A history of AI, from ancient myths to the Dartmouth Workshop, AI winters, and the breakthroughs that shaped today’s AI revolution.
Artificial Intelligence has never been a straight line of progress. Instead, the History of AI is a spiral of bold claims, breakthroughs, long winters, and comebacks. To understand where modern AI stands, you need to explore the History of AI that shaped today’s revolution.
Precursors in the History of AI: Myths and Automata
The dream of artificial minds is older than computing.
- Ancient myths: Greek legends like Talos, the bronze guardian, show early visions of artificial beings.
- Mechanical automata: Renaissance engineers built clockwork creatures. In the 18th century, “The Turk” wowed audiences as a chess-playing machine (though secretly human-operated).
- Logic as machinery: Boole’s algebra (1854) and Babbage/Lovelace’s programmable engine showed that reasoning could, in principle, be mechanized.
For a deeper dive into early automation, see The History of Automata.
Turing and the Dawn of Computation
The leap from myth to math began in the 20th century.
- 1936: Alan Turing formalized computation with the Turing Machine.
- WWII: His codebreaking work proved the practical power of computation.
- 1950 — The Turing Test: Instead of asking “Can machines think?”, Turing reframed it: if you can’t tell a machine from a human in conversation, call it intelligent.
The Dartmouth Workshop and the Golden Years in the History of AI (1956–1974)
AI was officially born at Dartmouth.
- The workshop (1956): John McCarthy coined “Artificial Intelligence.” Minsky, Rochester, and Shannon joined him to push the idea that every part of intelligence could, in principle, be simulated.
- Symbolic AI: Early systems used symbols and rules to represent thought.
Breakthroughs included:
- Logic Theorist (1956): Proved math theorems.
- General Problem Solver (1957): Tried to generalize reasoning.
- Perceptron (1958): Frank Rosenblatt’s neural net for pattern recognition.
- LISP (1958): McCarthy’s AI-first programming language.
- ELIZA (1966): Weizenbaum’s chatbot simulating therapy.
- Shakey the Robot (1966–72): The first robot to perceive, plan, and move.
First AI Winter (1970s)
Hype soon crashed into limits.
- Computation wasn’t ready: Symbolic programs collapsed under complexity.
- The Lighthill Report (1973): Condemned AI research as overhyped “toy problems,” triggering UK funding cuts.
- DARPA cuts: U.S. military funding dried up as AI underdelivered.
- Perceptron criticism: Minsky and Papert showed single-layer nets couldn’t solve basic problems like XOR.
As a result, neural nets went into hibernation.
The Expert Systems Boom (1980s)
AI rebounded with commercial promise.
- Knowledge-based systems encoded expert knowledge into rules.
- MYCIN: Diagnosed infections better than junior doctors.
- DENDRAL: Interpreted chemical data to suggest molecular structures.
- XCON (DEC): Configured computer orders, saving millions.
The Second AI Winter (Late 1980s–1990s)
Expert systems hit their ceiling.
- Brittle by design: They couldn’t adapt outside narrow domains.
- Maintenance hell: Updating thousands of rules was costly.
- Hardware collapse: Specialized LISP machines lost to cheap Unix workstations.
The Quiet Renaissance (1990s–2000s)
While headlines mocked AI, researchers quietly built the future.
- Backpropagation: Finally made multi-layer neural nets trainable.
- Statistical learning: Shift from hand-coded rules to machine learning.
- Support Vector Machines & Decision Trees: Delivered solid, interpretable ML tools.
- Big Data: The web provided the fuel for training.
- Deep Blue (1997): IBM’s brute-force chess engine beat Garry Kasparov.
Related reading: Deep Blue vs Kasparov.
The Modern AI Boom (2010s–2025)
Deep learning lit the fuse, and the field exploded outward.
- 2012 — AlexNet: Convolutional nets crushed ImageNet, proving deep learning’s dominance.
- Reinforcement learning (2016): AlphaGo beat Lee Sedol at Go, a game once thought untouchable.
- Transformers (2017): “Attention Is All You Need” introduced the architecture behind today’s large models.
- Generative AI (2020s): GPT-3, ChatGPT, and Stable Diffusion showed that AI could now create, not just classify.
- Multimodal & Agentic AI (2023–2025): Next-gen models like GPT-5 and Gemini reason across text, audio, video, and data.
- The Open-Source Race (2023–2025): Llama 3 and other open models democratized access, fueling innovation.
Lessons from the History of AI: Hype vs Reality
History shows a few patterns:
- Cycles are inevitable: optimism → hype → disappointment → renewal.
- The AI Effect: once an AI task works, it stops being called AI (OCR, spam filters).
- Winters matter: the quiet periods built the math and infrastructure we use today.
- Interdisciplinary fuel: breakthroughs always come from mixing computer science, math, neuroscience, and engineering.
Ethics and the Future
The History of AI shows progress moves in cycles, not straight lines. However, today’s scale raises new stakes:
- Bias and fairness: Models reflect human data unless corrected.
- Privacy: Training often uses internet-scale datasets without consent.
- Jobs: Routine work is at risk even as new AI roles emerge.
- Security: Systems can be manipulated by adversarial attacks or poisoned data.
- Agentic AI: Models that plan and act will raise deeper accountability questions.
For more on ethics, see UNESCO’s AI Ethics Framework.
Takeaway
AI progress isn’t linear. Instead, it spirals through cycles of hype and disappointment, only to re-emerge stronger.
The generative boom of 2022 evolved into the agentic era of 2025, solving some problems while exposing new limits in reasoning and reliability. History reminds us that even as capabilities explode, the core challenges of grounding, safety, and cost remain.
The survivors will be those who can ride each hype wave while delivering real, durable value.
Read other articles in this series – The AI Playbook
