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Alan Turing introduces the "Turing Test" in his paper "Computing Machinery and Intelligence."
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Dartmouth Conference establishes AI as a field of study.
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Development of symbolic AI and rule-based systems; funding and interest in AI grow globally.
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Decline in interest as AI systems struggle with real-world complexity.
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Despite progress in theory, AI systems struggle to solve real-world problems due to their dependence on pre-defined rules and limited computational power.
Governments and organizations begin questioning AI’s feasibility. -
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Lighthill Report in the UK criticizes AI's limited progress, leading to reduced funding, followed by other nations.
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AI research focuses on highly specialized tasks like chess and mathematical proofs, but general AI remains elusive. Skepticism grows among researchers and funders.
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Expert systems like MYCIN and DENDRAL show promise in specialized areas.
These systems offer a brief resurgence of interest but fail to adapt beyond narrow use cases. -
Governments and businesses begin pulling back from AI investments.
The U.S. and Europe shift focus to more commercially viable computing technologies. -
Commercial Lisp machines fail, leading to further AI skepticism. AI faces increasing skepticism in the tech industry.
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Many researchers abandon AI for other fields like software engineering, computer science, and robotics.
Universities reduce funding for AI programs, and new researchers avoid the field. -
Researchers such as Geoffrey Hinton and David Rumelhart refine backpropagation for training neural networks.
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Advances in machine learning, neural networks, and increasing computational power rekindle interest in AI.
New optimism for AI research emerges, leading to a second wave of development in the 1990s.