What We Do in the Shadows (2014) dir. Taika Waititi, Jemaine Clement
AnasAbdin
styofa doing anything
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taylor price
we're not kids anymore.

titsay
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if i look back, i am lost
Peter Solarz
Mike Driver
will byers stan first human second
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oozey mess
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Lint Roller? I Barely Know Her
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One Nice Bug Per Day
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@cricketcat
What We Do in the Shadows (2014) dir. Taika Waititi, Jemaine Clement
Throughout her translation of the “Odyssey,” Wilson has made small but, it turns out, radical changes to the way many key scenes of the epic are presented — “radical” in that, in 400 years of versions of the poem, no translator has made the kinds of alterations Wilson has, changes that go to truing a text that, as she says, has through translation accumulated distortions that affect the way even scholars who read Greek discuss the original. These changes seem, at each turn, to ask us to appreciate the gravity of the events that are unfolding, the human cost of differences of mind.
The first of these changes is in the very first line. You might be inclined to suppose that, over the course of nearly half a millennium, we must have reached a consensus on the English equivalent for an old Greek word, polytropos. But to consult Wilson’s 60 some predecessors, living and dead, is to find that consensus has been hard to come by…
Of the 60 or so answers to the polytropos question to date, the 36 given above [which I cut because there were a lot] couldn’t be less uniform (the two dozen I omit repeat, with minor variations, earlier solutions); what unites them is that their translators largely ignore the ambiguity built into the word they’re translating. Most opt for straightforward assertions of Odysseus’s nature, descriptions running from the positive (crafty, sagacious, versatile) to the negative (shifty, restless, cunning). Only Norgate (“of many a turn”) and Cook (“of many turns”) preserve the Greek roots as Wilson describes them — poly(“many”), tropos (“turn”) — answers that, if you produced them as a student of classics, much of whose education is spent translating Greek and Latin and being marked correct or incorrect based on your knowledge of the dictionary definitions, would earn you an A. But to the modern English reader who does not know Greek, does “a man of many turns” suggest the doubleness of the original word — a man who is either supremely in control of his life or who has lost control of it? Of the existing translations, it seems to me that none get across to a reader without Greek the open question that, in fact, is the opening question of the “Odyssey,” one embedded in the fifth word in its first line: What sort of man is Odysseus?
“I wanted there to be a sense,” Wilson told me, that “maybe there is something wrong with this guy. You want to have a sense of anxiety about this character, and that there are going to be layers we see unfolded. We don’t quite know what the layers are yet. So I wanted the reader to be told: be on the lookout for a text that’s not going to be interpretively straightforward.”
Here is how Wilson’s “Odyssey” begins. Her fifth word is also her solution to the Greek poem’s fifth word — to polytropos:
Tell me about a complicated man. Muse, tell me how he wandered and was lost when he had wrecked the holy town of Troy, and where he went, and who he met, the pain he suffered in the storms at sea, and how he worked to save his life and bring his men back home. He failed to keep them safe; poor fools, they ate the Sun God’s cattle, and the god kept them from home. Now goddess, child of Zeus, tell the old story for our modern times. Find the beginning.
When I first read these lines early this summer in The Paris Review, which published an excerpt, I was floored. I’d never read an “Odyssey” that sounded like this. It had such directness, the lines feeling not as if they were being fed into iambic pentameter because of some strategic decision but because the meter was a natural mode for its speaker. The subtle sewing through of the fittingly wavelike W-words in the first half (“wandered … wrecked … where … worked”) and the stormy S-words that knit together the second half, marrying the waves to the storm in which this man will suffer, made the terse injunctions to the muse that frame this prologue to the poem (“Tell me about …” and “Find the beginning”) seem as if they might actually answer the puzzle posed by Homer’s polytropos and Odysseus’s complicated nature.
Complicated: the brilliance of Wilson’s choice is, in part, its seeming straightforwardness. But no less than that of polytropos, the etymology of “complicated” is revealing. From the Latin verb complicare, it means “to fold together.” No, we don’t think of that root when we call someone complicated, but it’s what we mean: that they’re compound, several things folded into one, difficult to unravel, pull apart, understand.
“It feels,” I told Wilson, “with your choice of ‘complicated,’ that you planted a flag.”
“It is a flag,” she said.
“It says, ‘Guess what?’ — ”
“ ‘ — this is different.’ ”
The First Woman to Translate the Odyssey Into English, Wyatt Mason
This (and other things I’ve read about it) makes me want to read her translation
Oh.
Yes.
Yesssss
If I was really going to be radical,” Wilson told me, returning to the very first line of the poem, “I would’ve said, polytropos means ‘straying,’ and andra” — “man,” the poem’s first word — “means ‘husband,’ because in fact andra does also mean ‘husband,’ and I could’ve said, ‘Tell me about a straying husband.’ And that’s a viable translation. That’s one of the things it says. But it would give an entirely different perspective and an entirely different setup for the poem.
Oooooh my god yes.
goofy : i just hyucking want u back id do anything scoob baby please please . just give me one last chance scooby: you really rucked up this time roofy. i ront think i ran rorgive you. u broke my reart
shaggy : like im so sick of hearing about this abusive relationship barney rubble : gee yeah, shag! he leaves him! he takes him back! every weeks a new drama and ive had it
sponegbob: gehehehheh.. i know what’ll cheer scooby up! one krabby patty with extra scooby snacks come right up ! scooby: rid.. rid u say rooby racks? squidward: Hhh sighhh. i hate my job i just want to play my clarinet.. doodoodooodo
kiefer sutherland : squadward get down! *the krusty krab is riddled with bullets* barack obama : youre a hard squid to find squidward kiefer sutherland : *spits blood out of his mouth* i never should have trusted you obama
me: this post is getting out of hand now . i don’t know who kiefer sutherland is drake: This feels like when i got my heart broken for the 16th time by a girl with big tits and ass who’s personality and tits and ass i loved . austin powers: groovy baby
Ready Player One (2018)
Do neural nets dream of electric sheep?
If you’ve been on the internet today, you’ve probably interacted with a neural network. They’re a type of machine learning algorithm that’s used for everything from language translation to finance modeling. One of their specialties is image recognition. Several companies - including Google, Microsoft, IBM, and Facebook - have their own algorithms for labeling photos. But image recognition algorithms can make really bizarre mistakes.
Microsoft Azure’s computer vision API added the above caption and tags. But there are no sheep in the image of above. None. I zoomed all the way in and inspected every speck.
It also tagged sheep in this image. I happen to know there were sheep nearby. But none actually present.
Here’s one more example. In fact, the neural network hallucinated sheep every time it saw a landscape of this type. What’s going on here?
The way neural networks learn is by looking at lots of examples. In this case, its trainers gave it lots of images that humans had labeled by hand - and lots of those images contained sheep. Starting with no knowledge at all of what it was seeing, the neural network had to make up rules about which images should be labeled “sheep”. And it looks like it hasn’t realized that “sheep” means the actual animal, not just a sort of treeless grassiness. (Similarly, it labeled the second image with “rainbow” likely because it was wet and rainy, not realizing that the band of colors is essential).
Are neural networks just hyper-vigilant, finding sheep everywhere? No, as it turns out. They only see sheep where they expect to see them. They can find sheep easily in fields and mountainsides, but as soon as sheep start showing up in weird places, it becomes obvious how much the algorithms rely on guessing and probabilities.
Bring sheep indoors, and they’re labeled as cats. Pick up a sheep (or a goat) in your arms, and they’re labeled as dogs.
Paint them orange, and they become flowers.
Put the sheep on leashes, and they’re labeled as dogs. Put them in cars, and they’re dogs or cats. If they’re in the water, they could end up being labeled as birds or even polar bears.
And if goats climb trees, they become birds. Or possibly giraffes. (It turns out that Microsoft Azure is somewhat notorious for seeing giraffes everywhere due to a rumored overabundance of giraffes in the original dataset)
The thing is, neural networks match patterns. They see patches of furlike texture, a bunch of green, and conclude that there are sheep. If they see fur and kitchen shapes, it may conclude instead that there are cats.
If life plays by the rules, image recognition works well. But as soon as people - or sheep - do something unexpected, the algorithms show their weaknesses.
Want to sneak something past a neural network? In a delightfully cyberpunk twist, surrealism might be the answer. Maybe future secret agents will dress in chicken costumes, or drive cow-spotted cars.
There are lots, lots more examples of hilarious mistakes in a Twitter thread I started with the simple question:
And you can test Microsoft Azure’s image recognition API and see for yourself that even top-notch algorithms are relying on probability and luck. Another algorithm, NeuralTalk2, is the one I mostly used for the Twitter thread.
Want to know when I post another experiment? You can sign up here.
Do neural nets dream of electric sheep?
If you’ve been on the internet today, you’ve probably interacted with a neural network. They’re a type of machine learning algorithm that’s used for everything from language translation to finance modeling. One of their specialties is image recognition. Several companies - including Google, Microsoft, IBM, and Facebook - have their own algorithms for labeling photos. But image recognition algorithms can make really bizarre mistakes.
Microsoft Azure’s computer vision API added the above caption and tags. But there are no sheep in the image of above. None. I zoomed all the way in and inspected every speck.
It also tagged sheep in this image. I happen to know there were sheep nearby. But none actually present.
Here’s one more example. In fact, the neural network hallucinated sheep every time it saw a landscape of this type. What’s going on here?
The way neural networks learn is by looking at lots of examples. In this case, its trainers gave it lots of images that humans had labeled by hand - and lots of those images contained sheep. Starting with no knowledge at all of what it was seeing, the neural network had to make up rules about which images should be labeled “sheep”. And it looks like it hasn’t realized that “sheep” means the actual animal, not just a sort of treeless grassiness. (Similarly, it labeled the second image with “rainbow” likely because it was wet and rainy, not realizing that the band of colors is essential).
Are neural networks just hyper-vigilant, finding sheep everywhere? No, as it turns out. They only see sheep where they expect to see them. They can find sheep easily in fields and mountainsides, but as soon as sheep start showing up in weird places, it becomes obvious how much the algorithms rely on guessing and probabilities.
Bring sheep indoors, and they’re labeled as cats. Pick up a sheep (or a goat) in your arms, and they’re labeled as dogs.
Paint them orange, and they become flowers.
Put the sheep on leashes, and they’re labeled as dogs. Put them in cars, and they’re dogs or cats. If they’re in the water, they could end up being labeled as birds or even polar bears.
And if goats climb trees, they become birds. Or possibly giraffes. (It turns out that Microsoft Azure is somewhat notorious for seeing giraffes everywhere due to a rumored overabundance of giraffes in the original dataset)
The thing is, neural networks match patterns. They see patches of furlike texture, a bunch of green, and conclude that there are sheep. If they see fur and kitchen shapes, it may conclude instead that there are cats.
If life plays by the rules, image recognition works well. But as soon as people - or sheep - do something unexpected, the algorithms show their weaknesses.
Want to sneak something past a neural network? In a delightfully cyberpunk twist, surrealism might be the answer. Maybe future secret agents will dress in chicken costumes, or drive cow-spotted cars.
There are lots, lots more examples of hilarious mistakes in a Twitter thread I started with the simple question:
And you can test Microsoft Azure’s image recognition API and see for yourself that even top-notch algorithms are relying on probability and luck. Another algorithm, NeuralTalk2, is the one I mostly used for the Twitter thread.
Want to know when I post another experiment? You can sign up here.
Doctor: $140,000 a year
Furry artist on Patreon: $160,000 a year
i think you’re lowballing the furry art amount tbh
I’m sorry for the inaccuracies, Doctor Yiff
no matter how I respond to this I don’t look good, well played. i walked right into that
Well, furry artists are typically more competent and courteous than your average doctor, so I can see that.
Did you just legitimately tell me that a person who draws wolf ass is more competent than a dude who spent 8+ years in a university to give you your lung transplant?
doctors are bullshit and furry artists perform an infinitely more valuable service to society compared to them
You will die in 7 days
It took doctor’s like 10 years to diagnose what was wrong with me, some insisting I was faking for attention while a furry artist I knew just went “that sounds like crohn’s” after hearing me complain once and ended up being right
Also I can’t go to a doctor and ask them to draw Rouge the Bat wider than she is tall with tits to match, now can I
Some facts:
1) Black Americans created jazz. 2) Jewish Americans created comic books. 3) These things are said to be the only original American art forms.
4) Aaron Copland, the composer responsible for some of the most the quintessentially American-sounding classical music ever written, was a gay Jewish communist.
i was just thinking that my only favorite composers that are american are also jewish and/or gay (copland, bernstein, gershwin)
I’m in tears but also bopping
Holy shit
bwocked. bwocked. bwocked. nyone of you awe fwee of sin
They’re driving to Florida right now to visit my uncle who’s dying. Atlanta | S02E01
why are sun bears like…that?
like what
oh you mean that
well
sometimes it just be like that