If you only learned about AI from the last few years of news, you'd assume it appeared out of nowhere around 2022. It didn't. The field is over 70 years old, and it has already been through two major periods of hype followed by disappointment — periods researchers literally call "AI winters." Understanding that history isn't just trivia. It's what lets you tell the difference between genuine progress and the next hype cycle, because this won't be the last one.
1950s — Symbolic AI. Early researchers tried to build intelligence by hand-writing logical rules ("if the patient has a fever AND a rash, THEN consider measles"). This worked for narrow, well-defined problems, but writing enough rules to handle the real world's messiness proved impossible. This approach hit a wall, leading to the first AI winter in the 1970s, when funding collapsed because progress hadn't matched the promises.
1980s — Expert Systems. A revival built on the same rule-based idea, but applied to narrower domains like medical diagnosis and equipment troubleshooting. These systems worked well in their narrow lane but were brittle — they broke completely outside the exact scenarios their rules anticipated. This led to a second AI winter in the late 1980s/early 1990s.
1990s-2000s — Statistical Machine Learning. Researchers shifted strategy entirely: instead of hand-writing rules, let the computer learn patterns directly from data. This is the approach that underlies almost everything in this course track. Early wins included spam filters, early recommendation systems, and IBM's Deep Blue beating chess champion Garry Kasparov in 1997 (though Deep Blue was actually closer to brute-force search than modern ML).
2012 — The Deep Learning Breakthrough. A neural network called AlexNet dramatically outperformed every other approach in an image recognition competition. This single result convinced the field that deep neural networks, combined with enough data and computing power (especially GPUs), could outperform decades of hand-engineered approaches. This kicked off the deep learning era that built modern computer vision, speech recognition, and translation.
2017 — The Transformer. A paper called "Attention Is All You Need" introduced an architecture that became the foundation for every major language model since — you'll study this in detail in Tier 3. This is the single most consequential paper in the chain of events leading to today's AI.
2022-Present — The LLM Era. ChatGPT's public release in late 2022 made large language models a mainstream phenomenon almost overnight, even though the underlying research had been building for years. What changed wasn't a sudden invention — it was scale (much bigger models, much more training data) combined with a genuinely good user interface that let millions of non-technical people interact with the technology directly for the first time.