A group of Cornell researchers has received a $1 million grant from the U.S. Agency for International Development to use machine learning to rapidly analyze agricultural and food market conditions, aiming to better predict poverty and undernutrition in some of the world’s poorest regions.
Projects ranging from a soil-swimming robot that can sense conditions in the root zone in real time to computational models that can predict produce spoilage received seed funds from the Cornell Initiative for Digital Agriculture’s new Research Innovation Fund.
Cornell data scientists are developing models and mathematical techniques to address the world’s most vexing problems, from public health crises to climate change.
Artificial intelligence must be managed in ways that keep robots from doing harm accidentally, according to Daniel Weld, professor of computer science at the University of Washington.
A cross-campus collaboration led by materials science professor Uli Wiesner results in visual confirmation of 12-sided, nanoscale cage structures, which could have medical diagnostic and therapeutic applications.