Finally, a useful application for augmented reality: rendering virtual kitchen roaches

Laanlabs's showreel for 6d.ai meshing technology is an augmented reality demo in which virtual cockroaches crawl all over a very real kitchen. It's the best use of augmented reality I've ever seen. (via Beyond the Beyond) Read the rest

Machine learning classifiers are up to 20% less accurate when labeling photos from homes in poor countries

A new study from Facebook AI Research evaluates common machine-learning classifiers' ability to label photos of objects found in households in rich countries versus household objects from poor countries and finds that the models' performance lags significantly when being asked to classify the possessions of poor people. Read the rest

A deep dive into stalkerware's creepy marketing, illegal privacy invasions, and terrible security

Stalkerware -- spyware sold to people as a means of keeping tabs on their romantic partners, kids, employees, etc -- is a dumpster fire of terrible security (compounded by absentee management), sleazy business practices, and gross marketing targeted at abusive men who want to spy on women. Read the rest

DARPA's Spectrum Collaboration Challenge: finally some progress towards a "Cognitive Radio" future

For 17 years, I've been writing about the possibilities of "cognitive radio", in which radios sense which spectrum is available from moment to moment and collaborate to frequency-hop (and perform other tricks) to maximize the efficiency of wireless communications. Read the rest

A(nother) Lego Turing machine

Making a Turing machine is a kind of nerd rite of passage, like manually editing your X11 settings or building a two-second time-machine. As far back as 2005, we were chronicling the adventures of Lego Turing-machine builders (the state of the art advanced rather a lot by 2012), as well as the ongoing effort to attain Turing completeness in wood and also baked goods. Read the rest

Sleuthing from public sources to figure out how the Hateful Eight leaker was caught

In 2014, Quentin Tarantino sued Gawker for publishing a link to a leaked pre-release screener of his movie "The Hateful Eight." The ensuing court-case revealed that the screeners Tarantino's company had released had some forensic "traitor tracing" features to enable them to track down the identities of people who leaked copies. Read the rest

Explainer video: When does data become big data?

Rebecca Tickle is a PhD student in the Faculty of Science at the University of Nottingham, explains what big data is.  She uses a handy "Five Vees of Big Data Mnemonic" -- volume, velocity, variety, value, and veracity. She mentions that other people have come up with the "Seven Vees of Big Data Mnemonic" and even the "Ten Vees of Big Data Mnemonic."

Image: YouTube  Read the rest

Towards a method for fixing machine learning's persistent and catastrophic blind spots

An adversarial preturbation is a small, human-imperceptible change to a piece of data that flummoxes an otherwise well-behaved machine learning classifier: for example, there's a really accurate ML model that guesses which full-sized image corresponds to a small thumbnail, but if you change just one pixel in the thumbnail, the classifier stops working almost entirely. Read the rest

Why "collapse" (not "rot") is the way to think about software problems

For decades, programmers have talked about the tendency of software to become less reliable over time as "rot," but Konrad Hinsen makes a compelling case that the right metaphor is "collapse," because the reason software degrades is that the ground underneath it (hardware, operating systems, libraries, programming languages) has shifted, like the earth moving under your house. Read the rest

A 40cm-square patch that renders you invisible to person-detecting AIs

Researchers from KU Leuven have published a paper showing how they can create a 40cm x 40cm "patch" that fools a convoluted neural network classifier that is otherwise a good tool for identifying humans into thinking that a person is not a person -- something that could be used to defeat AI-based security camera systems. They theorize that the could just print the patch on a t-shirt and get the same result. Read the rest

A machine-learning wishlist for hardware designers

Pete Warden (previously) is one of my favorite commentators on machine learning and computer science; yesterday he gave a keynote at the IEEE Custom Integrated Circuits Conference, on the ways that hardware specialization could improve machine learning: his main point is that though there's a wealth of hardware specialized for creating models, we need more hardware optimized for running models. Read the rest

Mechanical calculators have the BEST divide-by-zero errors

In a delightful short video, Klara Sjöberg demonstrates the extreme and alarming freakout that you can trigger in a mechanical calculator by trying to divide a number by zero; in a followup, Lynn Grant tweets "That is why the old Friden calculators had a 'Divide Stop' key." Read the rest

What the rest of the world doesn't know about Chinese AI

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. Read the rest

Most paint-spatters are valid perl programs

If you run most paint-spatters through OCR software, it will generate valid perl programs. Read the rest

Front-line programmers default to insecure practices unless they are instructed to do otherwise

It's always sort of baffling when security breaches reveal that a company has stored millions of users' passwords in unencrypted form, or put their data on an insecure cloud drive, or transmitted it between the users' devices and the company's servers without encryption, or left an API wide open, or some other elementary error: how does anyone in this day and age deploy something so insecure? Read the rest

Creative Adversarial Networks: GANs that make art

Generative Adversarial Networks use a pair of machine-learning models to create things that seem very realistic: one of the models, the "generator," uses its training data to make new things; and the other, the "discerner," checks the generator's output to see if it conforms to the model. Read the rest

A machine-learning system that guesses whether text was produced by machine-learning systems

Gltr is an MIT-IBM Watson Lab/Harvard NLP joint project that analyzes texts and predicts whether that text was generated by a machine-learning model. Read the rest

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