For the first time ever, experts in veterinary medicine, AI researchers, industry pioneers, and thought leaders from around the world gathered at to the College of Veterinary Medicine (CVM) for the Symposium on Artificial Intelligence in Veterinary Medicine.
Cornell AI News
News Category
Filter by Topic
Satellite images of plants’ fluorescence can predict crop yields
Cornell researchers and collaborators have developed a new framework that allows scientists to predict crop yield without the need for enormous amounts of high-quality data – which is often scarce in developing countries, especially those facing heightened food insecurity and climate risk.
AI apps and robots take home awards at BOOM
Apps that use artificial intelligence to help with tutoring, labeling medical images and perfecting your form while exercising, websites that address social issues with technology, and a robot that may one day colonize Mars all won awards at the annual Bits On Our Minds student technology showcase.
Brooks School Tech Policy Institute focuses on intersection of national security and tech policy
We live in an era in which rapid technological change shifts the global security balance in real time. No one knows that better than Sarah Kreps, director of the Brooks School Tech Policy Institute (BTPI), and John L. Wetherill Professor in the Department of Government in the College of Arts & Sciences.
As Empire AI dawns, Cornell lays groundwork for public good
Empire AI, a $400 million effort to create a shared academic research computing facility, is set to advance dozens of ambitious, cross-disciplinary projects at Cornell.
Cornell Bowers CIS to launch minor in artificial intelligence
Beginning Fall 2024, Cornell undergraduates can minor in artificial intelligence (AI).
Scientists Harness Robots and AI to Revolutionize Farming
Yu Jiang is taking ag research to the next level. From robots to drones to AI, Yu is developing many exciting solutions for the New York farm industry.
Statistical machine learning finds unknown factors that underlie disease
A new method can now find previously unknown factors that underlie disease by using statistical machine learning to sort through mountains of complex biological data.