Multilingual 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.
Multilingual 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.
List of Figures
List of Tables
Preface
Part One: Fundamentals of multilingual artificial intelligence
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: Large Language models: theories and applications
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: Culture and multicultual AI
Chapter 8: Multilingual AI in practice
Chapter 9: Multicultural AI
Chapter 10: Multilingual and multicultural AIpedagogy, proficiency, policy,
and predictions
References
Index
Peng 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.
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