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anonymous asked: Once, I encountered the funny story of an Al image descriptor with a sheep obsession. It had been trained on pictures of fields of sheep. Therefore, it tagged anything in a field as 'sheep', including an empty field, because they work on statistical probability. Therefore, it thinks "ah, a field! there's probably a sheep here." (It's a bit more complicated but basically that.) It also couldn't recognise sheep in places that weren't fields, such as petrol stations or barns. [cont lauralot89 replied Now, the alarming aspect of this story is that the very same technology is probably what tumblr is using to identify p---. Now, if it can't tell that an empty field is not, in fact, full of sheep, what hope do we have that it can't tell an empty room isn't full of writing human forms engaged in passionate coitus? this really does sound like an episode of black mirron crazy-pages: crazy-pages: retroactivebakeries: oneiroteuthis But wait, it's even weirder than that Chappell EllisonChappellracker Dec So here's something.Users are testing the limits of Tumblr's new algorithm tht flags adult content (aka censorbot), This one found that a man's chest was flagged but a man's dest next a 50% scale owl went unnoticed. Things seem to be going great 546 Mincer the Chunk-Seeking Construct Oan gremlin ollow Replying to @Chappel Tracker @lagonegi They're using pn nsfw, a neural network trained to identify nsfww content which has a layer of two competing nsfw and sfw liking neurons that combine for the final result, so literally including a picture of a landscape with the image can hype up SFW Guy and lower the nsfw 96 2:10 PM-7 Dec 2018 This is gonna produce some absolutely baffling p---------- oh my f------ god they actually are using open source software. They're using a f------ one-layer unidirectional bicategory tag-trained neural network. This will never work. Literally, it will never work. There's just not onough algorithmic complexity to do what they're asking of it. I bet you I could prove on a mathematical level that this joke of a neural net fundamentally lacks the abstraction necessary to do its job This will never get better. Their algorithm will never stop f------ up, it will never actually flag p--- reliably and it will always require a massive quantity of human hours to deal with the deluge of mistagged pictures. This isn't just a case of an insufficiently trained algorithm, it's just. this is the most basic neural network you can make. It probably hasa a lot of neurons and has loads of training data but like. you can't just brute force this kind of stuff. One layer of neurons is just Not Enough Also, just to make this clear, Tumblr lied. I mean, we already know this, but I mean they llieeeeed. All that stuff they promised about what would or would not be censored? That cannot be delivered on with a system this simple. Nude classical sculptures, political protests, male-presenting n------ (really Tumblr?), nude art outside the context of sex, all that? You cannot train a bicategory one-layer neural network to exclude those things. It cannot be done Tumblr never intended for those things to actually be permitted, they were just lying. Because the system they have cannot actually do what they said it would and never will be able to Also, this kind of system is super vulnerable to counter-neural strategies. I bet you before the end of the month someone hooks up their own open source one layer bicategory neural network which puts an imperceptible (to humans) layer of patterned static over arbitrary images, and trains it by having it bot-post static-ed images to Tumblr and reinforcing based or whether the images are labeled nsfw or sfw. Seriously, within a month someone will have an input-output machine which can turn any image 'sfw in Tumblr's eyes This is genuinely pathetic. Like, I have real pity for whoever implemented this, because it's clear Tumblr doesn't actually have any engineers with any expertise with machine learning left at all and they foisted the job off on some poor bastard who has no idea what they're doing and is going to get all kinds of flak for their (perfectly reasonable and predetermined) failure from management.

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Your post was flagged Review The items in lines are not Used in the same manner where user sleps on deployed flap while cleaning boot is center chanael with brushes such that the product and claimed design are substantially the same, and would deceive an ondmary observer FIG. 9 claimod design are substantially the same, and would deceive an ondinay observer The itcms in dotted lines are not claimed Virtually same profile, shape and proportions such that the product and elaimed design are substantially the same, and would deceive an ondinary FIG. 3 observer S584,466 The iteas in dotted lines ase not claimed JOBSITE Same basic profile, shape and proportions such that the product and claimed design are substantially the same, and would deceive an ordinary ebserver Same folding flaps with tabs for deployment of tabs such that the product and claimed design are substantially the same, and would deceive an ordinary observer
Tumblr We are banning p--- to make Tumblr a better community! *One-third of Tumblr users leave the site because of the ban* Tumblr: imgflip.com
the minute tumblr banned p--- is the minute they signed over a million people to twitter and im the proof 12:21 PM-14 Mar 2019
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Tumblr Adult Content Ban - It’d be funny, if Verizon putting tumblr’s maintenance on the shoulders of a skeleton crew didn’t ma...

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Uploaded Dec 11, 2018 at 03:39AM EST

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