Loki, Episode 1Â Glorious Purpose

Xuebing Du
almost home
Cosimo Galluzzi
trying on a metaphor

⣠Chile in a Photography âŁ
Today's Document

pixel skylines
cherry valley forever
d e v o n

Andulka

Kaledo Art

shark vs the universe
AnasAbdin
Three Goblin Art
Cosmic Funnies
will byers stan first human second
Alisa U Zemlji Chuda
Misplaced Lens Cap
$LAYYYTER
seen from South Africa

seen from Brazil
seen from Malaysia

seen from United States

seen from Brazil

seen from Iraq
seen from United States
seen from Italy

seen from Saudi Arabia
seen from Austria
seen from Albania
seen from Chile
seen from Mauritius
seen from TĂźrkiye
seen from Uzbekistan

seen from Russia
seen from TĂźrkiye
seen from United States

seen from United States
seen from United States
@matthewraven
Loki, Episode 1Â Glorious Purpose

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SAM WILSON AND BUCKY BARNES âHATINGâ EACH OTHER THROUGH THE YEARSÂ Â Â Â Â
honestly one of my favorite jokes from this show
Mama Cass Elliot photographed by Henry Diltz, 1968.
lundqvistanna
Nakenbad đ
#sommar
#nakenbad
#livet

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instagram.com/bodyposipanda
Lol đ¤Łđ¤Ł
Omg đ¤Łđ¤Łđ¤Łđ¤Łđ¤Ł

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p4059
Girl on a Swing by George Dawney
There is categorically no trend on Twitter that will ever ever ever beat this âgirls do it betterâ thing weâve got going because honestly itâs just meaning weâre getting photos of beautiful girls recreating this look and my lesbian ass is living for it
Seeing as you guys are loving this as much as I am, let me bless your eyes with some more beautiful girls creating this iconic look. Creds of course to all of these wonderful ladies
im fucKIN

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Let Me See Your Face, Hiejin Yoo
Once, I encountered the funny story of an AI 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]
Now, the alarming aspect of this story is that the very same technology is probably what tumblr is using to identify porn. Now, if it canât tell that an empty field is not, in fact, full of sheep, what hope do we have that it canât tell an empty room isnât full of writing human forms engaged in passionate coitus?
this really does sound like an episode of black mirror
But wait, itâs even weirder than that!
This is gonna produce some absolutely baffling pornography.
âŚ. oh my fucking god they actually are using open source software. Theyâre using a fucking one-layer unidirectional bicategory tag-trained neural network. This will never work. Literally, it will never work. Thereâs just not enough algorithmic complexity to do what theyâre 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 fucking up, it will never actually flag porn reliably and it will always require a massive quantity of human hours to deal with the deluge of mistagged pictures. This isnât just a case of an insufficiently trained algorithm, itâs 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 canât 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 liiiieeeeed. 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 nipples (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 on whether the images are labeled nsfw or sfw. Seriously, within a month someone will have an input-output machine which can turn any image âsfwâ in Tumblrâs eyes.
This is genuinely pathetic. Like, I have real pity for whoever implemented this, because itâs clear Tumblr doesnât 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 theyâre doing and is going to get all kinds of flak for their (perfectly reasonable and predetermined) failure from management.
As has been pointed out before, there are no humans behind this at all. The review process just reruns either the same algorithm or another algorithm, but people have posted screen shots showing obviously SFW pictures that were still deemed NSFW on review, despite the fact that any human, no matter how overworked / tired would have seen that these pictures were not porn.