each frame.
I do think "image reasoning" could potentially be a viable captcha strategy.A classic example is "find the time traveller" pictures, where there are modern objects that gives away who the time traveller is.However, I think it shouldn't be too difficult to teach an AI to identify "odd" objects in an image, unless each image has some unique trick, in which case we would need to create millions of such puzzles somehow. Maybe it could be made harder by having "red herrings" that might seem out of place but actually aren't which might make the AI misunderstand part of the time.
Really interesting idea to make it 3D. I think it might be possible to combined with random tasks given by text, such as "find the part of the 3d object that is incorrect" or different tasks like that (and the object in this case might be a common object like a sofa but one of the pillows is made of wood or something like that)