How COVID-19 Broke AI, And Why AI May Break Again

A woman is frustrated by the answer given by a digital assistant. The last year has seen no shortage of unprecedented circumstances. All aspects of our lives, from work to travel to shopping, have changed. During this massive disruption, we have (unfortunately) learned why ML Ops - the practice of machine learning (ML) in production and the management of an ML lifecycle, should not be an afterthought but rather a critical element of getting value from AI. Figure 1 below shows a simplified example of an AI model in action. First trained by data - past examples of the environment, the model is then put into the real world to make predictions on new inputs - which are implicitly assumed to be sufficiently similar to what the training examples were.