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The explosion of artificial intelligence (AI) methods present many opportunities for future technology, while at the same time, there are some unresolved challenges. One example is the ability to explain, reproduce, and leverage AI methods for practical applications. AI, along with many other pattern recognition and information fusion techniques can augment a user in decision making. However, when difficult decisions arise, contextual knowledge can help discern the merits of the results. In this paper, we highlight the ability of AI to support “usable” man-machine support as for methods that support data at rest, data in use, and data in motion (RUM). An example is shown for deep multi modal image fusion in support of simultaneous tracking and identification.
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Blasch et al. (2019) studied this question.