In an article for Popular Mechanics, Tiana Cline spotlights SoFi, an autonomous, soft, robotic fish that can swim alongside real fish. “SoFi has the potential to be a new type of tool for ocean exploration and to open up new avenues for uncovering the mysteries of marine life,” Cline notes.
In an interview with Wynne Parry of NBC Mach, Prof. Sherry Turkle expresses concern that household robots can interfere with children learning to understand and connect with one another. “There are skills of listening, of putting oneself in the place of the other, that are required when two human beings try to deeply understand each other,” Turkle 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.”
Prof. Max Tegmark is one of a growing number of tech leaders who have signed a pledge agreeing not to develop lethal autonomous weapons, reports James Vincent in The Verge. “Tegmark said the pledge did what politicians have not: impose hard limits on the development of AI for military use,” writes Vincent.
In a recent blog post, Microsoft’s president and chief legal officer, Brad Smith, references research by MIT graduate student Joy Buolamwini while calling for government to regulate the use of facial recognition software. Buolamwini’s work “showed error rates of as much as 35% for systems classifying darker skinned women,” reports Dina Bass for Bloomberg.
A new paper by Prof. Erik Brynjolfsson argues that machines and automation will replace specific tasks rather than entire jobs, writes Adi Gaskell for Forbes. As an example, Gaskell notes that there are 26 tasks associated with radiologists, and while “analyzing medical images is well suited to AI, interpersonal skills are currently not.”
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.”
This BBC Click segment highlights the artificial intelligence system developed by CSAIL researchers that can monitor people’s movements through walls. Prof. Dina Katabi explains that the device helps preserve the privacy of those being monitored by separating and encrypting, “any identifiable information from the measurement.”
Fast Company reporter Steven Melendez writes that CSAIL researchers have created a new system that allows a robot to detect human brainwave patterns so it knows when it made a mistake. Melendez explains that, “Teaching robots to understand human nonverbal cues and signals could make them safer and more efficient at working with people.”