Algorithms

Graduate student Shashank Srikant speaks with The Economist about his work developing a new model that can detect computer bugs and vulnerabilities that have been maliciously inserted into computer code.

New York Times reporter Siobhan Roberts spotlights how Prof. Erik Demaine and his father Martin Demaine, a robotics engineer at CSAIL and an artist-in-resident at EECS, are designing “‘algorithmic puzzle fonts,’ a suite of mathematically inspired typefaces that are also puzzles.” The Demaines explained that: “Scientists use fonts every day to express their research through the written word. But what if the font itself communicated (the spirit of) the research? What if the way text is written, and not just the text itself, engages the reader in the science?”

Mashable spotlights Strolling Cities, a video project from the MIT-IBM Watson AI Lab, which uses AI to allow users to imagine what different words would like as a location. “Unlike other image-generating AI systems, Strolling Cities creates fictional cities every time,” Mashable notes.

An electric, autonomous boat developed by MIT researchers is being tested in the canals of Amsterdam as part of an effort to ease traffic, reports Aleksandar Furtula and Mike Corder for the AP. The Roboat project is aimed at developing “new ways of navigating the world’s waterways without a human hand at the wheel,” write Furtula and Corder. “The vessels are modular so they can be easily adapted for different purposes, carrying cargo or workers.”

A recent review by MIT researchers finds that “only about 23% of machine learning studies in health care used multiple datasets to establish their results, compared to 80% in the adjacent field of computer vision, and 58% in natural language processing,” writes Casey Ross for STAT. “If the performance results are not reproduced in clinical care to the standard that was used during [a study], then we risk approving algorithms that we can’t trust,” says graduate student Matthew McDermott. “They may actually end up worsening patient care.”

In a forthcoming book, photographer Jessica Wynne spotlights the chalkboards of mathematicians, including Professor Alexei Borodin’s and Associate Professor Ankur Moitra’s, reports Clara Moskowitz for Scientific American. 

Wired reporter Will Knight spotlights how MIT researchers have showed that “an AI program trained to verify that code will run safely can be deceived by making a few careful changes, like substituting certain variables, to create a harmful program.”

MIT researchers are applying machine learning algorithms typically used for natural language processing to identify coronavirus variants, reports Brian Browdie for Quartz. “Besides being able to quantify the potential for mutations to escape, the research may pave the way for vaccines that broaden the body’s defenses against variants or that protect recipients against more than one virus, such as flu and the novel coronavirus, in a single shot,” writes Browdie. 

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.