Algorithms

Popular Science reporter Charlotte Hu writes that MIT researchers have simulated an environment in which socially-aware robots are able to choose whether they want to help or hinder one another, as part of an effort to help improve human-robot interactions. “If you look at the vast majority of what someone says during their day, it has to do with what other [people] want, what they think, getting what that person wants out of another [person],” explains research scientist Andrei Barbu. “And if you want to get to the point where you have a robot inside someone’s home, understanding social interactions is incredibly important.”

MIT researchers have developed a new machine learning system that can help robots learn to perform certain social interactions, reports Brian Heater for TechCrunch. “Researchers conducted tests in a simulated environment, to develop what they deemed ‘realistic and predictable’ interactions between robots,” writes Heater. “In the simulation, one robot watches another perform a task, attempts to determine the goal and then either attempts to help or hamper it in that task.”

Quanta Magazine reporter Thomas Lewton spotlights Prof. Janet Conrad’s work on MiniBooNE, a neutrino particle detector that was in operation from 2002 until 2019.

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.

TechCrunch reporter Brian Heater spotlights RFusion, a fully-integrated robotic arm developed by MIT researchers that “uses an RF antenna and camera mounted to an arm to find lost objects.”

Graduate student Joy Buolamwini speaks with Good Morning America about her work uncovering bias in AI systems and how beauty data can marginalize people of color. “We can’t have social justice without algorithmic justice,” says Buolamwini.

Bloomberg’s Janet Wu speaks with alumna Nan-Wei Gong PhD '13, co-founder of Figur8, an MIT startup applying AI to help diagnose musculoskeletal problems. “Figur8 is a tool that really brings lab experience into the field so everyone can quantify their musculoskeletal injuries,” says Gong. “We invented a wearable system that allows you to capture biomarkers of your musculoskeletal health and pinpoint injury through our AI algorithm.”

Nuria Oliver PhD ’00 speaks with Wired reporter Willem Marx about her work developing a data gathering system to help combat Covid-19 in Valencia, Spain. Olivier and her team developed “a powerful predictor that's been road-tested during a time of unprecedented strain, and continues to be used across Valencia," writes Marx. "They have also created a system that can suggest a small number of specific, effective pandemic-related policies or interventions that a government can make.”

Inside Science reporter Tom Metcalfe writes that MIT researchers have developed a new method for taking the Earth’s temperature by examining basaltic rocks, and used the method to create a model of the Earth's oceanic ridges. "We are constantly stressing how [plate] tectonics operated in the past," says postdoc Stephanie Brown Krein. "And so I think it's really important for us to be able to understand how tectonics are working in the present day.”

Vice reporter Radhamely De Leon spotlights how researchers from MIT and Carnegie Mellon University have created “a search engine tool that shows what Google search results appear in different countries or languages, highlighting key differences in the algorithm between regions.”

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.”