Artificial intelligence can generate videos so realistic that distinguishing them from authentic footage is becoming increasingly difficult. But a computer science team led by researchers at UC Riverside has developed a tool that moves beyond simply identifying whether a video is fake. It also determines which AI system created it.
A new study led by Fabrice Niyigaba '27 reports the first digital tool for identifying online propaganda in Kinyarwanda, the national language of Rwanda's 15 million people—and possibly the first for any of the Bantu languages spoken by 350 million Africans.
The capabilities of large AI systems are constantly improving, but they consume a great deal of energy during training and operation. The human brain, by contrast, is extremely energy-efficient: It requires only around 20 watts.
A drone swoops low over an alpine forest. It climbs suddenly to follow the contours of the sharply rising landscape. Pulses from its lidar—a laser mapping instrument—rapidly scan the trees below.
Generating an image of a person on a computer using text prompts is easy. Generating one with two people is similarly simple. But creating an image of multiple people actually doing something, and faithfully reproducing not just the people but the thing they're doing? Not so easy.
Large language models (LLMs), the artificial intelligence (AI) systems underpinning ChatGPT and similar conversational platforms, are now used by many people worldwide to find and summarize information and generate different types of text. Despite their widespread use, these models still have notable limitations.
Researchers from the Institute of Science and Technology Austria (ISTA) presented two papers at SIGGRAPH in Los Angeles. One paper introduces new methods for texturing highly deformable surfaces, and the other proposes a faster, more precise way to compute geometric properties of shapes. Both show how to boost graphics accuracy while cutting computational cost, with potential applications across video gaming, visual effects and simulation. The studies have been published in ACM Transactions on Graphics.
Researchers at the Universities of Konstanz and Stuttgart have demonstrated a new approach to computation: a computer made from a few hundred microscopic particles oscillating in a liquid. Instead of processing information through carefully fabricated electronic circuits, their system performs computations by harnessing the complex collective dynamics of interacting particles.
Quantum computing and AI are among the most rapidly developing modern technologies. AI, in the form of machine learning, has been deployed for decades to recommend movies and TV shows and make it easier to search for images. Over the past several years, large language models have permeated even more facets of daily life, from writing emails to producing images, videos and songs in response to requests expressed in a few written lines.
When can we trust the results we get from AI, and when is learning impossible? Researchers have shown that there are some problems that even the most powerful AI cannot reliably solve, no matter how much data it is given.
The era of building "personalized AI" by training AI models on individual or corporate documents and data is beginning. However, while such customization can improve task performance, it can also weaken a model's existing safety safeguards. KAIST researchers have developed a core AI technology that preserves customized performance while further strengthening safety.
Artificial intelligence isn't just capable of translating between existing languages—it can also create entirely new ones.
An international research team involving the University of Bayreuth has, for the first time, analyzed the "inner workings" of AI language models when predicting political voting decisions. To do so, the researchers examined six national elections and AI-based election forecasts and developed a new method for making more precise predictions. They presented their findings at the International Conference on Machine Learning (ICML 2026) in Seoul, South Korea.
Training artificial intelligence to enforce even seemingly straightforward rules—like balls and strikes in Major League Baseball (MLB)—is a messy, dynamic process that takes time and careful evaluation of the technology in the wild, according to new Cornell research.
In 2022, global production of construction materials accounted for more than 7% of total carbon emissions. But how many of those materials were truly necessary to build houses, buildings and bridges?
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