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The Road to General Intelligence
Buch von Jerry Swan (u. a.)
Sprache: Englisch

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Beschreibung
Humans have always dreamed of automating laborious physical and intellectual tasks, but the latter has proved more elusive than naively suspected. Seven decades of systematic study of Artificial Intelligence have witnessed cycles of hubris and despair. The successful realization of General Intelligence (evidenced by the kind of cross-domain flexibility enjoyed by humans) will spawn an industry worth billions and transform the range of viable automation [...] recent notable successes of Machine Learning has lead to conjecture that it might be the appropriate technology for delivering General Intelligence. In this book, we argue that the framework of machine learning is fundamentally at odds with any reasonable notion of intelligence and that essential insights from previous decades of AI research are being forgotten. We claim that a fundamental change in perspective is required, mirroring that which took place in the philosophy of science in the mid 20th century. We propose a framework for General Intelligence, together with a reference architecture that emphasizes the need for anytime bounded rationality and a situated denotational semantics. We given necessary emphasis to compositional reasoning, with the required compositionality being provided via principled symbolic-numeric inference mechanisms based on universal constructions from category theory.
¿ Details the pragmatic requirements for real-world General Intelligence.
¿ Describes how machine learning fails to meet these requirements.
¿ Provides a philosophical basis for the proposed approach.
¿ Provides mathematical detail for a reference architecture.
¿ Describes a research program intended to address issues of concern in contemporary AI.
The book includes an extensive bibliography, with ~400 entries covering the history of AI and many related areas of computer science and [...] target audience is the entire gamut of Artificial Intelligence/Machine Learning researchers and industrial practitioners. There are a mixture of descriptive and rigorous sections, according to the nature of the topic. Undergraduate mathematics is in general sufficient. Familiarity with category theory is advantageous for a complete understanding of the more advanced sections, but these may be skipped by the reader who desires an overall picture of the essential concepts

This is an open access book.
Humans have always dreamed of automating laborious physical and intellectual tasks, but the latter has proved more elusive than naively suspected. Seven decades of systematic study of Artificial Intelligence have witnessed cycles of hubris and despair. The successful realization of General Intelligence (evidenced by the kind of cross-domain flexibility enjoyed by humans) will spawn an industry worth billions and transform the range of viable automation [...] recent notable successes of Machine Learning has lead to conjecture that it might be the appropriate technology for delivering General Intelligence. In this book, we argue that the framework of machine learning is fundamentally at odds with any reasonable notion of intelligence and that essential insights from previous decades of AI research are being forgotten. We claim that a fundamental change in perspective is required, mirroring that which took place in the philosophy of science in the mid 20th century. We propose a framework for General Intelligence, together with a reference architecture that emphasizes the need for anytime bounded rationality and a situated denotational semantics. We given necessary emphasis to compositional reasoning, with the required compositionality being provided via principled symbolic-numeric inference mechanisms based on universal constructions from category theory.
¿ Details the pragmatic requirements for real-world General Intelligence.
¿ Describes how machine learning fails to meet these requirements.
¿ Provides a philosophical basis for the proposed approach.
¿ Provides mathematical detail for a reference architecture.
¿ Describes a research program intended to address issues of concern in contemporary AI.
The book includes an extensive bibliography, with ~400 entries covering the history of AI and many related areas of computer science and [...] target audience is the entire gamut of Artificial Intelligence/Machine Learning researchers and industrial practitioners. There are a mixture of descriptive and rigorous sections, according to the nature of the topic. Undergraduate mathematics is in general sufficient. Familiarity with category theory is advantageous for a complete understanding of the more advanced sections, but these may be skipped by the reader who desires an overall picture of the essential concepts

This is an open access book.
Über den Autor
Zusammenfassung

Details the pragmatic requirements for real-world General Intelligence

Provides a philosophical basis for the proposed approach

Provides mathematical detail for a reference architecture

This book is open access, which means that you have free and unlimited access.

Inhaltsverzeichnis
Introduction.- Challenges for Deep Learning.- Challenges for Reinforcement Learning.- Work on Command: The Case for Generality.- Architecture.
Details
Erscheinungsjahr: 2022
Fachbereich: Technik allgemein
Genre: Mathematik, Medizin, Naturwissenschaften, Technik
Rubrik: Naturwissenschaften & Technik
Medium: Buch
Reihe: Studies in Computational Intelligence
Inhalt: xiv
136 S.
8 s/w Illustr.
18 farbige Illustr.
136 p. 26 illus.
18 illus. in color.
ISBN-13: 9783031080197
ISBN-10: 303108019X
Sprache: Englisch
Ausstattung / Beilage: HC gerader Rücken kaschiert
Einband: Gebunden
Autor: Swan, Jerry
Nivel, Eric
Steunebrink, Bas
Hedges, Jules
Atkinson, Timothy
Kant, Neel
Auflage: 1st ed. 2022
Hersteller: Springer International Publishing
Studies in Computational Intelligence
Verantwortliche Person für die EU: Books on Demand GmbH, In de Tarpen 42, D-22848 Norderstedt, info@bod.de
Maße: 241 x 160 x 13 mm
Von/Mit: Jerry Swan (u. a.)
Erscheinungsdatum: 23.06.2022
Gewicht: 0,401 kg
Artikel-ID: 121580558
Über den Autor
Zusammenfassung

Details the pragmatic requirements for real-world General Intelligence

Provides a philosophical basis for the proposed approach

Provides mathematical detail for a reference architecture

This book is open access, which means that you have free and unlimited access.

Inhaltsverzeichnis
Introduction.- Challenges for Deep Learning.- Challenges for Reinforcement Learning.- Work on Command: The Case for Generality.- Architecture.
Details
Erscheinungsjahr: 2022
Fachbereich: Technik allgemein
Genre: Mathematik, Medizin, Naturwissenschaften, Technik
Rubrik: Naturwissenschaften & Technik
Medium: Buch
Reihe: Studies in Computational Intelligence
Inhalt: xiv
136 S.
8 s/w Illustr.
18 farbige Illustr.
136 p. 26 illus.
18 illus. in color.
ISBN-13: 9783031080197
ISBN-10: 303108019X
Sprache: Englisch
Ausstattung / Beilage: HC gerader Rücken kaschiert
Einband: Gebunden
Autor: Swan, Jerry
Nivel, Eric
Steunebrink, Bas
Hedges, Jules
Atkinson, Timothy
Kant, Neel
Auflage: 1st ed. 2022
Hersteller: Springer International Publishing
Studies in Computational Intelligence
Verantwortliche Person für die EU: Books on Demand GmbH, In de Tarpen 42, D-22848 Norderstedt, info@bod.de
Maße: 241 x 160 x 13 mm
Von/Mit: Jerry Swan (u. a.)
Erscheinungsdatum: 23.06.2022
Gewicht: 0,401 kg
Artikel-ID: 121580558
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