For healthy hearing, timing matters
Machine-learning models let neuroscientists study the impact of auditory processing on real-world hearing.
Machine-learning models let neuroscientists study the impact of auditory processing on real-world hearing.
As the use of generative AI continues to grow, Lincoln Laboratory's Vijay Gadepally describes what researchers and consumers can do to help mitigate its environmental impact.
Inspired by the human vocal tract, a new AI model can produce and understand vocal imitations of everyday sounds. The method could help build new sonic interfaces for entertainment and education.
AeroAstro PhD student Sydney Dolan uses an interdisciplinary approach to develop collision-avoidance algorithms for satellites.
Associate Professor Matteo Bucci’s research sheds new light on an ancient process, to improve the efficiency of heat transfer in many industrial systems.
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.
MIT engineers developed AI frameworks to identify evidence-driven hypotheses that could advance biologically inspired materials.
With models like AlphaFold3 limited to academic research, the team built an equivalent alternative, to encourage innovation more broadly.
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.
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.
Researchers develop “ContextCite,” an innovative method to track AI’s source attribution and detect potential misinformation.
MIT engineers developed the largest open-source dataset of car designs, including their aerodynamics, that could speed design of eco-friendly cars and electric vehicles.