Artificial intelligence crashes are historically common:
First, what caused previous AI winters? There was one straightforward reason: The technology did not work. Expert systems weren’t experts. Language translators failed to translate. Even Watson, after winning Jeopardy, failed to provide useful answers in the real-world context of medicine. When technology fails, winters come.
Nearly all of AI’s recent gains have been realized due to massive increases in data and computing power that enable old algorithms to suddenly become useful. For example, researchers first conceived neural networks—the core idea powering much machine learning and AI’s notable advances—in the late 1950s. The worries of an impending winter arise because we’re approaching the limits of what massive data combined with hordes of computers can do. Brendan Dixon, “Is a bad “AI winter” looming?” at Mind Matters
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See also by Brendan Dixon: The “Superintelligent AI” Myth The problem that even the skeptical Deep Learning researcher left out