Digital humanities scholars have developed a computational system to mine maps from nearly 100,000 digitized books from the 19th and early 20th centuries, discovering that just 1.7% of novels include maps, mostly at the beginning or end.
The data inputs that enable modern search and recommendation systems were thought to be secure, but an algorithm developed by Cornell Tech researchers successfully teased out names, medical diagnoses and financial information from encoded datasets.
Cornell researchers, in partnership with the technology company NVIDIA, have developed a method for creating digital images of cloth that more accurately captures the texture, sheen and translucence of textiles.
A new paper co-authored by Cornell law professor Frank Pasquale argues that the current copyright system is ill-equipped to handle a world in which machines learn from, and compete with, human creativity at unprecedented scale.
A Cornell research team has introduced a new method that helps machines make connections between what’s on the ground and how it represented on a map – an advance that could improve robotics, navigation systems and 3D modeling.
A new study shows that using large language models like ChatGPT boosts paper production, especially for non-native English speakers, but the overall increase in AI-written papers is making it harder to separate the valuable contributions from the AI slop.
A new approach, called WildCAT3D, is making it easier to visualize lifelike 3D environments from everyday photos already shared online, opening new possibilities in industries such as gaming, virtual tourism and cultural preservation.
