Edge Intelligence or Edge AI moves AI computing from the cloud to edge devices, where data is generated. This is a key to building distributed and scalable AI systems in resource-intensive applications such as Computer Vision. In this article, we discuss the following topics: What is Edge Computing, and why do we need it? What is Edge Intelligence or Edge AI? Moving Deep Learning Applications to the Edge On-Device AI and Inference at the Edge Edge Intelligence enables AI democratization   Edge Computing Trends With the breakthroughs in deep learning, recent years have witnessed a booming of artificial intelligence (AI) applications and services. Driven by the rapid advances in mobile computing and the Artificial Intelligence of Things (AIoT), billions of mobile and IoT devices are connected to the Internet, generating zillions of bytes of data at the network edge. Accelerated by the success of AI and IoT technologies, there is an urgent need to push the AI frontiers to the network edge to fully unleash the potential of big data. To realize this trend, Edge Computing is a promising concept to support computation-intensive AI applications on edge devices. Edge Intelligence or Edge AI is a combination of AI and Edge Computing; it enables the deployment of machine learning algorithms to the edge device where the data is generated. Edge Intelligence has the potential to provide artificial intelligence for every person and every organization at any place.   What is Edge Computing Edge Computing is the concept of capturing, storing, processing, and analyzing data closer to the location where it is needed to improve response times and save bandwidth. Hence, edge computing is a distributed computing framework that brings applications closer to data sources such as IoT devices, local end devices, or edge servers. The rationale of edge computing is that computing should happen in the proximity of data sources. Therefore, we envision that edge computing could have as big an impact on our society as we have witnessed with cloud computing.   
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