Artificial intelligence and librarianship : notes for reading

Disponível em: https://softoption.us/AIandLibrarianship

Autor principal: Frické, Martin
Tipo de documento: Livro
Idioma: Inglês
Publicado em: SoftOption 2024
Assuntos:
Inteligência artificial
Chatbot
Linguagem de máquina
Modelo
Biblioteca
Bibliotecário
Preservação digital
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id 9780473722944
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spelling Artificial intelligence and librarianship : notes for reading Frické, Martin Inteligência artificial Chatbot Linguagem de máquina Modelo Biblioteca Bibliotecário Preservação digital Disponível em: https://softoption.us/AIandLibrarianship Carrossel de IA Table of contents: Chapter 1: intellectual background, 18 1.1 Introduction to Artificial Intelligence, 18 1.2 A Genuine Great Leap Forward, 24 1.3 Digitization and Transcription, 26 1.4 A Paean to Text in Structured Digital Form, 29 1.4.1 Text-to-Speech, 29 1.4.2 Machine Translation, 30 1.4.3 Search and Navigation, 32 1.4.4 Preservation and Archiving, 33 1.4.5 Free Books!, 33 1.4.6 Natural Language Processing, 33 1.4.7 Processing by Computer Software, 34 1.5 Data and the Need for Good Data, 34 1.6 Types of Machine Learning, 37 1.6.1 Supervised, 37 1.6.2 Unsupervised, 39 1.6.3 Semi-Supervised, 40 1.6.4 Self-Supervised, 41 1.6.5 Reinforcement, 43 1.6.6 Reinforcement Learning from Human Feedback (RLHF), 45 1.7 The Concept of Algorithm, 46 1.8 Annotated Readings for Chapter 1, 48 CHAPTER 2: CHATBOTS, 50 2.1 Introduction, 50 2.2 Dialog Processing, 51 2.3 ELIZA to ALICE, 54 2.4 The Turing Test, 57 2.5 Machine Learning Chit-Chat Bots, 57 2.6 LaMDA, 58 2.7 ChatGPT, 59 2.8 Task-Oriented, 62 2.9 GPTs, 65 2.10 Annotated Readings for Chapter 2, 68 CHAPTER 3: LANGUAGE MODELS, 70 3.1 Introduction, 70 3.2 Markov Chains, 71 3.3 Hidden Markov Models, 75 3.4 Shannon's Guessing Game, 77 3.4.1 Introduction, 77 3.4.2 Shannon's Approximations as Markov Processes, 79 3.4.3 Training a Shannon-Markov Model to Produce 'A Baby GPT', 82 3.5 Taylor's Cloze Procedure, 86 3.6 nanoGPT and an Illustration of Training, 87 3.7 Embeddings, 89 3.8 Word Embeddings and Word2Vec, 92 3.9 Adding Knowledge to Language Models, 94 3.10 InstructGPT and the Insights it Provides, 96 7 3.11 Annotated Readings for Chapter 3, 100 CHAPTER 4: LARGE LANGUAGE MODELS, 101 4.1 Introduction, 101 4.2 Seq2Seq, Encoder-Decoder Architecture, and Attention, 102 4.3 Attention and Transformers, 104 4.4 Large Language Models and Foundation Models, 105 4.5 Foundation Models, 105 4.5.1 BERT, 106 4.5.2 GPT-3, GPT-3.5, GPT-4, 107 4.6 Bigger is Better and Switch Transformers, 109 4.7 Base Models to Assistants to Agents, 110 4.8 Concerns and Limitations, 117 4.8.1 Hallucinations, 117 4.8.2 Fakes and Deepfakes, 118 4.8.3 Source Training Data Intellectual Property, Privacy, and Bias, 119 4.8.4 Intellectual Property of the Generated Output, 121 4.8.5 Cybersecurity, 123 4.8.6 Apparent Conflict with Chomsky’s Theories, 123 4.8.7 Environmental Costs, 124 4.8.8 Lack of Transparency, 125 4.9 Adding Knowledge and Reasoning to LLMs, 126 4.10 Annotated Readings for Chapter 4, 127 CHAPTER 5: LARGE MULTIMODAL MODELS, 130 5.1 Introduction, 130 5.2 Built in Safety Restrictions for GPT-4V, 132 8 5.2.1 ‘Inherited’ Restrictions, 132 5.2.2 Privacy, 133 5.2.3 Stereotypes and Ungrounded Inferences, 133 5.2.4 Be My Eyes— Be My AI, 135 5.2.5 An Assessment of the Restrictions, 135 5.3 A General Sense of What GPT-4V Can Do, 136 5.3.1 Follow Textual Instructions, 136 5.3.2 Read Printed or Handwritten Text, 137 5.3.3 Read Some Mathematics, 143 5.3.4 Read Data and Reason with It, 143 5.3.5 Follow Visual Pointing in Images, 143 5.3.6 Analyze Images Including Medical Images, 145 5.3.7 Use Ordinary Common-Sense Knowledge and Reasoning Across Modes, 149 5.3.8 Be an Educational Tutor, 150 5.3.9 Use Visual Diagrams When Writing Computer Code, 151 5.3.10 Have Temporal and Video Understanding, 151 5.3.11 Answer Intelligence Quotient (IQ) Tests, 152 5.3.12 Avoid False Presuppositions, 153 5.3.13 Navigate Real and Virtual Spaces, 153 5.4 Yang et al.’s Conclusion on GPT-4V, 154 5.5 GPT-4 Turbo (Early 2024), 155 5.6 GPT-4o (Later 2024), 156 5.7 Google’s Gemini, 156 5.8 Anthropic’s Claude, 157 5.9 Meta’s LLaMa, 158 5.10 Voice, 159 5.11 Possible Applications for LMMs, 159 5.11.1 Smartphone Uses, 159 5.11.2 Spot the Difference, 160 5.11.3 Producing Reports from Medical Images, 160 5.11.4 Assist with Image Generation, 160 5.11.5 Extension with Plugins, 161 5.11.6 Retrieval-Augmented Generation (RAG), 161 9 5.11.7 Label and Categorize Images, 162 5.11.8 Identify Objects, 162 5.11.9 ‘Igor’, AI Advantage and AI Community, 162 5.12 Annotated Readings for Chapter 5, 163 CHAPTER 6: EVALUATION AND THE FUTURE, 164 6.1 Reliability, Trustworthiness, and Alignment, 164 6.2 System 1 and System 2, 166 6.3 Benchmarks, 167 6.3.1 Introduction, 167 6.3.2 Multi-turn dialogs, 167 6.3.3 Chatbots, 168 6.3.4 Reasoning, 168 6.3.5 Common sense reasoning, 169 6.3.6 MMLU, 170 6.3.7 Coding, 171 6.4 Artificial General Intelligence (AGI), 173 6.5 The ARC-AGI Benchmark, 175 6.6 Artificial Super Intelligence (ASI), 176 6.7 Annotated Readings for Chapter 6, 178 CHAPTER 7: BIAS AND UNFAIRNESS, 179 7.1 Algorithmic Pipeline + Data = Machine Learning, 179 7.2 Some Clarification of the Terms 'Bias' and ‘Unfairness’, 181 7.3 Forms of Bias in Wider Machine Learning, 186 7.4 Bias in Natural Language Processing, 187 7.5 Some Clarification of the Term 'Algorithm', 192 10 7.6 Computer Program Inadequacy, 194 7.7 Bias in the Context of Wider Machine Learning Programs, 197 7.7.1 Fairness ('Distributive Justice'), 198 7.7.2 Debiasing Representation, 208 7.7.3 Panopticon Bias, the Panopticon Gaze, 209 7.7.4 Bias in (Librarianship) Classification, 212 7.8 Stochastic Psittacosis: LLMs and Foundation Models, 212 7.9 Supplement: The Bias of Programmers, 216 7.9.1 The 'Biases' of Professional Programmers, 216 7.9.2 The Biases of All of Us as Programmers, 218 7.10 Annotated Readings for Chapter 7, 218 CHAPTER 8: BIAS IN MACHINE LEARNING AND LIBRARIANSHIP, 221 8.1 Introduction, 221 8.2 Harms of Omission, 223 8.3 What to Digitize, 223 8.4 Search, Primarily Using Search Engines, 224 8.5 Social Media, Dis-, Mis- and False-Information, 231 8.6 Bias in the Organization of Information, 231 8.6.1 Introduction, 231 8.6.2 Be Careful, and Sparing, with Emotive Content, 233 8.6.3 Warrant and Controlled Vocabularies, 233 8.6.4 The Act of Classification Has Consequences, 239 8.6.5 Taxonomies Have Consequences, 241 8.6.6 The Current State of Libraries and Their Organizational Systems, 243 8.6.7 Designing Information Taxonomies for Librarianship, 245 8.7 Navigation: Metadata Supported and Otherwise, 247 11 8.8 Ethical Arguments to Underpin Assertions of Harms of Bias, 249 8.9 Annotated Readings for Chapter 8, 250 CHAPTER 9: WHAT MIGHT NATURAL LANGUAGE PROCESSING (NLP) BRING TO LIBRARIANSHIP?, 251 9.1 Introduction, 251 9.2 The Pre-Processing Pipeline, 252 9.3 Text Embeddings and Similarity, 254 9.3.1 Searching by Meaning (Semantic Search), 256 9.3.2 Research Trails, 257 9.3.3 Classification, 258 9.3.4 One Style of Recommendation, 258 9.3.5 Plagiarism Detection, 258 9.4 Named Entity Recognition, 259 9.5 Topic Modeling, 260 9.6 Text Classification Problems, 261 9.6.1 Shelving and Subject Classification, 262 9.6.2 Sentiment Analysis, 262 9.6.3 Author or Genre Recognition, 263 9.7 Controlled Vocabularies, Thesauri, and Ontological Vocabularies . 264 9.8 Indexing and Automatic Indexing, 265 9.9 Abstracts, Extracts, Key Phrases, Keywords, and Summaries, 268 9.10 Text Mining and Question Answering, 271 9.11 Machine Translation, 271 9.12 Evidence, 271 12 9.13 This Is Not Magic, 272 9.14 Text Processing and Laws, 273 9.15 Annotated Readings for Chapter 9, 274 CHAPTER 10: WHAT ARE THE OPPORTUNITIES FOR LIBRARIANS?275 10.1 Introduction, 275 10.2 Librarians as Synergists, 279 10.3 Librarians as Sentries, 283 10.4 Librarians as Educators, 284 10.5 Librarians as Managers, 286 10.6 Librarians as Astronauts, 287 10.7 Annotated Readings for Chapter 10, 288 CHAPTER 11: LIBRARIANS AS SYNERGISTS, 290 11.1 Intellectual Freedom, 290 11.1.1 Text Recognition, 292 11.1.2 Speech to Text, 302 11.1.3 Sign Language to Text, and Text to Sign Language, 304 11.1.4 Helping Filter and Personalize, 305 11.1.5 Scholarly Publishing, 306 11.1.6 What Can Be Done With Computer Text, 306 11.1.7 ELI5 Translation, 306 11.2 Improving the Intermediation Between 'Users' and 'Information Resources'., 307 11.2.1 Some Users Might Not Be Human, 307 11.2.2 Some Resources Might Not Be Resources, 308 11.2.3 Digital Archiving, 308 11.2.4 Enhanced Search Engines, 308 13 11.2.5 Personalization and Recommendation, 311 11.2.6 Recommender Systems, 312 11.2.7 Understanding What the User is Asking For, 315 11.2.8 Text Mining, 315 11.2.9 Information Assistants (and ‘GPTs’), 316 11.3 Improving Traditional Cataloging, Classification, and Retrieval Tools , 318 11.3.1 NLP Inspired Improvements, 321 11.3.2 Metadata Generation and Automatic Cataloging, 322 11.3.3 Some Retrieval Tools, 323 11.4 Chatbots, 330 11.4.1 Reference Interviews, 331 11.4.2 Virtual Services, 333 11.4.3 Chatbots as Continuous User Testing of a Library's Public Interface., 334 11.5 Release, Produce, or Curate Training Data, 334 11.6 Debunking, Disinformation, Misinformation, and Fakes, 336 11.7 Social Epistemology, 336 11.8 Robots, 339 11.9 Images, 341 11.10 Annotated Readings for Chapter 11, 342 CHAPTER 12: LIBRARIANS AS SENTRIES, 343 12.1 Copyright and Intellectual Property, 343 12.2 Intellectual Freedom, 343 12.3 Censorship and Algorithmic Curation, 344 12.4 Privacy, 346 14 12.5 Bias, 347 12.6 Social Epistemology, 347 12.6.1 Reliability, Validity, and Over Confidence, 347 12.6.2 Confirmation Bias and Poor Reasoning, 348 12.6.3 Misinformation, 348 12.6.4 Awareness of the Digital Literacy of Patrons, 348 12.7 Chatbots, 349 12.8 Personalization and Paternalism, 350 12.9 Images and Facial Recognition Technology, 352 12.10 Losing Jobs, 353 12.11 Annotated Readings for Chapter 12, 354 CHAPTER 13: LIBRARIANS AS EDUCATORS, 355 13.1 Information Literacy (for Consumers of Information), 355 13.2 Artificial Intelligence Literacy, 355 13.3 Data Information Literacy (for Producers of Information), 358 13.4 Changes in Learning and Teaching, 359 13.5 Scholarly Communication, 359 13.6 Academic Libraries Collaborating with other University Units, 360 13.7 AI Laboratories in the Library, 360 13.8 Automated Decision-Making, 361 13.9 Explainable Artificial Intelligence (XAI), 367 15 13.10 Annotated Readings for Chapter 12, 370 CHAPTER 14: LIBRARIANS AS MANAGERS, 372 14.1 Coming on Board, 372 14.2 Data and Analyses, 375 14.3 Evidence-Based Librarianship, 376 14.4 Data-Driven Decision Making, 377 14.4.1 Collection Building and Management, 377 14.4.2 Circulation and User Studies, 377 14.4.3 Processing in Libraries, 377 14.4.4 Research and Scholarship, 378 14.4.5 Service Quality, 378 14.5 Acquiring the Appropriate AI Tools, 378 14.6 Analysts and Staff, 379 14.7 Fear of AI, 379 14.8 Annotated Readings for Chapter 14, 380 CHAPTER 15: LIBRARIANS AS ASTRONAUTS, 381 15.1 Astronaut Training, 381 15.2 Why Should You Learn How To Do It?, 381 15.3 What are the Real Creative Possibilities, 382 15.4 Sitting in Your Tin Can, 384 15.5 Exploring World 3, 385 15.5.1 Undiscovered Public Knowledge (UPK), 385 15.5.2 Literature-Based Discovery (Text Based Informatics), 388 16 15.5.3 A Message to Librarian Astronauts, 388 15.6 Annotated Readings for Chapter 15, 389 APPENDIX A: SOME THEORETICAL BACKGROUND TO LIBRARIANSHIP, 390 A.1 Concepts, Classification, Taxonomies, and Items, 390 A.2 Controlled Vocabularies, and Thesauri, 391 A.3 Ontologies and Ontological Vocabularies, 393 A.4 Objective, Intersubjective, and Subjective, 395 A.5 Emotive and Descriptive Content, 397 A.6 Classification Schemes and the Act of Classification, 399 A.7 Annotated Readings for Appendix A, 401 APPENDIX B: WORKING WITH LLMS, 402 B.1 Introduction, 402 B.2 Prompts and Prompt Engineering, 403 B.2.1 Basic Examples of Zero-Shot Prompting, 405 B.2.2 Examples of Few-Shot Prompting, 411 B.2.3 Chain of Thought Prompting, 413 B.2.4 Tuning, or Configuring, the Models or Prompts, 415 B.3 Choices on Development, 418 B.4 Moving Forward With LangChain, 421 B.4.0 A Note on the Status of LangChain and Similar as of 11/6/2023, 421 B.4.1 What is LangChain?, 422 B.4.2 LangChain Experiments Displayed to a Web Page, 424 B.4.3 LangChain Using Jupyter, 435 B.4.4 Resources for LangChain using Jupyter, 438 17 B.5 Annotated Resources for Appendix B, 439 APPENDIX C: TWO IMPORTANT METHODOLOGICAL POINTS, 441 C.1 False Positives and False Negatives, 441 C.2 The Base-Rate Fallacy, 443 C.3 Annotated Readings for Appendix C, 447 APPENDIX D: CAUSAL DIAGRAMS, 449 D.1 Causation and Correlation, 449 D.2 Causal Diagrams, 451 D.3 Annotated Readings for Appendix D, 467 APPENDIX E: KNOWLEDGE GRAPHS, 468 E.1 Knowledge Graphs, 468 E.2 Annotated Readings for Appendix E, 470 GLOSSARY, 471 BIBLIOGRAPHY, 507 SoftOption 2024 Livro application/pdf 533 p. 9780473722944 https://biblioteca.sophia.com.br/terminal/9549/acervo/detalhe/461625 Inglês https://biblioteca.sophia.com.br/terminal/9549/acervo/detalhe/461625 Cover: https://biblioteca.sophia.com.br/terminal/9549/capa/capa?codigo=461625
institution TRF 3ª Região / SJSP
collection TRF 3ª Região / SJSP
language Inglês
topic Inteligência artificial
Chatbot
Linguagem de máquina
Modelo
Biblioteca
Bibliotecário
Preservação digital
spellingShingle Inteligência artificial
Chatbot
Linguagem de máquina
Modelo
Biblioteca
Bibliotecário
Preservação digital
Frické, Martin
Artificial intelligence and librarianship : notes for reading
description Disponível em: https://softoption.us/AIandLibrarianship
format Livro
author Frické, Martin
title Artificial intelligence and librarianship : notes for reading
title_short Artificial intelligence and librarianship : notes for reading
title_full Artificial intelligence and librarianship : notes for reading
title_fullStr Artificial intelligence and librarianship : notes for reading
title_full_unstemmed Artificial intelligence and librarianship : notes for reading
title_sort artificial intelligence and librarianship : notes for reading
publisher SoftOption
publishDate 2024
url https://biblioteca.sophia.com.br/terminal/9549/acervo/detalhe/461625
_version_ 1867002326513876992
score 12,069173

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