ππ΄π’π―π«@gwernJun 2Today's very On-Brandβ’ activity: making SF sourdough using my mutant starter bred over 6 years for herbal resistance and letting it rise in the exhaust heat from my GPUs whose neural nets are cropping anime images while I read about AI.
ππ΄π’π―π«@gwernJun 6The Postman Always Rings Twice (in accordance with the Prime Directive to avoid use of disruptive technologies & follow primitive norms on communication).
ππ΄π’π―π«@gwernJun 1But I do have time to explain, to people who I think the explanation would help or who deserve it; which is not people who repeatedly ask lazy questions and appear to be shamelessly incorrigible about it.
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ππ΄π’π―π«@gwernJun 3Hm... You can obviously predict someone's hair color/length, gender, race, body fat, and probably age from that cropped image (not just the ear), but I'm not sure about the facial features. That might just be plausible guessing by the model conditional on the previous variables.
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ππ΄π’π―π«@gwernMay 31'The way' is whatever works. 'Intellectually satisfying' is irrelevant and may be in fact the opposite of a good thing: if it was intellectually satisfying, it would have been done already.
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ππ΄π’π―π«@gwernJun 6Amazing data aug result: harmful if only on reals, very helpful if on both reals+fakes!
I checked StyleGAN, MSG-GAN, compare_gan, and Mooch's PyTorch BigGAN, and in every case, either no data aug is done or it's done only on reals as part of loading pipeline. We've screwed up!
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ππ΄π’π―π«@gwernJun 4Presumably the explainer will start with IAT & proceed to stereotype threat to help us understand what goes on inside the mind of monsters like Yglesias. Was he born like that or did society make him that way? Scientists now know the answer is a complex interplay of factors...
ππ΄π’π―π«@gwernJun 4Exactly. Everyone is always giving nothing to almost every single cause in the world, much like atheists believe in one less god than everyone else. By donating anything, folk morals hold you entangled & to blame: blog.jaibot.com/the-copenhagenβ¦ So if you don't give 'a lot', you're bad.
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