Researchers from MIT have found that, as a result of the Covid-19 pandemic, people are less likely to explore economically different parts of their home cities, reports Immanual John Milton for Bloomberg. “Fewer people are visiting attractions like museums, restaurants or parks that are outside their immediate mobility radius, and they’re spending less time among residents at different socioeconomic levels,” writes Milton.
Eleven fellows have been selected for the 2023-2024 Morningside Academy for Design (MIT MAD) program, reports Designboom, which is focused on offering “opportunities for students, faculty, and the general public to explore the intersection of design, technology, and social impact.” The fellowship program is aimed at helping designers have a “real-world impact in fields such as sustainability, architecture, health, and social justice.”
MIT researchers have found that interactions between people from different economic backgrounds have dropped significantly since the start of the Covid-19 pandemic, reports David Scharfenberg for The Boston Globe. Scharfenberg notes the “the phenomenon could hurt low-income people in direct ways – they’ll lose connections to better-off people – and indirect ways.”
Prof. Jongyoon Han and research scientist Junghyo Yoon have developed a new portable desalination device that can deliver safe drinking water at the push of a button, reports Meghan Gunn and Kerri Anne Renzulli for Newsweek. The device “requires less power than a cell phone charger to run and produces clean drinking water that exceeds World Health Organization standards,” writes Gunn and Renzulli.
Soledad O’Brien spotlights how researchers from MIT and Massachusetts General Hospital developed a new artificial intelligence tool, called Sybil, that an accurately predict a patient’s risk of developing lung cancer. “Sybil predicted with 86 to 94 percent accuracy whether a patient would develop lung cancer within a year,” says O’Brien.
Researchers from MIT have found that Twitter “bot detection tools can rely on funky, flawed data sets that replicate mistakes made within one another, rather than trying to accurately identify bots,” reports Chris Stokel-Walker for Fast Company. “We realized that this was a systemic issue in the data sets that are commonly used for bot detection,” says postdoc Zachary Schutzman.
MIT researchers have developed SoftZoo, “an open framework platform that simulated a variety of 3D model animals performing specific tasks in multiple environmental settings,” reports Andrew Paul for Popular Science. “This computational approach to co-designing the soft robot bodies and their brains (that is, their controllers) opens the door to rapidly creating customized machines that are designed for a specific task,” says CSAIL director, Prof. Daniela Rus.
Researchers at MIT have developed “SoftZoo,” a platform designed to “study the physics, look and locomotion and other aspects of different soft robot models,” reports Brian Heater for TechCrunch. “Dragonflies can perform very agile maneuvers that other flying creatures cannot complete because they have special structures on their wings that change their center of mass when they fly,” says graduate student Tsun-Hsuan Wang. “Our platform optimizes locomotion the same way a dragonfly is naturally more adept at working through its surroundings.”
Prof. Marzyeh Ghassemi speaks with NPR host Emily Kwong and correspondent Geoff Brumfiel about how artificial intelligence could impact medicine. “When you take state-of-the-art machine-learning methods and systems and then evaluate them on different patient groups, they do not perform equally,” says Ghassemi.
Popular Science reporter Jamie Dickman writes that using liquid neural networks, MIT researchers have “trained a drone to identify and navigate toward objects in varying environments.” Dickman notes that: “These robust networks enable the drone to adapt in real-time, even after initial training, allowing it to identify a target object despite changes in their environment.”
Researchers at MIT have developed a new type of autonomous drone that uses advanced neural networks to fly, reports Tony Ho Tran for The Daily Beast. “The new design allows the drone to make better decisions when flying through completely new environments,” writes Tran, “and could have future applications in self-driving cars, search and rescue operations, wildlife monitoring, or even diagnosing medical issues.”
NBC News highlights how researchers from MIT and MGH have developed a new AI tool, called Sybil, that can “accurately predict whether a person will develop lung cancer in the next year 86% to 94% of the time.” NBC News notes that according to experts, the tool "could be a leap forward in the early detection of lung cancer.”
Prof. Marzyeh Ghassemi speaks with WBUR reporter Geoff Brumfiel about her research studying the use of artificial intelligence in healthcare. “When you take state-of-the-art machine learning methods and systems and then evaluate them on different patient groups, they do not perform equally,” says Ghassemi.
Researchers at MIT have created a four-legged robot called DribbleBot, reports Caroline Goggin for WHDH. The robot “can dribble a soccer ball under the same conditions as humans, using onboard sensors to travel across different types of terrain.”
Popular Science reporter Andrew Paul spotlights how researchers from MIT CSAIL have developed a soccer-playing robot, dubbed DribbleBot, that can handle a variety of real-world terrains. “DribbleBot showcases extremely impressive strides in articulation and real-time environmental analysis. Using a combination of onboarding computing and sensing, the team’s four-legged athlete can reportedly handle gravel, grass, sand, snow, and pavement, as well as pick itself up if it falls.”