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.
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Paperback. Condición: new. Paperback. 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Nº de ref. del artículo: 9781663279279
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Paperback. Condición: new. Paperback. 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Nº de ref. del artículo: 9781663279279
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Paperback. Condición: new. Paperback. 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. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Nº de ref. del artículo: 9781663279279
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Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - As AI systems grow more capable, the next dimension shifts toward thequestion 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 gapbecomes increasingly consequential as systems scale.This book presents a systems-engineering framework for building autonomousintelligent systems. Here, functional self-awareness is treated as anarchitectural property arising from the integration of persistent self-models,metacognition, self-governance, and explicit uncertainty awareness. Thesemechanisms enable systems to monitor their own reasoning, constrain theiractions, and remain governable over time.Drawing on control theory, AI systems engineering, and real-world failuremodes, the book reframes self-aware AI as a practical requirement for safe,scalable intelligence. It off ers a rigorous, lifecycle-oriented approach forengineers, researchers, and product leaders designing AI systems that mustunderstand and regulate themselves. Nº de ref. del artículo: 9781663279279
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Taschenbuch. Condición: Neu. Engineering Functional Self-Awareness in AI Systems | From Metacognition to Closed-Loop Autonomy | Raghurami Reddy Etukuru | Taschenbuch | Englisch | 2026 | iUniverse | EAN 9781663279279 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand. Nº de ref. del artículo: 135578020
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