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Evaluating Architectural Safeguards for Uncertain AI Black-Box Components

Although tremendous progress has been made in Artificial Intelligence (AI), it entails new challenges. The growing complexity of learning tasks requires more complex AI components, which increasingly exhibit unreliable behaviour. In this book, we present a model-driven approach to model architectura...

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Збережено в:
Бібліографічні деталі
Автор: Scheerer, Max (auth)
Формат: Електронний ресурс Частина з книги
Мова:Англійська
Опубліковано: KIT Scientific Publishing 2023
Серія:The Karlsruhe Series on Software Design and Quality
Предмети:
Mathematics & science
self-adaptive systems; safeguarding AI; architectural reliability analysis; Software engineering; Selbst-Adaptive Systeme; Absicherung von KI; architekturelle Zuverlässigkeitsanalyse; Softwaretechnik
Онлайн доступ:DOAB: download the publication
DOAB: description of the publication
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Резюме:Although tremendous progress has been made in Artificial Intelligence (AI), it entails new challenges. The growing complexity of learning tasks requires more complex AI components, which increasingly exhibit unreliable behaviour. In this book, we present a model-driven approach to model architectural safeguards for AI components and analyse their effect on the overall system reliability.
Фізичний опис:1 electronic resource (472 p.)
ISBN:KSP/1000161585
Доступ:Open Access

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