Interactions between humans and dogs involve a continuous exchange of physical and verbal cues. A person may pet a dog, use a hand gesture, give a verbal command or call the pet from a distance, prompting changes in the dog's posture, position or movement. Understanding these human–animal interactions requires data that captures both participants simultaneously.
Researchers from Tokyo Metropolitan University have used an evolutionary algorithm to identify optimal shapes for ultra-thin, bio-inspired corrugated airfoils, the two-dimensional cut-throughs of wings. Lightweight membrane-like wings are a strong contender for use in unmanned aerial vehicles (UAVs) in thin atmospheres, like on Mars, and in UAV miniaturization.
For decades, the gold standard for capturing human movement and transferring these data into the digital world has required actors to wear suits with physical markers that cameras track in a specialized studio. While accurate, this capture process is time-consuming and expensive. Markers need to be placed manually, they can fall off, and if two people get too close—such as during a hug or a dance—the system fails to track all markers as one person blocks the other from the camera's view, and accuracy drops significantly. Extensive post-processing is needed to recover the motion in such challenging sequences. The creators of the Hollywood blockbusters "Avatar" or "The Lord of the Rings" will know what we mean.
A research team at the Faculty of Engineering at The University of Hong Kong (HKU), led by professor Ruibang Luo of the School of Computing and Data Science and professor Can Li of the Department of Electrical and Computer Engineering, has developed Gungnir, a blockchain-inspired DNA data storage codec. The framework improves digital information recovery from severely damaged DNA sequences, potentially extending the practical lifespan of DNA data archives from less than a decade to centuries. The paper is published in the journal Nature Communications.
Artificial intelligence models can give users the wrong answer and do so with great confidence. They can also hedge and warn that they are unsure—even when they get the answer right.
William Overman began his Ph.D. program at Stanford Graduate School of Business at an auspicious moment: just two months before ChatGPT launched publicly in November 2022, exploding the widely held understanding of what machines are capable of.
MIT researchers have developed a new technique that helps generative artificial intelligence models find solutions to high-stakes problems. In these settings, a plausible answer is not enough: The output often must also satisfy nonnegotiable safety, physical or task-specific requirements, known as hard constraints.
Artificial intelligence (AI) systems, particularly the large language models (LLMs) that underpin ChatGPT and similar platforms, are remarkably skilled at answering queries about a wide range of topics. However, the extent to which they can interpret social situations and predict how humans might respond in specific scenarios remains poorly understood.
Large language models (LLMs), the artificial intelligence (AI) systems supporting the functioning of ChatGPT, Gemini and similar conversational platforms, are often expected to answer queries, generate texts for specific purposes and make decisions without the supervision of human experts. Yet past studies suggest that these models can sometimes mislead users, producing unreliable answers that express high confidence in false or inaccurate statements.
Researchers at Maynooth University (MU) have developed a first-of-its-kind DNA computer, described as one of the most complex and fastest molecular computers yet reported.
MIT researchers have created a computing platform that could be used to develop intelligent and adaptive next-generation electronics that can perform multiple functions, like computing and memory, within one extremely compact, energy-efficient device.
Quantum error correction must detect and correct errors without directly reading the quantum information. Classical low-density parity check (LDPC) codes have a well-established design theory: by choosing how many checks connect to each bit, retaining appropriate randomness, and avoiding short loops, designers can pursue both a large minimum distance and a threshold phenomenon in which the decoding failure rate drops sharply below a predicted noise level.
When a website loads quickly or an operating system runs smoothly, you can thank caching—a widely used computing process for fast data access that works by storing frequently used pieces of data in a computer's memory.
A guiding principle for many software engineers—"What you see is what you get" —means creating programs where the content you're editing looks the same as the final product. But when you're using generative AI (genAI) systems to 3D-print, say, a mug, you'll likely get a cup that can't hold your coffee. Why is that?
AI systems that generate video frame by frame could follow a user's camera commands far more accurately, thanks to a new training method developed by researchers from the University of Surrey and NVIDIA. This improvement matters most when someone steers a generated scene rather than simply watching it. That includes video games built on AI-generated worlds, virtual production sets where a director can move a camera around, and simulated environments used to train robots. The findings are posted on the arXiv preprint server.
---- End of list Tech Xplore Computer Science News Articles on this page 2 of 2 total pages ----