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.ai
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Blue Book of AI
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Blue Book of AI
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The Little Blue Book of AI
by
Swift
L
e
a
r
n
.ai
A clear, accessible, and always-updatable guide to understanding artificial intelligence.
Table of Contents
Chapter 1: About This Resource
Overview
Who This Resource Is For
Why This Resource Exists
How to Use This Resource
What You’ll Find Inside
What This Resource Isn’t
Key Takeaway
Chapter 2: What Is AI?
Overview
A Simple Definition
The Three Core Ingredients of AI
What AI Does Well
Where AI Struggles
Everyday Examples of AI
Key Takeaway
Chapter 3: What AI Is Not
Overview
AI Is Not Human
AI Is Not Always Right
AI Is Not Magic
AI Is Not a Replacement for Human Judgment
Key Takeaway
Chapter 4: A Brief History of AI
Overview
Early Ideas (1940s–1950s)
The Birth of AI (1956)
The AI Winters (1970s–1990s)
Machine Learning Rises (2000s)
The Deep Learning Era (2010s)
The Rise of Generative AI (2020s)
Mass Adoption (Today)
Key Takeaway
Chapter 5: What Made AI Possible?
Overview
Algorithms: The Brains of AI
How These Components Fit Together
Data: The Fuel
Computing Power: The Engine
Scaling Laws: Bigger Models, Better Results
Key Takeaway
Chapter 6: Types of AI
Overview
Visual Overview
Narrow AI
Generative AI
General AI (AGI)
Small Language Models (SLMs)
Key Takeaway
Chapter 7: AI Under the Hood
Overview
How AI Systems Work
Neural Networks
Transformers
Models
Large Language Models (LLMs)
Multimodal AI
Hardware: CPUs, GPUs, TPUs, and NPUs
Power & Infrastructure
Autocomplete at Scale
Key Takeaway
Chapter 8: Should I Use AI?
Overview
A Simple Decision Framework
When AI Is Helpful
When You Should Be Careful
When Not to Use AI
The Helpful / Safe / Trustworthy Test
Key Takeaway
Chapter 9: AI Facts
Overview
AI Facts at a Glance
AI by the Numbers
Surprising Realities
AI in Everyday Life
AI Has Limits and Risks
Key Takeaway
Chapter 10: AI Use Around the World
Overview
Global AI Landscape (Visual)
North America
Europe
Asia
Middle East & Africa
Latin America
Global Takeaways
Key Takeaway
Chapter 11: Must-Know Concepts
Overview
Core Concepts at a Glance
Training vs. Inference
Parameters
Prompts
Tokens
Hallucinations
Bias
Alignment
Multimodal AI
Retrieval-Augmented Generation (RAG)
Key Takeaway
Chapter 12: Must-Know Terminology
Overview
AI Terminology at a Glance
Artificial Intelligence (AI)
Machine Learning (ML)
Neural Network
Transformer
Large Language Model (LLM)
Parameter
Token
Hallucination
Bias
Alignment
Inference
Training
Generative AI
Multimodal AI
Retrieval-Augmented Generation (RAG)
Key Takeaway
Chapter 13: How People Use AI Today
Overview
A Visual Overview of the 3 Modes
Automation
Augmentation
Agency
Choosing the Right Mode for Your Task
Key Takeaway
Chapter 14: Path to AI Fluency
Overview
The Four Dimensions of AI Fluency
Efficient
Effective
Ethical
Safe
Bringing It All Together
Key Takeaway
Chapter 15: AI’s Four Core Competencies (The 4Ds)
Overview
The Four Competencies at a Glance
Delegation
Description
Discernment
Diligence
Bringing the 4Ds Together
Key Takeaway
Chapter 16: What Can I Do with AI?
Overview
What You Can Do With AI (Visual)
Everyday Life
Work & Productivity
Creativity & Innovation
Specialized Uses
AI Can Play Many Roles
Key Takeaway
Chapter 17: Economic Considerations in the World of AI
Overview
AI Economics at a Glance
Business & Industry Impact
Market Impact & Investment
Infrastructure Requirements
Labor Market & Workforce Shifts
AI in the Global Economy
Risks, Costs, and Constraints
Key Takeaway
Chapter 18: Ethical Considerations in the World of AI
Overview
Core Pillars of Ethical AI
Transparency
Fairness & Bias
Privacy & Data Protection
Accountability & Responsibility
Safety & Harm Prevention
Intellectual Property (IP) & Ownership
Misuse & Harmful Applications
Equity & Access to AI Tools
Key Takeaway
Chapter 19: Educational Considerations in the World of AI
Overview
AI & Education at a Glance
Personalized Learning
On-Demand Tutoring & Support
Creativity & Exploration
Support for Teachers & Educators
Accessibility & Inclusion
Academic Integrity & Skill Development
AI Literacy for All Students
Equity Considerations
Key Takeaway
Chapter 20: Rapid-Fire Q&A
Overview
Rapid Q&A Overview
What is AI?
Is AI conscious?
Why is AI sometimes wrong?
Can AI replace humans?
Is my data safe when using AI?
Why did AI suddenly get so good?
How does AI generate text?
Does AI understand what it writes?
Can AI think creatively?
Will AI take all the jobs?
Can AI be biased?
How should I start using AI?
Do I need to learn coding to use AI well?
What skills matter most in the AI era?
Is AI safe for kids and students?
Key Takeaway
Chapter 21: Conclusion — The Journey Ahead
Conclusion
Looking Ahead
AI Works Best with Humans at the Center
AI Fluency Is an Ongoing Practice
A Shared Responsibility
A Future Full of Opportunity
Final Message
Key Takeaway