Prof. Peter Shor is one of four winners for this year’s Breakthrough Prize in Fundamental Physics, reports Zeeya Merali for Nature. Merali writes that Shor’s research “laid the groundwork for the development of ultra-secure communications and computers that might one day outperform standard machines at some tasks.”
MIT researchers have developed a new in-home device that can help monitor Parkinson’s patients by tracking their gait, reports Hiawatha Bray for The Boston Globe. “We know very little about the brain and its diseases,” says Professor Dina Katabi. “My goal is to develop non-invasive tools that provide new insights about the functioning of the brain and its diseases.”
Popular Science reporter Philip Kiefer writes that MIT researchers have developed an in-home device that could be used to track the progression of symptoms in Parkinson’s patients. “We can’t really ask patients to come to the clinic every day or every week,” explains graduate student Yingcheng Liu. “This technology gives us the possibility to continuously monitor patients, and provide more objective assessments.”
Research led by Prof. Michael Howland has found that adjusting the orientation of wind turbines on a farm can reduce the wake effect and boost the total output, reports Maria Perez Ortiz for Wired. “Howland and his team’s algorithm first uses atmospheric physics and operational farm data—such as temperature and wind conditions—to estimate the wakes that turbines are creating and how these are impacting other turbines,” writes Ortiz.
As the early images from the James Webb Space Telescope (JWST) were revealed in July 2022, astronomers such as Hubble Postdoctoral Scholar Rohan Naidu were able to uncover numerous galaxies within them, reports Jonathan O’Callaghan for Scientific American. Naidu recounts how an algorithm he developed “sifted out an object that, on closer inspection, was inexplicably massive and dated back to just 300 million years after the big bang, older than any galaxy ever seen before,” writes O’Callaghan.
Prof. Michael Howland speaks with Camille Bond at E&E News about his research, which suggests that finding the ideal position for individual wind turbines could increase the overall efficiency of the entire wind farm. “If we're expanding wind energy quite substantially, it's important that we design the wind farms in the best way and we control them in the best way to achieve that goal,” said Howland.
Researchers from MIT are working with the Staten Island Performing Provider System to develop an algorithm that can predict who in the system is at risk for an opioid overdose, reports Maddie Bender for the Daily Beast. “In preliminary testing, Conte’s team and MIT Sloan researchers found their model was highly accurate at predicting overdoses and fatal overdoses, even with delays in the data of up to 180 days,” writes Bender.
MIT scientists have discovered a new method to maximize wind farm output, reports Gwen Egan for Boston.com. “While there are pros and cons to this strategy, it’s possible that it could allow for smaller wind farms that take up less land to produce more energy,” says Prof. Michael Howland. “It’s critically important we do this now, as we embark on building much more offshore wind. We need to ensure that our future wind farms maximize efficiency to increase the pace of decarbonization.”
MIT scientists have found that changing the angle of turbine blades on wind farms could increase energy output, reports David Abel for The Boston Globe. “Given the scale of wind deployment needed to achieve state and federal climate goals, we need optimal wind farm performance to ensure efficient, rapid decarbonization,” says Prof. Michael Howland. “Our method resulted in significant energy gains over standard industry operations, and, importantly, it can be instituted with minimal cost.”
The MIT mini cheetah broke a speed record after learning to adapt to difficult terrain and upping its speed, reports Rienk De Beer for Popular Mechanics.
MIT researchers have developed a new system that enabled the mini robotic cheetah to learn to run, reports John Koetsier for Forbes. ““Traditionally, the process that people have been using [to train robots] requires you to study the actual system and manually design models,” explains Prof. Pulkit Agrawal. “This process is good, it’s well established, but it’s not very scalable. “But we are removing the human from designing the specific behaviors.”
MIT researchers have developed FuseBot, a new system that combines RFID tagging with a robotic arm to retrieve hidden objects from a pile, reports Brian Heater for TechCrunch. “As long as some objects within the pile are tagged, the system can determine where its subject is most likely located and the most efficient way to retrieve it,” writes Heater.
A study co-authored by MIT researchers finds that algorithms based on clinical medical notes can predict the self-identified race of a patient, reports Katie Palmer for STAT. “We’re not ready for AI — no sector really is ready for AI — until they’ve figured out that the computers are learning things that they’re not supposed to learn,” says Principal Research Scientist Leo Anthony Celi.
CSAIL graduate student Yunzhu Li and his colleagues have trained a robot to use two metal grippers to mold letters out of play dough, reports Jeremy Hsu for New Scientist. "Li and his colleagues trained a robot to use two metal grippers to mould the approximate shapes of the letters B, R, T, X and A out of Play-Doh," explains Hsu. "The training involved just 10 minutes of randomly manipulating a block of the modelling clay beforehand, without requiring any human demonstrations."
TechCrunch reporter Brian Heater spotlights multiple MIT research projects, including MIT Space Exploration Initiative’s TESSERAE, CSAIL’s Robocraft and the recent development of miniature flying robotic drones.