Overview

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.

Full Product Details

Author:   Robert Barton ,  Jerome Henry
Publisher:   Pearson Education (US)
Imprint:   Addison Wesley
ISBN:  

9780135429419

ISBN 10:   0135429412
Pages:   448
Publication Date:   04 April 2026
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Forthcoming
Availability:   Available To Order  
Limited stock is available. It will be ordered for you and shipped pending supplier's limited stock.

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 Isn’t 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 Decoder’s 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 Today’s 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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Author Information

Robert Barton is a Cisco Distinguished AI Engineer with Cisco’s AI Software Engineering Group. A graduate of the University of British Columbia in Engineering Physics, he has extensive expertise in networking, cybersecurity, and AI. Rob has authored books on AI, Wi-Fi networks, quality of service, and the Internet of Things (IoT). He has also co-authored numerous peer-reviewed research papers and holds patents in areas such as cybersecurity, cloud networking, and AI/machine learning. As the leader of Cisco’s AI research program, which collaborates with top universities around the globe, Rob is helping drive both research and innovation for academia and industry. He is also a sought-after public speaker at international AI and computer networking conferences and events. Jerome Henry is a Distinguished Engineer at Cisco Systems. A lead researcher in the CTO group, he started embracing AI and generative AI when they were conversation topics only between likeminded peer researchers, in years when access to powerful-enough GPUs was available only to elite groups. By developing new techniques to make AI applicable to several fields of physics and communications, Jerome has contributed to making AI and GenAI mainstream. He holds more than 500 patents, many of them in innovative AI and GenAI schemes, and has authored multiple books on topics ranging from networking, to IoT, to AI. He is based in Research Triangle Park, North Carolina.

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