AI winter is a quiet period for artificial intelligence research and development. The promotion, interest and funding for AI has gone through a number of active and inactive cycles over the years.
As claims for improving the speed and accuracy of predictive analytics are made and marketing hype around the promises of artificial intelligence builds, general interest grows and investments follow. When vendor promises fall short, however, and AI initiatives are more complicated than to carry out than promised or fail to deliver a robust return on investment (ROI), the pendulum swings and customers no longer buy into the idea that AI will be the solution to every problem. The resulting downturn in adoption typically results in a period of reduced funding which in turn, can lead to another AI winter during which time marketing and customer attention is focused elsewhere.
In the past few years, AI has been on a long, strong upswing, but after several years of hype, advances and implementations, some analysts are predicting another AI winter. Ethics is a hot-button topic of discussion for the technology industry right now, especially in the rapidly developing fields around using large data sets to train machine learning and automated decision systems.
Consumers and tech industry workers are raising questions about how automated decision-making systems are designed and what decisions they should be allowed to make, both in terms of industry verticals and specific applications within them. To forestall another AI winter, some vendors are choosing to label software features "predictive" instead of "artificially intelligent."