Cornell AI News
Cornell is spearheading the development and refinement of AI through extensive interdisciplinary collaborations.
Filter by Topic
Faculty and researchers invited to request time to run research projects on new AI tool
Faculty and researchers invited to request time to run research projects on new AI tool
Global AI among three projects funded to build better future
A multidisciplinary team aims to build a more inclusive AI shaped by global cultures and knowledge – one of three projects that make up Cornell’s new Global Grand Challenge: The Future.
Cornell EMPIRE AI Weekly Forum
Happening every Friday from 4-4:30 PM via Zoom, the forum is for Cornell faculty and staff (e.g., research operational support staff) who are interested in the EMPIRE AI’s current status and future plans. Earlier this spring, New York State and six academic partners,...
NSF grant supports AI-driven sustainability research, training
A new program at Cornell will tackle critical environmental challenges by integrating advanced artificial intelligence (AI) tools with sustainability research across the campus, thanks to a grant from the National Science Foundation’s Research Traineeship Program.
Reducing the cultural bias of AI with one sentence
“Cultural prompting” – asking an AI model to perform a task like someone from another part of the world – resulted in reduced bias in responses for the vast majority of the more than 100 countries tested by a Cornell-led research group.
Mixing Physical, Virtual Worlds to Drive Home Climate Urgency
The Communal eXtended-Reality (CXR) system is a cutting-edge blend of the physical and digital worlds in which virtual scenes are overlaid onto the real world, designed to engage communities in new ways.
Brevity is money when using AI for data analysis
A new computational system called Schemonic, developed by Cornell researchers, cuts the costs of using large language models such as ChatGPT and Google Bard by combing large datasets and generating what amounts to “CliffsNotes” versions of data
Diagnostic tool identifies puzzling inflammatory diseases in kids
A Cornell-led collaboration developed machine-learning models that use these cell-free molecular RNA dregs to diagnose pediatric inflammatory conditions that are difficult to differentiate. The diagnostic tool can accurately determine if a patient has Kawasaki disease (KD), Multisystem Inflammatory Syndrome in Children (MIS-C), a viral infection or a bacterial infection, while simultaneously monitoring the patient’s organ health.