Latest stories, updates, and announcements that highlight the people, research, and progress of AI throughout Cornell.

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AI Headlines from Around the University

Cornell leads project putting robots to work in US orchards

A Cornell-led research project is developing robots that can perform labor-intensive orchard operations; it’s supported by a four-year, $7.5 million grant from the U.S. Department of Agriculture’s Specialty Crop Research Initiative. 

Cornell expands AI Critical Literacy Program for incoming students

After a successful pilot launch in the spring, Cornell’s AI Critical Literacy Program will be offered in the fall to all incoming students as well as other interested students, faculty members and staff.

Strike a pose: Creating more realistic multi-person images

A Cornell research group has devised a way to generate an image of multiple people, incorporating the poses of all individuals to inform the image generation and make the scene more believable and accurate.

Weak AI regulation may backfire, making products less safe

A new modeling study finds no regulation of AI products and services may be safer than weak regulation.

Inside baseball: AI-enabled enforcement tech takes time, testing

Training artificial intelligence to enforce even seemingly straightforward rules – like balls and strikes in Major League Baseball – is a messy, dynamic process that takes time and careful evaluation of the technology.

Associate professor Shiri Azenkot delivering opening keynote at the Disability and Access in Tech and AI Summit.

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Cornell Tech Hosts First-Ever Summit on Disability, Access, and AI

October 15, 2025

Cornell Tech hosted the inaugural Disability and Access in Tech and AI Summit on Oct. 9-10 on its Roosevelt Island campus, bringing together researchers, technologists, and community advocates to explore how disability and accessibility intersect with innovation. The summit welcomed speakers, students, faculty, alumni, and community members from Cornell’s Ithaca campus, New York City, and around the United States.

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Using AI to learn quantum complexity

October 15, 2025

Cornell physicists and computer scientists have developed a machine learning architecture inspired by the large language models (LLMs) behind ChatGPT to help them study the vastly complicated interactions that happen when nature’s smallest particles interact.  

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AI models often fail to identify ableism across cultures

October 14, 2025

The artificial intelligence models underlying popular chatbots and content moderation systems struggle to identify offensive, ableist social media posts in English – and perform even worse in Hindi, new Cornell research finds.

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Two grants to study AI and worker voice awarded to ILR

October 13, 2025

Two new grants from the Washington Center for Equitable Growth will fund ILR research aimed at providing insights to policymakers, union leaders, managers and others who are shaping the future of AI in the workplace.

graphic of Lightbulb on computer chip

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Pain tolerance increases during social interaction in VR

October 1, 2025

Researchers in the Virtual Embodiment Lab found that engagement in social virtual reality, whether with loved ones or total strangers, enhances pain tolerance.

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Provost initiative to envision Cornell’s long-term future

October 1, 2025

A faculty-led committee will explore how the university can evolve to best serve future generations while pursuing its core mission.

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Faculty innovate with, and avoid, AI in the classroom

October 1, 2025

At the same time faculty are finding ways to use generative AI tools to help students learn, pen-and-paper assessments are returning to the classroom.

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Models feel hemmed in by AI

September 30, 2025

Using generative AI, fashion designers can use digital photos to adjust models’ features and even deploy fully digital avatars in place of humans. A team including an ILR School researcher has written a paper highlighting models’ challenges.

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‘Bottling’ human intuition for AI-led materials discovery

September 30, 2025

A Cornell researcher and collaborators have developed a machine-learning model that encapsulates and quantifies the valuable intuition of human experts in the quest to discover new quantum materials.