Epstein Secrets

oduction .......00..000 000 tte teen ee 271 14.2: Key Vertex atid Edge Typé$ casas cuine cs ce ees ee ses ems es emo ws Meee MERE OE 271 14.3 Derived Hypergraphs .......... 0.000. c cece tenet eae 272 14.3.1 SMEPH Vertices .........000 0000. tte 272 14.3.2 SMEPH Edges ...... 0... ee eee eens 273 14.4 Implicat

works... .......0.000. 0022 c cee ee 268 13.6.4 Neural-Symbolic Glocality in CogPrime ............. 0000.0 269 14 Representing Implicit Knowledge via Hypergraphs ....................... 271 14.1 Introduction .......00..000 000 tte teen ee 271 14.2: Key Vertex atid Edge Typé$ casas cuine cs ce ees ee ses ems es

epresentation...................... 245 13.1 Introduction ..........000 0002 eens 245 13.2 Localized Knowledge Representation using Weighted, Labeled Hypergraphs .... 246 13.2.1 Weighted, Labeled Hypergraphs............... 0000: c eee eee eee eee 246 13.3 Atoms: Their Types and Weights ............ 0.00. cee

he Ethical Treatment of AGIs ..........0...0 000 c cece ee 226 12.6.1 Possible Consequences of Depriving AGIs of Freedom ................. 228 12.6.2 AGI Ethics as Boundaries Between Humans and AGIs Become Blurred . 229 12.7 Possible Benefits of Closely Linking AGIs to the Global Brain................ 230 12.

the Brain 263 13.6.3 Glocal Hopfield Networks 268 13.6.4 Neural-Symbolic Glocality in CogPrime 269 14 Representing Implicit Knowledge via Hypergraphs 271 14.1 Introduction 271 14.2 Key Vertex and Edge Types 271 14.3 Derived Hypergraphs 272 14.3.1 SMEPH Vertices 272 14.3.2 SMEPH Ed

13 Local, Global and Glocal Knowledge Representation 245 13.1 Introduction 245 13.2 Localized Knowledge Representation using Weighted, Labeled Hypergraphs 246 13.2.1 Weighted, Labeled Hypergraphs 246 13.3 Atoms: Their Types and Weights 247 13.3.1 Some Basic Atom Types 247 13.3.2 Variable At

Five Key Words 9 1.7.1 Memory and Cognition in CogPrime 10 1.8 Virtually and Robotically Embodied Al 11 1.9 Language Learning 12 1.10 AGI Ethics 12 1.11 Structure of the Book 13 1.12 Key Claims of the Book 13 Section I Artificial and Natural General Intelligence 2 What Is Human-Lik

algorithms for creating localist knowledge from globalist knowledge are described 13.2 Localized Knowledge Representation using Weighted, Labeled Hypergraphs There are many different mechanisms for representing knowledge in AI systems in an explicit, localized way, most of them descending from various va

straint of AGI self-modification may be possible, but the feasibility and desirability of these scenarios is needful of further exploration. 12.6.2 AGI Ethics as Boundaries Between Humans and AGIs Become Blurred Another important reason for valuing ethical treatment of AGLs is that the boundaries between

n” algorithms for creating localist knowledge from globalist knowledge are described 13.2 Localized Knowledge Representation using Weighted, Labeled Hypergraphs There are many different mechanisms for representing knowledge in AI systems in an explicit, localized way, most of them descending from various var

estraint of AGI self-modification may be possible, but the feasibility and desirability of these scenarios is needful of further exploration. 12.6.2 AGI Ethics as Boundaries Between Humans and AGIs Become Blurred Another important reason for valuing ethical treatment of AGIs is that the boundaries between m