For all their impressive capabilities, large language models (LLMs) often fall short when given challenging new tasks that require complex reasoning skills.

In 1954, the world’s first successful organ transplant took place at Brigham and Women’s Hospital, in the form of a kidney donated from one twin to the other. At the time, a group of doctors and scientists had correctly theorized that the recipient’s antibodies were unlikely to reject an organ from an identical twin. One Nobel Prize and a few decades later, advancements in immune-suppressing drugs increased the viability of and demand for organ transplants. Today, over 1 million organ transplants have been performed in the United States, more than any other country in the world.

Marine scientists have long marveled at how animals like fish and seals swim so efficiently despite having different shapes. Their bodies are optimized for efficient aquatic navigation (or hydrodynamic) so they can exert minimal energy when traveling long distances.

Diffusion models like OpenAI’s DALL-E are becoming increasingly useful in helping brainstorm new designs. Humans can prompt these systems to generate an image, create a video, or refine a blueprint, and come back with ideas they hadn’t considered before.

In an office at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), a soft robotic hand carefully curls its fingers to grasp a small object. The intriguing part isn’t the mechanical design or embedded sensors – in fact, the hand contains none. Instead, the entire system relies on a single camera that watches the robot’s movements and uses that visual data to control it.

In 2025, MIT granted tenure to 11 faculty members across the School of Engineering. This year’s tenured engineers hold appointments in the departments of Aeronautics and Astronautics, Biological Engineering, Chemical Engineering, Electrical Engineering and Computer Science (EECS) — which reports jointly to the School of Engineering and MIT Schwarzman College of Computing — Materials Science and Engineering, Mechanical Engineering, and Nuclear Science and Engineering.

Launched in February of this year, the MIT Generative AI Impact Consortium (MGAIC), a presidential initiative led by MIT’s Office of Innovation and Strategy and administered by the MIT Stephen A. Schwarzman College of Computing, issued a call for proposals, inviting researchers from across MIT to submit ideas for innovative projects studying high-impact uses of generative AI models.

Research has shown that large language models (LLMs) tend to overemphasize information at the beginning and end of a document or conversation, while neglecting the middle.

Will the perfect storm of potentially life-changing, artificial intelligence-driven health care and the desire to increase profits through subscription models alienate vulnerable patients?

Understanding how molecules interact is central to biology: from decoding how living organisms function to uncovering disease mechanisms and developing life-saving drugs. In recent years, models like AlphaFold changed our ability to predict the 3D structure of proteins, offering crucial insights into molecular shape and interaction. But while AlphaFold could show how molecules fit together, it couldn’t measure how strongly they bind — a key factor in understanding all aforementioned. That missing piece is where MIT’s new AI model, Boltz-2, comes in.