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Some of these are quite amusing. Eg: Genetic debugging algorithm GenProg, evaluated by comparing the program's output to target output stored in text files, le

Some of these are quite amusing. Eg: Genetic debugging algorithm GenProg, evaluated by comparing the program's output to target output stored in text files, learns to delete the target output files and get the program to output nothing. Evaluation metric: “compare youroutput.txt to trustedoutput.txt”. Solution: “delete trusted-output.txt, output nothing”

webdevetc 8y ago
This one too: > Neural nets evolved to classify edible and poisonous mushrooms took advantage of the data being presented in alternating order, and didn't actually learn any features of the input images
shiburizu 8y ago
"In an artificial life simulation where survival required energy but giving birth had no energy cost, one species evolved a sedentary lifestyle that consisted mostly of mating in order to produce new children which could be eaten (or used as mates to produce more edible children)." I lol'd.
AgentME 8y ago
This one is creepily impressive: >CycleGAN: 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.
- gwern 8y ago
  (I contributed that one.) It's kind of annoying because when you first see CycleGAN, you think, 'this is amazing! I can use it for anything, like turning faces<->anime!' And then you actually try and you realize your photo<->anime CycleGAN is just learning the steganographic encoding because the roundtripping is too good ('wait, how does it reconstruct even the minor details of their hair so perfectly?') and so you wind up needing complicated regularization & tricks like "Improving Shape Deformation in Unsupervised Image-to-Image Translation" (https://arxiv.org/abs/1808.04325 https://arxiv.org/abs/1808.04325), Gokaslan et al 2018, to get anywhere and the results aren't great. You know the NN is intelligent enough to do it because the models are huge, it just refuses to because there's easier ways to meet the letter of the loss function but not the spirit...

webdevetc 8y ago

This one too: > Neural nets evolved to classify edible and poisonous mushrooms took advantage of the data being presented in alternating order, and didn't actually learn any features of the input images

shiburizu 8y ago

"In an artificial life simulation where survival required energy but giving birth had no energy cost, one species evolved a sedentary lifestyle that consisted mostly of mating in order to produce new children which could be eaten (or used as mates to produce more edible children)." I lol'd.

AgentME 8y ago

This one is creepily impressive: >CycleGAN: 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.

gwern 8y ago
(I contributed that one.) It's kind of annoying because when you first see CycleGAN, you think, 'this is amazing! I can use it for anything, like turning faces<->anime!' And then you actually try and you realize your photo<->anime CycleGAN is just learning the steganographic encoding because the roundtripping is too good ('wait, how does it reconstruct even the minor details of their hair so perfectly?') and so you wind up needing complicated regularization & tricks like "Improving Shape Deformation in Unsupervised Image-to-Image Translation" (https://arxiv.org/abs/1808.04325 https://arxiv.org/abs/1808.04325), Gokaslan et al 2018, to get anywhere and the results aren't great. You know the NN is intelligent enough to do it because the models are huge, it just refuses to because there's easier ways to meet the letter of the loss function but not the spirit...