New Scientist reporter Victoria Turk writes that MIT researchers have developed a system that can predict the future based off of a still image. Turk writes that the system could enable “an AI assistant to recognize when someone is about to fall, or help a self-driving car foresee an accident.”
Graduate student Maimuna Majumder writes for Wired about her research examining how the 2016 presidential election impacted the mental health of people in different states around the U.S. Majumder and her colleagues found that while susceptibility varies across groups, the presidential campaign, “has likely had adverse effects on the mental health and wellbeing of American citizens.”
Quentin Palfrey of J-PAL North America writes for The Hill about steps the Commission on Evidence-based Policymaking can take to support research and policymaking. “Analyzing administrative data to adjust government programs may seem like dry, behind-the-scenes work, but when scaled up to redirect national policies, it can have a significant impact on millions of Americans.”
Scott Clement of The Washington Post writes that researchers at the Laboratory for Social Machines have found that while the majority of Twitter conversation concerning the presidential campaign has centered around Donald Trump over the past week and a half, “battlegrounds differed in what particular issues or themes they focused on.”
A new book by Prof. Carlo Ratti and graduate student Matthew Claudel focuses on the impact technology has on cities, writes Kate Abbey-Lambertz for The Huffington Post. “Ratti and Claudel envision a potential future where new technology ― from individualized heating grids to neighborhood 3D-printing fabrication studios ― ‘weaves into a tapestry of citizen empowerment’.”
Emma Grey Ellis writes for Wired that MIT researchers are aiming to get a better grasp on public health in cities around the world by analyzing the microbes in sewage systems using a robot dubbed Luigi. Wilson explains that sewage “carries drug metabolites, DNA, even biomarkers for diabetes—and all that data is available in real time.”
MIT researchers have developed a database of annotated English words written by non-native English speakers, reports Kevin Hartnett for The Boston Globe. The database will provide “a platform for the study of learner English and also make it easier to develop technology like better search engines that supports non-native speakers.”
MIT researchers have developed a new system for protecting patient privacy in genomic databases, reports Anna Nowogrodzki for Nature. The system “masks the donor's identity by adding a small amount of noise, or random variation, to the results it returns on a user’s query,” Nowogrodzki explains.
In an article for The Huffington Post about why virtual assistants have trouble understanding accents, Philip Ellis highlights how researchers from MIT have compiled a database of written English composed by non-native speakers. Ellis explains that the aim is "to create a richer context for machine learning” systems.
In an article for The New Yorker, Frank Rose features “The City of Tomorrow”, a new book by Prof. Carlo Ratti and graduate student Matthew Claudel. Rose writes that the city Claudel and Ratti envision is “a hybrid of the digital and the physical, a ‘triumph of atoms and bits’ that yields a sort of augmented urban reality.”
Prof. Jonathan How speaks with Boston Globe reporter Steve Annear about how researchers from MIT and Ford are collaborating on a new project aimed at better understanding vehicle mobility and demand in dense urban areas. The project will allow researchers to “investigate new planning and prediction algorithms in a complex, but controlled, environment,” explains How.
Researchers from MIT and Ford are collaborating on a new project to measure pedestrian traffic and predict the need for on-demand shuttle services, reports Kirsten Korosec for Fortune. The researchers hope to use the data they collect to predict demand for shuttles, and then "routing those vehicles to areas where they’re needed most at the corresponding times.”
In an article for Wired, Tim Moynihan writes that a team of CSAIL researchers has created a machine-learning system that can produce sound effects for silent videos. The researchers hope that the system could be used to “help robots identify the materials and physical properties of an object by analyzing the sounds it makes.”
Writing for the Financial Times, Clive Cookson reports that MIT researchers have developed an artificial intelligence system capable of producing realistic sounds for silent movies. Cookson explains that another application for the system could be “to help robots understand objects’ physical properties and interact better with their surroundings."
Washington Post reporter Matt McFarland writes that MIT researchers have created an algorithm that can produce realistic sounds. “The findings are an example of the power of deep learning,” explains McFarland. “With deep learning, a computer system learns to recognize patterns in huge piles of data and applies what it learns in useful ways.”