Cornell AI News
Cornell is spearheading the development and refinement of AI through extensive interdisciplinary collaborations.
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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.
People, not design features, make a robot social
Researchers who develop social robots – ones that people interact with – focus too much on design features and not enough on sociological factors, like human-to-human interactions, the contexts where they happen and cultural norms involving robots.
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).
AI-powered ‘sonar’ on smartglasses tracks gaze and facial expressions
Cornell researchers have developed two technologies that track a person’s gaze and facial expressions through sonar-like sensing.
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.