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?
Students in the University of Washington's Prosocial Computing Group noticed a trend on social media: People were using generative artificial intelligence to make short science videos. The trouble was that these people weren't scientists, which, given AI's proclivity to be convincingly wrong, could accelerate the spread of misinformation. So the lab wondered how to enable scientists and other researchers to better adapt to platforms like TikTok.
When a standard large language model (LLM) is confronted with a problem, it tries to solve it by matching it to similar information it has seen before, and then give an answer based on those past patterns. But how it decides which information to use and what value it gives to different pieces of information can be somewhat inscrutable from the outside. An EPFL team has created a new large language model that is structured similarly to a human brain, allowing users more control and moving away from "black box" AI.
In his 1927 paper, "A law of comparative judgment," the American psychologist L. L. Thurstone proposed that when people select one option among multiple alternatives, they are picking the one that has the highest value to them, even though they cannot assign a particular number to that choice.
Two years ago, researchers at MIT proposed a provocative idea: As AI models become more powerful, they begin to see the world in the same way. But not everyone was convinced, and now EPFL scientists have shown that the picture is more nuanced.
Workshopping, an iterative process in which creators share ideas, test what works and refine what doesn't through collective feedback, is at the heart of any writers group. This collaborative dynamic inspired George Mason University Ph.D. student Shiwei Hong to explore whether artificial intelligence (AI) could benefit from a similar approach.
How do software developers respond when they come across code they do not intuitively understand? Neuropsychologists have now explored this question by recording brain activity alongside eye movements. A team of psycholinguists then compared the findings with established patterns from natural language processing and identified some surprising parallels. The interdisciplinary team from Saarland University and Chemnitz University of Technology has now published its study in Scientific Reports.
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