China unveils bionic antelope robot to observe endangered Tibetan species.
A lifelike bionic Tibetan antelope robot uses 5G and AI to monitor herds in Hoh Xil without disturbing them.
Category: robotics/AI – Page 98
NVIDIA Opens Portals to World of Robotics With New Omniverse Libraries, Cosmos Physical AI Models and AI Computing Infrastructure
NVIDIA today announced new NVIDIA Omniverse™ libraries and NVIDIA Cosmos™ world foundation models (WFMs) that accelerate the development and deployment of robotics solutions.
Meet IDEA: An AI assistant to help geoscientists explore Earth and beyond
A new artificial intelligence tool developed by researchers at the University of Hawai’i (UH) at Mānoa is making it easier for scientists to explore complex geoscience data—from tracking sea levels on Earth to analyzing atmospheric conditions on Mars.
Called the Intelligent Data Exploring Assistant (IDEA), the software framework combines the power of large language models, like those used in ChatGPT, with scientific data, tailored instructions, and computing resources.
By simply providing questions in everyday language, researchers can ask IDEA to retrieve data, run analyses, generate plots, and even review its own results—opening up new possibilities for research, education, and scientific discovery.
Using NVIDIA TensorRT-LLM to run gpt-oss-20b
This notebook provides a step-by-step guide on how to optimizing gpt-oss models using NVIDIA’s TensorRT-LLM for high-performance inference. TensorRT-LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and support state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs. TensorRT-LLM also contains components to create Python and C++ runtimes that orchestrate the inference execution in performant way.
Using geometry and physics to explain feature learning in deep neural networks
Deep neural networks (DNNs), the machine learning algorithms underpinning the functioning of large language models (LLMs) and other artificial intelligence (AI) models, learn to make accurate predictions by analyzing large amounts of data. These networks are structured in layers, each of which transforms input data into ‘features’ that guide the analysis of the next layer.
The process through which DNNs learn features has been the topic of numerous research studies and is ultimately the key to these models’ good performance on a variety of tasks. Recently, some computer scientists have started exploring the possibility of modeling feature learning in DNNs using frameworks and approaches rooted in physics.
Researchers at the University of Basel and the University of Science and Technology of China discovered a phase diagram, a graph resembling those used in thermodynamics to delineate liquid, gaseous and solid phases of water, that represents how DNNs learn features under various conditions. Their paper, published in Physical Review Letters, models a DNN as a spring-block chain, a simple mechanical system that is often used to study interactions between linear (spring) and nonlinear (friction) forces.
Video: China claims first drone hunt of ‘hostile warship’
The People’s Liberation Army has released rare footage showing its reconnaissance drones tracking a ‘hostile warship,’ highlighting China’s increasing integration of unmanned systems with intelligence operations.
The video, aired in Forging Ahead, the PLA’s latest military documentary, depicts a coordinated mission involving the WZ-7 and WZ-10 unmanned aerial vehicles. Both are high-altitude, long-endurance platforms built by the Aviation Industry Corp (AVIC) of China for surveillance missions.