Researchers claim to have permanently neutralized ad-blocking's most promising weapons

Last year, Princeton researchers revealed a powerful new ad-blocking technique: perceptual ad-blocking uses a machine-learning model trained on images of pages with the ads identified to make predictions about which page elements are ads to block and which parts are not. Read the rest

Kenyans from "the toughest neighborhood on earth" trace pixels all day to train autonomous vehicles

The Nairobi neighborhood of Kibera is Africa's largest slum, and it's home to an unlikely, Silicon-Valley-style tech park operated by Samasource (motto: "Artificial intelligence meets human dignity"), who serves clients from Google to Microsoft to Salesforce, using clickworkers who get paid $9/day, compared to the going wage of $2/day in the region's "informal economy" (the company believes that paying wages on par with rich-world clickworkers would "distort the local economy"). Read the rest

Using machine learning to teach robots to get dressed

In the Siggraph 2018 paper Learning to Dress: Synthesizing Human Dressing Motion via Deep Reinforcement Learning, a Georgia Institute of Technology/Google Brain research team describe how they taught body-shame to an AI, leaving it with an unstoppable compulsion to clothe itself before the frowning mien of God. Read the rest

DeOldify: a free/open photo-retoucher based on machine learning

Jason Antic's DeOldify is a Self-Attention Generative Adversarial Network-based machine learning system that colorizes and restores old images. It's only in the early stages but it's already producing really impressive results, and the pipeline includes a "defade" model that is "just training the same model to reconstruct images that augmented with ridiculous contrast/brightness adjustments, as a simulation of fading photos and photos taken with old/bad equipment." Read the rest

Customizable ethics checklists for Big Data researchers

Deon is a project to create automated "ethics checklists" for data science projects; by default, running the code creates a comprehensive checklist covering data collection and storage, modeling and deployment: the checklist items aren't specific actions, they're "meant to provoke discussion among good-faith actors who take their ethical responsibilities seriously. Because of this, most of the items are framed as prompts to discuss or consider. Teams will want to document these discussions and decisions for posterity." Read the rest

This AI-generated portrait just sold at auction for $432K, but not without controversy

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.

Christie's:

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.

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Amazon is actively pitching face-recognition to ICE

Despite an uprising of Amazon employees over the use of the company's AI facial recognition program ("Rekognition") in law enforcement, the company is actively courting US Immigration and Customs Enforcement in the hopes that it will use the wildly inaccurate technology. Read the rest

Robin "Sourdough" Sloan is using a machine-learning autocomplete system to write his next novel

Robin Sloan is a programmer and novelist whose books like Sourdough and Mr Penumbra's 24-Hour Bookstore are rich and evocative blends of self-aware nerdy playfulness and magical speculation. Read the rest

Compression could be machine learning's "killer app"

Pete Warden (previously) writes persuasively that machine learning companies could make a ton of money by turning to data-compression: for example, ML systems could convert your speech to text, then back into speech using a high-fidelity facsimile of your voice at the other end, saving enormous amounts of bandwidth in between. Read the rest

Stet, a gorgeous, intricate, tiny story of sociopathic automotive vehicles

Sarah Gailey's micro-short-story STET is a beautiful piece of innovative storytelling that perfectly blends the three ingredients for a perfect piece of science fiction: sharply observed technological speculation that reflects on our present moment; a narrative arc for characters we sympathize with; and a sting in the tail that will stay with you long after the story's been read. Read the rest

What would a "counterculture of AI" look like?

Shitty math kills: shitty math "proved" that being selfish produced optimal outcomes and torched the planet; shitty math rains hellfire missiles down on innocents; in the 1960s, shitty math drove the "hamlet pacification program," producing 90,000 pages a month in data about the endless hail of bombs the US dropped on Vietnam. Read the rest

Amazon trained a sexism-fighting, resume-screening AI with sexist hiring data, so the bot became sexist

Some parts of machine learning are incredibly esoteric and hard to grasp, surprising even seasoned computer science pros; other parts of it are just the same problems that programmers have contended with since the earliest days of computation. The problem Amazon had with its machine-learning-based system for screening job applicants was the latter. Read the rest

Survey: corporate execs vastly overestimate customers' satisfaction

A (somewhat dubious) survey of 850 business executives for firms of 500 or more employees "with involvement in the decision making process regarding customer experience in their organization" and 4,500 consumers "who have contacted a brand during the last six months with an enquiry or issue to be resolved" found a vast gap between how satisfied the executives believed their customers were and how the customers felt about their interactions. Read the rest

Hate-speech detection algorithms are trivial to fool

In All You Need is “Love”: Evading Hate Speech Detection, a Finnish-Italian computer science research team describe their research on evading hate-speech detection algorithms; their work will be presented next month in Toronto at the ACM Workshop on Artificial Intelligence and Security. Read the rest

There's a literal elephant in machine learning's room

Machine learning image classifiers use context clues to help understand the contents of a room, for example, if they manage to identify a dining-room table with a high degree of confidence, that can help resolve ambiguity about other objects nearby, identifying them as chairs. Read the rest

IBM developed NYPD surveillance tools that let cops pick targets based on skin color

The NYPD's secretive Lower Manhattan Security Coordination Center uses software from IBM in its video analytics system, which allows cops to automatically scan surveillance footage for machine-generated labels that identify clothing and other identifying classifiers. Read the rest

AI will try to paint what you tell it to, often generating surreal horrors

A research team wrote about how they trained a machine-learning AI to generate images from text descriptions. When fed birds as its dataset, it got very good at painting birds...

... But the more you feed it, the crazier it gets.

More collected here, and you can try it yourself thanks to Chris Valenzuela's online implementation.

Here are my efforts.

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