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The Road to General Intelligence.

By: Swan, Jerry.
Contributor(s): Nivel, Eric | Kant, Neel | Hedges, Jules | Atkinson, Timothy | Steunebrink, Bas.
Material type: materialTypeLabelBookSeries: Studies in Computational Intelligence Series: Publisher: Cham : Springer International Publishing AG, 2022Copyright date: �2022Edition: 1st ed.Description: 1 online resource (142 pages).Content type: text Media type: computer Carrier type: online resourceISBN: 9783031080203.Genre/Form: Electronic books.DDC classification: 006.3 Online resources: Click to View
Contents:
Intro -- Foreword by Melanie Mitchell -- Foreword by David Spivak -- Contents -- 1 Introduction -- Part I Requirements -- 2 Background -- 2.1 What we Mean by General Intelligence -- 2.2 Science as Extended Mind -- 2.3 The Death of `Good Old-Fashioned AI' -- 3 Where is My Mind? -- 3.1 A Sanity Check -- 3.2 Real-World Machine Learning -- 4 Challenges for Deep Learning -- 4.1 Compositionality -- 4.2 Strong Typing -- 4.3 Reflection -- 4.4 Implications and Summary -- 5 Challenges for Reinforcement Learning -- 5.1 A Priori Reward Specification -- 5.2 Sampling: Safety and Efficiency -- 6 Work on Command: The Case for Generality -- 6.1 Goals and Constraints -- 6.2 Planning -- 6.3 Anytime Operation -- Part II Semantically Closed Learning -- 7 Philosophy -- 7.1 The Problem of Machine Induction -- 7.2 Semantically Closed Learning (SCL) -- 7.3 Baseline Properties of SCL -- 7.4 High-Level Inference Mechanisms of SCL -- 7.5 Intrinsic Motivation and Unsupervised Learning -- 8 Architecture -- 8.1 SCL as a Distributed/Localist Hybrid -- 8.2 Reference Architecture -- 9 A Compositional Framework -- 9.1 Categorical Cybernetics -- 9.2 Hypothesis Generation -- 9.3 Abstraction and Analogy -- 9.4 Abduction -- 10 2nd Order Automation Engineering -- 10.1 Behavioral System Engineering -- 10.2 Reactive Synthesis -- 10.3 Proactive Synthesis -- 10.4 Safety -- 11 Prospects -- 11.1 Summary -- 11.2 Research Topics -- 11.2.1 Choice of Expression Language -- 11.2.2 Compositional Primitives -- 11.2.3 Links with Behavioral Control -- 11.2.4 Pragmatics via `Causal Garbage Collection' -- 11.3 Conclusion -- Appendix Bibliography.
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Intro -- Foreword by Melanie Mitchell -- Foreword by David Spivak -- Contents -- 1 Introduction -- Part I Requirements -- 2 Background -- 2.1 What we Mean by General Intelligence -- 2.2 Science as Extended Mind -- 2.3 The Death of `Good Old-Fashioned AI' -- 3 Where is My Mind? -- 3.1 A Sanity Check -- 3.2 Real-World Machine Learning -- 4 Challenges for Deep Learning -- 4.1 Compositionality -- 4.2 Strong Typing -- 4.3 Reflection -- 4.4 Implications and Summary -- 5 Challenges for Reinforcement Learning -- 5.1 A Priori Reward Specification -- 5.2 Sampling: Safety and Efficiency -- 6 Work on Command: The Case for Generality -- 6.1 Goals and Constraints -- 6.2 Planning -- 6.3 Anytime Operation -- Part II Semantically Closed Learning -- 7 Philosophy -- 7.1 The Problem of Machine Induction -- 7.2 Semantically Closed Learning (SCL) -- 7.3 Baseline Properties of SCL -- 7.4 High-Level Inference Mechanisms of SCL -- 7.5 Intrinsic Motivation and Unsupervised Learning -- 8 Architecture -- 8.1 SCL as a Distributed/Localist Hybrid -- 8.2 Reference Architecture -- 9 A Compositional Framework -- 9.1 Categorical Cybernetics -- 9.2 Hypothesis Generation -- 9.3 Abstraction and Analogy -- 9.4 Abduction -- 10 2nd Order Automation Engineering -- 10.1 Behavioral System Engineering -- 10.2 Reactive Synthesis -- 10.3 Proactive Synthesis -- 10.4 Safety -- 11 Prospects -- 11.1 Summary -- 11.2 Research Topics -- 11.2.1 Choice of Expression Language -- 11.2.2 Compositional Primitives -- 11.2.3 Links with Behavioral Control -- 11.2.4 Pragmatics via `Causal Garbage Collection' -- 11.3 Conclusion -- Appendix Bibliography.

Description based on publisher supplied metadata and other sources.

Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2023. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.

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