Demystifying artificial intelligence : symbolic, data-driven, statistical and ethical AI / edited by Emmanuel Gillain.

1 Preface and introduction.-- 2 A holistic view of AI techniques, their limitations and complementarities.-- 3 Solve problems by searching, including with constraints, a fundamental pillar.-- 4 Reasoning with first-order logic.-- 5 Knowledge representation and engineering with ontologies.-- 6 Probabilistic reasoning: When the environment is uncertain.-- 7 Learning from data.-- 8 Between language and knowledge.-- 9 Some words about ethics. The angles of fairness and transparency 10 Industry examples where different AI techniques are combined .-- 11 Conclusion.

This book is intended for business professionals that want to understand the fundamental concepts of Artificial Intelligence, their applications and limitations. Built as a collaborative effort between academia and the industry, this book bridges the gap between theory and business application, demystifying AI through fundamental concepts and industry examples. The reader will find here an overview of the different AI techniques to search, plan, reason, learn, adapt, understand and interact. The book covers the two traditional paradigms in AI: the statistical and data-driven AI systems, which learn and perform by ingesting millions of data points into machine learning algorithms, and the consciously modelled AI systems, known as symbolic AI systems, which use explicit symbols to represent the world and make conclusions. Rather than opposing those two paradigms, the book will also show how those different fields can complement each other.