Hard Fork AI The History of AI
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Feb 2, 2026 A fast tour through AI's origins, from early philosophical questions to the 1956 birth of the field. Short takes on symbolic AI, AI winters, and why rule‑based systems fell short. A look at the shift to machine learning, the deep learning breakthroughs of the 2010s, and how scaling models and data reshaped capabilities. Final thoughts on why the current boom feels different and what wider access might bring.
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Limits Of Rule-Based AI
- Early AI assumed human thought could be reduced to rules and logic.
- That symbolic approach worked in narrow tasks but failed in messy real-world problems.
When Symbolic Systems Hit Reality
- Jaeden recounts how symbolic systems succeeded at chess and logic but collapsed outside controlled settings.
- That failure led to funding drying up and the first AI winter.
Expert Systems' Narrow Win
- Expert systems revived AI in the 1980s by encoding specialist decision rules.
- They worked in narrow domains but were brittle, costly, and hard to maintain at scale.