Demystifying Generative AI: A Practical and Intuitive Introduction

Description

Demystifying Generative AI: A Practical and Intuitive Introduction

In an era where artificial intelligence is rapidly reshaping the world and redefining the way we work, Demystifying Generative AI: A Practical and Intuitive Introduction emerges as a key resource for professionals and enthusiasts seeking to leverage the transformative power of AI. Authored by AI experts Robert Barton and Jerome Henry, this book is a unique entry into the world of AI. Unlike traditional references that are either too technical or overly simplistic, this book strikes a balance by providing clear explanations and practical examples, all supported by real-world case studies. It is designed as an intuitive guide through the inner workings of AI, from foundational principles to deployment and security best practices. It is designed to make generative AI accessible to anyone interested in learning more about AI, including IT professionals, software developers, business analysts, tech managers, educators, and decision-makers. Rob and Jerome address the surging demand for AI literacy as organizations invest heavily in AI-driven solutions, aiming to boost productivity and maintain a competitive edge.

Key Topics:

• Foundations of AI: A historical and conceptual overview of artificial intelligence, including essential terminology and the broader AI landscape.
• How Generative AI Actually Works: An in-depth and intuitive analysis of LLMs, from the origins of language modeling into the modern world of Transformers and their applications.
• Unique Approach: Balances depth and accessibility, focusing on intuitive understanding with examples, adding practical application rather than dense theory or superficial summaries.
• Practical Applications: Features how GenAI and LLMs can be deployed in practices, using applications like RAG, fine-tuning techniques, and how to security LLMs from attack.
• Comprehensive Coverage: Covers foundational AI concepts, machine learning (classic and advanced), deep learning, large language models, Transformers, AI infrastructure, agentic AI systems, ethical considerations, security for LLMs, and deployment strategies.

Demystifying Generative AI emphasizes the growing necessity of AI literacy in a technology-driven world. By demystifying generative AI and equipping readers with both theoretical grounding and practical tools, the book aims to empower individuals and organizations to succeed in the era of intelligent automation. With its expert authorship and accessible format, this book is an essential resource for navigating the next wave of innovation.

Sample Content

Table of Contents

Preface
Part I The Foundations of Generative AI
Chapter 1 Ten Breakthroughs That Made Generative AI Possible
Breakthrough 1: The Turing Machine
Breakthrough 2: The Artificial Neuron
Breakthrough 3: The Dartmouth Conference
Breakthrough 4: The Perceptron
The Rise of Symbolic Reasoning (1960s)
The First AI Winter (Early 1970s to Early 1980s)
Breakthrough 5: Neural Networks and Backpropagation
Breakthrough 6: Recurrent Neural Networks
The Second AI Winter (Late 1980s to Mid-1990s)
Breakthrough 7: Invention of the GPU
Breakthrough 8: Reinforcement Learning
Breakthrough 9: Language Modeling
Breakthrough 10: The Transformer
Summary
References
Chapter 2 The Machinery of Learning
Types of Learning
Supervised Learning
Unsupervised Learning
Reinforcement Learning
The Machine Learning Family Tree
What Is a Model?
How Models Are Trained
Training, Validation, and Test Datasets
Inference Models
How to Measure Model Accuracy
Hyperparameters
Summary
Chapter 3 Foundational Algorithms
Linear Regression: One Stroke to Represent the Data
Describing a Line
Loss Functions and Other Hyperparameters
Classification
Support Vector Machines
Discovering Structures in Data
K-Means, the Clustering King
DBSCAN and Growing Clusters
Summary
Chapter 4 An Introduction to Neural Networks
Neural Networks Key Concepts
ANNs: General Structure and Terminology
Training a Neural Network
Training Models and Overcoming Challenges
The Importance of Clean Data
Labeled Data: The Backbone of Supervised Learning
Avoiding the Pitfalls: Overfitting and Underfitting
Scaling Up Training
Summary
Chapter 5 Neural Network Architectures
Feedforward Neural Networks
Traditional FFNs
Convolutional Neural Networks (CNNs)
Traditional Generative Models
Generative Adversarial Networks (GANs)
Variational Autoencoders (VAEs)
Diffusion Models
Recurrent Models
Recurrent Neural Networks (RNNs)
Long Short-Term Memory Networks (LSTMs)
Summary
Chapter 6 Reinforcement Learning: Teaching Machines to Learn by Trial and Error
An AI That Learns Like Us
Key Concepts of Reinforcement Learning
The Markov Decision Process (MDP)
The Bellman Equation
Model-Based Versus Model-Free Systems
On-Policy Versus Off-Policy Learning: Two Paths to Learning
Monte Carlo Reinforcement Learning
Temporal Difference (TD) Learning
Q-Learning
Deep Reinforcement Learning
Summary
References
Part II The Generative AI Revolution
Chapter 7 Language Modeling: The Birth of LLMs
An Introduction to LLMs
Foundations of Language Modeling
Next-Word Prediction
From Words to Tokens
Word Embedding: Turning Tokens into Numbers
How Word Embeddings Are Learned
Semantic Relationships in the Embedding Space
The Semantics of Language
Summary
Reference
Chapter 8 Attention Is All You Need: The Foundation of Generative AI
A New Architecture Begins to Take Shape
Attention Is All You Need
From Sequential to Parallel Processing
Positional Encoding
The Self-Attention Mechanism
Summary
References
Chapter 9 Attention Isnt All You Need: Understanding the Transformer Architecture
The Encoder Block
The Multi-Head Attention Layer
The Add and Norm Layers and Residual Connections
The Feedforward Network (FFN) Layer
Layers Upon Layers of Encoder Blocks
How Encoders Are Trained
The Decoder Block
The Decoders Output Classifier
How Decoders Are Trained
What Type of Machine Learning Is Involved in Training LLMs?
Case Study: The GPT-3 Transformer
Future Directions
Summary
References
Part III Living with Generative AI
Chapter 10 Making Models Smarter: Prompt and Context Engineering
Prompt and Context Windows
Prompt Engineering Techniques
Shot-Based Approaches
Chain-Based Approaches
Self-Ask Approaches
Prompt Engineering Limitations
Context Engineering
Types of Contexts in LLM Workflows
Tools and Protocols
Context Design Techniques
Summary
Chapter 11 Retrieval-Augmented Generation
The Need for RAG
Common Applications of RAG
RAG Trends and Practices
The RAG Pipeline
Query Formulation
Retrieval Filtering
Working with Knowledge Databases
Loading Documents
Chunking: Splitting Documents
Embedding and Storing Segments
Retrieving Segments
Summary
Chapter 12 Fine-Tuning LLMs
The Need for Fine-Tuning
Comparing Fine-Tuning and RAG
Inference Hyperparameter Tuning for LLMs
Temperature
Top-K Sampling
Top-P (Nucleus) Sampling
Repetition Penalty
Principles of Fine-Tuning with New Data
Fine-Tuning for Model Types and Objectives
Supervised Fine-Tuning (SFT)
Transfer Learning
Parameter-Efficient Fine-Tuning (PEFT) Methods
Retrieval-Augmented Fine-Tuning (RAFT)
Reinforcement Learning from Human Feedback (RLHF)
Benchmarking Model Performance
Summary
References
Chapter 13 Securing LLMs from Attack
What Makes AI Security Different
The Emergence and Importance of AI Security Frameworks
NIST AI Risk Management Framework
The OWASP Top 10
MITRE ATLAS
The ISO/IEC Suite of AI Standards
A Comparison of the AI Security Frameworks
AI Vulnerabilities and Attack Vectors
Direct Prompt Injection Attacks
Prompt Injections with Jailbreaking
Indirect Prompt Injection Attacks
Extraction and Inversion Attacks
AI Supply Chain Threats
Defending Models from Attack
Extending the Guardrail System
Architectural Safeguards
Continuous Monitoring and Detection System
Generative Adversarial Defense Techniques
Summary
References
Chapter 14 AI Ethics and Bias: Building Responsible Systems
Bias and Ethical Risks in GenAI
The Biased Data That Shapes GenAI
The Difficulty of Stopping GenAI Bias
When Generative AI Goes Wrong: Unethical and Harmful Outputs
Hallucination and Misinformation
Synthetic Media, Deepfakes, and Disinformation
Ownership, Consent, and Copyright
Transparency, Explainability, and Trust
Building Responsible Generative AI
Alignment and AI Safety
Practical Responses to Ethical AI Challenges
Summary
References
Chapter 15 The Future of AI: From Generative to General Intelligence
Where We Stand: A Snapshot of Todays Capabilities
Current Limitations and Known Pain Points
The Emergence Question: Are We Seeing Sparks of AGI?
What Makes AGI Different?
What Is AGI?
What Is Intelligence Anyway?
Do Reasoning LLMs Really Reason?
Predicting Versus Understanding
Are We Already on the Path to AGI?
Paths to AGI
The Scaling Hypothesis
The Modular Hypothesis
The Embodied System Hypothesis
Hybrid Models
Is AGI the End of Humanity?
The Alignment Problem Revisited
Black Boxes and Loss of Interpretability
The Singularity and the Skynet Problem
Controlling Existential Risks
Is AGI Helping or Hurting Society?
Will AI Take Your Job?
Education in the Age of Generative AI
Societal Identity and Stability
The Evolving Voice of Generative AI
From Single-Goal Prompting to Multimodal Partnering
Responsive Interfaces
Redefining Creativity
The Future We Choose
Scenario A: The Co-creative Society
Scenario B: The Automated Present
Scenario C: The Disrupted Path
Summary
References
Appendix A The History of AI
Appendix B A Summary of Neural Network Model Architectures
Glossary


9780135429419 TOC 12/19/2025

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