Along with the rapid developments in communication technologies and the surge in the use of mobile devices, a brand-new computation paradigm, Edge Computing, is surging in popularity. Meanwhile, Artificial Intelligence (AI) applications are thriving with the breakthroughs in deep learning and the many improvements in hardware architectures. Billions of data bytes, generated at the network edge, put massive demands on data processing and structural optimization. Thus, there exists a strong demand to integrate Edge Computing and AI, which gives birth to Edge Intelligence. In this paper, we divide Edge Intelligence into AI for edge (Intelligence-enabled Edge Computing) and AI on edge (Artificial Intelligence on Edge). The former focuses on providing more optimal solutions to key problems in Edge Computing with the help of popular and effective AI technologies while the latter studies how to carry out the entire process of building AI models, i.e., model training and inference, on the edge. This paper provides insights into this new inter-disciplinary field from a broader perspective. It discusses the core concepts and the research road-map, which should provide the necessary background for potential future research initiatives in Edge Intelligence.

In 2016, I visited the CEBIT conference in Hannover. It was full of so called'smart' things which I did not find smart at all. This'smart' things hype included, in fact, many devices which were simply'connected' and which, in most cases, delivered a narrowly defined single purpose benefit to the user. However, one very special presentation at CEBIT influenced my views on how AI might be delivered in the future. IBM presented a research project called SyNAPSE, developing an AI chip named'TrueNorth' which could deliver computing power equivalent to the brain of an ant while consuming just 73mW of energy.

In 2016, I visited the CEBIT conference in Hannover. It was full of so called'smart' things which I did not find smart at all. This'smart' things hype included, in fact, many devices which were simply'connected' and which, in most cases, delivered a narrowly defined single purpose benefit to the user. However, one very special presentation at CEBIT influenced my views on how AI might be delivered in the future. IBM presented a research project called SyNAPSE, developing an AI chip named'TrueNorth' which could deliver computing power equivalent to the brain of an ant while consuming just 73mW of energy.

Both edge computing and artificial intelligence (AI) have continued to gather attention over the past few years as mobile and Internet of Things (IoT) technologies become increasingly adopted by a vast number of industries around the world. These new technologies have also created new problems and challenges for those looking to implement and benefit from the advances and developments of the fourth industrial revolution. Edge computing is a computing technique used to move decision making closer to the source of data (the edge) and artificial intelligence is an area of computer science that looks to create intelligent machines and also includes sub-fields such as machine learning. Individually, these two technologies have been shown to work incredibly efficiently and both show huge potential for future development, however, combining the two for use within IoT systems could result in a match made in IoT heaven. In this article, we'll explain how edge computing and artificial intelligence are being developed to work together and put forwards some possible use cases that could be applied to AI at the edge.

How can businesses adapt to emerging AI solutions in edge computing? Artificial intelligence could unlock the Internet of Things (IoT) potential every CIO has dreamed of. Yet AI requires substantial compute power, so how close can AI get to the edge? IoT technologies are frequently criticised for being nascent, insecure, pointless or causing uncertainty around ROI. On the other hand, well-advised integration of IoT devices within an industry-standard architecture can improve efficiency, bring costs down, and reduce TCO infrastructure in the long-term. Internet of Things devices will inevitably be "at the edge" of the central compute platform (eg the Cloud) where data is stored, retrieved and processed. Yet AI could help distribute the workload so that edge devices could manage speedy computation at a distance from the data centre.