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.”
Engadget reporter Jon Fingas writes that MIT researchers have developed an encryption method that can secure sensitive data in neural networks without slowing machine learning systems. The method, notes Fingas, could “lead to more uses of internet-based neural networks for handling vital info, rather than forcing companies and institutions to either build expensive local equivalents or forget AI-based systems altogether.”
A study co-authored by Prof. Erik Brynjolfsson and graduate student Daniel Rock finds that specific tasks, not jobs, are likely to become automated, writes Joe McKendrick for Forbes. The researchers explain that, “machine learning technology can transform many jobs in the economy, but full automation will be less significant than the re-engineering of processes and the reorganization of tasks."
MIT researchers have developed a machine learning system that could reduce the number of chemotherapy and radiotherapy treatments that glioblastoma patients receive, reports the Xinhua News Agency. The system “finds an optimal treatment plan, with the lowest possible potency and frequency of doses that should still reduce tumor sizes,” Xinhua explains.
In an article for Forbes about how AI could improve healthcare, Bernard Marr highlights an algorithm developed by MIT researchers that can analyze 3-D scans up to 1,000 times faster than is currently possible. “When saving minutes can mean saving lives, AI and machine learning can be transformative,” writes Marr.
In an article for the Financial Times, Prof. Erik Brynjolfsson writes about his new research showing that advances in machine learning could help fuel a surge in productivity and economic growth around the world. Brynjolfsson writes that as machine learning systems develop, “we can have not only higher productivity growth, but also more widely shared prosperity.”
New Scientist reporter Chelsea Whyte spotlights Prof. Regina Barzilay’s quest to revolutionize cancer treatment by applying AI techniques in ways that could help doctors detect cancer earlier. Barzilay explains that she is committed to, "applying the best technologies available to what we care about the most – our health.”
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.
In an episode of BBC Click, host Spencer Kelly visits CSAIL to learn about developments in robotics and deep learning algorithms. Kelly notes that CSAIL is, “at the forefront of robotics, building machines in shapes and sizes that challenge our very idea of what a robot is.”
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.”
In an article for The New York Times, graduate student Joy Buolamwini writes about how AI systems can often reinforce existing racial biases and exclusions. Buolamwini writes that, “Everyday people should support lawmakers, activists and public-interest technologists in demanding transparency, equity and accountability in the use of artificial intelligence that governs our lives.”
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.”
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.