Researchers at Cornell Tech have released a dataset extracted from more than 300,000 public Reddit communities, and a report detailing how Reddit communities are changing their policies to address a surge in AI-generated content. The team collected metadata and community rules from the online communities, known as subreddits, during two periods in July 2023 and November 2024. The researchers will present a paper with their findings at the Association of Computing Machinery's CHI conference on Human Factors in Computing Systems being held April 26 to May 1 in Yokohama, Japan. One of the researchers' most striking discoveries is the rapid increase in subreddits with rules governing AI use. According to the research, the number of subreddits with AI rules more than doubled in 16 months, from July 2023 to November 2024. "This is important because it demonstrates that AI concern is spreading in these communities.

Human-robot interaction requires to be studied in the wild. In the summers of 2022 and 2023, we deployed two trash barrel service robots through the wizard-of-oz protocol in public spaces to study human-robot interactions in urban settings. We deployed the robots at two different public plazas in downtown Manhattan and Brooklyn for a collective of 20 hours of field time. To date, relatively few long-term human-robot interaction studies have been conducted in shared public spaces. To support researchers aiming to fill this gap, we would like to share some of our insights and learned lessons that would benefit both researchers and practitioners on how to deploy robots in public spaces. We share best practices and lessons learned with the HRI research community to encourage more in-the-wild research of robots in public spaces and call for the community to share their lessons learned to a GitHub repository.

Employing artificial intelligence to help improve outcomes for people with cardiovascular disease is the focus of a three-year, $15 million collaboration among Cornell Tech, the Cornell Ann S. Bowers College of Computing and Information Science (Cornell Bowers CIS) and NewYork-Presbyterian – with physicians from its affiliated medical schools Weill Cornell Medicine and Columbia University Vagelos College of Physicians and Surgeons (Columbia University VP&S). The Cardiovascular AI Initiative, to be funded by NewYork-Presbyterian, was launched this summer in a virtual meeting featuring approximately 40 representatives from the institutions. "AI is poised to fundamentally transform outcomes in cardiovascular health care by providing doctors with better models for diagnosis and risk prediction in heart disease," said Kavita Bala, professor of computer science and dean of Cornell Bowers CIS. "This unique collaboration between Cornell's world-leading experts in machine learning and AI and outstanding cardiologists and clinicians from NewYork-Presbyterian, Weill Cornell Medicine and Columbia will drive this next wave of innovation for long-lasting impact on cardiovascular health care." "NewYork-Presbyterian is thrilled to be joining forces with Cornell Tech and Cornell Bowers CIS to harness advanced technology and develop insights into the prediction and prevention of heart disease to benefit our patients," said Dr. Steven J. Corwin, president and chief executive officer of NewYork-Presbyterian. "Together with our world-class physicians from Weill Cornell Medicine and Columbia, we can transform the way health care is delivered."

A team of researchers at Cornell Tech, Cornell University's tech-focused research campus, has developed a forecast for how technologies like artificial intelligence could shape cities in the coming decade. After a year of work, the team released its first "Horizon Scan" report last week to discuss the potential risks and applications of recent advancements in urban tech. The forecast report predicts areas where the most radical and rapid changes in urban tech could take place, touching on topics such as "supercharged" smart city infrastructure, the use of sustainable building materials and machine learning in the public sector, among other areas of interest. The project was led by Anthony Townsend, urbanist in residence at the Jacobs Urban Tech Hub at Cornell Tech, who has spent years studying tech-related issues like the digital divide. He said the goal of the Horizon Scan was to create a road map "to make better decisions about applied research" in urban tech. Townsend said the need to weigh potential pros and cons of machine learning's applications in the public sector is a recurring factor in the report.

Serge Belongie, member of the Computer Science department and Associate Dean at Cornell Tech, has been named Andrew H. and Ann R. Tisch Professor. In response to his investiture as an endowed chair, which began on April 1st, Belongie says "I wish I had the words to express my gratitude for this remarkable honor." In his capacity as Associate Dean at Cornell Tech, Belongie is "busy with coronavirus pandemic-related planning for Fall semester course offerings." As professor, he is working on "growing our cross-campus research efforts in Mixed Reality." The latter initiative "gathers efforts from across Cornell's campuses that relate to augmented and virtual reality, and their core disciplines of computer vision, computer graphics, and human-computer interaction."

A research team at UT has developed an artificial intelligence program to improve people's clothing choices. Fashion is a program with the goal of making minimal edits for outfit improvement, such as changing the color or fit of a piece of clothing, said Kimberly Hsiao, a computer science graduate student. She said she is leading the project with UT computer science professor Kristen Grauman and students and professors from Cornell Tech, Georgia Tech and Facebook AI Research. "We wanted to come up with something that is useful in peoples' lives, and clothing is how people make statements about themselves," Hsiao said. Hsiao said the program works by users uploading a photo of their outfit.

Data sets and benchmarks have been some of the most effective tools to speed progress in AI. Our current renaissance in deep learning has been fueled in part by the ImageNet benchmark. Recent advances in natural language processing have been hastened by the GLUE and SuperGLUE benchmarks. "Deepfake" techniques, which present realistic AI-generated videos of real people doing and saying fictional things, have significant implications for determining the legitimacy of information presented online. Yet the industry doesn't have a great data set or benchmark for detecting them.

A team of researchers from the University of Texas at Austin and Cornell Tech have trained a software program to uncloak digitally-blurred or distorted images using deep learning, essentially teaching a computer to interpret a set of example data. Using this process, the team's software identified encrypted photographs with a 71% success rate. For context, the human success rate was 0.2%. The team purposely used an open source deep learning library to train their software, and their research exposes more weaknesses in the concept of online privacy. "The techniques we're using in this paper are very standard in image recognition, which is a disturbing thought," said Cornell Tech's Vitaly Shmatikov, pointing out that theirs was an "off-the-shelf, poor man's approach" to encrypted image recognition, and that a person or entity with bad intentions could do a lot of damage with this technology.

Pixelation has long been a familiar fig leaf to cover our visual media's most private parts. Blurred chunks of text or obscured faces and license plates show up on the news, in redacted documents, and online. The technique is nothing fancy, but it has worked well enough, because people can't see or read through the distortion. The problem, however, is that humans aren't the only image recognition masters around anymore. As computer vision becomes increasingly robust, it's starting to see things we can't.