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Scientific American

Scientists from MIT and other institutions have developed the largest, most detailed computer model of the universe’s first billion years, which could help shed light on how the early universe evolved, reports Charles Q. Choi for Scientific American. The model, named THESAN, “can track the birth and evolution of hundreds of thousands of galaxies within a cubic volume spanning more than 300 million light-years across.”

TechCrunch

TechCrunch reporter Brian Heater spotlights new MIT robotics research, including a team of CSAIL researchers “working on a system that utilizes a robotic arm to help people get dressed.” Heater notes that the “issue is one of robotic vision — specifically finding a method to give the system a better view of the human arm it’s working to dress.”

The Boston Globe

Researchers from MIT and other institutions have developed a new simulation that illuminates how stars formed in the early universe, reports Martin Finucane for The Boston Globe. “It was a neutral, dark cosmos that became bright and ionized as light began to emerge from the first galaxies,” explains Aaron Smith, a NASA Einstein Fellow in MIT’s Kavli Institute for Astrophysics and Space Research.

VICE

MIT researchers have developed a new simulation of the early universe, shedding light onto the period when the first stars were formed, reports Audrey Carleton for Vice. “Using existing models of the early universe and of cosmic dust, matched with new code created to interpret how light and gas interacted with one another, they created a visual depiction of the growth of the universe,” writes Carleton.

Axios

Axios reporter Alison Snyder writes that a new study by MIT researchers demonstrates how AI algorithms could provide insight into the human brain’s processing abilities. The researchers found “Predicting the next word someone might say — like AI algorithms now do when you search the internet or text a friend — may be a key part of the human brain's ability to process language,” writes Snyder.

Scientific American

Using an integrative modeling technique, MIT researchers compared dozens of machine learning algorithms to brain scans as part of an effort to better understand how the brain processes language. The researchers found that “neural networks and computational science might, in fact, be critical tools in providing insight into the great mystery of how the brain processes information of all kinds,” writes Anna Blaustein for Scientific American.

The Washington Post

Washington Post reporter Paige Winfield Cunningham spotlights how MIT researchers have developed a new way to estimate the impact of Covid-19. The researchers “developed a way to compare and merge more than two dozen different models from universities and analytics groups around the country.”

Economist

Research scientist Brian Subirana speaks with The Economist’s Babbage podcast about his work developing a new AI system that could be used to help diagnose people asymptomatic Covid-19.

Boston 25 News

MIT researchers have developed a new model that could be used to help determine “how long you will be safe in a room with someone who is positive for COVID-19 based on room type, size and even the ventilation and filtration system,” reports Boston 25 News.

Fast Company

Fast Company reporter Mark Sullivan writes that Prof. John Bush and Prof. Martin Z. Bazant have developed a mathematical model that “simulates the fluid dynamics of virus-loaded respiratory droplets in any space, from a cozy kitchen to a gigantic concert hall.”

BBC News

A new algorithm developed by MIT researchers could be used to help detect people with Covid-19 by listening to the sound of their coughs, reports Zoe Kleinman for BBC News. “In tests, it achieved a 98.5% success rate among people who had received an official positive coronavirus test result, rising to 100% in those who had no other symptoms,” writes Kleinman.

Mashable

Mashable reporter Rachel Kraus writes that a new system developed by MIT researchers could be used to help identify patients with Covid-19. Kraus writes that the algorithm can “differentiate the forced coughs of asymptomatic people who have Covid from those of healthy people.”

Gizmodo

A new took developed by MIT researchers uses neural networks to help identify Covid-19, reports Alyse Stanley for Gizmodo. The model “can detect the subtle changes in a person’s cough that indicate whether they’re infected, even if they don’t have any other symptoms,” Stanley explains.

TechCrunch

TechCrunch reporter Devin Coldewey writes that MIT researchers have built a new AI model that can help detect Covid-19 by listening to the sound of a person’s cough. “The tool is detecting features that allow it to discriminate the subjects that have COVID from the ones that don’t,” explains Brian Subirana, a research scientist in MIT’s Auto-ID Laboratory.

CBS Boston

MIT researchers have developed a new AI model that could help identify people with asymptomatic Covid-19 based on the sound of their cough, reports CBS Boston. The researchers hope that in the future the model could be used to help create an app that serves as a “noninvasive prescreening tool to figure out who is likely to have the coronavirus.”