
Premium
AI Simplified
1/3/2026
Dedication
1/3/2026
About the Author
1/3/2026
Acknowledgement
1/3/2026
Preface
1/3/2026
Chapter 1: Introduction to Artificial Intelligence
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1.1 What Is AI?, Understanding It in Simple Language
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1.2 A Brief History and Evolution of AI
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1.3 AI vs Automation vs Human Intelligence
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1.4 Examples of AI in Everyday Life
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1.5 Why AI Matters Today, Career and Business Impact
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1.6 Four Case Studies in Modern AI
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1.7 Why Now? The Three Forces Behind the Current Boom
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1.8 Five Misconceptions Worth Clearing Up Now
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1.9 Frequently Asked Questions
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1.10 Putting It Together
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Chapter 2: Types of AI Systems
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2.1 Narrow AI vs General AI vs Super AI
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2.2 Reactive Machines vs Self-Learning Systems
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2.3 Rule-Based AI vs Learning-Based AI
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2.4 Real-Life Examples: Netflix, Google Maps, Alexa and More
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2.5 The Capability Ladder: A Framework for Thinking About AI Systems
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2.6 The Memory Spectrum: From Goldfish to Lifelong Learner
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2.7 Hybrid Architectures: How Real Systems Combine Approaches
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2.8 The Marketing Translation Guide
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2.9 Frequently Asked Questions
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2.10 Putting It Together
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Chapter 3: Understanding Machine Learning Basics
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3.1 What Is Machine Learning?
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3.2 Three Families of Machine Learning
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3.3 Training Data, Models, and Predictions
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3.4 Everyday Machine Learning Use Cases
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3.5 What Machine Learning Cannot Do (Yet)
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3.6 A Tour of the Most Important ML Algorithms
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3.7 The Machine-Learning Project Lifecycle
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3.8 Three Real-World Stories
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3.9 Frequently Asked Questions
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3.10 Putting It Together
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Chapter 4: Data, The Fuel of AI
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4.1 What Is Data?
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4.2 Structured vs Unstructured Data
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4.3 The Importance of Data Quality
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4.4 Data Collection Basics
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4.5 Bias in Data, Simple Examples
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4.6 An Introduction to Datasets
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4.7 Privacy, Consent, and Governance, A First Look
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4.8 Data Architecture: How Real Organisations Store Their Data
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4.9 The Hidden Workforce: How Training Data Actually Gets Labelled
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4.10 Privacy in Depth
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4.11 Building Your Own Dataset: A Walk-through
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4.12 The Open Dataset Ecosystem
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4.13 Frequently Asked Questions
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4.14 Putting It Together
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Chapter 5: How AI Makes Decisions
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5.1 The Three-Step Flow: Input, Processing, Output
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5.2 Pattern Recognition: The Heart of It All
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5.3 Algorithms, Models, and Predictions
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5.4 Accuracy, Errors, and the Confusion Matrix
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5.5 When AI Gets It Wrong
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5.6 Calibration: When the Confidence Itself Is Wrong
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5.7 Threshold Tuning and the Operating Point
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5.8 Decision Boundaries in Higher Dimensions
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5.9 Real-World Decision Stories
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5.10 Frequently Asked Questions
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5.11 Putting It Together
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Chapter 6: AI in Everyday Life and Industry
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6.1 AI in Healthcare
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6.2 AI in Finance
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6.3 AI in Education
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6.4 AI in Marketing
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6.5 AI in Customer Service
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6.6 AI in Manufacturing and Operations
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6.7 The Realistic Adoption Curve
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6.8 Cross-Industry Patterns Worth Spotting
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6.9 Three Case Studies in Depth
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6.10 Spotting AI Opportunities in Your Own Workplace
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6.11 Frequently Asked Questions
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6.12 Putting It Together
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Chapter 7: Introduction to Generative AI
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7.1 What Generative AI Is, and What It Is Not
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7.2 How a Language Model Generates Text
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7.3 The Major Flavours of Generative AI
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7.4 The Hallucination Problem
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7.5 What Generative AI Is Great At, and What It Is Not
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7.6 Using Generative AI Well: Prompting Basics
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7.7 Behind the Scenes: How These Models Are Built
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7.8 Retrieval-Augmented Generation: Grounding the Model in Real Data
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7.9 The Ethics of Training Data
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7.10 Working with Generative AI Across Languages
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7.11 Frequently Asked Questions
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7.12 Putting It Together
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Chapter 8: Hands-on with No-Code AI Tools
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8.1 The Current Chatbot Landscape
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8.2 The Anatomy of a Good Prompt
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8.3 Image Generation in Practice
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8.4 Building Your Own Custom Assistant
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8.5 Teachable Machine and Other No-Code Model Builders
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8.6 The Wider No-Code AI Ecosystem
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8.7 The Iteration Loop
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8.8 Workflow Automation: Where AI Quietly Saves Hours
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8.9 Voice and Audio Tools in Practice
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8.10 The Iteration Discipline: A Closer Look
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8.11 Building a Personal Prompt Library
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8.12 Safety, Privacy, and Common Pitfalls
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8.13 Frequently Asked Questions
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8.14 Putting It Together
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Chapter 9: Ethics, Risks, and Responsible AI
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9.1 The Risk Landscape
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9.2 Bias and Fairness in AI Systems
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9.3 Privacy in the AI Era
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9.4 Misinformation, Deepfakes, and Synthetic Content
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9.5 AI and Jobs
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9.6 Accountability and Recourse
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9.7 Safety and Catastrophic Risk
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9.8 The State of AI Regulation
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9.9 A Practical Posture for the Engaged User
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9.10 The Concentration of Power
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9.11 The Indian Context Specifically
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9.12 How to Run an Informal AI Ethics Review
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9.13 Evaluating AI Vendors and Products
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9.14 The Long View on Responsible AI
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9.15 Lessons from the First Five Years of Modern Generative AI
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9.16 Case Studies in Responsible and Irresponsible AI
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9.17 Working Through Ethical Trade-Offs
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9.18 What Companies, Governments, and Civil Society Are Doing
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9.19 Sector Spotlights: How Ethics Plays Out in Practice
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9.20 The Environmental Cost of AI
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9.21 The Geopolitics of AI
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9.22 Frequently Asked Questions
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9.23 Putting It Together
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Chapter 10: The Future of AI and Your Path Forward
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10.1 Three Views of the Next Decade
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10.2 The AGI Debate, Honestly
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10.3 Skills That Will Compound
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10.4 A 90-Day Personal AI Roadmap
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10.5 The Discipline of Deliberate Practice
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10.6 Staying Current Without Burning Out
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10.7 Unsolved Questions Worth Caring About
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10.8 What AI Literacy Means for the Citizen You Are Becoming
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10.9 Imagined Scenarios for the Next Decade
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10.10 Ten Things Every AI-Literate Person Should Be Able to Do
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10.11 A Letter to the Reader
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10.12 What This Book Has Tried to Do
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10.13 A Curated Resource Map
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10.14 Mistakes Beginners Make Once They Have Some Knowledge
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10.15 Building an AI Portfolio: From Reader to Practitioner
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10.16 Conversation Patterns for Working with AI in Teams
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10.17 Career Paths Into and With AI
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10.18 Going Deeper: Math, Code, and Research
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10.19 Examples of AI Literacy in Real Lives
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10.20 A Glossary for Future Reference
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10.21 A Final Reflection on Working Alongside Machines
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10.22 Frequently Asked Questions
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10.23 Putting It Together: A Closing Note
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