A new computational model can predict antibody structures more accurately
Using this model, researchers may be able to identify antibody drugs that can target a variety of infectious diseases.
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Using this model, researchers may be able to identify antibody drugs that can target a variety of infectious diseases.
Biodiversity researchers tested vision systems on how well they could retrieve relevant nature images. More advanced models performed well on simple queries but struggled with more research-specific prompts.
Five MIT faculty and staff, along with 19 additional alumni, are honored for electrical engineering and computer science advances.
The neuroscientist turned entrepreneur will focus on advancing the intersection of behavioral science and AI across MIT.
With models like AlphaFold3 limited to academic research, the team built an equivalent alternative, to encourage innovation more broadly.
Researchers at MIT, NYU, and UCLA develop an approach to help evaluate whether large language models like GPT-4 are equitable enough to be clinically viable for mental health support.
The MIT senior will pursue graduate studies in the UK at Cambridge University and Imperial College London.
Five MIT faculty members and two additional alumni are honored with fellowships to advance research on beneficial AI.
SERC Scholars from around the MIT community examine the electronic hardware waste life cycle and climate justice.
The “PRoC3S” method helps an LLM create a viable action plan by testing each step in a simulation. This strategy could eventually aid in-home robots to complete more ambiguous chore requests.
In a recent commentary, a team from MIT, Equality AI, and Boston University highlights the gaps in regulation for AI models and non-AI algorithms in health care.
Using high-powered lasers, this new method could help biologists study the body’s immune responses and develop new medicines.
A new technique identifies and removes the training examples that contribute most to a machine-learning model’s failures.
Using LLMs to convert machine-learning explanations into readable narratives could help users make better decisions about when to trust a model.
MIT CSAIL director and EECS professor named a co-recipient of the honor for her robotics research, which has expanded our understanding of what a robot can be.