Industrial AI Technologies for Next-Generation Autonomous Operations with Sustainable Performance

This book lays down the technological foundation for and introduces key artificial intelligence (AI) concepts and technologies for the digitising industry. While this chapter does not exhaustively cover all types of AI, it comprehensively prioritises the features of AI-based industrial applications and designs and defines the reference terminology used in the other chapters of the book. AI integrates several interrelated technologies to solve problems and perform tasks to achieve defined objectives; hence, AI can be approached from many viewpoints, such as mathematics and computer science, linguistics, psychology, neurology, and philosophy. The approach in this chapter is from a technological and industrial perspective, and concepts and functions are presented intuitively and visually, focusing on AI, as it is applied to embedded systems, with industrial automation, interactivity, and sustainability in mind. This already reflects the next-generation deployment of AI into edge devices (called edge AI) and the emergence of different edge layers (i.e., micro-, deep- and meta-edge), which contrasts existing solutions that are currently deployed in the cloud. The edge processing continuum includes sensing, processing and communication devices (micro-edge) close to the physical industrial assets under monitoring, gateways and intelligent controller processing devices (deep-edge) and on-premise multi-use computing devices (meta-edge). Furthermore, instead of attempting to present a definition of AI that is common to all industries, the chapter relies on a framework of classifications and continuums along various dimensions, including the industrial intelligence spectrum, the intelligent capabilities spectrum, the edge-cloud continuum, the symbolic reasoning – pattern recognition continuum and, not the least, the problem-solving spectrum. The chapter introduces some of the main pillars of problem solving, such as expert systems, genetic and evolutionary computation, intelligent agents, machine learning (ML) and more. This chapter, in particular, will detail ML approaches and neural networks. During the past decades, the trends and developments in AI have followed a recurring pattern, where the focus has moved back and forth between logic (symbolic reasoning) and pattern recognition (neural networks), driven by the varying abilities of technologies to acquire data, learn, derive new information and reason to reach decisions. In the last years, machine learning and