AI winter

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In the history of artificial intelligence, an AI winter is a period of reduced funding and interest in artificial intelligence research.^[1] The field has experienced several hype cycles, followed by disappointment and criticism, followed by funding cuts, followed by renewed interest years or even decades later.

The term first appeared in 1984 as the topic of a public debate at the annual meeting of AAAI (then called the "American Association of Artificial Intelligence").^[2] Roger Schank and Marvin Minsky—two leading AI researchers who experienced the "winter" of the 1970s—warned the business community that enthusiasm for AI had spiraled out of control in the 1980s and that disappointment would certainly follow. They described a chain reaction, similar to a "nuclear winter", that would begin with pessimism in the AI community, followed by pessimism in the press, followed by a severe cutback in funding, followed by the end of serious research.^[2] Three years later the billion-dollar AI industry began to collapse.

There were two major winters approximately 1974–1980 and 1987–2000,^[3] and several smaller episodes, including the following:

• 1966: failure of machine translation
• 1969: criticism of perceptrons (early, single-layer artificial neural networks)
• 1971–75: DARPA's frustration with the Speech Understanding Research program at Carnegie Mellon University
• 1973: large decrease in AI research in the United Kingdom in response to the Lighthill report
• 1973–74: DARPA's cutbacks to academic AI research in general
• 1987: collapse of the LISP machine market
• 1988: cancellation of new spending on AI by the Strategic Computing Initiative
• 1990s: many expert systems were abandoned
• 1990s: end of the Fifth Generation computer project's original goals

Enthusiasm and optimism about AI has generally increased since its low point in the early 1990s. Beginning about 2012, interest in artificial intelligence (and especially the sub-field of machine learning) from the research and corporate communities led to a dramatic increase in funding and investment, leading to the current (as of 2024^[update]) AI boom.