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.
Sprache
Verlagsort
Verlagsgruppe
Zielgruppe
Für höhere Schule und Studium
Für Beruf und Forschung
Academic, Postgraduate, and Undergraduate Advanced
Illustrationen
8 s/w Zeichnungen, 7 s/w Tabellen, 3 s/w Photographien bzw. Rasterbilder, 11 s/w Abbildungen
7 Tables, black and white; 8 Line drawings, black and white; 3 Halftones, black and white; 11 Illustrations, black and white
Maße
Höhe: 246 mm
Breite: 174 mm
Dicke: 10 mm
Gewicht
ISBN-13
978-1-032-74722-4 (9781032747224)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Klassifikation
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.
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 AI-pedagogy, proficiency, policy, and predictions
References
Index
