Engineering Functional Self-Awareness in AI Systems From Metacognition to Closed-Loop Autonomy
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Sprache:Englisch
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Produktdetails
Format
ePUB
Kopierschutz
Nein
Family Sharing
Nein
Text-to-Speech
Ja
Erscheinungsdatum
02.06.2026
Verlag
IUniverseSeitenzahl
280 (Printausgabe)
Dateigröße
592 KB
Sprache
Englisch
EAN
9781663279286
As AI systems grow more capable, the next dimension shifts toward the
question of the self in AI. Modern systems can reason and act across domains,
yet they remain fundamentally passive, unable to evaluate their own reliability,
regulate their behavior, or recognize the limits of their competence. This gap
becomes increasingly consequential as systems scale.
This book presents a systems-engineering framework for building autonomous
intelligent systems. Here, functional self-awareness is treated as an
architectural property arising from the integration of persistent self-models,
metacognition, self-governance, and explicit uncertainty awareness. These
mechanisms enable systems to monitor their own reasoning, constrain their
actions, and remain governable over time.
Drawing on control theory, AI systems engineering, and real-world failure
modes, the book reframes self-aware AI as a practical requirement for safe,
scalable intelligence. It off ers a rigorous, lifecycle-oriented approach for
engineers, researchers, and product leaders designing AI systems that must
understand and regulate themselves.
question of the self in AI. Modern systems can reason and act across domains,
yet they remain fundamentally passive, unable to evaluate their own reliability,
regulate their behavior, or recognize the limits of their competence. This gap
becomes increasingly consequential as systems scale.
This book presents a systems-engineering framework for building autonomous
intelligent systems. Here, functional self-awareness is treated as an
architectural property arising from the integration of persistent self-models,
metacognition, self-governance, and explicit uncertainty awareness. These
mechanisms enable systems to monitor their own reasoning, constrain their
actions, and remain governable over time.
Drawing on control theory, AI systems engineering, and real-world failure
modes, the book reframes self-aware AI as a practical requirement for safe,
scalable intelligence. It off ers a rigorous, lifecycle-oriented approach for
engineers, researchers, and product leaders designing AI systems that must
understand and regulate themselves.
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