BACKGROUND

Artificial Intelligence (AI) is a broad term used to describe computer systems that improve with the processing of more data, giving them the appearance of having human-like intelligence. More specific industry terms are Machine Learning, or a subset of machine learning called Deep Learning (DNN or Deep Neural Networks). Currently the data to train these systems and the deployment and testing of the systems use physical data and real environments. For example, developing a retail store AI system that understands product stock availability, proper product merchandising, and shopper behavior requires physical retail store mockups or test stores, actors or others performing the shopping tasks, and a very large number of product types, product shelf positions, stock in/out configurations, plus physical cameras, and shelf and other sensors that comprise the AI system. Providing the data variability needed to train the AI system requires that months or years of camera or sensor data be collected, while the product types, stock levels and shelf positions are randomly varied. The test data must represent many years of store operation in order to create an AI system that understands situations and actions it has not been exposed to before.

