AI data processing at the edge reduces costs, data latency – Urgent Comms

A race is on to accelerate artificial intelligence (AI) at the edge of the network and reduce the need to transmit huge amounts of data to the cloud. The edge, or edge computing, brings data processing resources closer to the data and devices that need them, reducing data latency, which is important for many time-sensitive processes, such as video streaming or self-driving cars. Development of specialized silicon and enhanced machine learning (ML) models is expected to drive greater automation and autonomy at the edge for new offerings, from industrial robots to self-driving vehicles. Vast computer resources in centralized clouds and enterprise data centers are adept at processing large volumes of data to spot patterns and create machine learning training models that "teach" devices to infer what actions to take when they detect similar patterns. But when those models detect something out of the ordinary, they are forced to seek intervention from human operators or get revised models from data-crunching systems.