Professor Jesse Thaler describes a vision for a two-way bridge between artificial intelligence and the mathematical and physical sciences — one that promises to advance both.
A new approach could help users know whether to trust a model’s predictions in safety-critical applications like health care and autonomous driving.
By leveraging idle computing time, researchers can double the speed of model training while preserving accuracy.
Research from the MIT Center for Constructive Communication finds leading AI models perform worse for users with lower English proficiency, less formal education, and non-US origins.
A new method developed at MIT could root out vulnerabilities and improve LLM safety and performance.
MIT Sports Lab researchers are applying AI technologies to help figure skaters improve. They also have thoughts on whether five-rotation jumps are humanly possible.
Removing just a tiny fraction of the crowdsourced data that informs online ranking platforms can significantly change the results.
CSAIL researchers find even “untrainable” neural nets can learn effectively when guided by another network’s built-in biases using their guidance method.
MIT-IBM Watson AI Lab researchers developed an expressive architecture that provides better state tracking and sequential reasoning in LLMs over long texts.
The technique can help scientists in economics, public health, and other fields understand whether to trust the results of their experiments.
