![]() Connectivity cost also drops, as processing part of the data locally reduces bandwidth and cellular data usage. By embedding AI locally, manufacturers can reduce latency issues and accelerate the generation of insights while lowering cloud services usage and cost. The sudden and dramatic changes in network traffic that have accompanied Covid-19 lockdowns and the shift to working from home are likely to accelerate the move already underway toward edge computing.īenefits of edge computing include preserving bandwidth and increasing efficiency by processing information closer to the users and devices that require it, rather than sending that data for processing in central locations in the cloud. Edge AI transplants brains to factory tools and machinery.Ĭonsidered the next wave of artificial intelligence, “edge AI” or “AI on the edge” is a network infrastructure that makes it possible for AI algorithms to run on the edge of a network, meaning closer to or even on the devices collecting the data. ![]()
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