In Artificial Condition, Martha Well's soap opera loving rogue security AI remains cantankerous and awesome.
Murderbot is an AI security robot with a busted autonomy regulator. So long as they can keep the regulator a secret, they can remain fully aware and independent. Mostly they want to watch soap operas. Soap operas and to be left the hell alone.
I absolutely adore Murderbot. Murderbot wants quality time on their own.
In the second installment Murderbot sets out to learn about the event from which they named themselves, wherein many humans died and their AI regulator was broken. Murderbot has no direct recollection of what went on and believes this knowledge will change everything.
Murderbot teams up with an AI research ship named ART and heads off to the mining colony where it all went down.
Artificial Condition: The Murderbot Diaries Book 2 via Amazon Read the rest
Janelle Shane is an AI researcher. In this TED talk she explains that we should not be afraid that AIs are going to rebel against us. We should be afraid of AIs because they are going to do exactly what we tell them to do. "It's really easy to accidentally give AI the wrong problem to solve," she says, "and often we don't realize that until something has actually gone wrong." Read the rest
Even the covers to this collection of AI-written science fiction novels were created by AI. The reviews are also written by AI. Titles include Bitches of the Points, Auro-Minds and the Hungers, The Table in 10, and Breath Chanter. Of course it's an art project and the writing is a mess, but an excerpt from any of the books could be slipped in the pages of a Gor novel without anyone being the wiser.
[via Bruce Sterling] Read the rest
Roko's Basilisk is a notorious thought experiment regarding artificial intelligence and our own perceptions of reality, particularly as it relates to a hypothetically powerful AI. It's kind of like Newcomb's Paradox, with a little more Battlestar Galactica-style AI genocide.
If you want to know more about it, feel free to click the link. But be warned: the whole point of Roko's Basilisk is that the mere knowledge of Roko's Basilisk also makes you complicit in Roko's Basilisk. If Roko's Basilisk is real—a question which is intrinsic to the thought experiment itself—then the potential contained within that hypothetical idea is enough to sow the seeds to self-destructive doubt. And that's how Roko's Basilisk wins.
You don't need to know the specific details of Roko's Basilisk to understand how the concept could relate to the growing phenomenon of deepfakes—the manipulation of deep Learning technology to create deceptively realistic videos, like adding Nicholas Cage's face into every movie. The cybersecurity firm DeepTrace recently released a report on the myriad ways that deepfakes threaten our trust in knowledge, and in our own eyes. And their conclusion? The mere idea of deepfakes is enough to bring the worst case scenario to life—even if we never actually reach that worst case scenario in practice.
Nicholas Cage as Amy Adams, because Deepfakes.
In reality, deepfakes haven't actually been used to successfully falsify videos of politicians to use as large-scale propaganda; like most things on the internet, they're mostly used for porn. But the fact that they could be used to deceive the public is itself enough to make public trust spiral downwards, causing us to debate both what is true, and the methods by which we determine what is true. Read the rest
Google DeepMind co-founder Mustafa Suleyman has been placed on leave from the applied artificial intelligence lab he ran. Read the rest
“The cameras are already transforming modern day policing in Uganda, with facial recognition and artificial intelligence as part of policing and security.” — Ugandan Police.
GPT-2 is OpenAI's language model that produces astonishingly lucid text responses to short text inputs. I've been playing around with a small model of GPT-2 (here are installation instructions) for a while and am shocked by the quality of many of its responses. The language model is probabilistic, too, so it gives a new output every time you enter the same prompt. For example, I fed it with this fake news prompt:
Food scientists have developed a process to turn people who have died from ebola into delicious food. The disease victims' bodies are freeze dried, then ground into fine powder and mixed with salt and spices. "Tastes like chicken," say taste testers. The first batch of “Ebola Bowls” will be released on a limited basis at Whole Foods stores by Thanksgiving.
Here are five outputs from the above prompt:
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GPT-2 OUTPUT (1): The bowl is made from raw chicken and other animals, which are ground and cooked in water to release oil and protein. This is the best way to cook the human body, says Dr. John D'Agostino, who led the study and now works for the Food and Drug Administration (FDA).
The researchers believe that the Ebola bite is the most common way that people develop Ebola and that some people may carry the virus to other parts of the body. The virus can spread from person to person by contact with bodily fluids.
The study was published in the journal Infectious Diseases. Other scientists are still studying the safety and efficacy of the meal.
AI Portraits does an amazing job of creating original portraits based on photos of faces. The generative network was trained with 45,000 portrait images.
"Not only colors, but shapes, the lines of the face are completely redesigned by the model. In the above example, the model decides upon a Renaissance style, highlighting the elegance of the aquiline nose, the smoothness of the forehead."
Notice how the AI didn't show Isabella Rossellini's teeth. I tried it with a photo where I'm smiling and got the same result:
According to the folks who made AI Portraits, "Portrait masters rarely paint smiling people because smiles and laughter were commonly associated with a more comic aspect of genre painting, and because the display of such an overt expression as smiling can seem to distort the face of the sitter. This inability of artificial intelligence to reproduce our smiles is teaching us something about the history of art." Read the rest
Instagram launched a new feature today, Restrict, intended to help vulnerable users avoid abuse. Facebook's Head of Instagram Adam Mosseri says the company will also be focusing on new uses for AI to crack down on bullying. Read the rest
Military research and Chinese firms had access to the data Microsoft scraped under Creative Commons licenses.
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.