Prof. Julie Shah speaks with The Economist about her work developing systems to help robots operate safely and efficiently with humans. “Robots need to see us as more than just an obstacle to maneuver around,” says Shah. “They need to work with us and anticipate what we need.”
Physics World reporter Tim Wogan writes that MIT researchers used machine learning techniques to identify a mysterious “X” particle in the quark–gluon plasma produced by the Large Hadron Collider. “Further studies of the particle could help explain how familiar hadrons such as protons and neutrons formed from the quark–gluon plasma believed to have been present in the early universe,” writes Wogan.
Using machine learning techniques, MIT researchers have detected “X particles” produced by the Large Hadron Collider, reports Rahul Rao for Popular Science. “The results tell us more about an artifact from the very earliest ticks of history, writes Rao. “Quark-gluon plasma filled the universe in the first millionths of a second of its life, before what we recognize as matter—molecules, atoms, or even protons or neutrons—had formed.”
Scientists have discovered “X-particles” in the aftermath of collisions produced in the Large Hadron Collider, which could shed light on the structure of these elusive particles, reports Becky Ferreira for Vice. “X particles can yield broader insights about the type of environment that existed in those searing and turbulent moments after the Big Bang,” writes Ferreira.
Prof. David Mindell writes for Forbes about the premise behind his new book, “The Work of the Future: Building Better Jobs in the Age of Intelligent Machine,” which he wrote with Prof. David Autor, and Elisabeth Reynolds, former director of MIT’s Industrial Performance Center. The new book “concerns demographic shifts in the United States that will generate consistent labor shortages for a generation; the continued profusion of information technology and mobile phones into legacy sectors such as logistics, construction, and transportation; technology-enabled remote work, conferencing, and training; and a long-term need for improved training, reeducation, and upskilling among low – and middle – skill workers,” writes Mindell.
Prof. David Autor speaks with New York Times columnist Peter Coy about the new book he wrote with Prof. David Mindell and Elisabeth Reynolds, “The Work of the Future: Building Better Jobs in an Age of Intelligent Machines.” Autor explains that: “Most people’s fear of technology is really a fear of capitalism, what the markets will do with the technology. You can’t make a lot of progress if you’re making people poorer at the same time.”
A new study by MIT researchers finds people are more likely to interact with a smart device if it demonstrates more humanlike attributes, reports Brian Heater for TechCrunch. The researchers found “users are more likely to engage with both the device — and each other — more when it exhibits some form of social cues,” writes Heater. “That can mean something as simple as the face/screen of the device rotating to meet the speaker’s gaze.”
STAT reporters Katie Palmer and Casey Ross spotlight how Prof. Regina Barzilay has developed an AI tool called Mirai that can identify early signs of breast cancer from mammograms. “Mirai’s predictions were rolled into a screening tool called Tempo, which resulted in earlier detection compared to a standard annual screening,” writes Palmer and Ross.
In an article for The Wall Street Journal about next generation technologies that can create and quantify personal health data, Laura Cooper spotlights Prof. Dina Katabi’s work developing a noninvasive device that sits in a person’s home and can help track breathing, heart rate, movement, gait, time in bed and the length and quality of sleep. The device “could be used in the homes of seniors and others to help detect early signs of serious medical conditions, and as an alternative to wearables,” writes Cooper.
IEEE Spectrum reporter Prachi Patel writes that researchers from MIT and Google Brain have developed a new open-source tool that could streamline solar cell improvement and discovery. The new system should “speed up development of more efficient solar cells by allowing quick assessment of a wide variety of possible materials and device structures,” writes Patel.
Prof. Regina Barzilay speaks with Good Morning America about her work developing a new AI tool that could “revolutionize early breast cancer detection” by identifying patients at high risk of developing the disease. “If this technology is used in a uniform way,” says Barzilay, “we can identify early who are high-risk patients and intervene.”
Washington Post reporter Steve Zeitchik spotlights Prof. Regina Barzilay and graduate student Adam Yala’s work developing a new AI system, called Mirai, that could transform how breast cancer is diagnosed, “an innovation that could seriously disrupt how we think about the disease.” Zeitchik writes: “Mirai could transform how mammograms are used, open up a whole new world of testing and prevention, allow patients to avoid aggressive treatments and even save the lives of countless people who get breast cancer.”
STAT reporter Katie Palmer writes that MIT researchers have developed a new machine learning model that can "flag treatments for sepsis patients that are likely to lead to a ‘medical dead-end,/ the point after which a patient will die no matter what care is provided.”
Wise Systems, an AI-based delivery management platform originating from MIT’s Media Lab, has applied machine learning to real-time data to better plan delivery routes and schedules for delivery drivers, reports Susan Galer for Forbes. “The system can more accurately predict service times, taking into account the time it takes to complete a stop, and factoring in the preferences of the retailer, hotel, medical institution, or other type of client,” says Allison Parker of Wise Systems.
MIT researchers developed a new control system for the mini robotic cheetah that allows the robot to jump and traverse uneven terrain, reports Jules Suzdaltsev for Mashable. “There’s a camera for processing real-time input from a video camera that then translates that information into body movements for the robot,” Suzdaltsev explains.