AI and ML History — From Perceptrons to LLMs

Why it matters

History reveals why current techniques work: deep learning had precursors that failed for lack of compute and data. Understanding what changed clarifies what modern AI actually does.

The architecture

1950s-60s: symbolic AI. Programs manipulate symbols per hand-coded rules. Prolog, expert systems.

1960s-90s: perceptrons and early neural nets. Interest waxed and waned.

2012: AlexNet wins ImageNet. Deep learning revolution begins.

How it works end to end

2012 breakthrough: GPU compute + large labeled datasets + neural net advances (ReLU, dropout, better initialization) combined.

2017: Transformer architecture. Attention is all you need.

2020-present: LLMs at scale. GPT-3, ChatGPT, and current generation.

Winters: two AI winters (1970s and late 80s) when hype exceeded delivery.