AI winter

In the history of artificial intelligence, an AI winter is a period of reduced funding and interest in artificial intelligence research. 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"). Roger Schank and Marvin Minsky—two leading AI researchers who had survived 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. The described of 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. Three years later the billion-dollar AI industry began to collapse. There were two major winters approximately 1974–1980 and 1987–2000 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 () AI boom.

EdgeAI-Aware Design of In-Memory Computing Architectures

EdgeAI-Aware Design of In-Memory Computing Architectures