James' World 2

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Matt Edmondson, a federal agent with the Department of Homeland Security for the last 21 years, got a call for help last year. A friend working in another part of government—he won’t say which one—was worried that someone might have been tailing them when they were meeting a confidential informant who had links to a terrorist organization. If they were being followed, their source’s cover may have been blown. “It was literally a matter of life and death,” Edmondson says.

“If you’re trying to tell whether you’re being followed, there are surveillance detection routes,” Edmondson says. If you’re driving, you can change lanes on a freeway, perform a U-turn, or change your route. Each can help determine whether a car is following you. But it didn’t feel like enough, Edmondson says. “He had those skills, but he was just looking for an electronic supplement,” Edmondson explains. “He was worried about the safety of the confidential informant.”

After not finding any existing tools that could help, Edmondson, a hacker, and digital forensics expert, decided to build his own anti-tracking tool. The Raspberry Pi-powered system, which can be carried around or sit in a car, scans for nearby devices and alerts you if the same phone is detected multiple times within the past 20 minutes. In theory, it can alert you if a car is tailing you. Edmondson built the system using parts that cost around $200 in total and will present the research project at the Black Hat security conference in Las Vegas this week. He’s also open-sourced its underlying code.

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When I was first dating my future wife, we went to an event that required us to go through a metal detector. The guard stopped me and our exchange went like this:

Guard: “Hey. You and her–are you together?”

Me: “Together? I mean, things are going well, but I don’t want to put a label on it.”

Future wife (laughing): “OMG. He wants to know if he can put our jackets in the same bin.”

I admit it: I’m an over-thinker. Are you? If you had been there, would you have laughed? Or would you have thought: “Gosh, that could totally have been me?”

I’d like to know your answer, but I’d also like to explain the reasoning behind the “You and her together?” story, which I’ve told more times and to more people than I can remember.

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In Uttar Pradesh, India, where I grew up, temperatures can soar as high as 120 degrees in May and June. But very few people have access to an air conditioner.

With a per capita income of around a $1,000 a year, many people in this part of the country can’t afford to buy an A/C unit or pay the power bills that come with using one.

So how do people keep cool?

That’s a question that people are asking themselves as the world faces unprecedented heat waves, including in the U.S. Pacific Northwest and Europe, where many do not have air conditioners. Severe heat has already killed thousands of people this summer.

Yet people in India and in other countries across the Global South have long figured out ways to deal with the horrible heat. And so, I’d like to share a few tips on how to stay cool that I’ve learned from my upbringing and elders in Uttar Pradesh. Some of the advice is just what you’d think – like drinking lots of liquids and staying out of the sun – but others might surprise you.

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Malaka Gharib/ NPR

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History shows civil wars to be among the messiest, most horrifying of human affairs. So Princeton professor Arvind Narayanan and his Ph.D. student Sayash Kapoor got suspicious last year when they discovered a strand of political science research claiming to predict when a civil war will break out with more than 90 percent accuracy, thanks to artificial intelligence.

A series of papers described astonishing results from using machine learning, the technique beloved by tech giants that underpins modern AI. Applying it to data such as a country’s gross domestic product and unemployment rate was said to beat more conventional statistical methods at predicting the outbreak of civil war by almost 20 percentage points.

Yet when the Princeton researchers looked more closely, many of the results turned out to be a mirage. Machine learning involves feeding an algorithm data from the past that tunes it to operate on future, unseen data. But in several papers, researchers failed to properly separate the pools of data used to train and test their code’s performance, a mistake termed “data leakage” that results in a system being tested with data it has seen before, like a student taking a test after being provided the answers.

“They were claiming near-perfect accuracy, but we found that in each of these cases, there was an error in the machine-learning pipeline,” says Kapoor. When he and Narayanan fixed those errors, in every instance they found that modern AI offered virtually no advantage.

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