Awhile ago I tried an interesting experiment. I fed a famous psychology image into a bunch of different image recognition systems. This image: https://i.imgur.

Awhile ago I tried an interesting experiment. I fed a famous psychology image into a bunch of different image recognition systems. This image: https://i.imgur.com/2aCqMx2.png https://i.imgur.com/2aCqMx2.png And here are the results: https://imgur.com/a/8ndyq https://imgur.com/a/8ndyq This doesn’t really prove anything, but I thought it was interesting. It is of course, unreasonable to expect ML algorithms to perform decently, so far outside of the space they were trained on. But I suspect that part of the reason they don’t do well is that they are purely feed forward. Humans also don’t see the image at first. It takes time to find the pattern, and then everything clicks into place and you can’t unsee it. This might have something to do with recurrency. But more importantly, information feeds down the hierarchy as well as up. Features above, give information back down to features below. So once you see the dog, that tells the lower level features that they are seeing legs and heads, which says they are seeing outlines of more basic 3 dimensional shapes, and so on. I think it also requires a descent understanding of 3d space, to fit the observed pattern to 3d models which could have produced it. I’m not certain if regular NNs observing static images, are optimal for learning that. More here: https://www.reddit.com/r/MachineLearning/comments/399ooe/testing_image_recognition_platforms_on_a_famous/ https://www.reddit.com/r/MachineLearning/comments/399ooe/tes...