A Short History and the Main Approaches

See how AI moved from hand-written rules to machine learning, deep learning and generative AI.

In this lesson you will

Describe the main approaches to AI
Place them on a simple timeline

Key concepts

rule-based (expert) systemsmachine learningdeep learninggenerative AI

The lesson

Early AI systems followed rules written by experts: if the temperature is above 38 °C and there is a cough, suggest a test. They were transparent but brittle — they failed on cases the rules did not cover.

Machine learning lets systems learn patterns from examples instead. Deep learning, using large neural networks, made big advances in recognising images and speech from around 2012.

Generative AI — systems that create text, images, audio and code, such as chat assistants — became widely available from 2022. Each approach is still used where it fits best.

Step by step

Read short descriptions of five AI systems.
Classify each as rule-based, machine learning, deep learning or generative.
Place the approaches on a timeline.
Discuss when rules are still the better choice.

Practice activity

Create a one-page timeline of AI approaches with one example each.

Expected result: An accurate timeline with correctly classified examples.

Check your understanding

What is the main weakness of rule-based systems?

Show answer
They fail on cases the rules do not cover.
What does generative AI produce?

Show answer
New content such as text, images, audio or code.

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