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AI For Everyone (AI4E)

Part 2 · How AI Actually Works

Nine plain-language lessons on what AI is, how models learn from examples, and where they reliably fail. No maths, no code, no jargon without a definition.

  1. Lesson 16

    What AI Actually Is (Not Magic, Not Movies)

    Explain what AI is in plain language, give 3 everyday examples of AI already in use, and describe why 'AI is just maths' (demystification).

    3 min · self-check

  2. Lesson 17

    How AI Models Are Trained (The Hokkien Mee Analogy)

    Describe the seven steps of building an AI system using the Hokkien Mee analogy, and map a real-world AI project to those steps.

    4 min · self-check

  3. Lesson 18

    Predicting Numbers: Regression

    Explain what regression does, identify tasks where it applies, and describe why some predictions are possible and others are not.

    3 min · self-check

  4. Lesson 19

    Sorting Into Categories: Classification

    Explain how classification works, distinguish it from regression, and identify tasks where classification applies.

    4 min · self-check

  5. Lesson 20

    Finding Patterns Without Labels: Clustering

    Describe how clustering differs from classification, explain when clustering is the right approach, and identify tasks where discovering hidden groups would be valuable.

    4 min · self-check

  6. Lesson 21

    Computer Vision: How Machines See

    Describe how a computer represents and processes images, explain how CV models recognise objects, and identify practical CV applications in your industry.

    4 min · self-check

  7. Lesson 22

    Generative AI: How It Actually Works

    Describe how an LLM generates text (next-word prediction), explain why responses vary, and describe how AI-generated images are created.

    4 min · self-check

  8. Lesson 23

    Data Is Everywhere (Documents, Emails, Spreadsheets)

    Recognise that 'data' for AI includes documents, emails, reports, chat logs, and spreadsheets, not just databases. Identify 5 data sources in your daily work that AI could process.

    4 min · self-check

  9. Lesson 24

    AI Has Limitations (Hallucinations, Bias, Errors)

    Recognise that AI can hallucinate (invent plausible-sounding nonsense), exhibit bias, and make confident errors. Give 2 examples of each.

    4 min · self-check