Writing for Forbes about efforts to improve air travel safety, Tanya Eves highlights the Air-Guardian system, an eye-tracking monitor for pilots developed by CSAIL researchers that assists when attention wavers. “In tests, it reduced flight risk and improved navigation success rates,” writes Eves. “It's a model for how the virtual co-pilot relationship should work: not replacement, but a seamless, intelligent partnership that understands when to act and when to stay silent.”
Writing for Forbes, contributor Ron Schmelzer highlights Describe Anything, Anywhere, at Any Moment (DAAAM), a new system developed by MIT researchers that could enable robots to capture details of objects they see while exploring an environment. In the future, the system could allow factory workers to send robotic assistants to find items. DAAAM “lets a robot build a detailed map of a space, attach descriptions to objects in that map, and answer plain English questions later,” Schmelzer explains.
Associated Press reporter Rodrique Ngowi visits MIT to learn about how researchers in Prof. Xuanhe Zhao’s lab developed an ultrasound wristband that gathers data on human hand motions as part of an effort to help train humanoid robots to undertake complex tasks, from housework to surgery. “Imagine people doing housework,” says Zhao. “We can use the data obtained by our system to train a robot to do exactly (that) housework with this dexterous hand motion.”
Nature reporter Jyoti Madhusoodanan features Prof. Regina Barzilay and Prof. James Collin’s work developing AI tools aimed at accelerating the process of drug discovery and tackling the growing problem of antibiotic resistance. Barzilay notes that the goal of AI-based drug design is not to have the perfect method, but to find working solutions to the antibiotic-resistance crisis. “To me, the art is really in taking the tools we currently have, which are already doing quite a bit, and translating them into something which is useful in clinic,” she explains.
In an article for Forbes, Senior Lecturer Guadalupe Hayes-Mota '08 MS '16, MBA '16 explores how “AI is now embedded in the critical path of drug discovery, and it is making consequential decisions at a speed and scale that existing governance structures were simply not designed to handle.” Hayes-Mota emphasizes: “The goal is to ensure that as AI accelerates the machine of drug development, we have deliberate mechanisms for human accountability threaded through every critical junction.”
Prof. Regina Barzilay speaks with WBUR’s Priyanka Dayal McCluskey about her work developing an AI risk detection tool that can analyze mammogram images and help predict risk of breast cancer before it happens or spreads. Barzilay, who describes the tool as a hi-tech weather forecast for breast health, notes that: “We really need to have tools that can help, rather than just staring at an image and trying to guess.”
Sybil, a new AI tool developed by researchers from MIT and Mass General Brigham Cancer Institute, “analyzes a single CT scan and generates a risk score predicting the likelihood of developing lung cancer over a period of up to six years,” reports Ivan Rodriguez for WCVB-TV. “In 2023, researchers reported that Sybil achieved an accuracy rate of 86% to 94% in distinguishing high-risk patients from low-risk patients within a year.”
Cambridge Day reporter Zoe Beketova, a student in MIT’s Graduate Program in Science Writing, visits Prof. Xuanhe Zhao’s lab to get a hands-on look at the group’s ultrasound wristband that can map movements of the human body using sound waves, part of the group’s work aimed at changing “how we gather information from inside the body.” Says Zhao: “The mission of my lab is really merging humans with machines and AI. We believe there’s a huge opportunity [with] this interface.”
Reporting for CNN, Caleb Hellerman spotlights how MIT computer scientists developed an AI program called Sybil that can “‘look’ at a single CT scan and generate a ‘risk score’ corresponding to the likelihood of the person developing cancer over any period up to six years.”
Prof. Xuanhe Zhao speaks with Tech Briefs reporter Andrew Corselli about his team’s work developing an ultrasound wristband that precisely tracks a wearer’s hand movements in real time and can communicate device these motions to a robot or a virtual environment. “For the future of human society, humanized robots will do lots of different work for us. For that work, we need a dexterous robotic hand,” explains Zhao. “We believe this ultrasound wristband, based on variable imaging, could be the future of really knowing the human hand motions.”
MIT researchers have developed a new traffic navigation system that more accurately reflects travel time by including parking data, reports Catherine Parotta for Boston 25. “What we can do is figure out if you’re best off trying this parking lot first, even if it’s farther than the closest parking lot,” explains Prof. Cathy Wu. Graduate student Cameron Hickert adds that: “We hope that this can help people make better decisions."
Two new studies from researchers at MIT and elsewhere have described “the machine-learning algorithms they developed to screen bacterial genomes and identify proteins that are involved in protecting the microorganisms against viral invaders,” reports Miryam Naddaf for Nature. “There’s a hope that maybe there’s a next generation of molecular tools that would come from some of these new systems,” says Prof. Michael Laub.
MIT researchers have developed an ultrasound wristband that can transmit a user’s motions to a robotic hand or a virtual environment, reports Mack Degeurin for Popular Science. “Volunteers wearing the device could direct the robotic hand to grab tennis balls, make hand signs, and even play notes on a piano,” Degeurin explains. “That same technique can also be applied to digital environments, which means future wearers could control a phone screen without ever touching it, or interact with virtual reality in ways that feel more immersive.”
Prof. Adam Berinsky speaks with Washington Post reporter Shira Ovide about American’s relationship with AI and social media. “Berinsky said the unfavorable opinions about AI aren’t that bad compared with Americans’ low confidence in many institutions, including big business, Congress and newspapers,” writes Ovide.
Writing for the Financial Times, Prof. Danielle Li examines the risks for highly skilled workers whose expertise is used as training data for AI systems. “As workers, people should think about how to use AI to expand their skills: whether by building complementary capabilities or by finding ways to scale their expertise through AI systems,” Li writes. “As citizens, they should press for policies that give workers clearer rights over the data generated by their work and compensation for it.”