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History & Evolution of AI

Trace artificial intelligence from the Turing test and early symbolic systems, through the AI winters, to deep learning and the transformer models behind the assistants in use today.

Material Overview
History & Evolution of AI

Follow the ideas, breakthroughs, and setbacks that shaped modern artificial intelligence. You will study early symbolic AI and expert systems, the funding collapses known as the AI winters, the revival driven by neural networks, cheap computing power, and large datasets, and the transformer architecture that made large language models possible. Understanding this arc explains why current systems behave the way they do, which limitations keep returning, and how to read new announcements with useful historical context.

Curriculum

Learning Path

Each level is enrolled separately. Work through them in order, or start at the level that matches your experience.

  1. Level 01

    Beginner

    Purpose

    Meet the founding ideas of artificial intelligence and the people who argued about whether a machine could think at all.

    Outcome

    You can describe where the field began and what the first generation of AI could and could not do.

    What You'll Learn
    • Follow the Turing test and the Dartmouth workshop that named the field
    • Learn how early programs played games and proved theorems with hand written rules
    • See why the first wave of optimism ran ahead of the available computing power

    249 CREDS

  2. Level 02

    Intermediate

    Purpose

    Study the expert system era and the funding collapses that followed, and learn why hype cycles keep repeating.

    Outcome

    You can recognise the pattern behind an AI hype cycle and judge current claims against it.

    What You'll Learn
    • Examine expert systems, knowledge bases, and why maintaining rules by hand did not scale
    • Trace the two AI winters and the promises that caused them
    • Compare the claims of that period with the ones being made today

    499 CREDS

  3. Level 03

    Advanced

    Purpose

    Understand the deep learning revival, from backpropagation to the moment large datasets and graphics processors changed everything.

    Outcome

    You can explain why deep learning succeeded where earlier approaches failed.

    What You'll Learn
    • Learn why neural networks stalled and what made them viable again
    • Follow the results in image recognition and speech that convinced the field to switch approach
    • See how open datasets, hardware, and shared code accelerated progress

    797 CREDS

  4. Level 04

    Expert

    Purpose

    Trace the transformer era and place current systems in a longer arc so you can reason about what is likely to come next.

    Outcome

    You can situate any new model release inside the history of the field and judge how significant it really is.

    What You'll Learn
    • Study the attention mechanism and why it displaced earlier sequence models
    • Follow scaling laws, instruction tuning, and the shift from research demos to consumer products
    • Analyse which limitations have been solved by scale and which have not moved at all

    1,086 CREDS