>A cooperative GAN architecture for converting images from one genre to another (eg horses<->zebras) has a loss function that rewards accurate reconstruction of

>A cooperative GAN architecture for converting images from one genre to another (eg horses<->zebras) has a loss function that rewards accurate reconstruction of images from its transformed version; CycleGAN turns out to partially solve the task by, in addition to the cross-domain analogies it learns, steganographically hiding autoencoder-style data about the original image invisibly inside the transformed image to assist the reconstruction of details. I am pretty sure a lot of image-related AIs today do this kind of thing. Unfortunately, researchers almost never test for it explicitly, because proving your algorithm is stupider than it looks is not good for publishing. AI research today needs three things. 1. All AI degrees should contain a class on the history of the field. 2. Every paper should include description of cases/datasets where the algorithm fails, preferably compiled by a different team. 3. Research in "stupid" AI, i.e. in trying to bring old algorithms close to SOTA results using modern hardware and optimizations. Almost no one does that. Almost no one talks about it. I bet many people don't even understated why it's important.