AI winter
A period when AI research funding and public optimism dropped sharply after early hype exceeded practical results.
An AI winter refers to one or more historical slumps: the first in the mid-1970s and the second around late 1980s–early 1990s, when progress in symbolic AI and expert systems disappointed funders and industry. Critics argued many tasks remained out of reach; budgets shrank despite earlier promises of human-level reasoning soon.
Recoveries came partly from expert systems in narrow domains, then from renewed machine learning and deep learning when data and compute caught up.