WORLD MODELS How Intelligent Machines Build an Internal Understanding of Reality (Paperback)
Idioma: inglés
Editorial: Intelligloss Press, 2026
- Tapa blanda
- Nuevo
Librería: Grand Eagle Retail, Bensenville, IL, Estados Unidos de AmericaGrand Eagle Retail
Vendedor de AbeBooks desde 12 de octubre de 2005
Condición: Nuevo
EUR 80,28
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Paperback. WORLD MODELS: How Intelligent Machines Build an Internal Understanding of Reality introduces readers to one of the most important ideas in advanced artificial intelligence: how intelligent systems can develop internal representations that help them interpret information, recognize relationships, anticipate outcomes, and make decisions about the world around them.Humans constantly build mental models. We observe our surroundings, remember previous experiences, recognize patterns, imagine what might happen next, and adjust our actions accordingly. Artificial intelligence approaches these abilities differently. World models are computational representations that can help AI systems organize information about environments, actions, objects, relationships, and possible future states.Written in clear and accessible language, this book explores how world models can contribute to increasingly capable intelligent systems. Readers learn how AI can use data and experience to form useful representations, predict possible outcomes, simulate situations, plan actions, learn from interactions, and adapt when circumstances change.The book examines the connections between world models and important areas of artificial intelligence, including machine learning, reasoning, memory, multimodal intelligence, autonomous systems, robotics, planning, decision-making, and artificial general intelligence (AGI). It also explains an essential distinction: an AI system's internal representation of the world is not the same as human consciousness or human understanding.Beyond the technology, World Models encourages readers to consider important questions about reliability and responsibility. What happens when an AI system develops an inaccurate representation of a situation? How can humans identify errors? How should intelligent systems handle uncertainty? When should people intervene? And who remains accountable when AI-generated predictions influence real-world decisions?Through explanations, examples, reflections, worksheets, and learning activities, students are encouraged to think critically about both the possibilities and limitations of increasingly sophisticated AI systems.Designed as part of the IntelliGloss AI Education Series, World Models helps students build AI literacy while developing the critical-thinking skills necessary for a future in which intelligent systems may play increasingly important roles in education, science, transportation, robotics, business, and everyday life.The goal is not simply to teach students what artificial intelligence can do. It is to help them understand how intelligent systems represent information, why those representations can sometimes be wrong, and why human judgment, safety, accountability, and responsible oversight remain essential.Learn AI. Understand AI. Use AI.- Susie Hala Discover how AI builds internal representations of the world to recognize patterns, predict outcomes, plan actions, and make decisions-and why human judgment, safety, and responsible oversight remain essential. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…
N° de ref. del artículo 9781972925348
- Título
- WORLD MODELS How Intelligent Machines Build an Internal Understanding of Reality (Paperback)
- Autor
- Susie Hala
- Editorial
- Intelligloss Press
- Año de publicación
- 2026
- Estado
- new
- Encuadernación
- Paperback
- Idioma
- inglés
- ISBN 10
- 1972925342
- ISBN 13
- 9781972925348
WORLD MODELS: How Intelligent Machines Build an Internal Understanding of Reality introduces readers to one of the most important ideas in advanced artificial intelligence: how intelligent systems can develop internal representations that help them interpret information, recognize relationships, anticipate outcomes, and make decisions about the world around them.
Humans constantly build mental models. We observe our surroundings, remember previous experiences, recognize patterns, imagine what might happen next, and adjust our actions accordingly. Artificial intelligence approaches these abilities differently. World models are computational representations that can help AI systems organize information about environments, actions, objects, relationships, and possible future states.
Written in clear and accessible language, this book explores how world models can contribute to increasingly capable intelligent systems. Readers learn how AI can use data and experience to form useful representations, predict possible outcomes, simulate situations, plan actions, learn from interactions, and adapt when circumstances change.
The book examines the connections between world models and important areas of artificial intelligence, including machine learning, reasoning, memory, multimodal intelligence, autonomous systems, robotics, planning, decision-making, and artificial general intelligence (AGI). It also explains an essential distinction: an AI system's internal representation of the world is not the same as human consciousness or human understanding.
Beyond the technology, World Models encourages readers to consider important questions about reliability and responsibility. What happens when an AI system develops an inaccurate representation of a situation? How can humans identify errors? How should intelligent systems handle uncertainty? When should people intervene? And who remains accountable when AI-generated predictions influence real-world decisions?
Through explanations, examples, reflections, worksheets, and learning activities, students are encouraged to think critically about both the possibilities and limitations of increasingly sophisticated AI systems.
Designed as part of the IntelliGloss AI Education Series, World Models helps students build AI literacy while developing the critical-thinking skills necessary for a future in which intelligent systems may play increasingly important roles in education, science, transportation, robotics, business, and everyday life.
The goal is not simply to teach students what artificial intelligence can do. It is to help them understand how intelligent systems represent information, why those representations can sometimes be wrong, and why human judgment, safety, accountability, and responsible oversight remain essential.
Learn AI. Understand AI. Use AI.
- Susie Hala
“Sinopsis” puede pertenecer a otra edición de este título.
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Vendedor de AbeBooks desde 12 de octubre de 2005
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