Writing for Wired, Prof. Joi Ito, director of the Media Lab, writes about the need for creating more open, global datasets for such critical issues as air quality monitoring. “We need to start using data for more than commercial exploitation,” argues Ito, “deploying it to understand the long-term effects of policy, and create transparency around those in power—not of private citizens.”
Boston Globe reporter Jessie Scanlon spotlights Prof. Regina Barzilay’s work developing machine learning systems that can identify patients at risk of developing breast cancer. Barzilay is creating “software that aims to teach a computer to analyze mammogram images more effectively than the human eye can and to catch signs of cancer in its earliest phases.”
MIT researchers have found that it’s easy to reidentify anonymized data compiled in massive datasets, reports Kelsey Campbell-Dollaghan for Fast Company. The findings show that urban planners, tech companies and designers, “who stand to learn so much from these big urban datasets,” writes Campbell-Dollaghan, “need to be careful about whether all that data could be combined to deanonymize it.”
A new study by MIT researchers provides evidence that compiling massive anonymized datasets of people’s movement patterns can put their private data at risk, reports the Xinhua news agency. The researchers found “data containing ‘location stamps’ – information with geographical coordinates and time stamps – could be used to easily track the mobility trajectories of how people live and work.”
MIT researchers examined why a third of Wikipedia deliberations go unresolved and developed a new tool that could be used to help resolve more discussions, reports Samantha Cole for Motherboard. Cole explains that “the tool uses the data they found and analyzed in this research, to summarize threads and predict when they’re at risk of going stale.”
TechCrunch reporter Ingrid Lunden highlights RapidSOS, an MIT startup that “helps increase the funnel of information that is transmitted to emergency services alongside a call for help.”
Smithsonian reporter Randy Rieland writes that MIT researchers have developed a machine learning model that can detect speech and language patterns associated with depression. The researchers note that the system is intended to assist, not replace clinicians. “We’re hopeful we can provide a complementary form of analysis,” explains Senior Research Scientist James Glass.
Motherboard reporter Daniel Oberhaus writes that MIT researchers have developed an AI system that can generate theories about the physical laws of imaginary universes. Oberhaus writes that in the future the system could be used to help understand “massively complex datasets, such as those used in climate modeling or economics.”
MIT researchers have developed a language translation model that operates without human annotations and guidance, reports Liangyu for Xinhua news agency. The system, which may enable computer-based translations of the thousands of languages spoken worldwide, is “a step toward one of the major goals of machine translation, which is fully unsupervised word alignment,” Liangyu explains.
MIT researchers have developed a new technique to store information using lasers, reports Meriame Berboucha for Forbes. “By using pulses of light, a material can be flipped from one state to another and return to its original state,” Berboucha explains. “As a result, a wave of new-generation data storage devices could be in our homes and workplaces very soon.”
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.
Prof. Tim Berners-Lee has created a new technology aimed at allowing people more control over their online data, reports the BBC News. Berners-Lee felt that the “current model of handing over lots of data to many different online services did not serve people well,” the BBC explains.
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.”