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.
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.
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.
Researchers develop “ContextCite,” an innovative method to track AI’s source attribution and detect potential misinformation.
First organized MIT delegation highlights the Institute’s growing commitment to addressing climate change by showcasing research on biodiversity conservation, AI, and the role of local communities.
