Prof. Jacob Andreas speaks with CNN’s Lisa Eadicicco about deciphering the chain-of-thought behind complex AI neural networks. “I think we’re still very far from being able to claim that we have any sort of fine-grained understanding of how the model is actually arriving at a solution,” says Andreas.
Prof. Ankur Moitra and Prof. John Urschel share their thoughts with Forbes’ Alex Knapp about OpenAI releasing more than 700 math preprint papers online. “We’re used to landmark results being accompanied by new insights and theories,” writes Moitra. “But these feel different. The claims are spectacular, the papers are incomprehensible and there’s no punchline in sight that explains why the mysteries suddenly unraveled.” Adds Urschel: “It’s obvious that how we do and evaluate mathematics is going to have to change. What I think is constant is the fundamental idea of understanding that underpins our profession. And I’m excited to learn from these breakthroughs.”
Prof. Pat Pataranutaporn and graduate student Manuel Cherep speak to the Scientific American’s Mary Randolph about companies creating AI agents to assist users in personal tasks. “We have this narrative crisis about AI right now. There’s two opposite visions of the future, and while people worry about AI,” says Pataranutaporn, “there’s this cute AI tool that’s being released.”
Prof. John Urschel speaks with Wall Street Journal reporter Andrew Beaton about how he called an audible and pivoted from the NFL to a career in mathematics, delving into his time playing for the Baltimore Ravens, his graduate studies at MIT and the publication of his latest research, which proves a decades-old mathematical conjecture. “I feel like I bet on myself in a big way,” Urschel says. “I’m living my best life right now.”
Prof. John Urschel found that the Gaussian elimination algorithm, used to calculate shared solutions to a group of equations, specifically linear equations, is close to 100 percent reliable, writes reporter Miriam Waldvogel for The Washington Post. “It just seems so interesting that the most fundamental algorithm we have in the field, we do not fully understand,” says Urschel. “I really love this question of why versus how, and this idea of understanding what’s going on.” Remarking on his previous career as an NFL player, he shares: “If a student thinks that’s cool and [it] gets them to come take my class, and they learn a lot of cool, numerical math, that sounds great to me.”
Forbes’ Rashi Shrivastava highlights MIT spinout Liquid AI, which is building “liquid neural networks that process data more efficiently and don’t need expensive chips stored in data centers.” Shrivastava writes, “Liquid AI’s models are small enough that they can run directly on laptops and phones. There’s no cost to host them.”
Research Scientist Julian Klein and Prof. Frances Ross speak with speak with Physics reporter Akashni Raghubar Latchanna about an electron microscopy technique they developed to move atoms within a material, creating new possibilities for engineering electrical, optical, and quantum properties. “If we can control the electronic or optical functions of the materials, we might potentially make those devices more brain-like, more portable, and less power-hungry,” Ross said.
Forbes’ Amit Sinha spotlights nearly 50 years of the RSA algorithm, “one of the most important practical implementations of public-key cryptography,” developed by Prof. Ronald Rivest and his colleagues Profs. Adi Shamir and Len Adleman. RSA turned public-key cryptography “into a practical method for secure communication between parties who had never met and shared no prior secret,” notes Sinha. “It also made digital signatures practical, enabling identity verification and authentication across open networks.”
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.”