CSAIL researchers trained a robot to analyze and make scheduling suggestions in a hospital labor ward, writes Kelsey Atherton for Popular Science. Atherton writes that “by adding in a robot that can analyze scheduling needs, hospitals could make better informed decisions.”
The curved origami sculptures created by Prof. Erik Demaine and his father Martin Demaine are featured in the exhibit “Above the Fold: New Expressions in Origami,” writes Solvej Schou for the Associated Press. The father-son duo use math algorithms to solve paper-folding problems. "Our work grows directly out of our decades collaborating together in mathematics and sculpture," explains Prof. Demaine.
CSAIL researchers used videos of popular TV shows to train an algorithm to predict how two people will greet one another. “[T]he algorithm got it right more than 43 percent of the time, as compared to the shoddier 36 percent accuracy achieved by algorithms without the TV training,” notes Janet Burns in Forbes.
Mary Beth Griggs writes for Popular Science that CSAIL researchers have created an algorithm that can predict human interaction. Griggs explains that the algorithm could “lead to artificial intelligence that is better able to react to humans or even security cameras that could alert authorities when people are in need of help.”
Dan Misener writes for CBC News that CSAIL researchers have developed an algorithm that can predict interactions between two people. PhD student Carl Vondrick explains that the algorithm is "learning, for example, that when someone's hand is outstretched, that means a handshake is going to come."
CSAIL researchers have trained a deep-learning program to predict interactions between two people, writes Hope King for CNN. “Ultimately, MIT's research could help develop robots for emergency response, helping the robot assess a person's actions to determine if they are injured or in danger,” King explains.
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.”
Ryan Mandelbaum of Popular Science speaks with David Shoemaker, who leads MIT’s LIGO Lab and Advanced LIGO, about the second successful detection of gravitational waves. "It’s wonderful," says Shoemaker. "It’s so different from the first one ... but its importance is no less."
Boston Globe reporter Eric Moskowitz writes that scientists have been able to detect gravitational waves for the second time. “It’s a wondrous thing,” said David Shoemaker, who leads the MIT lab that helped build the detectors. “Three months apart, 1.4 billion years ago, these two events happened at two different places in the sky.”
Scientists have observed a second pair of black holes colliding using the twin detectors of the Laser Interferometer Gravitational Wave Observatory (LIGO), reports Dennis Overbye for The New York Times. Overbye writes that LIGO provides “a way of hearing the universe instead of just looking at it.”
For the second time, scientists have detected gravitational waves produced by the collision of two black holes, reports Irene Klotz for Reuters. “We are starting to get a glimpse of the kind of new astrophysical information that can only come from gravitational-wave detectors,” says David Shoemaker, who leads Advanced LIGO.
In an article for New Scientist, Lisa Grossman writes that for the second time the Laser Interferometer Gravitational Wave Observatory (LIGO) has detected gravitational waves. “This gives us confidence,” says MIT research scientist Salvatore Vitale. “It was not just a lucky accident. Seeing a second one tells us clearly that there is a population of black holes there.”
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.”
Popular Science reporter Mary Beth Griggs writes that MIT researchers have developed an algorithm that can learn how to predict sound. The algorithm “can watch a silent movie and create sounds that go along with the motions on screen. It's so good, it even fooled people into thinking they were actual, recorded sounds from the environment.”