by Clifford B. Anderson and Douglas H. Fisher
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Authors’ Blog on book (with conceptual updates); Errata
Anderson, Clifford B. and Fisher, Douglas H. (2025). Artificial Intelligence for Academic Libraries. Routledge. ISBN: 978-1-032-75353-9 (hbk) ISBN: 978-1-032-68035-4 (pbk) ISBN: 978-1-003-47360-2 (ebk)
Table of Contents
Preface
1 Introduction
1.1 Concerns with AI
1.2 What Is AI?
1.2.1 AIs Explore Alternatives
1.2.2 Characteristics of AI Systems
1.3 Types of AI
1.3.1 Narrow, General, and Intermediate AI
1.3.2 Deliberative, Reflexive, and Hybrid AI
1.3.3 Machine Learning and Other Subfields of AI
1.4 Relevance of AI to Academic Libraries
1.5 Looking Ahead
1.6 Points for Reflection and Discussion
Notes
2 Milestones in Deliberative AI Development
2.1 Symbol Systems
2.2 Expert Systems
2.3 Commonsense Reasoning
2.4 The Rise of Machine Learning
2.5 Machine Learning Approaches of Deliberative AI
2.6 Computational Creativity
2.6.1 Narrative Generation
2.6.2 Interactive Narrative
2.7 Back to Academic Libraries
2.8 Points for Reflection and Discussion
Notes
3 The Rise of Connectionist AI
3.1 Early History
3.2 Deep Learning
3.3 Language Models
3.3.1 Word2Vec and Word Embeddings
3.3.2 Large Language Models
3.4 Liquid Neural Networks
3.5 Points for Reflection and Discussion
Notes
4 Philosophies of Artificial Intelligence
4.1 The Turing Test
4.1.1 Intelligence Need Not Be Human-Like
4.1.2 A Test of Interrogator
4.1.3 Relevance Today
4.2 The Chinese Room
4.3 Implications of False Perceptions about AI Cognition
4.4 Points for Reflection and Discussion
Notes
5 Ethical and Societal Considerations
5.1 AI Safety
5.2 Alignment
5.3 Latent Bias
5.3.1 Bias Runs Deep
5.3.2 Attempts at Debiasing
5.4 Misinformation
5.4.1 Hallucinations
5.4.2 Creativity, Error, Dream Machines, and the Chinese Room
5.4.3 A Rush to Market
5.5 Disinformation
5.6 Environmental Implications of AI
5.7 The Ethical Responsibilities of Librarians
5.8 Points for Reflection and Discussion
Notes
6 Intellectual Property Rights and Obligations
6.1 Scholarship
6.1.1 Peer Review
6.1.2 Scientific Figures
6.1.3 Logic of Discovery
6.2 Copyright
6.2.1 Is Training an LLM on Copyrighted Data a Fair Use?
6.2.2 Is AI Generated Content Copyrightable?
6.3 Plagiarism and Scholarly Standards
6.4 Teaching Composition in the Age of AI
6.5 Human-AI Collaboration
6.6 Points for Reflection and Discussion
Notes
7 Hybrid AIs
7.1 Playing to Respective Strengths
7.1.1 Reflex and Reasoning
7.1.2 Reasoning and Communication
7.1.3 Setting the Stage
7.1.4 Retrieval Augmented Generation
7.1.5 Layering AIs
7.2 Chain-of-Thought Reasoning
7.3 Agentic AI
7.4 Mixture of Experts
7.5 Compilation of Deliberative AI into Connectionist AI
7.6 Points for Reflection and Discussion
Notes
8 Robust Conversational AI
8.1 Open-Domain Question Answering
8.1.1 Identifying Faulty Presuppositions
8.1.2 Identifying and Revising Unanswerable Questions
8.2 In-Context Learning
8.2.1 Is In-Context Learning Really Learning?
8.2.2 In-Context Learning Activates Latent Knowledge
8.3 Retrieval-Augmented Generation
8.4 Fine-Tuning
8.4.1 What is Fine-Tuning?
8.4.2 Instruction Tuning
8.4.3 Safety Tuning
8.4.4 Computational Cost of Fine-Tuning
8.5 Collaborating on Conversational AI
8.6 Points for Reflection and Discussion
Notes
9 Professional Development
9.1 Communicating and Teaming with AIs
9.2 Safety Constraints and Alignment
9.3 Automation
9.4 Grant Support and Participation
9.5 Transparency and Research Reproducibility
9.6 Computer Programming
9.7 Mathematics
9.8 Keeping Up with Rapid Change
9.9 Points for Reflection and Discussion
Notes
10 Looking Forward
10.1 Future Scenarios
10.2 Integration
10.3 Socialization
10.4 Curation
Notes
Bibliography
Index