Tom Loftus of The Wall Street Journal highlights a study co-authored by MIT Prof. Maryam Farboodi that finds big data plays an important role in raising capital from investors and could contribute to the growing divide between large and small companies.
In this video, Reuters reporter Matthew Stock explores how researchers at the MIT Senseable City Lab are using big data to help make crowded cities better places to live. Stock explains that researchers hope to use anonymous data to improve public infrastructure and living spaces to “make the metropolis fit for future generations.”
Justin Chen of STAT writes that Biobot Analytics, which was founded by former MIT researchers, is measuring traces of drugs in sewers in an attempt to detect emerging public health threats. The technology could “first pinpoint communities that need interventions, like substance abuse programs, and later measure the success of those programs in lowering drug use,” explains Chen.
Writing for The Verge, Angela Chen highlights advances in AI that are allowing researchers to discover and understand new materials at a rapid pace. Chen cites a study co-authored by Assistant Prof. Elsa Olivetti, who “developed a machine-learning system that scans academic papers to figure out which ones include instructions for making certain materials.”
TechCrunch’s Devin Coldewey profiles ReviveMed, a biotech startup out of MIT that turns drug discovery into a big data problem. “ReviveMed’s approach is a fundamentally modern one that wouldn’t be possible just a few years ago, such is the scale of the data involved,” writes Coldeway.
In a new working paper, Prof. Daron Acemoglu and his co-author argue that the rise in automation is linked to the aging of the blue-collar population. “The study shows that workers feeling the brunt of automation in lost jobs and lower wages are between the ages of 36 and 55. Those findings should make it easier for policy makers to track down the most affected workers—and help them survive the robot rush,” writes Ana Campoy for Quartz.
Empatica, a startup founded by Prof. Rosalind Picard, makes a wearable sensor that detects a person’s seizures, as well as certain physiological factors. Picard discussed the potential benefits of her work with Co.Design’s Katherine Schwab: “[W]hen you get personalized, long-term data from a watch or a phone, we can start to help an individual learn [their] patterns, not on average for some group you may be an outlier in.”
In an article for the Financial Times, Robin Wigglesworth highlights Profs. Alberto Cavallo and Roberto Rigobon’s work with the Billion Prices Project. Wigglesworth notes that the project is an, “example of a broader trend of trawling the swelling sea of big data for clues on how companies, industries or entire economies are performing.”
Wall Street Journal reporter Jason Bordoff writes that MIT researchers have produced a map of the Nairobi bus system using GPS data collected from riders’ mobile phones. “With these networks now mapped, users can access the system more easily and efficiently, and local officials can plan bus and train routes around it,” writes Bordoff.
A new study by MIT researchers provides evidence that running can be socially contagious, reports Gretchen Reynolds for The New York Times. Prof. Sinan Aral explains that the study showed, “In general, if you run more, it is likely that you can cause your friends to run more.”
Los Angeles Times reporter Amina Khan writes that a study by MIT researchers shows that exercise can be contagious. The researchers found that a “runner’s peers did influence him or her to run more — but they also discovered that not all users influenced their buddies equally. Individuals were more likely to be prodded to up their game by less-active peers.”
CSAIL researchers have found that ride-sharing taxis controlled by a citywide computer system could decrease the number of cars on the road in New York City, reports Clive Cookson for the Financial Times. The researchers found that “3,000 four-passenger cars could satisfy 98 per cent of the city’s demand.”
MIT researchers have developed a technique to make big data more manageable, writes Kevin Murnane for Forbes. The technique creates "coresets" that can be used by data analysis tools “often applied in computer vision, natural language processing, neuroscience, weather prediction, recommendation systems and more.”
A new system developed by MIT researchers can predict how a scene will unfold, similar to how humans can visually imagine the future, reports Ed Gent for Scientific American. Graduate student Carl Vondrick explains that the system is “an encouraging development in suggesting that computer scientists can imbue machines with much more advanced situational understanding."
Steven Melendez of NBC News writes that a new system developed by CSAIL researchers can predict the future by examining a photograph. Grad student Carl Vondrick explains that the system’s ability to forecast normal behavior could allow it to be used for applications like self-driving cars.