Engineering Artificially Intelligent Systems: A Systems Engineering Approach to Realizing Synergistic Capabilities: 13000 (Information Systems and Applications, incl. Internet/Web, and HCI) - Tapa blanda

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Many current AI and machine learning algorithms and data and information fusion processes&nbsp;attempt in software to estimate situations in our complex world of nested feedback loops. Such&nbsp;algorithms and processes must gracefully and efficiently adapt to technical challenges such as&nbsp;<div>data quality induced by these loops, and interdependencies that vary in complexity, space, and&nbsp;time.</div><div><br></div><div>To realize effective and efficient designs of computational systems, a Systems Engineering&nbsp;perspective may provide a framework for identifying the interrelationships and patterns of&nbsp;change between components rather than static snapshots. We must study cascading&nbsp;interdependencies through this perspective to understand their behavior and to successfully&nbsp;adopt complex system-of-systems in society.&nbsp;</div><div><br></div>This book derives in part from the presentations given at the AAAI 2021 Spring Symposium&nbsp;session on Leveraging Systems Engineering to Realize Synergistic AI / Machine Learning&nbsp;Capabilities. Its 16 chapters offer an emphasis on pragmatic aspects and address topics in&nbsp;systems engineering; AI, machine learning, and reasoning; data and information fusion;&nbsp;intelligent systems; autonomous systems; interdependence and teamwork; human-computer&nbsp;interaction; trust; and resilience.<div><br></div><div>The chapter&nbsp;“How Interdependence Explains the World of Team work” is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.<br></div>