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.
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.”
Researchers at MIT have created an algorithm that transforms faces and popular landmarks into scarier versions with impressionistic, sketchy qualities, according to the BBC News. To help teach the algorithm about the concept of scariness, the researchers are asking people vote for the scariest images.
Just in time for Halloween, MIT researchers have launched a website that uses algorithms to generate scary images based off of pictures of popular landmarks and public figures, reports Rebecca Hersher for NPR. The deep-learning algorithm creates “artistic images of high perceptual quality based on examples of images created by humans,” Hersher reports.
Alyssa Newcomb writes for NBC News about the Nightmare Machine, a new system developed by MIT researchers that generates scary images based off of familiar faces and locations. “The Nightmare Machine gets scarier with help from humans, who are asked to vote on which images are the scariest,” Newcomb explains.
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.”
Researchers from MIT and IBM are joining forces to develop systems that enable machines to recognize images and sounds as people do, reports Hiawatha Bray for The Boston Globe. James DiCarlo, head of the Department of Brain and Cognitive Sciences, notes that as researchers build systems that can interpret events, “we learn ways our own brains might be doing that.”
MIT researchers are studying the possibility of developing autonomous boats and floating vessels, writes Stephanie Mlot in a Fox News article. The research, which is being conducted in collaboration with the Amsterdam Institute for Advanced Metropolitan Solutions, “aims to serve as an inspiration for urban areas around the globe.”
Writing for The New York Times, Steve Lohr features Prof. Tomaso Poggio’s work “building computational models of the visual cortex of the brain, seeking to digitally emulate its structure, even how it works and learns from experience.” Lohr notes that efforts like Poggio’s could lead to breakthroughs in computer vision and machine learning.
Prof. Ramesh Raskar has been awarded the Lemelson-MIT prize for his “trailblazing work which includes the co-invention of an ultra-fast imaging camera that can see around corners, low-cost eye-care solutions and a camera that enables users to read the first few pages of a book without opening the cover,” writes Krishna Pokharel for The Wall Street Journal.
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.
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.”
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.