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Artificial intelligence (AI) is one of the broadest and most all-encompassing of the data analytics references the reader will hear. It is the over-arching theory and science of development of computer systems and processes that can consider facts and variables to perform processes that typically require human intelligence and the uniquely human capability of learning new things and applying them. Any number of sciences and disciplines are brought to AI such as mathematics, computer science, psychology, and linguistics, among many others. One need only picture the ways that humans think, interact, and understand one another to perform daily tasks to see the breadth of fields, disciplines, and specialty branches of learning that must be brought to bear.

At one time, this term included virtually all the individual technologies that will be introduced in this chapter, in one form or another. However, once the loosely organized science gives birth to a proven discipline, the emergent capability is purged from the definition of AI and can stand alone. Therefore, AI is by definition the nebulous and nondescript potential technologies that may ultimately emerge to emulate human thinking, capabilities, and interactions. Prior to building critical mass and emerging successfully as individual disciplines, optical character recognition (OCR), intelligent character recognition (ICR), speech recognition, observing any combination or sequence of variables for compound decision making, language translation, robotic process automation (RPA), neural networks and machine learning – all of these lived in the vague, blurred, and ambiguous land of potential to emulate human-like capabilities – artificial intelligence.