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.
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.”
Wired reporter Brian Barrett writes that MIT researchers have developed a new system that allows virtual reality headsets to operate wirelessly. The system uses “millimeter wave (mm wave) technology, a large band of spectrum whose potential has gone largely untapped,” Barrett explains.
Hal Hodson writes for New Scientist that MIT researchers have developed a system that allows virtual reality headsets to operate wirelessly. To solve the problem posed by sending vast amounts of data wirelessly, the researchers used a “different wireless technology called millimeter wave (mmWave), which is in a higher band of the frequency spectrum to that used by Wi-Fi.”
MIT researchers have developed a new algorithm to create videos from still images, writes G. Clay Whittaker for Popular Science. “The system "learns" types of videos (beach, baby, golf swing...) and, starting from still images, replicates the movements that are most commonly seen in those videos,” Whittaker explains.
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’.”
A new WiFi system developed by CSAIL researchers is three times faster than a normal wireless network, writes Thomas Tamblyn for The Huffington Post. The new system allows transmitters “to work together and make sure that they’re sending information that isn’t clashing to each device,” explains Tamblyn.
In an article for Forbes, Kevin Murnane writes that a new system developed by MIT researchers significantly improves WiFi performance. Murnane writes that the system “could be used at locations like concert halls and sports stadiums to eliminate the poor WiFi performance people often experience in these venues.”
Matt McFarland writes for CNN that CSAIL researchers have created a new system that can transfer wireless data 3.3 times faster than usual. McFarland explains that to increase the speed of data transfer, researchers “developed algorithms that process a router's signal so that multiple routers can send information on the same wireless spectrum without causing interference.”
CSAIL researchers recently presented an algorithm that teaches computers to predict sounds, writes Kevin Hartnett for The Boston Globe. The ability to predict sounds will help robots successfully navigate the world and “make sense of what’s in front of them and figure out how to proceed,” writes Hartnett.
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.”