Most visions for decision support and information technology anticipate the use of machine learning to enable software torespond to an adapting environment, including the ability to learn capabilities while on-the-job. Currently, systems andsoftware engineering processes hinder employment of task learning technology, because the adaptation it provides runscounter to our notions of stability. Similarly, systems must typically demonstrate satisfaction of requirements beforedeployment, rather than learn tasks while on the job. This paper introduces new problems for the field of softwareengineering and discusses an approach for preparing cognitive systems for deployment. We describe one approach to a bootcamp for cognitive systems and present the results of simulations of the boot camp. The results of our experiments providethresholds and patterns for knowledge, and the requirement for specific patterns of human use of cognitive systems. Theseresults are then used to infer requirements for a boot camp and measures for the prediction of successful employment of theassistant.
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