A photograph of the sky by Trevor Paglen can look like a massive abstraction, except for a tiny speck, a surveillance drone, spotted like a malignant dot on a chest x-ray. His images of secluded military sites in Nevada can also ooze with colour from the churning heat and dust. In the new documentary film Unseen Skies, directed by Yaara Bou Melhem, Paglen calls the effect "impressionistic haze". Photographing those places, often from miles away (or farther), is about "seeing and not seeing at the same time," Paglen says. "For me those images were about capturing that paradox."

The "Training Humans" exhibition was held at the Milan Osservatorio from September 2019 to February 2020.

Supervised learning is a type of machine learning in which a model is trained using labeled data. You begin with a very large collection of labeled data. For the Iris Data Set, the data all refer to individual iris flowers, which can be divided into three related species. For the MNIST dataset, the data are images of about 70,000 handwritten numbers, 0 through 9.) You divide the dataset into two parts, the training data and the test data. The split might be 30/70, or 40/60.

ImageNet will remove 600,000 images of people stored on its database after an art project exposed racial bias in the program's artificial intelligence system. Created in 2009 by researchers at Princeton and Stanford, the online image database has been widely used by machine learning projects. The program has pulled more than 14 million images from across the web, which have been categorized by Amazon Mechanical Turk workers -- a crowdsourcing platform through which people can earn money performing small tasks for third parties. According to the results of an online project by AI researcher Kate Crawford and artist Trevor Paglen, prejudices in that labor pool appear to have biased the machine learning data. Training Humans -- an exhibition that opened last week at the Prada Foundation in Milan -- unveiled the duo's findings to the public, but part of their experiment also lives online at ImageNet Roulette, a website where users can upload their own photographs to see how the database might categorize them.

The tool, called ImageNet Roulette, detects human faces in any uploaded photo and assign them labels using ImageNet, an academic training set with millions of pictures depicting almost anything imaginable, and WordNet, the corresponding text tags. As viral examples on Twitter have shown, the results of this process are more often than not completely useless--nonsensical at best and racist or otherwise offensive at worst. In some cases, it would label black men as "offenders" or "wrongdoers," while other times it would spit out racial slurs against Asians or outdated and offensive terms for black people. I might have a bad sense of humor but I don't think this particularly funny #imagenetroulette pic.twitter.com/RR578nhCOU The offensiveness was more or less the point, says co-creator, Kate Crawford, who is also a co-founder of New York University's AI Now Institute, which studies the social implications of artificial intelligence.

How are you supposed to react when a robot calls you a "gook"? At first glance, ImageNet Roulette seems like just another viral selfie app – those irresistible 21st-century magic mirrors that offer a simulacrum of insight in exchange for a photograph of your face. Want to know what you will look like in 30 years? If you were a dog what breed would you be? That one went viral in 2016.

Modern artificial intelligence is often lauded for its growing sophistication, but mostly in doomer terms. If you're on the apocalyptic end of the spectrum, the AI revolution will automate millions of jobs, eliminate the barrier between reality and artifice, and, eventually, force humanity to the brink of extinction. Along the way, maybe we get robot butlers, maybe we're stuffed into embryonic pods and harvested for energy. But it's easy to forget that most AI right now is terribly stupid and only useful in narrow, niche domains for which its underlying software has been specifically trained, like playing an ancient Chinese board game or translating text in one language into another. Ask your standard recognition bot to do something novel, like analyze and label a photograph using only its acquired knowledge, and you'll get some comically nonsensical results.

The news that Christie's would sell an artwork made by artificial intelligence this October captured worldwide headlines and imaginations alike. Portrait of Edmond de Belamy (2018), an uncanny, algorithm-created rendering of an aristocratic gentleman, will hit the auction block in New York with an estimate of $7,000 to $10,000. But the piece's inclusion in such a high-profile sale is creating controversy far ahead of the auction itself. While images generated using AI technology have been circulating relatively widely since Google's pattern-finding software DeepDream roared onto the scene in 2015, the field was still young, and the artworks produced via AI were neither aesthetically nor conceptually rich enough to hold the attention of the art world for long. But after the heavyweight auction house announced it was ready to sell this latest work, the mysterious portrait--and the even more mysterious algorithm behind it--were cast by many in the media as the new standard-bearers for the genre.

Around the time Leonardo Da Vinci was painting the Mona Lisa, he was also writing his Codex on the Flight of Birds, a roughly 35,000-word exploration of the ways in which man might take to the air. His illustrations included diagrams positing pre-Newtonian theories of physics, a rudimentary plan for a flying machine and many, many sketches of birds in flight. The Mona Lisa, with her secretive smile, is a universe of intimacy captured on a relatively small panel of wood. But the landscape behind his captivating subject shows the world as you would see it from atop a tall hill--or from the vantage point you would get if you had hitched a ride on the back of a giant bird. Even as da Vinci was perfecting one way of seeing a face, he was dreaming of other ways of looking.

Images used to be made by people, for people. Today, there's an entirely new kind of image: pictures taken by machines, for other machines to use. This new genre–created by cameras mounted on traffic lights, in shopping malls, on advertisements, and on computers and smartphones–is teaching computers how to see. "You have a moment where for the first time in history most of the images in the world are made by machines for other machines, and humans aren't even in the loop," says the Berlin-based artist Trevor Paglen. "I think the automation of vision is a much bigger deal than the invention of perspective."