Jetson Orin Nano 2 is the module NVIDIA announced on 25 August 2026 to put physical AI inside delivery drones, inspection drones, home robots and factory vision systems. The headline everyone ran was the same one NVIDIA led with: twice the inference performance of the outgoing Jetson Orin Nano Super, in the same form factor, […]
Hugging Face and its Pollen Robotics subsidiary opened pre-orders on 27 August 2026 for Microduck, a 25 cm bipedal robot duck priced at $399 before taxes and shipping, with first deliveries targeted before Christmas 2026. The machine carries 15 motors, a wide-angle camera, an 8×8 time-of-flight LiDAR array, two IMUs and an articulated beak that works as a gripper, on a Rockchip RK3566 with 1 GB of RAM and 32 GB of storage running its neural policy at 50 Hz. Seven behaviours ship on the device — walking, rolling on skates, grasping, kicking, standing up after a fall, sitting and a generated audio identity — and every one was trained in MuJoCo with proximal policy optimisation and transferred to hardware. The control stack and the full training pipeline are published under Apache 2.0, but the hardware is not open and Pollen Robotics says it has no plans to change that. This breakdown covers the pack pricing, the complete published specification, the Open Duck Mini project the design grew out of, how it compares with the Reachy Mini line, and the battery, payload and compute ceilings that decide who should actually buy one.
Workers in China are living through the fastest AI adoption drive on earth. Enterprise use of AI models and agents jumped from 9.6% to 47.5% in a single year, Beijing’s AI Plus plan targets 90% penetration by 2030, and programmers, scriptwriters and translators are already losing work to the tools their employers adopted. This article follows the Associated Press reporting on three workers who lived through it, sets their stories against the verified numbers — youth unemployment at 17.9%, a record 12.7 million graduates, 54% of the world’s industrial robot installations — and draws out what China’s adapt-or-be-replaced moment means for businesses everywhere else.
AI agents create virtual playgrounds that are transforming the way robots learn, adapt, and perform real-world tasks. Instead of relying solely on expensive physical testing environments, robotics companies are increasingly using AI-powered simulation platforms where robots can safely practice millions of interactions before ever touching the real world. These virtual environments generate vast amounts of […]
LingBot-Map is an open-source streaming 3D reconstruction model that turns ordinary RGB video into online mapping, pose estimation, and scene understanding.
In the ever-evolving landscape of computing, the integration of heterogeneous technologies has emerged as a powerful strategy to optimize performance for specific workloads. The marriage of CPUs, GPUs, and FPGAs in cluster configurations has become a cornerstone in the realm of high-performance computing (HPC). Concurrently, the integration of machine learning (ML) and artificial intelligence (AI) into cluster servers has fueled groundbreaking advancements across various industries. In this article, we delve into the symbiotic relationship between heterogeneous computing and machine learning integration, exploring how this convergence is shaping the future of scientific simulations, AI applications, and beyond.
The data deluge of the digital age has transformed our world into a symphony of information, pulsating through every facet of our lives. But unlike a carefully orchestrated concert, much of this data embarked on a long-distance trek to centralized data centers in the cloud for processing. While powerful, this centralized approach resembled a cumbersome waltz – latency delays, bandwidth oversaturation, and security vulnerabilities cast a shadow on its efficiency.
Imagine a world where robots not only wield tools but intuit human emotions, where smart homes weave tapestries of personalized comfort based on your sighs and smiles, and where self-driving cars navigate urban symphonies of honks and gestures. This isn’t a futuristic pipe dream; it’s the nascent melody of Multimodal AI, a technology weaving a symphony of senses poised to revolutionize everything from healthcare to our very interactions with machines.
A practical analysis of MIT research that channels hand gestures through AI to create robot training data, with implications for robotics, teleoperation, and enterprise deployment.
