Re:Optical illusions that flummox computers
by candiodari on Saturday April 15, 2017 @11:09AM
(#14121049)
TLDR: high-energy (sudden changes from one pixel to the next) overlays "fool" AIs. If you think about it you can see why : they change the statistical properties of an image a lot "without" disturbing it.
Less so, obviously, if you do something like downsample it or otherwise soften or ... with filters first. Nor do they fool neural networks with attention (they simply at some point decide it's not worth looking at and identify the picture by something else).
And the ridiculous example given does not work without being able to read the mind of the neural network (the misidentified panda).
Most neural network classification mistakes are "understandable" (e.g. look at the misidentified carousel). These are really 99.99% or more of the total mistakes make. Also that network probably needs more Indian elephants in it's training set (kids make stupid mistakes classifying animals they've never seen or only seen very few times as well [1]).
Given how a lot of animals look, I wonder if this doesn't work on "real" brains as well. I for one have trouble seeing zebras in pictures, and it's of course not for lack of contrast. Counting them or accurately judging distance is just out of the question. But many animals look way more colorful and contrast-rich than seems advisable, from chickens, of course peacocks, to ladybugs.
A number of optical illusions seem based on high contrast patterns being included in images. Especially if, like in the examples here, the high contrast patterns don't line up with the objects in the image (e.g. moving a vertical and horizontal slit filter over an image and you will not be able to see through it, however in any freeze frame you won't have that problem).
Less so, obviously, if you do something like downsample it or otherwise soften or ... with filters first. Nor do they fool neural networks with attention (they simply at some point decide it's not worth looking at and identify the picture by something else).
And the ridiculous example given does not work without being able to read the mind of the neural network (the misidentified panda).
Most neural network classification mistakes are "understandable" (e.g. look at the misidentified carousel). These are really 99.99% or more of the total mistakes make. Also that network probably needs more Indian elephants in it's training set (kids make stupid mistakes classifying animals they've never seen or only seen very few times as well [1]).
Given how a lot of animals look, I wonder if this doesn't work on "real" brains as well. I for one have trouble seeing zebras in pictures, and it's of course not for lack of contrast. Counting them or accurately judging distance is just out of the question. But many animals look way more colorful and contrast-rich than seems advisable, from chickens, of course peacocks, to ladybugs.
A number of optical illusions seem based on high contrast patterns being included in images. Especially if, like in the examples here, the high contrast patterns don't line up with the objects in the image (e.g. moving a vertical and horizontal slit filter over an image and you will not be able to see through it, however in any freeze frame you won't have that problem).
[1] https://www.youtube.com/watch?v=bnJ8UpvdTQY