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.
Read MoreFive MIT faculty members and two additional alumni are honored with fellowships to advance research on beneficial AI.
Read MoreThe “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.
Read MoreIn 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.
Read MoreA new technique identifies and removes the training examples that contribute most to a machine-learning model’s failures.
Read MoreUsing LLMs to convert machine-learning explanations into readable narratives could help users make better decisions about when to trust a model.
Read MoreMIT 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.
Read MoreResearchers develop “ContextCite,” an innovative method to track AI’s source attribution and detect potential misinformation.
Read MoreMIT engineers developed the largest open-source dataset of car designs, including their aerodynamics, that could speed design of eco-friendly cars and electric vehicles.
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