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E-raamat: Engineering Artificially Intelligent Systems: A Systems Engineering Approach to Realizing Synergistic Capabilities

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Engineering Artificially Intelligent Systems: A Systems Engineering Approach to Realizing Synergistic CapabilitiesSuurem pilt
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Many current AI and machine learning algorithms and data and information fusion processes attempt in software to estimate situations in our complex world of nested feedback loops. Such algorithms and processes must gracefully and efficiently adapt to technical challenges such as 
data quality induced by these loops, and interdependencies that vary in complexity, space, and time.

To realize effective and efficient designs of computational systems, a Systems Engineering perspective may provide a framework for identifying the interrelationships and patterns of change between components rather than static snapshots. We must study cascading interdependencies through this perspective to understand their behavior and to successfully adopt complex system-of-systems in society. 

This book derives in part from the presentations given at the AAAI 2021 Spring Symposium session on Leveraging Systems Engineering to Realize Synergistic AI / Machine Learning Capabilities. Its 16 chapters offer an emphasis on pragmatic aspects and address topics in systems engineering; AI, machine learning, and reasoning; data and information fusion; intelligent systems; autonomous systems; interdependence and teamwork; human-computer interaction; trust; and resilience.


Introduction: Motivations for and Initiatives on AI Engineering.-
Architecting Information Acquisition To Satisfy Competing Goals.- Trusted
Entropy-Based Information Maneuverability for AI Information Systems
Engineering.- BioSecure Digital Twin: Manufacturing Innovation and
Cybersecurity Resilience.- Finding the path toward design of synergistic
humancentric complex systems.- Agent Team Action, Brownian Motion and
Gamblers Ruin.- How Deep Learning Model  Architecture and Software Stack
Impacts Training Performance in the Cloud.- How Interdependence Explains the
World of Teamwork.- Designing Interactive Machine Learning Systems for GIS
Applications.- Faithful Post-hoc Explanation of Recommendation using
Optimally Selected Features.- Risk Reduction for Autonomous Systems.- Agile
Systems Engineering in Building Complex AI Systems.- Platforms for Assessing
Relationships: Trust with Near Ecologically-valid Risk, and Team
Interaction.- Principles for AI-Assisted Attention Aware Systems in
Human-in-the-loo[ p Safety Critical Applications.- Interdependence and
vulnerability in systems: A review of theory for autonomous human-machine
teams.- Principles of a Accurate Decision and Sense-Making for Virtual Minds. 
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