RECURSIVE SELF-IMPROVEMENT The Algorithmic Engines Driving the Transition to General Intelligence (Paperback)
Idioma: inglés
Editorial: Intelligloss Press, 2026
- Tapa blanda
- Nuevo
Librería: AussieBookSeller, Truganina, VIC, AustraliaAussieBookSeller
Vendedor de AbeBooks desde 22 de junio de 2007
Condición: Nuevo
EUR 74,46
Cantidad disponible: 1 disponibles
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Paperback. RECURSIVE SELF-IMPROVEMENT: The Algorithmic Engines Driving the Transition to General Intelligence introduces readers to one of the most important and challenging ideas in the future of artificial intelligence: the possibility that intelligent systems may become increasingly capable of evaluating and improving aspects of their own performance.Written in accessible language for educational settings, this book explores recursive self-improvement as a concept while carefully distinguishing present-day artificial intelligence from theoretical future systems. Readers examine how AI systems learn, adapt, evaluate performance, use feedback, interact with tools, and participate in increasingly sophisticated development processes.The book explores the relationship between recursive improvement and the potential transition from specialized artificial intelligence toward artificial general intelligence (AGI). Rather than presenting AGI as an established reality, it encourages students to investigate the scientific questions, technical challenges, uncertainties, and competing possibilities surrounding advanced AI development.Students are also introduced to the human side of increasingly capable intelligent systems. Who determines an AI system's objectives? How should improvements be evaluated? What safeguards should exist? When should human approval be required? How can organizations balance innovation with safety, accountability, transparency, and responsible oversight?Throughout the book, readers are encouraged to think critically rather than simply accept predictions about the future of AI. Educational activities help students examine assumptions, compare possibilities, evaluate risks and benefits, and consider why human judgment remains important as artificial intelligence advances.Designed as part of the IntelliGloss AI Education Series, Recursive Self-Improvement helps bridge technical AI literacy with responsible decision-making. It provides students and educators with a foundation for understanding how increasingly capable AI systems could evolve while emphasizing that technological progress must be accompanied by thoughtful human governance, safety practices, and accountability.Learn AI. Understand AI. Use AI. Explore how AI systems may become increasingly capable, how recursive self-improvement relates to the path toward AGI, and why human oversight, safety, accountability, and responsible decision-making matter. 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.…
N° de ref. del artículo 9781972925355
- Título
- RECURSIVE SELF-IMPROVEMENT The Algorithmic Engines Driving the Transition to General Intelligence (Paperback)
- Autor
- Susie Hala
- Editorial
- Intelligloss Press
- Año de publicación
- 2026
- Estado
- new
- Encuadernación
- Paperback
- Idioma
- inglés
- ISBN 10
- 1972925350
- ISBN 13
- 9781972925355
RECURSIVE SELF-IMPROVEMENT: The Algorithmic Engines Driving the Transition to General Intelligence introduces readers to one of the most important and challenging ideas in the future of artificial intelligence: the possibility that intelligent systems may become increasingly capable of evaluating and improving aspects of their own performance.
Written in accessible language for educational settings, this book explores recursive self-improvement as a concept while carefully distinguishing present-day artificial intelligence from theoretical future systems. Readers examine how AI systems learn, adapt, evaluate performance, use feedback, interact with tools, and participate in increasingly sophisticated development processes.
The book explores the relationship between recursive improvement and the potential transition from specialized artificial intelligence toward artificial general intelligence (AGI). Rather than presenting AGI as an established reality, it encourages students to investigate the scientific questions, technical challenges, uncertainties, and competing possibilities surrounding advanced AI development.
Students are also introduced to the human side of increasingly capable intelligent systems. Who determines an AI system's objectives? How should improvements be evaluated? What safeguards should exist? When should human approval be required? How can organizations balance innovation with safety, accountability, transparency, and responsible oversight?
Throughout the book, readers are encouraged to think critically rather than simply accept predictions about the future of AI. Educational activities help students examine assumptions, compare possibilities, evaluate risks and benefits, and consider why human judgment remains important as artificial intelligence advances.
Designed as part of the IntelliGloss AI Education Series, Recursive Self-Improvement helps bridge technical AI literacy with responsible decision-making. It provides students and educators with a foundation for understanding how increasingly capable AI systems could evolve while emphasizing that technological progress must be accompanied by thoughtful human governance, safety practices, and accountability.
Learn AI. Understand AI. Use AI.
“Sinopsis” puede pertenecer a otra edición de este título.
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