After rapid optimism in the 1960s, the 1970s brought tougher benchmarks, critical reviews, and limited computing resources. Governments and funders grew cautious as projects under-delivered against ambitious goals in machine translation, robotics, and general problem solving.

The downturn reshaped priorities and evaluation standards. While the “winter” slowed some symbolic programs, it also prepared the ground for future revivals in statistical methods, data-driven learning, and later deep learning.