How-to

Privacy

By combining smart watch sensor data with an algorithm to infer key entry sequences from even the smallest of hand movements, the team [Yan Wang and colleagues] was able to crack private ATM PINs with 80 percent accuracy on the first try and more than 90 percent accuracy after three tries.

Safe in Police hands? shows that between June 2011 and December 2015 there were at least 2,315 data breaches conducted by police staff. Over 800 members of staff accessed personal information without a policing purpose and information was inappropriately shared with third parties more than 800 times. Specific incidents show officers misusing their access to information for financial gain and passing sensitive information to members of organised crime groups.

In America, surveillance has always played an outsized role in the relationship between creditors and debtors. In the 19th century, credit bureaus pioneered mass-surveillance techniques. Today the American debtor faces remote kill switches in their devices, GPS tracking on their leased cars, and surreptitious webcam recordings from their rent-to-own laptops. And where our buying and borrowing habits were once tracked by shopkeepers, our computers score our creditworthiness without us knowing.

Soon anybody with a high-resolution camera and the right software will be able to determine your identity. That's because several technologies are converging to make this accessible. Recognition algorithms have become far more accurate, the devices we carry can process huge amounts of data, and there's massive databases of faces now available on social media that are tied to our real names. As facial recognition enters the mainstream, it will have serious implications for your privacy.

A new app called FindFace, recently released in Russia, gives us a glimpse into what this future might look like. Made by two 20-something entrepreneurs, FindFace allows anybody to snap a photo of a passerby and discover their real name — already with 70% reliability. The app allows people to upload photos and compare faces to user profiles from the popular social network Vkontakte, returning a result in a matter of seconds. According to an interview in the Guardian, the founders claim to already have 500,000 users and have processed over 3 million searches in the two months since they've launched.

[...] While there are reasons to be skeptical of their claims, FindFace is already being deployed in questionable ways. Some users have tried to identify fellow riders on the subway, while others are using the app to reveal the real names of porn actresses against their will. Powerful facial recognition technology is now in the hands of consumers to use how they please.

Tech

0.10.0 also marks a shift in project goals for python-ggplot. While the initial intention was to mimic the R-ggplot API, it's now clear that this isn't 100% necessary (and can make for some really strange looking code). In the future, you can expect there to be significant feature overlap, but not necessarily mimicry (after all, R is a little weird).

[...] These guys [Computational Story Lab at the University of Vermont in Burlington] have used sentiment analysis to map the emotional arcs of over 1,700 stories and then used data-mining techniques to reveal the most common arcs. "We find a set of six core trajectories which form the building blocks of complex narratives," they say.

Obligatory.

Artificial intelligence (AI) — once the stuff of science fiction — is fuelling the efforts of two Vancouver-based entrepreneurs to give industrial drones the ability to explore distant mining sites, search for missing people in remote locations and even deliver your favourite pizza.

[...] Without a way to avoid mid-air collisions, drones risk crashing into a Cessna, a flock of geese or a 747. Worst case scenario: a drone gets sucked into a jet engine causing catastrophic engine failure as high-velocity bits of metal penetrate fuel tanks, hydraulic lines and the cabin.

Researchers from the University of Southern California have developed a new machine learning tool capable of detecting certain speech-related diagnostic criteria in patients being evaluated for depression. Known as SimSensei, the tool listens to patient's voices during diagnostic interviews for reductions in vowel expression characteristic of psychological and neurological disorders that may not be sufficiently clear to human interviewers. The idea is (of course) not to replace those interviewers, but to add additional objective weight to the diagnostic process.

Google DeepMind, the London-based artificial intelligence unit owned by Alphabet Inc., announced a research partnership today with the National Health Service to gain access to a million anonymous eye scans. DeepMind will use the data to train its computers to identify eye defects. The aim is to give doctors a digital tool that can read an eye-scan test and recognize problems faster.

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