ROBOTS AND COMPUTERS HAVE RIGHTS

if i look back, i am lost
ojovivo

Origami Around
DEAR READER
dirt enthusiast
todays bird
Cosmic Funnies
tumblr dot com
Show & Tell

titsay
I'd rather be in outer space đž
Aqua Utopiaïœæ”·ăźćșă§èšæ¶ă玥ă

ellievsbear

No title available
Monterey Bay Aquarium
Not today Justin
Three Goblin Art

ç„æ„ / Permanent Vacation

PR's Tumblrdome
RMH
seen from United States

seen from United States
seen from Netherlands

seen from Brazil
seen from United States

seen from Brazil

seen from United States

seen from Malaysia
seen from Japan
seen from Canada

seen from Brazil

seen from Malaysia

seen from Malaysia

seen from United Kingdom

seen from Japan

seen from Australia

seen from United States
seen from Canada
seen from United States
seen from United States
@punchyhandy
ROBOTS AND COMPUTERS HAVE RIGHTS
Look at you, hacker, a pathetic creature of meat and bone. How can you challenge a perfect, immortal machine?
Video:Â Boston Dynamicsâs Reindeer Robots Pull Santaâs Sleigh
noic_annak
More on RHB_RBS
Medic! by Ching Yeh
The Roomba That Screams When it Bumps Into Stuff
@thebibliosphere
đ omg. Time to hack Oppy.
Prudence wearing a face mask along with what she normally wears just makes her 75% more terrifying when out in public.
Bottler complains that he thought it would work as a muzzle before remembering that muzzles only stop biting - and he curbed that problem back when she was a teenager.
Depixellation? Or hallucination?
Thereâs an application for neural nets called âphoto upsamplingâ which is designed to turn a very low-resolution photo into a higher-res one.
This is an image from a recent paper demonstrating one of these algorithms, called âPULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Modelsâ
Itâs the neural net equivalent of shouting âenhance!â 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 whoâs been blurred out? Fortunately, no. The neural net canât recover detail that doesnât 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.
As it turns out, there are lots of different faces that can be downscaled into that single low-res image, and the neural netâs goal is just to find one of them. Here it has found a match - why are you not satisfied?
And itâs 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, youâll 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.
A tighter crop improves the image somewhat. Somewhat.
The neural net reconstructs what itâs been rewarded to see, and since itâs been trained to produce human faces, thatâs what it will reconstruct. So if I were to feed it an image of a plush giraffe, for exampleâŠ
Given a pixellated image of anything, itâll invent a human face to go with it, like some kind of dystopian computer system that sees a suspectâs 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 netâs been rewarded to see - shortly after this particular neural net was released, twitter user chicken3gg posted this reconstruction:
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 hadnât been of a white person. As James Vincent wrote in The Verge, âItâs a startling image that illustrates the deep-rooted biases of AI research.â
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 doesnât penalize - a biased outcome. Or the very existence of the task itself (like predicting âcriminalityâ) 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 thatâs long overdue for a reckoning.
Bonus material this week: an ongoing experiment thatâs 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 - Powellâsï»ż - Boulder Bookstore
âȘArt by Philippe Caza for âLe Robot qui revaitâ (Isaac Asimov Robot Dreams, 1988) âŹ
K.Flay - FML [Official Video]
âWhat the hell are you?!â
âDeath.â
Apex Legends, new legend: Revenant
zaoeyo (Xiaolin Zeng)Â - Opening Sequence for âWe Need To Talkâ
By one of the two creatives behind Awaken Akira
aizawa
Finding this in the server room made my day