Researchers are increasingly exploring a future in which robots and AI systems do not operate as isolated machines, but instead learn collectively across connected networks – sharing information, adapting to changing environments, and continuously optimizing their own behavior in real time.
That growing area of research – often referred to as “networked AI” – is now the focus of a new special issue from the IEEE Signal Processing Society and the IEEE Journal of Selected Topics in Signal Processing, which have issued a call for papers examining “Autonomous and Evolutive Optimization in Networked AI”.
While the academic terminology may sound abstract, many of the themes are closely connected to emerging trends already reshaping robotics and industrial automation, including multi-agent robotics, distributed AI systems, edge intelligence, autonomous vehicles, warehouse robot fleets, and collaborative industrial automation. [Read more…] about IEEE explores future of ‘networked AI’ where robots learn collectively