A team of researchers from MIT and Princeton participating in the Amazon Robotics Challenge are using GelSight technology to give robots a sense of touch, reports Tom Simonite for Wired. Simonite explains that the, “rubbery membranes on the robot’s fingers are tracked from the inside by tiny cameras as they are deformed by objects it touches.”
In this video for USA Today, Sean Dowling highlights Pic2Recipe, the artificial intelligence system developed by CSAIL researchers that can predict recipes based off images of food. The researchers hope the app could one day be used to help, “people track daily nutrition by seeing what’s in their food.”
Researchers at MIT have developed an algorithm that can identify recipes based on a photo, writes BBC News reporter Zoe Kleinman. The algorithm, which was trained using a database of over one million photos, could be developed to show “how a food is prepared and could also be adapted to provide nutritional information,” writes Kleinman.
MIT researchers have developed a new machine learning algorithm that can look at photos of food and suggest a recipe to create the pictured dish, reports Matt Reynolds for New Scientist. Reynolds explains that, “eventually people could use an improved version of the algorithm to help them track their diet throughout the day.”
CSAIL researchers have trained an AI system to look at images of food, predict the ingredients used, and even suggest recipes, writes Matt Burgess for Wired. The system could also analyze meals to determine their nutritional value or “manipulate an existing recipe to be healthier or to conform to certain dietary restrictions," explains graduate student Nick Hynes.
Forbes reporter Kevin Murnane writes about how MIT researchers have used a computer vision system to examine how several American cities physically improved or deteriorated over time. Murnane writes that the study “provides important support for nuanced versions of traditional theories about why urban neighborhoods change over time.”
UPI reporter Amy Wallace writes that MIT researchers have applied a computer vision system to help quantify the physical improvement of American neighborhoods. The researchers found that “density of highly educated residents, proximity to central business districts and other attractive areas, and the initial safety score assigned by the computer system are strongly related to improvements.”
Graduate student Joy Buolamwini speaks with Guardian reporter Ian Tucker about her work fighting algorithmic biases. Buolamwini explains that she is, “trying to identify bias, to point out cases where bias can occur so people can know what to look out for, but also develop tools where the creators of systems can check for a bias in their design.”
CSAIL researchers have developed a system that allows robots to teach one another learned skills, reports Grace Williams for FOX News. Williams explains that the system, “gives non-coders the ability to teach robots various tasks using information about manipulating objects in a single demonstration. These skills can then be passed along to other robots that move in different ways.”
Prof. Regina Barzilay’s research group is working with MGH to use artificial intelligence and machine learning to improve cancer diagnoses, reports CNBC’s Meg Tirrell. The group also hopes to allow doctors to use “the huge quantities of data available on patients to make more personalized treatment decisions,” explains Tirrell.
Anthony Cuthbertson of Newsweek reports that PhD student Claudia Pérez D’Arpino has developed a system that allows robots to learn a skill and teach it to another robot. Armed with knowledge of how to perform a task, a 3-D interface demonstrates the tasks “allowing [the robot] to understand the motions it is being taught in the real world,” explains Cuthbertson.
This CNN video highlights a new system developed by CSAIL researchers that allows noncoders to teach robots to perform a task after a single demonstration. The new programming method also enables robots to learn from other robots, which could enable “a variety of robots to perform similar tasks.”
Wired reporter Matt Simon writes that CSAIL researchers have developed a new system that allows noncoders to be able to teach robots a wide range of tasks, and enables robots to transfer new skills to other robots. Simon notes that the development is a “glimpse into a future where, more and more, robots communicate without humans at all.”
BBC News reporter Zoe Kleinman writes that graduate student Joy Buolamwini has developed an initiative aimed at tackling algorithmic bias. "If we are limited when it comes to being inclusive that's going to be reflected in the robots we develop or the tech that's incorporated within the robots,” says Buolamwini.
President Reif spoke with Abraham González of El Financiero about the rapid advance of technology. “Machine learning will not replace us, on the contrary, it will help us. Just as computers help us get the job done today and just as cars help us get from one place to another,” explains Reif.