MIT is launching a center for autism research at the McGovern Institute with $20 million in funding from MIT alumnus Hock Tan and Lisa Yang, writes Barb Darrow for Fortune. Darrow writes that Yang told Fortune she was "greatly impressed by both the collegiality and focus of the institute's researchers.”
CSAIL researchers have developed a wearable AI system that allows users to detect the tone of a conversation in real-time, reports Janet Burns for Forbes. Using two algorithms to analyze data, the researchers were able to “classify each five-second chunk of conversation as positive, neutral, or negative,” explains Burns.
Dan Misener of CBC News writes that a wearable device developed by MIT researchers detects the tone of conversation by listening to the interaction and measuring the physiological responses of the user. “All of that data is fed into a neural network that's been trained to identify certain cues,” explains Misener.
CSAIL researchers have developed a wearable system that can gauge the tone of a conversation based on a person’s speech patterns and vitals with 83 percent accuracy, writes Brian Barrett for Wired. The system could be useful for people with social anxiety or Asperger’s, Barrett explains.
Hallie Smith writes for Boston Magazine that CSAIL researchers have developed a system that can help detect of the tone of a conversation. The system could be especially useful “for those who struggle with emotional and social cues, such as individuals with Asperger’s Syndrome,” Smith explains.
An algorithm developed by MIT researchers helps extract the correct answer from a large group of people even when the majority of people answer incorrectly, writes Erin Ross for Nature. While previous assumptions viewed the average opinion of a crowd as correct, the algorithm identifies “specialists with special knowledge, like doctors,” explains Prof. Dražen Prelec.
Scientific American reporter Simon Makin writes that MIT researchers have developed a new approach to extract correct answers from a crowd. “The new method performed better than majority or confidence-based methods alone, reducing errors by between 21 and 35 percent,” Makin explains.
CSAIL researchers have a developed a system that can predict human emotions by using wireless signals to monitor breathing and heartbeats, writes Edd Gent in a Scientific American article. "The idea is that you can enable machines to recognize our emotions so they can interact with us at much deeper levels," says graduate student Fadel Adib.
To encourage safer driving in Boston, Mayor Martin Walsh has announced a competition that uses a smartphone app developed by MIT startup Cambridge Telematics to reward driver performance, reports Dante Ramos for The Boston Globe. The app gives motorists “star ratings if they stay off their phones; drive at reasonable speeds; and brake, accelerate, and turn carefully.”
CSAIL researcher have created a device that uses changes in heart beat and breathing to detect emotions, writes Forbes’ Kevin Murnane. The heart of the system,” writes Murnane, “is the algorithm that extracts the heartbeat from the RF signal. It’s an impressive achievement that solves a difficult problem.”
A device created by CSAIL researchers can detect emotions by wirelessly measuring heartbeats, according to the Associated Press. The device is “87 percent accurate in using heartrate and what it’s already learned about a person to recognize joy, pleasure, sadness or anger.”
CSAIL researchers have developed a device that can determine a person’s mood using wireless signals, write Jordan Graham and Donna Goodison for The Boston Herald. “We view this work as the next step in helping develop computers that can better understand us at an emotional level,” explains Mingmin Zhao.
CSAIL researchers have developed a device that can determine emotion by analyzing reflections from wireless signals bounced off the human body, writes Mary Beth Griggs for Popular Science. “Because it can measure heart rate, it might also be a less invasive way for doctors to monitor patient's heartbeats, potentially watching for conditions like arrhythmias,” writes Griggs.
Matt McFarland writes for CNN that CSAIL researchers have developed a non-invasive device that uses radio waves to detect human emotion. "Imagine if the machines around you can understand when you are stressed or your emotional state is negative,” explains Prof. Dina Katabi."[It could] try to detect depression."
In a study of students offered $100 in bitcoin, MIT researchers found that early users were more likely to not use new technology if there were delays in accessing it, writes Kevin Lewis for The Boston Globe. The researchers found early adopters, “were more likely to cash out of their bitcoin if they had been delayed in getting it.”