Perplexity AI The History of AI
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Feb 2, 2026 A brisk tour of AI's origins, from early questions about machine thought to the 1956 naming moment. Short-lived booms, funding crashes, and expert systems' limits get attention. The shift to machine learning, the deep learning surge powered by data and GPUs, and how scaling birthed large language models are highlighted. A forward look at cheaper, more accessible AI rounds out the conversation.
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Limits Of Symbolic AI
- Early AI relied on symbolic rules and assumed human reasoning could be fully encoded as math and logic.
- Those rule-based systems worked in narrow domains but failed in messy real-world tasks like language and vision.
The AI Winter Reversal
- Funding and interest dried up after symbolic approaches failed to generalize, triggering an 'AI winter.'
- Governments and universities pulled back because the systems were brittle and expectations collapsed.
Expert Systems' Short-Lived Resurgence
- Expert systems revived AI in the 1980s by encoding specialist decision-making for narrow tasks.
- They delivered value but remained expensive, brittle, and hard to maintain or scale.