Edge AI refers to the deployment of artificial intelligence models directly on devices and machines rather than relying solely on centralized cloud infrastructure. This approach enables real-time decision-making, reduced latency, improved reliability, and greater data privacy for robotics and automated systems. The topic covers edge processors, embedded AI platforms, model optimization, and the trade-offs between edge and cloud computing. It also explores use cases in robotics, industrial automation, smart devices, and autonomous systems operating in bandwidth- or connectivity-constrained environments. As AI moves closer to the physical world, Edge AI plays a critical role in enabling responsive and scalable intelligent systems.
The global hybrid robots market is forecast to grow from $4.93 billion in 2025 to $10.03 billion by 2030 at a 15.3% CAGR, driven by AI advances, edge computing, autonomous field operations, and accelerating smart factory adoption across manufacturing, logistics, and defense.
DEEPX and Hyundai Motor Group's Robotics LAB have announced a strategic collaboration to co-develop an ultra-low-power AI computing platform capable of running large-scale generative AI models...
Qualcomm CEO Cristiano Amon says robotics will become a major revenue stream by 2028, as the company positions its Dragonwing chip at the center of the...
Asus is winding down new smartphone development as it redirects resources toward AI computing, robotics, and physical AI systems for industrial and enterprise markets.
CES 2026 marked a turning point for Physical AI, as humanoids, autonomous machines, and AI-driven platforms moved from experiments to scalable, real-world systems.
Samsung unveiled its “Companion to AI Living” vision at CES 2026, outlining how AI will connect entertainment, home appliances, health, and services into a unified ecosystem.
