Algorithms

CSAIL researchers have developed a new material with embedded sensors that can track a person’s movement, reports Mashable. The clothing could “track things like posture or give feedback on how you’re walking.”

Fast Company reporter Elizabeth Segran spotlights how CSAIL researchers have crafted a new smart fabric embedded with sensors that can sense pressure from the person wearing it. “Sensors in this new material can be used to gather data about people’s posture and body movements,” writes Segran. “This could be useful in a variety of settings, including athletic training, monitoring the health of elderly patients, and identifying whether someone has fallen over.”

Wired reporter Will Knight writes that MIT researchers have found that many of the key AI data sets used to train algorithms could contain many errors. “What this work is telling the world is that you need to clean the errors out,” says graduate student Curtis Northcutt. “Otherwise the models that you think are the best for your real-world business problem could actually be wrong.”

Boston.com reporter Mark Gartsbeyn spotlights “Coded Bias,” a new documentary that chronicles graduate student Joy Buolamwini’s work uncovering bias in AI systems. Gartsbeyn writes that in 2018, Buolamwini “co-authored an influential study showing that commercially available facial recognition programs had serious algorithmic bias against women and people of color.”

The Economist spotlights how MIT researchers created a virtual technique to decipher the contents of a letter that was sealed 300 years ago. The letter was originally sealed by its sender using the historical practice of securing correspondence called letterlocking. The new virtual technique “seems to hold plenty of promise for future research into a fascinating historical practice.”

A new imaging technique created by researchers from MIT and other institutions has been used to shed light on the contents of an unopened letter from 1697, writes Matt Simon for Wired. “With fancy letterlocking techniques, you will forcibly rip some part of the paper, and then that will become detectable,” says Prof. Erik Demaine, of the method used to seal the letter.

Guardian reporter Alison Flood explores the new technique created by MIT researchers to virtually unseal an unopened letter written in 1697. The researchers, “worked with X-ray microtomography scans of the letter, which use X-rays to see inside the document, slice by slice, and create a 3D image,” writes Flood.

Researchers from MIT and other institutions have developed a new virtual-reality technique that has allowed them to unearth the contents of letters written hundreds of years ago, without opening them, writes New York Times reporter William J. Broad. “The new technique could open a window into the long history of communications security,” writes Broad. “And by unlocking private intimacies, it could aid researchers studying stories concealed in fragile pages found in archives all over the world.”

Using X-ray imaging and algorithms, MIT researchers have been able to virtually open and read letters that been sealed for more than 300 years, writes Priti Parikh for New Scientist. “Studying folding and tucking patterns in historic letters allows us to understand technologies used to communicate,” says Jana Dambrogio, a conservator at the MIT Libraries.

Researchers from MIT and other institutions have used algorithms and an X-ray scanner to decipher the secrets inside a letter that has been sealed since 1697, reports Sara Castellanos for The Wall Street Journal. “This is a dream come true in the field of conservation,” said Jana Dambrogio, the Thomas F. Peterson Conservator at MIT Libraries.

Bloomberg reporter Ashlee Vancee spotlights the work of alumnus Youyang Gu SB ’15, MEng’19, who developed a forecasting model for Covid-19 last spring that was widely considered to be one of the most accurate models of the pandemic’s trajectory. “The novel, sophisticated twist of Gu’s model came from his use of machine learning algorithms to hone his figures,” writes Vancee.

TechCrunch reporter Darrell Etherington writes that MIT researchers have developed a new “liquid” machine learning system that can learn on the job. Etherington notes that the system has “the potential to greatly expand the flexibility of AI technology after the training phase, when they’re engaged in the actual practical inference work done in the field.”

Wired reporter Will Knight spotlights how MIT researchers built a machine learning system that can help predict which patients are most likely to develop breast cancer. “What the AI tools are doing is they're extracting information that my eye and my brain can't,” says Constance Lehman, a professor of radiology at Harvard Medical School and division chief of breast imaging at MGH.

TechCrunch reporter Darrell Etherington writes that MIT researchers have developed a new system that devises hardware architecture that can speed up a robot’s operations. Etherington notes that “this research could help unlock the sci-fi future of humans and robots living in integrated harmony.”

Writing for Wired, Will Knight spotlights how MIT researchers developed a new technique to squeeze an AI vision algorithm onto a low-power computer chip that can run for months on a battery. The advance “could help bring more advanced AI capabilities, like image and voice recognition, to home appliances and wearable devices, along with medical gadgets and industrial sensors.”