ISBN: 9798400731983 - common sense in large language models (8 resultados)

Condición: New.

Paperback. Condición: new. Paperback. This book provides the first comprehensive scientific examination of the structural divide between massive knowledge acquisition and the fluid "common sense" reasoning required for human-level intelligence in machines. Written for researchers, artificial intelligence practitioners, and graduate students, this volume offers a roadmap for building capable intelligence by considering the strengths and limitations of large language models through the important lens of machine common sensea grand challenge since the founding days of AI in the 1950s. It serves as an essential reference for advanced seminars seeking to bridge the conceptual gaps between fields like cognitive science, the history of computing, and modern-day AI. It also suggests ideas and frameworks for developing the next generation of reasoning-capable machines. While modern large language models (LLMs) like ChatGPT and Gemini appear deceptively capable in dialogue, they still occasionally fail at the intuitive physical and social tasks that define human common sense. This text synthesizes decades of researchfrom the symbolic logic of the Cyc project many decades ago to modern neuro-symbolic hybridsto provide a conceptual grounding for both LLMs' failures and successes in achieving major milestones in machine common sense. A central focus of the work is the growing crisis in evaluating machine common sense. As LLMs increasingly "solve" benchmarks through surface-level pattern matching and data contamination, traditional performance metrics have become dangerously misleading proxies for intelligence. This text offers a rigorous critique of current testing paradigms, exposing how issues like human annotation noise and statistical artifacts can create a false sense of progress that masks systemic brittleness. It then advocates for a new science of machine common sense that prioritizes rigorous, theory-driven reasoning over raw accuracy scores. Bridging vast data capture with human-like reasoning, the work examines AIs struggle to achieve machine common sense. It questions evaluation methods reliant on surface-level matching, revealing inherent flaws and proposing theory-driven strategies for developing more intuitive, robust AI systems. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

PAP. Condición: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book provides the first comprehensive scientific examination of the structural divide between massive knowledge acquisition and the fluid 'common sense' reasoning required for human-level intelligence in machines. Written for researchers, artificial intelligence practitioners, and graduate students, this volume offers a roadmap for building capable intelligence by considering the strengths and limitations of large language models through the important lens of machine common sense-a grand challenge since the founding days of AI in the 1950s. It serves as an essential reference for advanced seminars seeking to bridge the conceptual gaps between fields like cognitive science, the history of computing, and modern-day AI. It also suggests ideas and frameworks for developing the next generation of reasoning-capable machines.While modern LLMs like ChatGPT and Gemini appear deceptively capable in dialogue, they still occasionally fail at the intuitive physical and social tasks that define human common sense. This text synthesizes decades of research-from the symbolic logic of the Cyc project many decades ago to modern neuro-symbolic hybrids-to provide a conceptual grounding for both LLMs' failures and successes in achieving major milestones in machine common sense.A central focus of the work is the growing crisis in evaluating machine common sense. As LLMs increasingly 'solve' benchmarks through surface-level pattern matching and data contamination, traditional performance metrics have become dangerously misleading proxies for intelligence. This text offers a rigorous critique of current testing paradigms, exposing how issues like human annotation noise and statistical artifacts can create a false sense of progress that masks systemic brittleness. It then advocates for a new science of machine common sense that prioritizes rigorous, theory-driven reasoning over raw accuracy scores.…

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Paperback. Condición: new. Paperback. This book provides the first comprehensive scientific examination of the structural divide between massive knowledge acquisition and the fluid "common sense" reasoning required for human-level intelligence in machines. Written for researchers, artificial intelligence practitioners, and graduate students, this volume offers a roadmap for building capable intelligence by considering the strengths and limitations of large language models through the important lens of machine common sensea grand challenge since the founding days of AI in the 1950s. It serves as an essential reference for advanced seminars seeking to bridge the conceptual gaps between fields like cognitive science, the history of computing, and modern-day AI. It also suggests ideas and frameworks for developing the next generation of reasoning-capable machines. While modern large language models (LLMs) like ChatGPT and Gemini appear deceptively capable in dialogue, they still occasionally fail at the intuitive physical and social tasks that define human common sense. This text synthesizes decades of researchfrom the symbolic logic of the Cyc project many decades ago to modern neuro-symbolic hybridsto provide a conceptual grounding for both LLMs' failures and successes in achieving major milestones in machine common sense. A central focus of the work is the growing crisis in evaluating machine common sense. As LLMs increasingly "solve" benchmarks through surface-level pattern matching and data contamination, traditional performance metrics have become dangerously misleading proxies for intelligence. This text offers a rigorous critique of current testing paradigms, exposing how issues like human annotation noise and statistical artifacts can create a false sense of progress that masks systemic brittleness. It then advocates for a new science of machine common sense that prioritizes rigorous, theory-driven reasoning over raw accuracy scores. Bridging vast data capture with human-like reasoning, the work examines AIs struggle to achieve machine common sense. It questions evaluation methods reliant on surface-level matching, revealing inherent flaws and proposing theory-driven strategies for developing more intuitive, robust AI systems. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Taschenbuch. Condición: Neu. Common Sense in Large Language Models | Mayank Kejriwal | Taschenbuch | ACM Books | Einband - flex.(Paperback) | Englisch | 2026 | Association for Computing Machinery | EAN 9798400731983 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand. …

Paperback. Condición: new. Paperback. This book provides the first comprehensive scientific examination of the structural divide between massive knowledge acquisition and the fluid "common sense" reasoning required for human-level intelligence in machines. Written for researchers, artificial intelligence practitioners, and graduate students, this volume offers a roadmap for building capable intelligence by considering the strengths and limitations of large language models through the important lens of machine common sensea grand challenge since the founding days of AI in the 1950s. It serves as an essential reference for advanced seminars seeking to bridge the conceptual gaps between fields like cognitive science, the history of computing, and modern-day AI. It also suggests ideas and frameworks for developing the next generation of reasoning-capable machines. While modern large language models (LLMs) like ChatGPT and Gemini appear deceptively capable in dialogue, they still occasionally fail at the intuitive physical and social tasks that define human common sense. This text synthesizes decades of researchfrom the symbolic logic of the Cyc project many decades ago to modern neuro-symbolic hybridsto provide a conceptual grounding for both LLMs' failures and successes in achieving major milestones in machine common sense. A central focus of the work is the growing crisis in evaluating machine common sense. As LLMs increasingly "solve" benchmarks through surface-level pattern matching and data contamination, traditional performance metrics have become dangerously misleading proxies for intelligence. This text offers a rigorous critique of current testing paradigms, exposing how issues like human annotation noise and statistical artifacts can create a false sense of progress that masks systemic brittleness. It then advocates for a new science of machine common sense that prioritizes rigorous, theory-driven reasoning over raw accuracy scores. Bridging vast data capture with human-like reasoning, the work examines AIs struggle to achieve machine common sense. It questions evaluation methods reliant on surface-level matching, revealing inherent flaws and proposing theory-driven strategies for developing more intuitive, robust AI systems. 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.…