Multilingual Artificial Intelligence by Peng Wang, Pete Smith. Abingdon: Routledge, 2025. ISBN: 978 1 032 74724 8 Price £145.00 (hardback), ISBN 978 1 003 47055 7 Price £35.99 (eBook). xii+178 pages.

In the current technological landscape, the field of artificial intelligence (AI), specifically natural language processing (NLP), is experiencing a paradigm shift from technology-centric to humancentric (Shneiderman, 2022).This shift is spurred by the growing recognition that purely technical uses of AI can reinforce bias, be inscrutable, and lack human values.The evolution of generative AI took this challenge to new heights, transforming AI into a 'partner' that can collaborate deeply with humans (Krakowski, 2025;Wessel et al. 2025), along with AI pedagogy and governance frameworks (Capel and Brereton, 2023;Schmager, Pappas, and Vassilakopoulou, 2025).Consequently, a central challenge has emerged: how to ensure these powerful language technologies are designed and applied in a manner that is fair, transparent, interpretable, and respectful of cultural diversity.In response to this, Peng Wang and Pete Smith's Multilingual Artificial Intelligence (Wang and Smith, 2025) emerges not merely as a technological guide, but also a conceptual exploration of how to practice the 'human-centered AI' philosophy in a multilingual, multicultural world.It provides a strategic road map rather than a hands-on programming manual, aiming to bridge the gap between the diverse stakeholders in the multilingual communication process.Ultimately, the authors frame multilingual AI as a dual-purpose tool: one that not only enhances human productivity but also acts as a reflective medium to understand our own cognition.The book's conceptual map is elegantly structured into a three-part journey that guides readers from foundational theory to applications, and finally, into an analytical and humanistic reflection.Such an organization, which keeps the humanistic perspective on technology in mind for a general reader, sets the book apart from overly technical NLP engineering manuals on the one hand and purely theoretical critiques of AI in the absence of a technological context on the other hand.Part One, 'Fundamentals of multilingual artificial intelligence' (Chapters 1-4), establishes the theoretical groundwork for the book.It starts unconventionally but effectively in Chapter 1 by re-describing multilingual AI as not solely a computational issue, but as a communication process governed by Shannon's (Shannon, 1948) mathematical theory.By positioning the Shannon-Weaver model and the concept of the 'noisy channel' theory as the theoretical focus, the authors establish a powerful, unifying metaphor for the human-machine interactions that follow.From this conceptual anchor, the book progresses by outlining the data landscape (Chapter 2) and the elementary paradigms of AI (Chapter 3), presenting linguistic data as the 'great open basement'-a common ground for communication between deductive, human symbolic reasoning and inductive, data-driven machine learning.The first part culminates in Chapter 4 by tracing the representation of meaning from the structuralist and semiotic theories of Saussure (De Saussure, 1989) and Peirce (Peirce, 1931(Peirce, -1958) ) to modern vector semantics.A word2vec case study effectively demonstrates how contextual meaning can be extracted with machines, learning successfully to 'know a word by the company it keeps' (p.81).