Graduate student Joy Buolamwini is featured on NPR’s TED Radio Hour explaining the racial bias of facial recognition software and how these problems can be rectified. “The minimum thing we can do is actually check for the performance of these systems across groups that we already know have historically been disenfranchised,” says Buolanwini.
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.”
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.”
MIT researchers have identified the brain circuit that process the “when” and “where” components of memories, reports Robby Berman for Slate. “The newly discovered circuit connects the hippocampus and the entorhinal cortex,” writes Berman. “The entorhinal cortex splits each memory into two streams of information: one for location and one for timing.”
In this video, the BBC’s LJ Rich reports on the 3-D printed, soft robotic hand developed by researchers at the MIT Computer Science and Artificial Intelligence Lab. Rich explains that the robotic hand can “handle objects as delicate as an egg and as thin as a compact disk.”
Washington Post reporter Rachel Feltman writes that MIT researchers have designed a new robotic hand with soft, 3-D printed fingers that can identify and lift a variety of objects. Prof. Daniela Rus explains that her group’s robotic hand operates in a way that is “much more analogous to what we do as humans."
Writing for Popular Science, Mary Beth Griggs reports on the soft robotic gripper developed by researchers at MIT CSAIL. “The silicone fingers are equipped with sensors that analyze the object they are touching and compare it to other items in its database,” Griggs writes.
CNBC reporter Robert Ferris writes about how MIT researchers have developed a soft robotic hand that can identify and safely grasp delicate objects. Ferris explains that the researchers designed a “soft silicone ‘hand’ with embedded sensors that they can train to recognize different things.”
MIT CSAIL researchers have developed a silicon gripper that allows robots to grasp a wide variety of items, reports Nidhi Subbaraman for BetaBoston. Subbaraman explains that the hand expands “to accommodate a shape, and grasps radially – surrounding an object instead of picking it up with pincers.”
MarketWatch reporter Sally French writes that researchers from MIT CSAIL have developed an algorithm that can be used to predict how memorable a person’s is. “The algorithm was created from a database of more than 2,000 images that were awarded a “memorability score” based on human volunteers’ ability to remember the pictures,” French writes.
“A team of MIT researchers found that an existing computer vision system can achieve object recognition as well as humans and other primates,” writes Jamie Ducharme for Boston Magazine. Professor James DiCarlo’s team compared the visual recognition abilities of primates to those of the advanced computer system Super Vision.