A more effective way to train machines for uncertain, real-world situations
Researchers develop an algorithm that decides when a “student” machine should follow its teacher, and when it should learn on its own.
Researchers develop an algorithm that decides when a “student” machine should follow its teacher, and when it should learn on its own.
It’s more important than ever for artificial intelligence to estimate how accurately it is explaining data.
Senior Ananya Gurumurthy adds her musical talents to her math and computer science studies to advocate using data for social change.
With the artificial intelligence conversation now mainstream, the 2023 MIT-MGB AI Cures conference saw attendance double from previous years.
A new machine-learning model makes more accurate predictions about ocean currents, which could help with tracking plastic pollution and oil spills, and aid in search and rescue.
The CSAIL scientist describes natural language processing research through state-of-the-art machine-learning models and investigation of how language can enhance other types of artificial intelligence.
Models trained using common data-collection techniques judge rule violations more harshly than humans would, researchers report.
Researchers identify a property that helps computer vision models learn to represent the visual world in a more stable, predictable way.
The system they developed eliminates a source of bias in simulations, leading to improved algorithms that can boost the performance of applications.
A collaborative research team from the MIT-Takeda Program combined physics and machine learning to characterize rough particle surfaces in pharmaceutical pills and powders.