Research

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.