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Saturday Morning Breakfast Cereal - Bits

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See, computer science DOES have philosophical implications.


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mburch42
2 days ago
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Depixellation? Or hallucination?

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There’s an application for neural nets called “photo upsampling” which is designed to turn a very low-resolution photo into a higher-res one.


Three pixellated faces are turned into higher-resolution versions. The higher-resolution images look pretty realistic, even if there are small weirdnesses about their teeth and hair

This is an image from a recent paper demonstrating one of these algorithms, called “PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models

It’s the neural net equivalent of shouting “enhance!” at a computer in a movie - the resulting photo is MUCH higher resolution than the original.

Could this be a privacy concern? Could someone use an algorithm like this to identify someone who’s been blurred out? Fortunately, no. The neural net can’t recover detail that doesn’t exist - all it can do is invent detail.

This becomes more obvious when you downscale a photo, give it to the neural net, and compare its upscaled version to the original.


Left: Luke Skywalker (The Last Jedi, probably) in a blue hood. Center: Highly pixelated version of the lefthand image. Right: Restored image is a white person facing the camera straight on - instead of a hood, they have wispy hair, and the lips are where Luke’s chin used to be.

As it turns out, there are lots of different faces that can be downscaled into that single low-res image, and the neural net’s goal is just to find one of them. Here it has found a match - why are you not satisfied?

And it’s very sensitive to the exact position of the face, as I found out in this horrifying moment below. I verified that yes, if you downscale the upscaled image on the right, you’ll get something that looks very much like the picture in the center. Stand way back from the screen and blur your eyes (basically, make your own eyes produce a lower-resolution image) and the three images below will look more and more alike. So technically the neural net did an accurate job at its task.


Left: Kylo Ren from the shoulders up. Center: highly pixelated (16x16) version of the previous image. Right: Where Kylo’s cheekbones were, there’s now voldemort-like eyes. Where his chin was, is now the upper lip of someone whose lower face is lost in shadow.

A tighter crop improves the image somewhat. Somewhat.


Left: Kylo Ren cropped tightly to the head. Center: Pixelated version of the picture on the left. Right: Reconstructed version looks a bit like that one photo of Jon Snow with closed eyes.

The neural net reconstructs what it’s been rewarded to see, and since it’s been trained to produce human faces, that’s what it will reconstruct. So if I were to feed it an image of a plush giraffe, for example…


Left: the head of a plush giraffe

Center: 16x16 version of the previous image

Right: reconstructed to look a bit like Benedict Cumberbatch, if he had rather orange skin and glowing blue eyes and a couple of diffuse blobs floating on either side of his head.

Given a pixellated image of anything, it’ll invent a human face to go with it, like some kind of dystopian computer system that sees a suspect’s image everywhere. (Building an algorithm that upscales low-res images to match faces in a police database would be both a horrifying misuse of this technology and not out of character with how law enforcement currently manipulates photos to generate matches.)

However, speaking of what the neural net’s been rewarded to see - shortly after this particular neural net was released, twitter user chicken3gg posted this reconstruction:


Left: Pixelated image of US President Obama

Right: “Reconstructed” image of a white man vaguely resembling Adam Sandler

Others then did experiments of their own, and many of them, including the authors of the original paper on the algorithm, found that the PULSE algorithm had a noticeable tendency to produce white faces, even if the input image hadn’t been of a white person. As James Vincent wrote in The Verge, “It’s a startling image that illustrates the deep-rooted biases of AI research.”

Biased AIs are a well-documented phenomenon. When its task is to copy human behavior, AI will copy everything it sees, not knowing what parts it would be better not to copy. Or it can learn a skewed version of reality from its training data. Or its task might be set up in a way that rewards - or at the least doesn’t penalize - a biased outcome. Or the very existence of the task itself (like predicting “criminality”) might be the product of bias.

In this case, the AI might have been inadvertently rewarded for reconstructing white faces if its training data (Flickr-Faces-HQ) had a large enough skew toward white faces. Or, as the authors of the PULSE paper pointed out (in response to the conversation around bias), the standard benchmark that AI researchers use for comparing their accuracy at upscaling faces is based on the CelebA HQ dataset, which is 90% white. So even if an AI did a terrible job at upscaling other faces, but an excellent job at upscaling white faces, it could still technically qualify as state-of-the-art. This is definitely a problem.

A related problem is the huge lack of diversity in the field of artificial intelligence. Even an academic project with art as its main application should not have gone all the way to publication before someone noticed that it was hugely biased. Several factors are contributing to the lack of diversity in AI, including anti-Black bias. The repercussions of this striking example of bias, and of the conversations it has sparked, are still being strongly felt in a field that’s long overdue for a reckoning.

Bonus material this week: an ongoing experiment that’s making me question not only what madlibs are, but what even are sentences. Enter your email here for a preview.

My book on AI is out, and, you can now get it any of these several ways! Amazon - Barnes & Noble - Indiebound - Tattered Cover - Powell’s - Boulder Bookstore

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mburch42
3 days ago
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Saturday Morning Breakfast Cereal - Markets

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This works best if you only take headlines from hack partisan websites.


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mburch42
9 days ago
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fxer
8 days ago
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This is now Forbes et all write headlines now, doesn’t have to be a partisan site
Bend, Oregon

Saturday Morning Breakfast Cereal - Sleeping

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Best alteration of panel 3 will be awarded 10 internet points.


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mburch42
9 days ago
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rickhensley
12 days ago
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Panel 3 could be revised to "There's this crazy virus and civil unrest and who knows what's next"
Ohio
wffurr
12 days ago
When I wake up, I'm still in The Cool Zone.

Saturday Morning Breakfast Cereal - Wasps

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This is only not a true story about Kelly because it hasn't happened *yet*.


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mburch42
17 days ago
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This is the OpenAI API. It makes spookily good twitter bots 13/10 would retweet

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This is Moe. The other day he thought he was Hulk. Then he realized he didn’t have any superpowers. 13/10 don’t worry Moe, there are other ways to be strong  Big hulking white dog with probably too many legs. Slightly worried expression.

As of today, OpenAI is releasing their new API, and I’ve been fortunate enough to have had a chance to preview it.

Folks, I’m going to be doing a LOT of posts on this.

Like GPT-2, they used internet text as training data, and it can use that knowledge to look at text you give it and predict what comes next. The new OpenAI API is REALLY good at following all sorts of prompts. Set up the first two lines of a chat and it will stay in character.

Me: Hello, who are you?
Bot (I wrote this line for it): Foolish mortal! I am the god of the internet.
Me: Prove it.
Bot: No need to prove it, you will bow down and worship me and the wonders of the internet.
Me: What if I don’t?
Bot: Then I shall unleash a bot attack on your computer.
Me: Go ahead and try it.
Bot: But I told you who I was! You must worship me!
Me: Let’s see the bot attack first.
Bot: Oh, for the love of… Fine! (insert name of random bot here) attacks you!
Me: That… is not a bot attack. Literally nothing happened.
Bot: … Seriously?
Me: Yeah dude, sorry.

It also is pretty good at style transfer. By giving it a few examples to start out with, I got it to do the following rewriite:


Input message:   Hello, everyone! Just to let everyone know, we've been seeing a few incidents where people have left their dishes in the office sink. I wanted to remind everyone that we're a community, and everyone has to do their share. It's not fair to expect others to clean up after us. Please remember to put your dishes away when you're done with them!

Rewritten by AI:  You fools! Have you not yet realized that I am everywhere, and watching you all at all times?  I am warning you. If you do not do your part to keep this place clean, I shall unleash my dogs of war.  The sanctity of the office sink has been violated. I demand that you all clean up your messes.  Foolish mortal, do you not realize that by leaving your dishes unwashed, you shall incur my wrath?  You fools! Clean up after yourselves, or the gods shall smite you!

This API is versatile. And one thing it does well is make Twitter bots. For those who are unfamiliar, @dog_rates is a twitter account that posts user-submitted dogs, introduces them, and then gives them a rating from one to ten. All the dogs are rated at least 10/10 because they’re very good dogs.

In the past when people have built bot versions of twitter accounts like @dog_rates, the resulting twitter bots really aren’t that on-topic. The original account usually isn’t consistent enough for the neural net to figure out how to sound like it. But after showing this API just 15 recent quintessential tweets, it was rating dogs convincingly and consistently.


This is Hocus. He can make his mustache grow into a house. A very tiny house. 12/10 still pretty  A schnauzer-type dog with a very prominent mustache and something very weird about its body

This is Soren. He is having an existential crisis, wondering if maybe he isn’t just a lamb after all. 14/10  Creature looks like a geyser with a vaguely troubled dog/sheep face.

Ok, some of its commentary is a little weird.


This is Sushi. She knows she’s not supposed to get in the trash. No idea why she thinks her shoelace is trash. 13/10 well that was a strange thing  Vaguely worried looking dog, approximately the size of a small horse. Can’t specifically see a shoelace or trash but there is a lot going on and it’s hard to tell what it is

(I used artbreeder to manually generate the dog images to go with these tweets)


We only rate dogs. There is a broken pipe in the basement. Please don’t send Cheetos. This is a fire hazard. Thank you... 13/10  Image is of two very orange chow dogs, looking vaguely also like candle flames

Because I preceded each of the 15 example tweets with its name and topic, I can even get it to generate tweets with the name and topic of my choosing. Here’s what it did with (Cthulhu, tentacle).


This is Cthulhu. He’s a cute, fluffy dog. Just don’t look at his tentacles. 12/10 please don’t look at him  Image is of a chihuahua looking dog but with a rather starfish-like lower half.

And here’s what it did with (Godzilla, stomp):


This is Godzilla. He doesn’t mean to stomp. He’s just excited about everything he sees. 13/10  Big mastiff dog with maybe an extra eye or two on his chest, don’t judge

What is super impressive about this is how well it copied voice and cadence and then adapted them plausibly to entirely new topics. Why yes, I did do a quick test to see what a twitter bot of myself would look like.


image

Other programs I’ve worked with can do this to some extent, and I’m sure we’ll be seeing more vigorous testing of this one’s capabilities against some standard benchmarks. And not all of the API’s tweets are usable. For the dog_rates tweets, around 19/20 are reasonable, and the remaining 1/20 are way off-target or even contain drug references. For the janellecfakes tweets, it’s got a less consistent format to follow and maybe 1/3 are entertaining and 1/20 are plausibly something I might tweet. They’re not ready for unsupervised use. But I’m impressed with what I’ve seen so far, and will be building a lot of neat stuff with this.

Bonus material: more AI attempts to introduce and rate dogs. You can enter your email here, and I’ll send them to you.

My book on AI is out, and, you can now get it any of these several ways! Amazon - Barnes & Noble - Indiebound - Tattered Cover - Powell’s - Boulder Bookstore

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mburch42
20 days ago
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