Algorithms

MIT researchers have developed Ataraxos, an AI system capable of winning the challenging game Stratego. The system could one day help decision makers with real-world problems that involve hidden information, reports Jacek Krywk for Ars Technica. “Board games have fixed rules and clear winners, while real-world problems like negotiations, financial markets, or military conflicts usually don’t,” writes Krywk. “But the Ataraxos team argues the gap is smaller than it looks, since tackling any real problem starts with building a simplified model of it.” 

Jeffrey Riley, executive director of MIT RAISE’s “Day of AI” K-12 program, speaks to WBUR’s Suevon Lee about the importance of teaching school-aged children AI literacy skills. “Kids need to know about voice clones and deepfakes and plagiarism,” says Riley. “We need to teach kids so that they're healthy skeptics of this technology, that they mistrust and verify the information they're getting from it.”  

MIT researchers are leading a new project documenting what major AI systems say about elections in an effort to understand how the technology influences the democratic process, writes Tiffany Hsu for The New York Times. “AI is becoming part of the way people encounter and make sense of political information, and yet we know relatively little about what that information environment actually looks like, how it differs for different user demographics, political identities and geographies,” says Prof. Chara Podimata, a leader of the project.  

Prof. Daniela Rus, Director of MIT CSAIL, and Prof. David Autor, department head of Economics, are featured on Time’s “TIME100 AI 2026” list of 100 innovators, leaders, and thinkers reshaping the world through their advances in AI. “Over three decades, [Rus] has conducted pioneering work in robotics, extending the understanding of what form robots can take,” writes Tharin Pillay, while Steven Freiss describes Autor as “one of the nation’s most vocal and prominent economists aiming to ascertain AI’s potential impacts on labor.” 

Prof. Joshua Tenenbaum discusses whether AI systems will perform better if they have increased awareness of the world around them with New Scientist’s Daniel Cossins. “One of the big misconceptions is that intelligence is a single thing, that there is a single world model in the brain,” says Tenenbaum. “What we actually have is the ability to run many different models depending on the context, task and goal.” 

Graduate student Peter Zhi Xuan Li speaks to Tech Briefs’ Andrew Corselli about his team’s work developing a new chip that allows small, autonomous robots and other battery-limited devices to construct detailed 3D maps of their environments using a fraction of the power required by other systems. “Today, a robot uses separate, incompatible internal representations for different jobs: one for mapping, another for tracking its own motion, another for rendering what it sees, and every conversion adds memory footprint and energy,” says Li. “Our goal is a single shared representation.” 

Prof. Daniela Rus, director of CSAIL, joins Hakeem Oluseyi, host of GBH’s “Particles of Thought,” to discuss her group’s work developing liquid neural networks, AI that runs locally on a given device rather than a data center to increase privacy and improve energy efficiency. By using liquid AI, "you avoid privacy concerns and you avoid the security concerns associated with accessing the cloud,” says Rus. “Furthermore, the cost of running your models on a device is much, much lower than running a huge model in the cloud.” 

Research Scientist Maria Jesús Saénz discusses the benefits and barriers to companies adopting AI in their supply chains for a Financial Times article by reporter Lucy Colback. “If you are having humans change their process for automation in order to substitute themselves, this is a very perverse thing,” Saénz says. “They want to keep their salary and might boycott the AI. [They may] be algorithm averse.” 

MIT researchers have created a new building design model that could enable engineers to construct buildings and bridges that use less materials, writes Tech Briefs’ Andrew Corselli. “Traditional topology optimization essentially starts with a blank space and tries to figure out at each point in this blank space: ‘Should there be material,’ ‘should there not be material’ from an efficiency standpoint,” says Prof. Josephine Carstensen. “Our approach populates the space with a bunch of lines that are instead candidates for ‘should there be material’ or ‘should there not be material.’ By using this line approach, we have the opportunity to have more control.” 

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. 

MIT researchers have developed a new method for precisely moving columns of individual atoms within a material, which could give rise to exotic quantum properties and shed light on quantum behavior, reports Tim Wogan for Physics World. “I’m excited because of the scalability of this that allows us to look at the interactions between the defects rather than just creating a defect itself,” explains Prof. Frances Ross. 

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.”

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.”

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."