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.
Research from the MIT Center for Constructive Communication finds this effect occurs even when reward models are trained on factual data.
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.
Researchers propose a simple fix to an existing technique that could help artists, designers, and engineers create better 3D models.
This new device uses light to perform the key operations of a deep neural network on a chip, opening the door to high-speed processors that can learn in real-time.
Marzyeh Ghassemi works to ensure health-care models are trained to be robust and fair.
The technique could make AI systems better at complex tasks that involve variability.
