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.”
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.”
Boston.com reporter Sanjay Salomon writes about how “Duckietown,” a model city developed by MIT researchers, could help make self-driving cars a reality. “We realized if you scale down autonomous driving to something very small there’s lots of research to do on a smaller scale with none of the logistical challenges of real autonomous vehicle research,” explains postdoc Liam Paull.
Popular Science reporter Mary Beth Griggs writes that in an MIT course students developed a fleet of duckie-adorned self-driving taxis for a village called “Duckietown.” “Each of the robot taxis is equipped with only a single camera, and makes its way around the roads without any preprogrammed maps."
Cade Metz writes for Wired that MIT researchers have developed a system that allows robots to predict how objects will move. Postdoc Ilker Yildirim explains that in order for a robot to be able to assist with household tasks like washing the dishes, it must “deeply understand its physical environments.”
MIT computer scientists have developed a program that can predict how objects will move with the same accuracy as humans, reports Mary Beth Griggs for Popular Science. The researchers hope to eventually be able to program the system to “make predictions in the natural world even faster than we can.”
An AI system created by MIT researchers can predict how physical objects move through the world with human-like accuracy, reports Curt Woodward for BetaBoston. “Where humans learn to make such judgments intuitively, we essentially had to teach the system each of these properties,” explains postdoc Ilker Yildirim.
MIT researchers have created an algorithm that can identify what traits make an image memorable, reports Matt McFarland for The Washington Post. The algorithm could prove useful in developing educational tools as “textbooks and teaching aids could start to use visual aids that have been proven to stick in our heads,” McFarland explains.
MIT researchers have developed an algorithm that can predict the memorability of images with human-like accuracy, reports Adrienne Lafrance for The Atlantic. The researchers explained that their work demonstrates that “predicting human cognitive abilities is within reach for the field of computer vision.”
Boston.com reporter Megan McGinnes writes that MIT researchers are developing software that can predict how well people will remember certain images. Users of the new software “can feed images into the database, which are then overlaid with a heat map to show regions of the photo viewers are most likely to remember.”