Chapter 1 analyses how current research on AI has begun investigating the feasibility of producing autonomous weapon systems (AWS) and the challenges that they represent for current IHL . The fact that they collect and process their own data and make lethal targeting selection without human intervention constitutes the ‘third revolution in military affairs’. AWS will deliver enormous advantages such as the ‘dissociation of risk’ for soldiers, and the ‘dissociation of communication’ between human operators and weapons systems. The element of ‘dissociation of communication’ has been made possible due to the progress in the area of machine learning. Thus, Chapter 1 analyses how machine learning algorithms operate. Indeed, the algorithm designed and programmed for a mission can have a tripartite structure: an algorithm for situation assessment; a selection algorithm; and an algorithm for situation management. In spite of the advantages that AWS may introduce on the battlefield, machine learning algorithms are not deterministic but predictable algorithms and operate as black-box systems. Consequently, AWS cannot be said to be reliable but rather remain unpredictable. Their unpredictable nature will introduce challenges not only in terms of legal reviews and levels of reliability, but also especially in terms of accountability for violations of IHL caused by AWS.

In a cloud-edge environment, data are generated by different types of devices, and these devices have various computation capabilities and storage sizes. It is unrealistic to execute all the tasks in the cloud, instead, putting some work into edge servers that are close to end-users would be more reasonable. Edge Learning is a powerful paradigm for big data analytics in the cloud-edge environment. Edge Learning exploits pervasive data generated not only by user devices but also by other sensing devices and those stored in the cloud/edge servers (e.g., data from social networks). Moreover, EL leverages various computing entities (all the devices with computing capabilities ranging from cloud, edge servers, to various edge devices) in an efficient, reliable, and robust manner.

In this chapter, we first introduce the deep learning models that are widely used in Edge Learning. Then, we introduce the basic machine learning algorithms, architectures, and synchronization mode for Edge Learning.