Latent Space AI The History of AI
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Feb 2, 2026 A brisk tour of AI’s origins, from early philosophical debates to the 1956 naming moment. It covers symbolic AI, the crashes of AI winters, and the 1980s expert system comeback. The shift to machine learning, the data and compute boom that enabled deep learning, and the role of scaling large models are highlighted. It closes with why today’s AI surge feels different and what future accessibility might look like.
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Limits Of Symbolic AI
- Early AI relied on symbolic, hand-coded rules and failed outside narrow domains.
- Jaeden Schafer explains that real-world messiness made rule-based systems brittle and insufficient.
AI Winters And Expert Systems Return
- Funding and interest dried up after symbolic approaches failed, producing an "AI winter."
- Jaeden Schafer recounts how expert systems later revived interest but proved expensive and brittle.
Why Machine Learning Took Over
- Machine learning shifted focus from rules to learning from data, inspired by brain-like connections.
- Jaeden Schafer highlights data, cheaper compute, and better training as the turning points for neural networks.