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OverviewMultilingual Artificial Intelligence is a guide for non-computer science specialists and learners looking to explore the implementation of AI technologies to solve real-life problems involving language data. Focusing on multilingual, multicultural, pre-trained large language models and their practical use through fine-tuning and prompt engineering, Wang and Smith demonstrate how to apply this new technology in areas such as information retrieval, semantic webs, and retrieval augmented generation, to improve both human productivity and machine intelligence. Finally, they discuss the human impact of language technologies in the cultural context, and provide an AI competence framework for users to design their own learning journey. This innovative text is essential reading for all students, professionals, and researchers in language, linguistics, and related areas looking to understand how to integrate multilingual and multicultural artificial intelligence technology into their research and practice. Full Product DetailsAuthor: Peng Wang , Pete SmithPublisher: Taylor & Francis Ltd Imprint: Routledge Weight: 0.330kg ISBN: 9781032747224ISBN 10: 1032747226 Pages: 164 Publication Date: 29 April 2025 Audience: College/higher education , Professional and scholarly , Tertiary & Higher Education , Professional & Vocational Format: Paperback Publisher's Status: Active Availability: In Print ![]() This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us. Table of ContentsList of Tables List of Figures Preface Part One Chapter 1: Multilingual AI in a mathematical theory of communication Chapter 2: Data landscape for multilingual AI Chapter 3: Basic techniques to achieve artificial intelligence Chapter 4: Symbolic meaning and vector semantics Part Two Chapter 5: Multilingual large language models, fine-tuning and prompt engineering Chapter 6: Multilingual and cross-lingual information retrieval Chapter 7: Augmenting LLM performance with human knowledge Part Three Chapter 8: Multilingual AI in Practice Chapter 9: Multicultural AI Chapter 10: Multilingual and Multicultural AI—Pedagogy, Proficiency, Policy, and Predictions References IndexReviewsAuthor InformationPeng Wang is an IT analyst and the chair of the Multilingual AI Track. She is the co-author of Machine Learning in Translation. Pete Smith is Professor of Modern Languages at the University of Texas Arlington, where he also serves as Chief Analytics and Data Officer. Tab Content 6Author Website:Countries AvailableAll regions |