Introduction to Generative AI

Table of contents

1. inside front cover
2. Introduction to Generative AI
3. Copyright
4. dedication
5. contents
6. front matter
   1. foreword
   2. preface
   3. acknowledgments
   4. about this book
      1. Who should read this book
      2. How this book is organized: A road map
      3. liveBook discussion forums
      4. Other online resources
   5. about the author
   6. about the cover illustration
7. 1 Large language models: The power of AI
   1. Evolution of natural language processing
   2. The birth of LLMs: Attention is all you need
   3. Explosion of LLMs
   4. What are LLMs used for?
      1. Language modeling
      2. Question answering
      3. Coding
      4. Content generation
      5. Logical reasoning
      6. Other natural language tasks
   5. Where do LLMs fall short?
      1. Training data and bias
      2. Limitations in controlling machine outputs
      3. Sustainability of LLMs
   6. Revolutionizing dialogue: Conversational LLMs
      1. OpenAI’s ChatGPT
      2. Google’s Bard/LaMDA
      3. Microsoft’s Bing AI
      4. Meta’s LLaMa/Stanford’s Alpaca
   7. Summary
8. 2 Training large language models
   1. How are LLMs trained?
      1. Exploring open web data collection
      2. Demystifying autoregression and bidirectional token prediction
      3. Fine-tuning LLMs
   2. The unexpected: Emergent properties of LLMs
      1. Quick study: Learning with few examples
      2. Is emergence an illusion?
   3. What’s in the training data?
      1. Encoding bias
      2. Sensitive information
   4. Summary
9. 3 Data privacy and safety with LLMs
   1. Safety-focused improvements for LLM generations
      1. Post-processing detection algorithms
      2. Content filtering or conditional pre-training
      3. Reinforcement learning from human feedback
      4. Reinforcement learning from AI feedback
   2. Navigating user privacy and commercial risks
      1. Inadvertent data leakage
      2. Best practices when interacting with chatbots
   3. Understanding the rules of the road: Data policies and regulations
      1. International standards and data protection laws
      2. Are chatbots compliant with GDPR?
      3. Privacy regulations in academia
      4. Corporate policies
   4. Summary
10. 4 The evolution of created content
    1. The rise of synthetic media
       1. Popular techniques for creating synthetic media
       2. The good and the bad of synthetic media
       3. AI or genuine: Detecting synthetic media
    2. Generative AI: Transforming creative workflows
       1. Marketing applications
       2. Artwork creation
    3. Intellectual property in the LLM era
       1. Copyright law and fair use
       2. Open source and licenses
    4. Summary
11. 5 Misuse and adversarial attacks
    1. Cybersecurity and social engineering
    2. Information disorder: Adversarial narratives
    3. Political bias and electioneering
    4. Why do LLMs hallucinate?
    5. Misuse of LLMs in the professional world
    6. Summary
12. 6 Accelerating productivity: Machine-augmented work
    1. Using LLMs in the professional space
       1. LLMs assisting doctors with administrative tasks
       2. LLMs for legal research, discovery, and documentation
       3. LLMs augmenting financial investing and bank customer service
       4. LLMs as collaborators in creativity
    2. LLMs as a programming sidekick
    3. LLMs in daily life
    4. Generative AI’s footprint on education
    5. Detecting AI-generated text
    6. How LLMs affect jobs and the economy
    7. Summary
13. 7 Making social connections with chatbots
    1. Chatbots for social interaction
    2. Why humans are turning to chatbots for relationship
       1. The loneliness epidemic
       2. Emotional attachment theory and chatbots
    3. The good and bad of human-chatbot relationships
    4. Charting a path for beneficial chatbot interaction
    5. Summary
14. 8 What’s next for AI and LLMs
    1. Where are LLM developments headed?
       1. Language: The universal interface
       2. LLM agents unlock new possibilities
       3. The personalization wave
    2. Social and technical risks of LLMs
       1. Data inputs and outputs
       2. Data privacy
       3. Adversarial attacks
       4. Misuse
       5. How society is affected
    3. Using LLMs responsibly: Best practices
       1. Curating datasets and standardizing documentation
       2. Protecting data privacy
       3. Explainability, transparency, and bias
       4. Model training strategies for safety
       5. Enhanced detection
       6. Boundaries for user engagement and metrics
       7. Humans in the loop
    4. AI regulations: An ethics perspective
       1. North America overview
       2. EU overview
       3. China overview
       4. Corporate self-governance
    5. Toward an AI governance framework
    6. Summary
15. 9 Broadening the horizon: Exploratory topics in AI
    1. The quest for artificial general intelligence
    2. AI sentience and consciousness?
    3. How LLMs affect the environment
    4. The game changer: Open source community
    5. Summary
16. references
    1. Chapter 1
    2. Chapter 2
    3. Chapter 3
    4. Chapter 4
    5. Chapter 5
    6. Chapter 6
    7. Chapter 7
    8. Chapter 8
    9. Chapter 9
17. index
18. inside back cover