AI Studies Syllabus

A complete, free curriculum for understanding artificial intelligence — not as engineers, but as citizens, workers, and voters. What AI is, how it works, who controls it, what it changes, and how to navigate the world it's building. Eighteen courses drawn from six Quarex libraries.

19Courses
225Chapters
1,600+Topics

Foundations

What AI actually is, where it came from, and the core ideas that make it work.

1 What Is AI, Really? 12 ch.
  1. Definitions of AI and Why They Matter
  2. A Brief History of the Idea of Thinking Machines
  3. Strong AI vs. Narrow AI
  4. Symbolic AI, Statistical AI, and Modern Machine Learning
  5. What AI Can Do Today
  6. What AI Cannot Do
  7. How AI Learns: Training Data and Its Consequences
  8. The Companies Behind AI
  9. AI and Jobs: What Changes and What Doesn't
  10. AI and Trust: When Should You Believe It?
  11. AI in the News: Separating Hype from Reality
  12. Why Understanding AI Matters for Everyone
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2 The History of AI 12 ch.
  1. The Dream of Thinking Machines
  2. The Birth of a Field: Dartmouth and the 1950s
  3. The Golden Years: 1960s Optimism
  4. The First AI Winter: 1974–1980
  5. Expert Systems and the Knowledge Boom: 1980s
  6. The Second AI Winter: Late 1980s–1990s
  7. The Statistical Turn: Machine Learning Rises
  8. Deep Learning Breaks Through: 2006–2015
  9. The Language Revolution: Attention and Transformers
  10. The LLM Moment: 2020–Present
  11. The Race for AI Dominance
  12. What History Teaches Us
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3 Key Ideas in Machine Learning 12 ch.
  1. What Is Machine Learning?
  2. Data, Patterns, and Generalization
  3. Supervised Learning: Learning from Examples
  4. Unsupervised Learning: Finding Hidden Structure
  5. Reinforcement Learning: Learning by Doing
  6. Training, Testing, and Validation
  7. Features, Representations, and Embeddings
  8. Why More Data Often Beats Clever Algorithms
  9. Bias in Machine Learning
  10. The Black Box Problem
  11. How Machine Learning Is Evaluated
  12. Machine Learning in Your Life
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How It Works

The technical architecture behind modern AI — neural networks, transformers, probability — explained for understanding, not engineering.

4 Neural Networks Explained 12 ch.
  1. What Is a Neural Network?
  2. Neurons, Layers, and Activation Functions
  3. How Neural Networks Learn
  4. Convolutional Neural Networks: Seeing
  5. Recurrent Neural Networks: Sequences and Time
  6. Deep Networks: Why Depth Matters
  7. Training at Scale: GPUs, Data Centers, and Compute
  8. Generative Networks: Creating New Content
  9. Transfer Learning and Pre-trained Models
  10. Why Neural Networks Fail
  11. Interpreting Neural Networks
  12. The Future of Neural Networks
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5 Transformers and LLMs 12 ch.
  1. What Is a Language Model?
  2. From Word Embeddings to Transformers
  3. Attention: The Core Mechanism
  4. How Large Language Models Are Trained
  5. Fine-Tuning and RLHF: Making Models Useful
  6. What LLMs Can Do
  7. What LLMs Cannot Do
  8. The Major LLMs: GPT, Claude, Gemini, Llama, and Others
  9. Prompting: How to Talk to an LLM
  10. LLMs and Truth
  11. The Economics and Politics of LLMs
  12. Where LLMs Are Going
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6 Probability, Statistics, and Uncertainty 12 ch.
  1. Why AI Is Built on Probabilities
  2. Basic Probability: The Language of Uncertainty
  3. Bayes’ Theorem: Updating Beliefs with Evidence
  4. Distributions: How Data Spreads Out
  5. Correlation, Causation, and the Stories Data Tells
  6. Noise, Signal, and the Limits of Data
  7. Bias and Variance: The Fundamental Tradeoff
  8. Uncertainty in AI Outputs
  9. Why AI Can Be Wrong but Confident
  10. How Statistics Can Mislead
  11. Probability in Everyday Decisions
  12. Living with Uncertainty in an AI World
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AI in the World

Where AI is already deployed — in daily life, creative work, and healthcare — and what it changes in each domain.

7 AI in Everyday Life 12 ch.
  1. AI You Already Use
  2. Recommender Systems and Personalization
  3. Search Engines and AI Assistants
  4. AI in Phones, Cars, and Smart Devices
  5. AI in Shopping and Commerce
  6. AI in Social Media
  7. AI in Finance and Banking
  8. AI in Entertainment and Media
  9. AI in Education
  10. Invisible AI in Infrastructure and Services
  11. AI and Your Privacy
  12. Being an Informed AI User
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8 AI and Creativity 12 ch.
  1. What Does It Mean to Create?
  2. A Brief History of Machines and Art
  3. How Generative AI Actually Works
  4. AI in the Visual Arts
  5. AI in Music and Sound
  6. AI in Writing and Language
  7. AI in Film, Video, and Performance
  8. The Authorship Question
  9. Training Data and the Ethics of Influence
  10. The Economic Disruption
  11. Bias, Representation, and Aesthetic Defaults
  12. What Makes Human Creativity Different?
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9 AI in Healthcare 12 ch.
  1. Why Healthcare Is an AI Battleground
  2. Diagnosis: AI That Reads Images and Scans
  3. Drug Discovery and Development
  4. Clinical Decision Support
  5. Mental Health and AI
  6. Bias in Medical AI
  7. Patient Data, Privacy, and Consent
  8. Surgical Robotics and AI-Assisted Procedures
  9. Public Health and Epidemiology
  10. Regulation and Approval
  11. The Economics of Healthcare AI
  12. The Future of the Doctor-Patient Relationship
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Ethics and Responsibility

The moral dimensions of AI — bias, harm, governance, and the hidden costs of the technologies we adopt.

10 Responsible Use and Human Judgment 12 ch.
  1. AI as a Tool, Not an Oracle
  2. When AI Gets It Right — and When It Doesn’t
  3. Verification, Cross-Checking, and Skepticism
  4. The Human Skills AI Cannot Replace
  5. Keeping Humans in the Loop
  6. AI and Professional Responsibility
  7. Teaching Children and Students About AI
  8. AI and Decision-Making Under Pressure
  9. Building Personal AI Literacy
  10. AI in Democracy: The Citizen’s Responsibility
  11. Organizational Responsibility for AI
  12. The Judgment That Matters Most
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11 Risks, Harms, and Governance 12 ch.
  1. Bias, Discrimination, and Unequal Impact
  2. Privacy, Surveillance, and Data Exploitation
  3. AI in Criminal Justice
  4. AI and Employment Harm
  5. Misinformation, Deepfakes, and Manipulation
  6. Safety, Misuse, and Dual-Use Concerns
  7. Environmental Costs of AI
  8. Concentration of Power
  9. Accountability: Who Is Responsible When AI Causes Harm?
  10. Regulation: What Governments Are Doing
  11. Standards, Audits, and Transparency
  12. The Future of AI Governance
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12 Technology's Hidden Tradeoffs 25 ch.
  1. Why Technology Is Never Neutral
  2. The Surveillance Bargain
  3. Algorithmic Decision-Making
  4. Artificial Intelligence: Promise and Peril
  5. Social Media's Psychological Costs
  6. Novel Technologies and Missing Cultural Defenses
  7. Children in the Digital World
  8. The Automation of Work
  9. The Gig Economy's Hidden Costs
  10. Platform Power and Digital Monopolies
  11. Data Ownership and Consent
  12. Technology and Democracy
  13. Facial Recognition and Biometric Surveillance
  14. Autonomous Weapons and Military AI
  15. Biotechnology and Human Enhancement
  16. Environmental Costs of Technology
  17. Digital Divide and Technological Inequality
  18. Smart Cities and Public Space
  19. The Right to Repair
  20. Health Technology and Medical AI
  21. Cryptocurrency and Decentralized Finance
  22. Content Moderation Dilemmas
  23. Technology in Education
  24. Emerging Technologies on the Horizon
  25. Who Gets to Decide?
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Power, Economics, and Politics

Who controls AI, how it concentrates power, what the real risks are versus the imagined ones, and how nations are racing to govern it.

13 AI + Billionaires = Hyperagency 10 ch.
  1. What Is Hyperagency?
  2. How AI Amplifies Billionaire Power
  3. Who Has Hyperagency Today?
  4. Platform Control and Algorithmic Manipulation
  5. AI Disinformation and Synthetic Media
  6. The Asymmetry Problem
  7. What We Cannot See
  8. Contested Perspectives
  9. Remedies and Democratic Defenses
  10. The Stakes: What Happens If We Fail?
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14 AI Risks: Real vs. Imaginary 12 ch.
  1. Framing the Debate: What Counts as a Real Risk?
  2. Real Danger: Economic Displacement and Job Loss
  3. Real Danger: Billionaire Control and Platform Manipulation
  4. Real Danger: AI-Powered Disinformation
  5. Real Danger: Surveillance, Bias, and Institutional Harms
  6. Imaginary Danger: Sentient AI and Machine Consciousness
  7. Imaginary Danger: Superintelligence and Extinction
  8. Who Benefits from the Imaginary Risks Narrative?
  9. The Opportunity Cost of Misplaced Fear
  10. Defenses Against AI Risk Misinformation
  11. Contested Perspectives
  12. Focusing on What Matters
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15 The Ungovernable Economy: How Billionaire AI Could Break Global Finance 16 ch.
  1. The New Gilded Age
  2. Who Owns the AI Infrastructure
  3. AI-Driven Market Dynamics
  4. Pathways to Crisis: Coordination vs Emergence
  5. Historical Precedents
  6. Infrastructure Chokepoints
  7. Regulatory Failure
  8. From Trigger to Cascade
  9. Winners and Losers
  10. The Benign Scenario
  11. Early Warning Signs
  12. Policy Responses
  13. National Defense Strategies
  14. The Normie's Survival Guide
  15. Connection to Dollar Dominance
  16. After the Singularity
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16 Global AI Policy 12 ch.
  1. Why AI Needs Governance
  2. The European Approach: The EU AI Act
  3. The American Landscape: Fragmented Regulation
  4. China's AI Strategy: State Control and Ambition
  5. The Global South and AI Colonialism
  6. Military AI and Autonomous Weapons
  7. Facial Recognition and Biometric Surveillance
  8. Algorithmic Accountability and Transparency
  9. AI and Labor Rights
  10. International Cooperation and Competition
  11. Open Source vs. Closed AI: The Access Debate
  12. What Good AI Governance Could Look Like
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Practical Navigation

How to protect your career, spot synthetic content, and make informed decisions in an AI-saturated world.

17 AI and Your Career: Adaptation Strategies 12 ch.
  1. Understanding the AI Job Landscape
  2. Assessing Your Own Vulnerability
  3. Skills That Remain Valuable
  4. Working With AI: Augmentation Strategies
  5. Retraining and Education Options
  6. Career Pivots: Where to Go
  7. Entrepreneurship and Self-Employment
  8. Freelancing and the Gig Economy
  9. Financial Survival During Transition
  10. Collective Action and Worker Power
  11. Managing the Emotional and Identity Impact
  12. Long-Term Planning in an Uncertain Economy
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18 AI, Bots, and Synthetic Media 12 ch.
  1. What Is Synthetic Media?
  2. How AI Generates Text
  3. How AI Generates Images and Video
  4. How AI Generates and Clones Audio
  5. Tell-Tale Signs of AI-Generated Content
  6. Bot Networks and Engagement Farms
  7. Astroturfing and Manufactured Consensus
  8. Political Manipulation and Election Interference
  9. The Trust Crisis: When Nothing Looks Real
  10. Tools and Techniques for Verification
  11. Legal, Regulatory, and Platform Responses
  12. Living in a World of Synthetic Content
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19 AI as a Force Multiplier for Knowledge Work 10 ch.
  1. Chatbot vs. Production Tool: Two Ways to Use AI
  2. The Architect and the Engine
  3. Building Compounding Assets
  4. Systematizing the Workflow
  5. The Judgment Layer
  6. Quality Control: Using the System to Check the System
  7. Scale Without Loss of Coherence
  8. One Person, Many Outputs: The Solo Builder Model
  9. Ethics of AI-Assisted Production
  10. What Comes Next: AI as Infrastructure
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