Multilingual Artificial Intelligence
Autor Peng Wang, Pete Smithen Limba Engleză Paperback – 29 apr 2025
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.
| Toate formatele și edițiile | Preț | Express |
|---|---|---|
| Paperback (1) | 263.69 lei 3-5 săpt. | +89.69 lei 6-12 zile |
| Taylor & Francis – 29 apr 2025 | 263.69 lei 3-5 săpt. | +89.69 lei 6-12 zile |
| Hardback (1) | 842.25 lei 3-5 săpt. | |
| Taylor & Francis – 29 apr 2025 | 842.25 lei 3-5 săpt. |
Preț: 263.69 lei
Preț vechi: 335.19 lei
-21%
Puncte Express: 396
Preț estimativ în valută:
46.67€ • 54.54$ • 40.52£
46.67€ • 54.54$ • 40.52£
Carte disponibilă
Livrare economică 29 ianuarie-12 februarie
Livrare express 14-20 ianuarie pentru 99.68 lei
Preluare comenzi: 021 569.72.76
Specificații
ISBN-13: 9781032747224
ISBN-10: 1032747226
Pagini: 178
Ilustrații: 22
Dimensiuni: 174 x 246 x 14 mm
Greutate: 0.3 kg
Ediția:1
Editura: Taylor & Francis
Colecția Routledge
Locul publicării:Oxford, United Kingdom
ISBN-10: 1032747226
Pagini: 178
Ilustrații: 22
Dimensiuni: 174 x 246 x 14 mm
Greutate: 0.3 kg
Ediția:1
Editura: Taylor & Francis
Colecția Routledge
Locul publicării:Oxford, United Kingdom
Public țintă
Academic, Postgraduate, and Undergraduate AdvancedCuprins
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
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
Notă biografică
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.
Pete Smith is Professor of Modern Languages at the University of Texas Arlington, where he also serves as Chief Analytics and Data Officer.
Descriere
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.