Engineering artificially intelligent systems (14 resultados)

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Paperback. Condición: New. 1st ed. 2021. 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.…

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Condición: New. 1st ed. 2021 edition NO-PA16APR2015-KAP.

Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - Many current AI and machine learning algorithms and data and information fusion processesattempt in software to estimate situations in our complex world of nested feedback loops. Suchalgorithms and processes must gracefully and efficiently adapt to technical challenges such asdata quality induced by these loops, and interdependencies that vary in complexity, space, andtime.To realize effective and efficient designs of computational systems, a Systems Engineeringperspective may provide a framework for identifying the interrelationships and patterns ofchange between components rather than static snapshots. We must study cascadinginterdependencies through this perspective to understand their behavior and to successfullyadopt complex system-of-systems in society.This book derives in part from the presentations given at the AAAI 2021 Spring Symposiumsession on Leveraging Systems Engineering to Realize Synergistic AI / Machine LearningCapabilities. Its 16 chapters offer an emphasis on pragmatic aspects and address topics insystems engineering; AI, machine learning, and reasoning; data and information fusion;intelligent systems; autonomous systems; interdependence and teamwork; human-computerinteraction; trust; and resilience.…

Taschenbuch. Condición: Neu. Engineering Artificially Intelligent Systems | A Systems Engineering Approach to Realizing Synergistic Capabilities | William F. Lawless (u. a.) | Taschenbuch | Lecture Notes in Computer Science | xii | Englisch | 2021 | Springer | EAN 9783030893842 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.…

Paperback. Condición: New. 1st ed. 2021. 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.…

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PF. Condición: New. This item is printed on demand. Brand new book, sourced directly from publisher. Dispatch time is 6-7 days from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Many current AI and machine learning algorithms and data and information fusion processesattempt in software to estimate situations in our complex world of nested feedback loops. Suchalgorithms and processes must gracefully and efficiently adapt to technical challenges such asdata quality induced by these loops, and interdependencies that vary in complexity, space, andtime.To realize effective and efficient designs of computational systems, a Systems Engineeringperspective may provide a framework for identifying the interrelationships and patterns ofchange between components rather than static snapshots. We must study cascadinginterdependencies through this perspective to understand their behavior and to successfullyadopt complex system-of-systems in society.This book derives in part from the presentations given at the AAAI 2021 Spring Symposiumsession on Leveraging Systems Engineering to Realize Synergistic AI / Machine LearningCapabilities. Its 16 chapters offer an emphasis on pragmatic aspects and address topics insystems engineering; AI, machine learning, and reasoning; data and information fusion;intelligent systems; autonomous systems; interdependence and teamwork; human-computerinteraction; trust; and resilience. 296 pp. Englisch.…

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Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 296 pp. Englisch.…