This chapter provides an introduction to the paradigm, concepts, and algorithms of Computational Intelligence (CI). It highlights the main technologies of CI, including neural networks, fuzzy sets, and Granular Computing, and discusses their synergistic nature. The chapter also explores the advantages and limitations of CI technologies and their connections with Software Engineering, particularly its quantitative aspects. The content is structured top-down, starting with a concise introduction to CI as a synergistic environment and then delving into neurocomputing, evolutionary optimization, and Granular Computing. It emphasizes the role of information granularity in signal representation and the design of semantically sound abstractions for information granules. The chapter concludes by visualizing the role of CI in software engineering. The notation used follows common practices, with patterns treated as vectors in n-dimensional space and fuzzy sets described by capital letters. The chapter also includes definitions of CI, emphasizing its adaptivity, fault tolerance, speed, and error rates, and its role in substituting intensive computation for insight.This chapter provides an introduction to the paradigm, concepts, and algorithms of Computational Intelligence (CI). It highlights the main technologies of CI, including neural networks, fuzzy sets, and Granular Computing, and discusses their synergistic nature. The chapter also explores the advantages and limitations of CI technologies and their connections with Software Engineering, particularly its quantitative aspects. The content is structured top-down, starting with a concise introduction to CI as a synergistic environment and then delving into neurocomputing, evolutionary optimization, and Granular Computing. It emphasizes the role of information granularity in signal representation and the design of semantically sound abstractions for information granules. The chapter concludes by visualizing the role of CI in software engineering. The notation used follows common practices, with patterns treated as vectors in n-dimensional space and fuzzy sets described by capital letters. The chapter also includes definitions of CI, emphasizing its adaptivity, fault tolerance, speed, and error rates, and its role in substituting intensive computation for insight.