AI History & Milestones
From Turing test to modern transformers — key breakthroughs and AI winters
AI research has gone through cycles of excitement and disillusionment — known as "AI winters" — since its formal founding in 1956. The field has been transformed by three major waves: symbolic AI, statistical ML, and the current deep learning revolution driven by big data and GPU compute.
Key Points
- 1950: Alan Turing proposes the Turing Test as a measure of machine intelligence
- 1956: Dartmouth Conference — the term "Artificial Intelligence" is coined by John McCarthy
- 1970s–80s: First AI Winter — overhyped expectations, underpowered hardware
- 1980s: Expert systems boom — rule-based knowledge engineering
- 1990s: Second AI Winter — expert systems brittle, machine learning emerges
- 1997: Deep Blue beats Garry Kasparov at chess
- 2012: AlexNet wins ImageNet — deep learning revolution begins
- 2017: "Attention is All You Need" paper introduces the Transformer architecture
- 2020: GPT-3 — 175B parameter language model stuns the world
- 2022: ChatGPT launches, reaching 100M users in 2 months
- 2024+: Multimodal models (GPT-4V, Gemini), AI agents, reasoning models
Real-World Example
The 2012 ImageNet breakthrough reduced image classification error from ~26% to ~15% — a leap that would have taken decades at the previous rate of progress. This single event catalysed the modern deep learning era and triggered massive investment in AI infrastructure.