02. History of AI
Why History Matters
Section titled “Why History Matters”Understanding how AI evolved shows you why certain ideas exist, why the field stalled, and what breakthroughs made modern AI possible.
Timeline
Section titled “Timeline”1940s–1950s: The Birth of the Idea
Section titled “1940s–1950s: The Birth of the Idea”| Year | Event |
|---|---|
| 1943 | McCulloch & Pitts — first mathematical model of a neuron |
| 1950 | Alan Turing publishes Computing Machinery and Intelligence — proposes the Turing Test |
| 1956 | Dartmouth Conference — John McCarthy coins the term “Artificial Intelligence” |
The Turing Test: Can a machine fool a human into thinking it’s human through conversation? Still a reference point today.
1960s–1970s: Early Optimism
Section titled “1960s–1970s: Early Optimism”- First chatbot ELIZA (1966) — simulated a therapist using pattern matching
- Early chess programs developed
- Researchers wildly optimistic: “AI will solve everything in 20 years”
- Reality: computers too slow, data too scarce
1970s–1980s: First AI Winter
Section titled “1970s–1980s: First AI Winter”AI Winter = period when funding dried up because AI couldn’t deliver on its promises.
- Government funding cut after disappointing results
- Rule-based Expert Systems emerged — used hand-coded “if-then” logic
- Still too brittle for real-world complexity
1980s–1990s: Second AI Winter & Seeds of Revival
Section titled “1980s–1990s: Second AI Winter & Seeds of Revival”- Expert systems peaked, then failed at scale
- Backpropagation algorithm (1986) — key to training neural networks
- Second funding drought in early 1990s
- Deep Blue beats Garry Kasparov at chess (1997) — symbolic milestone
2000s: Data + Internet Changes Everything
Section titled “2000s: Data + Internet Changes Everything”- Internet creates massive datasets
- SVM, Random Forests become practical ML tools
- Google, Amazon use ML for search, recommendations
2010s: Deep Learning Revolution
Section titled “2010s: Deep Learning Revolution”| Year | Milestone |
|---|---|
| 2012 | AlexNet wins ImageNet — deep learning proves itself on vision |
| 2014 | GANs invented — AI can generate realistic images |
| 2016 | AlphaGo beats world champion at Go |
| 2017 | Transformer architecture published (“Attention Is All You Need”) |
| 2018 | BERT, GPT-1 released — LLMs emerge |
2020s: The LLM Era
Section titled “2020s: The LLM Era”| Year | Milestone |
|---|---|
| 2020 | GPT-3 — 175B parameters, fluent text generation |
| 2022 | ChatGPT — mainstream AI adoption |
| 2023 | GPT-4, Claude, Gemini — multimodal LLMs |
| 2024–25 | AI Agents, reasoning models, MCP protocol |
The Pattern
Section titled “The Pattern”Hype → Disappointment (Winter) → Quiet Progress → Breakthrough → RepeatEvery AI winter was followed by a breakthrough that changed everything. We are currently in a sustained breakthrough period driven by transformers + scale.
Interview Questions
Section titled “Interview Questions”Q: What was the Dartmouth Conference and why is it significant?
A: The 1956 Dartmouth Conference was where John McCarthy coined the term “Artificial Intelligence.” It’s considered the founding event of AI as a formal academic discipline — it defined the goal: making machines that simulate human intelligence.
Q: What is an AI Winter?
A: An AI Winter is a period of reduced funding and interest in AI research, typically following inflated expectations that weren’t met. There were two major winters (1970s and late 1980s). They ended when new breakthroughs — like deep learning — delivered real results.