A team of researchers from MIT, Northeastern, and Harvard has found links between cell phone usage and unemployment, reports Janelle Nanos for BetaBoston. The researchers found that “cellphone use and mobility dropped significantly in areas which eventually reported massive unemployment spikes,” Nanos explains.
Wall Street Journal reporter Josh Zumbrun writes about a new study co-authored by MIT researchers that found that cell-phone records can indicate if a person has been laid off. The researchers found that “people’s social lives and mobility contracted following a layoff.”
Team Raptor Maps received the top prize in MIT’s annual $100K Entrepreneurship Competition, reports Nidhi Subbaraman for BetaBoston. Founded by three MIT students, Raptor Maps “proposes to use camera-carrying drones to survey farmland and pinpoint damage before pests and diseases can decimate crops.”
Jordan Graham writes for The Boston Herald about a system created by Prof. Brian Williams that allows unpiloted underwater vehicles to make decisions without human intervention. Williams explains that the system was developed so that an underwater robot would not need low-level commands, “you just give it your goals.”
Brooks Hays of UPI writes that Prof. Brian Williams has developed a new system that allows autonomous underwater vehicles to operate independently. Robots using the new system “are able to navigate underwater expanses and execute research tasks on their own. Researchers simply dictate high-level goals, and the submersible calculates the most efficient path forward."
MIT researchers have developed a new system that gives underwater robots more decision-making capabilities, reports Kelsey Atherton for Popular Science. Atherton explains that developing machines that can operate without human control could “usher in a whole new age of discovery.”
Lauren Beavin of Boston Magazine speaks with A.M. Turing Award recipient Michael Stonebraker about why Boston is such a great place for computer scientists. The Boston tech scene "is way above critical mass, and the quality of life here is very, very high,” Stonebraker explains.
The ACM has awarded the A.M. Turing Award, widely regarded as the “Nobel Prize in Computing,” to CSAIL researcher and adjunct professor Michael Stonebraker, reports Barb Darrow for Fortune. Stonebraker is “famous for arguing that database is not a one-size-fits-all category."
Michael Stonebraker, a principal investigator at the MIT Computer Science and Artificial Intelligence Lab and an adjunct professor at MIT, has won the A.M. Turning Award for his work with database management systems, reports Nidhi Subbaraman for BetaBoston. “This is every computer scientist’s lifetime dream, and it came true for me,” said Stonebraker.
A new study by MIT scientists has found that metadata provides enough information to identify consumers in anonymous data sets. Aviva Rutkin writes for New Scientist that “for 90 per cent of people, just four pieces of information about where they had gone on what day was enough to pick out which card record was theirs.”
In this video, Robert Lee Hotz of The Wall Street Journal discusses how MIT researchers have found that individuals in an anonymous data set can be identified using just a few pieces of information about their shopping habits. “We're really being shadowed by our credit cards,” Lee Hotz explains.
A new MIT study examining anonymous credit card data shows that individuals can be identified using just a few pieces of information, writes Wall Street Journal reporter Robert Lee Hotz. “This touches on the fundamental limit of anonymizing data,” explains Yves-Alexandre de Montjoye.
In a piece for Scientific American, Larry Greenemeier writes about new MIT research showing how easy it is to identify individuals in anonymous data sets. “We have to think harder and reform how we approach data protection and go beyond anonymity, which is very difficult to achieve given the trail of information we all leave digitally,” says Yves-Alexandre de Montjoye.
MIT researchers were able to accurately identify individuals in an anonymous data set by looking at the date and location of four credit card transactions, reports Boer Deng for Nature. “Even when researchers only had estimates of time and location of a purchase to within a few days or neighbo[u]rhood blocks, they could still identify cardholders,” explains Deng.
Rebecca Jacobson writes for the PBS NewsHour about how MIT researchers have found that individuals in anonymous data sets can be identified using just a few pieces of outside information. The researchers found that there is a “94 percent chance of tracking all of your purchases with three pieces of extra information.”