MIT researchers have developed a system that “lets a person control a robotic arm with brainwaves and subtle hand gestures,” reports Jesus Diaz for Co.Design. According to Prof. Daniela Rus, the goal is “to develop robotic systems that are a more natural and intuitive extension of us.”
Engadget reporter Jon Fingas writes that MIT researchers have developed a tiny computer chip small enough to fit on a honeybee-sized drone that can help the drone navigate. The technology could eventually be applied to, “smart pills that navigate to where they're needed, or virtually any vehicle that may need to last for a very long time on one battery charge.”
A new system developed by MIT researchers analyzes radio signals that bounce off of human bodies to track their movement and posture from behind walls, write Saqib Shah for Fox News. Shah suggests that the system could allow military personal “to ‘see’ hidden enemies by wearing augmented reality headsets.”
By measuring how radio waves bounce off of human bodies, MIT researchers have developed a system that can track movements from behind a wall, writes Andrew Liszewski for Gizmodo. The researchers are working to improve on the current stick figure icons by “generating 3D representations that include subtle and small movements,” writes Liszewski.
Researchers led by Prof. Dina Katabi have developed a system to track people’s movements from behind a wall, writes Kaleigh Rogers of Motherboard. Earlier versions were unable to track precise movements, but the system can now interpret signals bouncing off bodies and “translate it into the movement of 14 different key points on the body, including the head, elbows, and knees.”
CSAIL researchers have developed a new system that uses low-power radio waves to detect and track people behind walls, reports Matt Simon for Wired. The system, which can be used to detect signs of distress in elderly patients, also “distinguishes one person from another in the same way your fingerprint distinguishes you,” explains Prof. Dina Katabi.
CSAIL researchers have created a system that can sense a person’s movements through walls, writes John Biggs for TechCrunch. The system is primarily intended as a healthcare device and could help with “passive monitoring of a subject inside a room without cameras or other intrusions,” and could provide insight into disease progression, Biggs explains.
Fast Company reporter Melissa Locker writes that CSAIL researchers have developed a system that allows wireless devices to sense a person’s movement through walls. Locker explains that the technology was created as a way to help those who are elderly, as it could be used to “monitor diseases like Parkinson’s and multiple sclerosis and provide a better understanding of disease progression.”
In response to a reader’s question about self-driving cars, Mercury News reporter Gary Richards describes new technology in the works by MIT researchers to allow, “driverless cars to change lanes more like human drivers do.”
WCVB reporter Mike Wankum visits the Camera Culture Group at the MIT Media Lab to learn more about a device that uses photon imaging to see through dense fog. Wankum explains that the device can, “calculate how fog typically reflects laser light and then removes the fog from the equation, revealing an image hidden inside.”
To prove that the data used to train machine learning algorithms can greatly influence its behavior, MIT researchers input gruesome and violent content into an AI algorithm, writes Benjamin Fearnow for Newsweek. The result is “Norman,” an AI system in which “empathy logic simply failed to turn on,” explains Fearnow.
In this video, BBC Click spotlights VirtualHome, a simulator developed by CSAIL researchers that could be used to teach robots to perform household chores. The researchers hope the system could one day allow for seamless human-robot collaboration by allowing robots to, “cooperate with [humans] in finishing their activity,” explains graduate student Xavier Puig.
MIT researchers have developed a virtual reality environment in order to train drones, writes Logan Booker of Gizmodo. “It's a neat use of an emerging technology, one that makes a lot of sense when you think about it,” Booker concludes.
In an effort to determine how different data impacts the view of AI, Media Lab researchers used gruesome images to train a system, which ultimately created a psychopathic AI, writes BBC reporter Jane Wakefield. "It highlights the idea that the data we use to train AI is reflected in the way the AI perceives the world and how it behaves," says Prof. Iyad Rahwan.
CSAIL researchers have developed a new system that could be used to train machines to complete tasks, writes Patrick Lucas Austin for Gizmodo. The researchers hope the system could eventually be used to, “teach robots how to accomplish tasks simply by showing them actual instructional videos,” Austin explains.