AI winter is a quiet period for artificial intelligence research and development. Over the years, funding for AI initiatives has gone through a number of active and inactive cycles. The label "winter" is used to describe dormant periods when customer interest in artificial intelligence declines.
Historically, AI winters have occurred because vendor promises have fallen short and AI initiatives have been more complicated to carry out than promised. When AI-washed products fail to deliver a robust return on investment (ROI), buyers become disappointed and direct their attention elsewhere. Use of the season winter to describe the resulting downturn emphasizes the idea that the quiet period will be a temporary state, followed again by growth and renewed interest.
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."