1950s ai hype cycle

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The first AI hype cycle began in the 1950s, following the birth of the field at a conference organized by John McCarthy at Dartmouth University in 1956. This event marked the start of the boom phase of AI, where the field saw significant theoretical and practical advances, including the development of rule-based systems, shallow and deep neural networks, natural language processing, and speech processing. The period between 1956 and 1973 was particularly fruitful, but it was followed by a phase where research slowed down, and even the term "artificial intelligence" was less used. Notable inventions like backpropagation and recurrent neural networks were largely overlooked during this time. The hype cycle concept suggests that new technologies often go through phases of inflated expectations followed by a trough of disillusionment before becoming productive. AI's history reflects this pattern, with periods of high enthusiasm and investment followed by disappointment and reduced funding, known as "AI winters". The first AI winter occurred after the initial hype cycle, as expectations were not met and funding was cut. The term "AI winter" was first used in 1984, and since then, the field has experienced several cycles of hype and disillusionment. Despite these cycles, AI has made significant progress over the decades, and the current interest in AI, as of 2024, is part of a new hype cycle that is reminiscent of the first one. It is important to learn from past cycles to manage expectations and sustain progress in AI research and development.
what were some of the achievements during the first ai hype cycle
what is the ai winter and why did it happen
how has the current hype cycle in ai differ from the first one