AI's Winter

  • Before Winter

    Alan Turing introduces the "Turing Test" in his paper "Computing Machinery and Intelligence."
  • Before Winter

    Dartmouth Conference establishes AI as a field of study.
  • Before Winter

    Development of symbolic AI and rule-based systems; funding and interest in AI grow globally.
  • Early Challenges

    Decline in interest as AI systems struggle with real-world complexity.
  • Early Limitations of AI

    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.
  • Period: to

    WINTER

  • UK point of view

    Lighthill Report in the UK criticizes AI's limited progress, leading to reduced funding, followed by other nations.
  • Shift to Specialized Applications

    AI research focuses on highly specialized tasks like chess and mathematical proofs, but general AI remains elusive. Skepticism grows among researchers and funders.
  • Expert System Boom (Temporary High Point)

    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.
  • Reduced Funding

    Governments and businesses begin pulling back from AI investments.
    The U.S. and Europe shift focus to more commercially viable computing technologies.
  • Lisp Machine Failures

    Commercial Lisp machines fail, leading to further AI skepticism. AI faces increasing skepticism in the tech industry.
  • Decline in AI Research Interest

    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.
  • Backpropagation Algorithm Introduced

    Researchers such as Geoffrey Hinton and David Rumelhart refine backpropagation for training neural networks.
  • End of the Winter

    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.