A stretch of time when hopes for AI collapsed and funding and interest dried up.
An AI winter is a period when expectations for AI ran far ahead of what it could actually do, then collapsed, taking research money and interest with them. People usually count two: one in the mid-1970s and one in the late 1980s.
Around the first, big promises such as machine translation came along slower than hoped, and the 1969 book Perceptrons showed the limits of one-layer neural networks, which cooled that research too. The second came when the boom in expert systems, built from rules typed in by hand, fizzled out. Research never fully stopped, and that work later fed into deep learning.
Think of it like this
You know a game with an amazing trailer that turns out disappointing? Players leave, and you hear nothing about a sequel for ages. AI winters were like that: the technology didn't vanish, the hype cooled first.
For example
Whenever AI hype gets very loud, someone asks: "Is another AI winter coming?"
Records about this
Source-checked moments from AI history.
Pinned down the limits mathematically, triggering a slump in neural network research that lasted 10 years or more