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The Cycles of AI Winters: A Historical Analysis and Modern Perspective
- AI has gone through periods called 'winters', where research and development have slowed down due to oversize expectations and limitations of technology.
- AI winters are periods marked by cooling off of support for AI technologies.
- The first major AI winter came from oversize expectations about what early AI technologies could achieve.
- A 1966 report, called the ALPAC report, was a principal catalyst for the first AI winter, as it detailed that machine translation was no better than human translation at the time.
- Early AI researchers promised they could solve problems that couldn't be met with the technology of their era.
- The AI winters have led to huge research funding cuts by governments and corporations.
- Research on Natural Language Processing projects, Computer Vision, and General Problem Solving programmes were scaled back considerably in the AI winters.
- The AI winter has affected industries and researchers, who rebranded their work using less ambitious terms.
- Modern deep learning systems today face challenges that echo past concerns in AI development.
- To avoid another AI winter, the AI community has to strike a balance between what it aspires to and what is functionally possible.
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