ChinAI Jeff Ding's weekly newsletter reporting on the Chinese AI scene; on the occasion of the newsletter's first anniversary, Ding has posted a roundup of things about the Chinese AI scene that the rest of the world doesn't know about, or harbors incorrect beliefs about.
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I'm heading back to Austin for the SXSW Interactive festival and you can catch me three times this weekend: first on the Untold AI panel with Malka Older, Rashida Richardson and Christopher Noessel (5-6PM, Fairmont
Manchester AB); then at the EFF Austin Party with Cindy Cohn and Bruce Sterling (7PM, 1309 Bonham Terrace); and on Sunday, I'm giving a keynote for Berlin's Re:Publica conference, which has its own track at SXSW; I'm speaking about Europe's new Copyright Directive and its dread Article 13 at 1PM at Buffalo Billiards, 201 East 6th Street.
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There aren't many details in Trump's “American A.I. Initiative,” but the point appears to be: send a message of technological dominance to China.
Stanford folklorist and science historian Adrienne Mayor has a fascinating-sounding new book out, titled "Gods and Robots: Myths, Machines, and Ancient Dreams of Technology
." It's a survey of how ancient Greeks, Romans, Indian, and Chinese myths imagined and grappled with visions of synthetic life, artificial intelligence, and autonomous robots. From Mayor's interview
at Princeton University Press:
Who first imagined the concept of robots?
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Most historians of science trace the first automatons to the Middle Ages. But I wondered, was it possible that ideas about creating artificial life were thinkable long before technology made such enterprises possible? Remarkably, as early as the time of Homer, ancient Greek myths were envisioning how to imitate, augment, and surpass nature, by means of biotechne, “life through craft”—what we now call biotechnology. Beings described as fabricated, “made, not born,” appeared in myths about Jason and the Argonauts, the sorceress Medea, the bronze robot Talos, the ingenious craftsman Daedalus, the fire-bringer Prometheus, and Pandora, the female android created by Hephaestus, god of invention. These vivid stories were ancient thought experiments set in an alternate world where technology was marvelously advanced.
Modern sci-fi movies pop up in several chapters. How do they relate to ancient myths?
Some 2,500 years before movies were invented, ancient Greek vase painters illustrated popular stories of the bronze robot warrior Talos, the techno-wizard Medea, and the fembot Pandora dispatched to earth on an evil mission, in ways that seem very “cinematic...”
Movies and myths about imagined technology are cultural dreams. Like contemporary science fiction tales, the myths show how the power of imagination allows humans to ponder how artificial life might be created—if only one possessed sublime technology and genius.
Editor's Note: Richard Metzger is a connoisseur of cannabis, and recently started growing his own. He's test-driving high-end rig good for small-scale grows from Cloudponics. This is not a sponsored post, Boing Boing is not getting anything from Cloudponics. Metzger's just really *that* enthusiastic about weed, and so far he likes the Cloudponics setup. Here's part two in Richard's ongoing series. — Xeni
In the first installment of This TARDIS Grows Weed with Artificial Intelligence, I explained how incredibly overwhelming it was for me to contemplate setting up a decent small grow situation as a rank novice. There were not only wildly varying philosophical approaches one might employ growing the dankest of nugs, but also a dizzying number of products, potions, pitfalls and problems. The proper cohort of gear needs to be amassed and assembled and it looked like there would inevitably be mistakes made along the way, some of them expensive, or at least time consuming. Growing pot seems easy if everything goes smoothly, but if one tiny thing goes wrong, then all can be lost. What are you going to do about spider mites? Mold? Nutrient burn? What is nutrient burn anyway? Read the rest
Forbes publishes 300 stories a day, and is developing AI software that writes first drafts of articles.
With Bertie, a contributor who writes regularly about the automobile industry might open up the tool to find the makings of an article about Tesla, complete with links to relevant, related articles published both on Forbes and elsewhere. The tool will surface images that might improve the story as well.
Bertie adds Forbes to the list of publishers using artificial intelligence products to help drive editorial output. The Washington Post’s Heliograf tool, which generates short stories based on structured data about things like election results or Olympics events, has generated thousands of stories since it was introduced two years ago; Reuters’ Lynx Insights tool has been helping the business publisher crank out stories since March 2018, and the Associated Press is years into using AI to write stories on topics including company earnings and minor league sports.
Hopefully Forbes will also develop AI to read these stories and click on the tooth implant adverts that accompany them. Read the rest
A neural network became a expert at detecting art forgeries by learning how famous artists drew their line strokes. Researchers at Rutgers University and the Atelier for Restoration and Research of Paintings in the Netherlands used a sample set of 300 line drawings from well-known artists such as Picasso and Matisse. From those drawings, the AI examined 80,000 lines strokes and learned what characteristics in the strokes were unique to the different artists.
From Technology Review:
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The researchers also trained a machine-learning algorithm to look for specific features, like the shape of the line in a stroke. This gave them two different techniques to detect forgeries, and the combined method proved powerful. Looking at the output of the machine-learning algorithm also provided some insight into the RNN, which acts as a “black box”—a system whose outputs are difficult for researchers to explain.
Since the machine-learning algorithm was trained on specific features, the difference between it and the RNN probably points to the characteristics the neural network was looking at to detect forgeries. In this case, it was using the changing strength along a stroke—that is, how hard an artist was pushing, based on the weight of the line—to identify the artist. With both algorithms working in tandem, the researchers were able to correctly identify artists around 80 percent of the time.
The researchers also commissioned artists to create drawings in the same style as the pieces in the data set to test the system’s ability to spot fakes. The system was able to identify the forgeries in every instance, simply by looking at a single stroke.
Remove.bg, a new free web app released by developer Benjamin Groessing on Monday, keeps popping up in my feeds so I thought I'd try it out. With one click, it removes the background of a photo using AI:
To test it out, I chose this photo cribbed from Aliexpress:
Remove.bg spit this out in less than 10 seconds:
Not bad. I suspect that the app read that pipe as "fingers." Anyone care to take this image and set her in an exotic locale by giving her a nice new background? Read the rest
First, take this quiz to see how good you are at distinguishing between real faces and fake ones made with generative adversarial networks (GANs). Then, read this article that teaches you how to spot the fakes. In a few years AI will be able to generate images that don't have recognizable tells. Read the rest
BB pal Lissa Soep of YR Media (formerly Youth Radio) writes:
Our Interactive team delved into Spotify's algorithm to discover how songs on the platform are scored for their "danceability." We were intrigued by this use of Artificial Intelligence to quantify something as personal and cultural as what makes us want to move our bodies. So we built a tool that invites users to rate a curated playlist for each song’s “danceability” and compare that rating against the one Spotify produced algorithmically. Our writer Deborah Raji uses the project to raise fascinating questions about what it means for AI to be making its way into so many corners of our lives.
"Can You Teach AI to Dance?" (YR Media)
(Image: detail of illustration by Symone Woodruff-Hardy) Read the rest
You can preorder an Alexa-enabled Big Mouth Billy Bass for $40. Read the rest
Two of the MIT researchers behind the provocative Deep Angel project, an algorithm that digitally erases objects from photos, have now delivered a strange and beautiful system to "conjure phantasms into being." Read the rest
This blurry portrait of a man may not look like much but it just sold at auction for $432,500, nearly 45 times its high estimate. What makes it so special? The Portrait of Edmond Belamy is the work of Artificial Intelligence and it's the first of its kind to sell at a major auction house.
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This portrait, however, is not the product of a human mind. It was created by an artificial intelligence, an algorithm defined by that algebraic formula with its many parentheses. And when it went under the hammer in the Prints & Multiples sale at Christie’s on 23-25 October, Portrait of Edmond Belamy sold for an incredible $432,500, signalling the arrival of AI art on the world auction stage.
The painting, if that is the right term, is one of a group of portraits of the fictional Belamy family created by Obvious, a Paris-based collective consisting of Hugo Caselles-Dupré, Pierre Fautrel and Gauthier Vernier. They are engaged in exploring the interface between art and artificial intelligence, and their method goes by the acronym GAN, which stands for ‘generative adversarial network’.
‘The algorithm is composed of two parts,’ says Caselles-Dupré. ‘On one side is the Generator, on the other the Discriminator. We fed the system with a data set of 15,000 portraits painted between the 14th century to the 20th. The Generator makes a new image based on the set, then the Discriminator tries to spot the difference between a human-made image and one created by the Generator.
Chris Veltri, proprietor of San Francisco's legendary Groove Merchant record shop, posted this astounding artifact to his Instagram wunderkammer of outré culture paper ephemera @collagedropoutsf! It's a poster for a lecture by artificial intelligence pioneer Herbert Simon that took place at UC Berkeley in 1974. The speech was titled "How Man and Computers Understand Language."
Far fucking out.
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James Mickens (previously) has a well-deserved reputation for being the information security world's funniest speaker, and if that were all he did, he would still be worth listening to.
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The theme of this year's Burning Man is I, Robot, which focuses "on the many forms of artificial intelligence that permeate our lives..." So, naturally, someone trained a neural network to come up with some camp names.
It spit out believable names like Spankles, Astro Sparkin, and Space Rock Screamin Camp, as well as weirder names like Corn Viral Hammers, Wiq Renames Spaghette, and Hellball Lounge. Then it went with some truly bizarre ones like Cohnie Stacefur Ass Chaos, Sir Liberains the Wreck Middle, and Awes Orpoop.
The woman behind the experiment, research scientist Janelle Shane, writes:
Thanks to an anonymous burner, I had a list of 1593 past Burning Man camps to feed to a neural network. A neural network is a kind of machine learning algorithm that learns to imitate the data it sees. My starting point was a textgen-rnn neural net that had been previously trained on metal bands and roller derby names, so it had a few ideas of its own to bring to the table. It did not disappoint.
There's a bunch more of these machine-learned camp names over at Shane's site.
Let's hope life imitates art and some Burners out there actually create one (or more) of these camps this year on the playa!
Image via simon of the playa
Thanks, Dan S.! Read the rest
The always entertaining and inspiring YouTuber Code Bullet decided to build a 2048-playing AI from scratch. It's really interesting to see him also build 2048 as part of the process. Read the rest