Artificial intelligence and machine learning move to the edge

We often associate artificial intelligence (AI) and machine learning (ML) with exotic applications - self-driving cars, speech and facial recognition, robotic control and medical diagnosis - all powered by massive rows of servers filled with CPUs or GPUs, at some distant data center. But in fact, AI and ML are getting closer and closer to all of us. That's because companies such as Google, Microsoft, Nvidia and others have recently introduced technologies, platforms and devices that can cost-effectively extend AI and ML capabilities to the edge of the network. Working in concert with cloud services, these devices are capable of processing large volumes of data locally, and enabling highly localized and timely "inference," industry jargon for AI- and ML-driven predictions executed at the edge after having been trained in the cloud; where data storage and processing power are plentiful and scalable. Previously, if you wanted to deploy machine learning capability you had to run it on some kind of server.