Technological Foundations of AI

Nils Urbach (), Daniel Feulner () and Tobias Guggenberger ()
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Nils Urbach: Frankfurt University of Applied Sciences
Daniel Feulner: University of Bayreuth
Tobias Guggenberger: University of Bayreuth

Chapter 2 in Managing Artificial Intelligence, 2026, pp 21-49 from Springer

Abstract: Abstract This chapter explores the core technical components that make AI possible, including data quality, storage architectures, computational power, and tools for AI development and deployment. It examines key learning methods—supervised, unsupervised, and reinforcement learning—alongside foundational algorithms such as regression, classification, and clustering, which drive AI-driven insights and automation. The goal is to provide a structured introduction to AI’s technological foundations, enabling readers to understand its core mechanisms and make informed decisions about its application.

Date: 2026
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DOI: 10.1007/978-3-032-13308-3_2

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