MIT Media Lab

Congratulations to Carolina Warneryd and Duncan Lee, researchers in the Biomechatronics group, on being recognized at the 2026 MIT Mechanical Engineering De Florez Awards Competition!

Warneryd won first place in the Graduate Design category for "Cam-eleon: A Cam-Actuated Quasi-Passive Ankle-Foot Prosthetic," and Lee won third place for "Design and Evaluation of a Terrain-Adaptive Variable Stiffness Prosthesis."

Two Biomechatronics researchers among winners of De Florez Awards at MExpo 2026 – MIT Media Lab Biomechatronics researchers Carolina Warneryd and Duncan Lee win first and third place De Florez Awards at MExpo 2026.

In Quartz, Cris Tolomia looks back at 25 tech predictions from the 1990s, including some from Media Lab researchers. “What's most useful about revisiting these predictions isn't the scorecard,” Tolomia writes. “It's the pattern. The forecasters who got the big things right shared a common quality: they understood the underlying technology well enough to reason from first principles about what it would enable.”

Tech predictions from 30 years ago: what futurists got right and wrong From Bill Gates' wallet PC to Clifford Stoll's internet skepticism, the tech forecasts of the mid-1990s reveal as much about human nature as about technology itself

In a study presented at CHI ‘26, Media Lab PhD students Anku Rani and Valdemar Danry, Professor Paul Liang, Dr. Andrew Lippman, and Professor Pattie Maes show that while chatbots can help people detect misinformation, over time, users may become worse at spotting fake news on their own. However, the study also identified conversational strategies that support active learning and skill development, rather than simply helping in the moment.

“AIs that ‘tell’ by providing direct answers are more likely to foster reliance, while those that ‘ask’ via Socratic questioning are better at engaging someone to actually learn how to discern the truth on their own,” Danry says. “But it’s very much a trade-off between speed and effort.”

The consequences of relying on AI for accurate news Research from the MIT Media Lab found that, over the course of a month, participants who relied on AI systems to verify facts actually got worse at detecting misinformation on their own when their chatbots were taken away.

Researchers from City Science - MIT Media Lab have developed a framework that helps large language models (LLMs) better interpret sensor data. They note that although LLMs excel at understanding text, most of the world’s data does not arrive as text. Instead, it’s encoded in measurements from sensors, satellites, machines, and infrastructure.

Their framework, presented in MDPI AI Sensors, combines time-series measurements with contextual information about people, infrastructure, and local conditions. This significantly improves the quality and reliability of AI-generated explanations, enabling more actionable insights from sensing systems deployed in complex real-world environments, such as informal settlements.

Authors: Andres Rico and Professor Kent Larson

Project Overview ‹ AxolRAG – MIT Media Lab We show that LLMs can provide significantly more reliable explanations of sensor data when it receives contextual information about people, infrastructure, and…

Researchers from the Media Lab’s Conformable Decoders group have developed a new approach to ultrasound imaging that allows the user to visualize a 3D augmented-reality image, making it easier to identify and analyze the object being scanned. The system could improve the training process for ultrasound technicians and other healthcare providers. It could also be deployed for use in hospitals, for tasks such as using ultrasound to place a needle in the right location for a biopsy.

“For training, this could make ultrasound more intuitive and more understandable. On the clinical side, it could be less time-consuming, more accurate, and also give health care providers more peace of mind. They wouldn’t have to wonder if they missed anything,” says Professor Canan Dagdeviren, the senior author of the study.

Authors: Jason F. Hou, Shrihari Viswanath, Cinay Dilibal, Bowen Wu, Tanisha Shende, and Canan Dagdeviren

Augmented reality system could make medical ultrasounds easier to interpret MIT researchers developed a new approach to ultrasound imaging that allows the user to visualize a 3D, augmented-reality image of the object being scanned. This technique could be deployed in hospitals or used to assist training technicians in ultrasound interpretation.

Congratulations to Professor Canan Dagdeviren on being selected as one of the four winners of the 2026 Nature Awards Healthspan Accelerator (Nature Portfolio)! These awards identify human health research with the potential to transform the human healthspan.

Professor Dagdeviren leads the Conformable Decoders group and WHx, the Women's Health Program at the MIT Media Lab. Her research group develops technologies that turn patterns from nature and the human body into useful signals and energy. She founded WHx to advance women's health innovation through collaboration with clinical, industry, and policy leaders, supporting better and more equitable breast imaging for early cancer detection.

The Nature Awards Healthspan Accelerator is designed to maintain scientific rigor and integrity throughout the translation process, providing researchers with the tools and connections to translate groundbreaking research for maximum impact.

Canan Dagdeviren among winners of 2026 Nature Awards Healthspan Accelerator – MIT Media Lab Canan Dagdeviren is one of four winners of the 2026 Nature Awards Healthspan Accelerator for health research translation.

During the MIT Ethics of Computing Research Symposium, hosted by the MIT Schwarzman College of Computing’s Social and Ethical Responsibilities of Computing (SERC) initiative, speakers including Media Lab Professor Pat Pataranutaporn considered how AI is shaping the world and its implications for society.

The crucial human component in computing and AI The MIT Ethics of Computing Research Symposium brings together experts and researchers working at the heart of ethical and social impact in technology.

Although the ocean covers 70 percent of the Earth’s surface, we have visually explored only a tiny fraction of the deep seafloor — less than 0.001 percent, about half of which is within 200 nautical miles of the US, Japan, or New Zealand. The Ocean Discovery League, founded by former Labber Dr. Katy Croff Bell, created the Global Deep Sea Exploration Goals to close that gap. The initiative, launched earlier this year, is a coordinated international effort to create a representative global dataset of deep-sea ocean imagery.

The Global Deep Sea Exploration Goals | Ocean Discovery League The Global Deep Sea Exploration Goals are an ambitious international effort to visually explore 10,000 strategically selected locations across the deep seafloor.

Professor Fadel Adib, who leads the Signal Kinetics group, is also the co-founder of Cartesian, which uses technology based on Professor Adib’s research to help retail stores track inventory, down to its exact location — whether it’s on a shelf or in a box in the back storage room. Cartesian’s platform could also improve indoor location tracking for manufacturers, logistics operators, and robotics companies.

“The big problem we’re solving is that about 50 percent of working hours in retail stores go to managing inventory,” Professor Adib says. “That is roughly a $15 billion problem in the U.S. alone. We use algorithms to decipher indoor locations using wireless signals. The core technology enables a new level of indoor localization.” He adds, “The broad vision for what we are doing is spatial AI. Today, AI does extremely well in the digital world. Now it has to move into the physical world. That means allowing machines to perceive their environment in such a way that they can interact with it. That’s where spatial AI comes in and where Cartesian sits.”

Startup helps retailers track their products in real-time The startup Cartesian is helping retailers keep track of inventory with a technology invented at MIT. Using wireless signals from attached RFID tags, the system finds items’ precise location in a store, from the stockroom to the shop floor.