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.
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.
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.
Inhaltsangabe
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 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
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