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.
Gabrielle DiBenedetto writes for Boston Magazine that researchers from MIT CSAIL have developed a robot that can help nurses schedule tasks. DiBenedetto writes that the robot “learns how to perform the scheduling job similarly to how a human would: through observation.”
Washington Post reporter Emily Langer chronicles the life and work of Prof. Emeritus Seymour Papert, who died last week at age 88. Langer explains that Papert “led an early campaign to revolutionize education with the personal computer, a tool he championed not as a classroom gadget but as a key to unlocking a child’s excitement for learning.”
Barb Darrow writes for Fortune about the career of Prof. Emeritus Seymour Papert, who died July 31. “In the 1960s, when computers were pricey and huge, Papert saw them as a way to help children learn by doing. He developed the Logo programming language for children, who initially used it to program and animate a small robot turtle.”
Lisa Mullins of WBUR’s All Things Considered speaks with Suzanne Massie, wife of the late Prof. Emeritus Seymour Papert, about Papert’s dedication to using technology to provide children around the world access to education. Massie notes that Papert was “the visionary who first saw the potential of the computer as an instrument of education of children.”
Prof. Emeritus Seymour Papert, a leading expert on using technology to help children learn, died on July 31, reports Glenn Rifkin for The New York Times. Prof. Mitchel Resnick notes that Papert was “the first person to see that the computer could be used to support children’s learning and development.”
Prof. Emeritus Robert Fano, known for his instrumental work in the development of interactive computers, died on July 13 at age 98, reports John Markoff for The New York Times. Markoff writes that Fano made “fundamental theoretical advances, both in the ways computers handled information and in the design of interactive software.”
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.
Scarlett Ho writes for The Huffington Post about an MIT startup, fireflies.ai, aimed at helping people foster and maintain connections. “All you have to do is forward an email from the contact you wish to keep in touch with to Fireflies, set reminders, add notes, and Fireflies will adapt over time, sending meaningful insights for you.”
Hae Young Yoo writes for The Boston Globe that Ori, a spinoff out of the MIT Media Lab’s CityHome research project, “is creating furniture for urban spaces -- not just smaller pieces, but smarter ones, equipped with robotics that move on demand.”
Wired reporter Margaret Rhodes writes that Media Lab spinoff Ori is developing transformable furniture to help maximize living spaces. “With the push of a button—or, with future versions of the software, at the sound of a voice or wave of a hand—pieces of Ori furniture will slide up, down, or over, reconfiguring spaces in mere moments.”
April Glasper writes for Wired about the robot Prof. Cynthia Breazeal created specifically for domestic purposes. Glasper explains that robot, dubbed Jibo “learns by listening and asking questions. Jibo uses machine learning, speech and facial recognition, and natural language processing to learn from its interactions with people.”
By watching TV shows and video clips, CSAIL researchers show that artificially intelligent systems can learn and predict human behavior, writes Tim Moynihan for Wired. Researchers say these findings could lead to analyzing hospital video feeds to alert emergency responders or allow robots to respond.
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.”