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.”
Fast Company reporter Mark Wilson spotlights Strolling Cities, a new AI video project developed by researchers from the MIT-IBM Watson AI Lab, which recreates the streets of Italy based on millions of photos and words. “I decided that the beauty and sentiment, the social, historical, and psychological contents of my memories of Italy could become an artistic project, probably a form of emotional consolation,” says Mauro Martino of the MIT-IBM Watson AI Lab. “Something beautiful always comes out of nostalgia.”
Fortune reporter Jonathan Vanian writes that researchers from the MIT-IBM Watson AI Lab and IBM Research have published a paper and data set “intended to help researchers create deep learning systems that can create software code.” Vanian notes that “research like this could pave the way for more efficient methods for companies to test software, analyze code, and potentially build apps.”
Times Higher Education reporter Simon Baker writes that Media Lab researchers have developed a new machine learning model that can predict research studies that will have the highest impact. The tool has the potential to “aid funders and research evaluators in making better decisions and avoiding the kind of biases and gaming that occurred with simpler metric assessments.”
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.”
In an article for New Scientist, Vijaysree Venkatraman reviews a new book by Kate Darling of the Media Lab, which explores whether we should think of robots as more like animals than humans. “Unlike animals, robots are designed, peddled and controlled by people, Darling reminds us. Her timely book urges us to focus on the legal, ethical and social issues regarding consumer robotics to make sure the robotic future works well for all of us,” writes Venkatraman.
Graduate student Joy Buolamwini joins Kara Swisher on The New York Times' “Sway” podcast to discuss her crusade against bias in facial recognition technologies. “If you have a face, you have a place in this conversation,” says Buolamwini.
USA Today reporter Barbara VanDenburgh spotlights Media Lab research specialist Kate Darling’s new book, “The New Breed: What Our History with Animals Reveals about Our Future with Robots.” VanDenburgh writes that in the book, “An MIT Media Lab researcher and technology policy expert argues that treating robots more like we treat animals, with a bit of humanity, will serve mankind well.”
Writing for Wired, Media Lab research specialist Kate Darling makes the case that robots are more like animals than people. “Despite the AI pioneers’ original goal of recreating human intelligence, our current robots are fundamentally different,” writes Darling. “They’re not less-developed versions of us that will eventually catch up as we increase their computing power; like animals, they have a different type of intelligence entirely.”
Rebellions Inc., a company founded by alumnus Park Sunghyun S.M. ’11, PhD ’14, is developing a microchip aimed at “running artificial intelligence more efficiently, which could cut precious millionths of a second off the reaction times of automatic-trading machines,” reports Hooyeon Kim and Whanwoong Choi for Bloomberg.
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.
MIT researchers have developed a new robot, dubbed RF Grasp, that can sense hidden objects using radio waves, reports Brian Heater for TechCrunch. “The tech allows RF Grasp to pick up things that are covered up or otherwise out of its line of vision,” writes Heater.
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.”
TechCrunch reporter Brian Heater spotlights how MIT researchers have devised a neural network to help optimize sensor placement on soft robots to help give them a better picture of their environment.