Demystifying Artificial Intelligence: Symbolic, Data-Driven, Statistical and Ethical AI

Readable as a whole or by chapter, this book is intended for business practitioners that have a bachelor or master's degree outside of the field of computer science or AI but still want to go deeper in their understanding of the AI technologies, their applicability and limitations. Such reading can also be useful as a general introduction for students taking an MBA class, or similar. The reader will find in this book a solid overview of the different AI technologies supporting systems that search, plan, reason, learn, adapt, understand or interact. All these terms are demystified in the book. The book covers the two traditional paradigms in AI: on one side, data-driven AI systems, that learn and perform by ingesting millions of data points into machine learning algorithms, and on the other side the consciously modelled AI systems, known as "symbolic AI" systems, that explicitly use symbolic representations. Rather than opposing those two paradigms, the book also shows how those different fields can complement each other and can be combined for even richer applications. Chapters are all structured in a pragmatic way that answers common sense questions about the why, what, how and limitations. The theory is illustrated with 22 real-world examples from the industry, giving altogether a solid understanding of AI concepts, applicability, and limitations.