Engineering Artificially Intelligent Systems: A Systems Engineering Approach to Realizing Synergistic Capabilities

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 Gambler's 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.