Univerzitní knihovna OU - Úplné zobrazení záznamu

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.

Anotace

Annotation