Behavior

MIT researchers have developed an AI system that can predict Alzheimer’s risk by forecasting how patients will perform on a test measuring cognitive decline up to two years in advance, reports Casey Ross for STAT. 

A gift from alumnus Charles Broderick will enable researchers at MIT and Harvard to investigate how cannabis effects the brain and behavior, reports Felice Freyer for The Boston Globe. Prof. John Gabrieli explains that it has been “incredibly hard” to get funding for marijuana research. “Without the philanthropic boost, it could take many years to work through all these issues,” he notes.

WBUR reporter Carey Goldberg spotlights how a gift from alumnus Charles Broderick is funding research on cannabis and its impacts on the brain. "We were saying, 'Wouldn't it be great to study this?'” says Prof. Myriam Heiman of the need to study the impacts of cannabis. "And then this gift comes along and really is enabling us to do everything we wanted to do."

A new paper co-authored by Prof. David Rand shows that people are more likely to buy solar panels if the salesperson actually uses them. Study participants believed the salesperson’s personal behavior “was a stronger signifier of their beliefs than any sales pitch, no matter how sincere,” reports Tarpley Hitt for The Daily Beast.

Popular Science reporter Rob Verger highlights how an MIT spinout and MIT researchers are developing tools to detect depression. “The big vision is that you have a system that can digest organic, natural conversations, and interactions, and be able to make some conclusion about a person’s well-being,” says grad student Tuka Alhanai.

MIT researchers have developed neural networks that can recognize speech patterns that are indicative of depression, writes Anna Powers for Forbes. “Because the model is generalized and does not rely on specific questions to be asked,” explains Powers, “the hope is that this model can be implemented into mobile apps that will allow people to detect depression through natural conversation.”

MIT researchers have developed a model that can help detect depression by analyzing an individual’s speech patterns, reports Kaveh Waddell for Axios. Waddell explains that the researchers, “trained an AI system using 142 recorded conversations to assess whether a person is depressed and, if so, how severely.”

MIT researchers have developed a new system that can detect depression by examining a patient’s speech and writing, reports John Biggs for TechCrunch. Biggs writes that the system could “help real therapists find and isolate issues automatically versus the long process of analysis. It’s a fascinating step forward in mental health.”

Boston Globe reporter Martin Finucane writes that MIT researchers have identified the region of the brain responsible for generating negative emotions. “The findings could help scientists better understand how some of the effects of depression and anxiety arise, and guide development of new treatments,” Finucane explains.

Kate Baggaley writes for NBC News that movement tracking technology developed by MIT researchers can be helpful for monitoring the elderly or sick. The system could be used to monitor an elderly relative and, “receive an instant alert if he or she falls,” or a doctor could use it to monitor the progression of a patient’s disease, explains Baggaley.

CSAIL researchers have created a deep learning system that can isolate individual musical instruments in a video by clicking on the specific instrument, writes Andrew Liszewski for Gizmodo. The researchers suggest the system, “could be a vital tool when it comes to remixing and remastering older performances where the original recordings no longer exist,” explains Liszewski.

BBC Click reporter Gareth Mitchell speaks with postdoc Oggi Rudovic about his work developing a system that allows autism therapy robots to help teach children how to decipher different emotions. Rudovic explains that the technology can “assist the therapist and also to make the whole therapy process engaging for the child.”

Popular Mechanics reporter David Grossman writes that MIT researchers have developed a new system that helps robots used in autism therapy better estimate how engaged a child is during an interaction. Grossman explains that, “using the personalized algorithm, the robot was able to correctly interpret a child's reaction 60 percent of the time.”

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.”