...a group of researchers from the University of Washington and the University of California at San Diego found that they could "fingerprint" drivers based only on data they collected from internal computer network of the vehicle their test subjects were driving, what's known as a car's CAN bus. In fact, they found that the data collected from a car's brake pedal alone could let them correctly distinguish the correct driver out of 15 individuals about nine times out of ten, after just 15 minutes of driving. With 90 minutes driving data or monitoring more car components, they could pick out the correct driver fully 100 percent of the time.

Since 2008, the FBI has been assembling a massive database of biometric information on Americans. This database, called Next Generation Identification (NGI), includes fingerprints, face recognition, iris scans and palm prints—collected not just during arrests, but also from millions of Americans for non-criminal reasons like immigration, background checks, and state licensing requirements. Now the FBI wants to exempt this vast collection of data from basic requirements guaranteed under the federal Privacy Act—and it's giving you only 21 business days to object.

Uber has your home address. It has the addresses of the places you want to get to. It knows when you're going to church, to your boyfriend's house, to the union hall, to the doctor's office. And if you’re a driver for Uber, it's tracking you for hours and hours each day.

[...] Uber tracks its drivers via GPS. In Hangzhou, China, the company used tracking to find drivers that were attending taxi protests, and threatened to terminate them as drivers.


A new analysis of 358 mass shootings in America in 2015 found that three-quarters of the victims whose race could be identified were black.

Roughly a third of the incidents with known circumstances were drive-by shootings or were identified by law enforcement as gang-related. Another third were sparked by arguments, often among people who were drunk or high.

The analysis, conducted by the New York Times with data collected by Reddit's mass shooting tracker and the Gun Violence Archive, used law enforcement reports on shootings that left four or more people injured or dead in 2015.

According to a new study, the 2015 election focused on the manliness of federal party leaders, with Trudeau often portrayed as subordinate in a field of aggressive alpha males.

[...] Sabin and co-author Kyle Kirkup reviewed 756 editorials and op-eds in Canada's top 10 English-language newspapers as well as interviewing journalists who covered the 2015 campaign. They looked for key words reflecting traditional masculinity, such as strength, stoicism and decisiveness. They also tracked emasculating words such as boyish, florid and emotional.

Today, Google's newest machine learning project released its first piece of generated art, a 90-second piano melody created through a trained neural network, provided with just four notes up front. The drums and orchestration weren't generated by the algorithm, but added for emphasis after the fact. It's the first tangible product of Google's Magenta program, which is designed to put Google's machine learning systems to work creating art and music systems.