Epstein Secrets

re Nodes 254 13.3.7 Links for Special External Data Types 254 13.3.8 Truth Values and Attention Values 255 13.4 Knowledge Representation via Attractor Neural Networks 256 EFTA00623773 xviii Contents 13.4.1 The Hopfield neural net model 256 13.4.2 Knowledge Representation via Cell Assemblies 257 13.5

Neural Darwinism 259 13.6 Glocal Memory 260 13.6.1 A Semi-Formal Model of Glocal Memory 262 13.6.2 Glocal Memory in 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

ruth values, allowing logical semantics. EFTA00624031 256 13 Local, Global and Global Knowledge Representation 13.4 Knowledge Representation via Attractor Neural Networks Now we turn to global, implicit knowledge representation — beginning with formal neural net models, briefly discussing the brain, and then turning

of glocality in human memory will doubtless improve tremendously. EFTA00624043 268 13 Local, Global and Glace! Knowledge Representation 13.6.3 Glocal Hopfield Networks The ideas in the previous section suggest that, if one wishes to construct an AGI, it is worth seriously considering using a memory with some sort

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60 13.6.1 A Semi-Formal Model of Glocal Memory .............0...0 00 e eevee 262 13.6.2 Glocal Memory in the Brain ............ 000... eee 263 13.6.3 Glocal Hopfield Networks... .......0.000. 0022 c cee ee 268 13.6.4 Neural-Symbolic Glocality in CogPrime ............. 0000.0 269 14 Representing Implicit Knowledge via Hyper

ues, allowing logical semantics. HOUSE_OVERSIGHT_013171 256 13 Local, Global and Glocal Knowledge Representation 13.4 Knowledge Representation via Attractor Neural Networks Now we turn to global, implicit knowledge representation — beginning with formal neural net models, briefly discussing the brain, and then turning b

cality in human memory will doubtless improve tremendously. HOUSE_OVERSIGHT_013183 268 13 Local, Global and Glocal Knowledge Representation 13.6.3 Glocal Hopfield Networks The ideas in the previous section suggest that, if one wishes to construct an AGI, it is worth seriously considering using a memory with some sort o