Epstein Secrets

arding the representation of knowledge using attractor neural nets. It is a mix of well-established fact with more speculative material. 13.4.1 The Hopfield neural net model Hopfield networks Iliop821 are attractor neural networks often used as associative memories. A Hopfield network with N neurons can

tation of glocal memory in attractor neural net systems • Chapter 23 presents Glocal Economic Attention Networks (ECANs), rough analogues of glocal Hopfield nets that play a central role in CogPrime. Our hypothesis of the potential general importance of glocality as a property of memory, systems (beyond

moved without significantly impacting the net- work's capacity or dynamics. Our experimental work uses sparse Hopfield networks. 13.4.1.1 Palimpsest Hopfield nets with a modified learning rule In JSV99J a new learning rule is presented, which both increases the Hopfield network capacity and turns it into

receives. It's a very complex matter that is richly intermixed with all the other aspects of intelligence, and here we will treat it as such. 12.2 Review of Current Thinking on the Risks of AGI Before proceeding to outline our own perspective on AGI ethics in the context of CogPrime, we will review the main existing strains of thought on t

garding the representation of knowledge using attractor neural nets. It is a mix of well-established fact with more speculative material. 13.4.1 The Hopfield neural net model Hopfield networks [Hop82] are attractor neural networks often used as associative memories. A Hopfield network with N neurons can b

ntation of glocal memory in attractor neural net systems e Chapter 23 presents Glocal Economic Attention Networks (ECANs), rough analogues of glocal Hopfield nets that play a central role in CogPrime. Our hypothesis of the potential general importance of glocality as a property of memory systems (beyond j

moved without significantly impacting the net- work’s capacity or dynamics. Our experimental work uses sparse Hopfield networks. 13.4.1.1 Palimpsest Hopfield nets with a modified learning rule In [SV99] a new learning rule is presented, which both increases the Hopfield network capacity and turns it into

t receives. It’s a very complex matter that is richly intermixed with all the other aspects of intelligence, and here we will treat it as such. 12.2 Review of Current Thinking on the Risks of AGI Before proceeding to outline our own perspective on AGI ethics in the context of CogPrime, we will review the main existing strains of thought on th

th 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 Foundations of Learning 258 13.5.1 Hebbian Learni

ve pattern recognition as well as static pattern recognition. Audition likely utilizes a similar hierarchy. Olfaction may use something more like a Hopfield attractor neural network, as described in Chapter 13. The networks corresponding to different sense modalities have multiple cross-linkages, more a

11.4.4 The Reflexive Stage 202 EFTA00623772 Contents xvii 12 The Engineering and Development of Ethics 205 12.1 Introduction 205 12.2 Review of Current Thinking on the Risks of AGI 206 12.3 The Value of an Explicit Goal System 209 12.4 Ethical Synergy 210 12.4.1 Stages of Development of Declarative Ethics 211 12.4.2 S

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tive pattern recognition as well as static pattern recognition. Audition likely utilizes a similar hierarchy. Olfaction may use something more like a Hopfield attractor neural network, as described in Chapter 13. The networks corresponding to different sense modalities have multiple cross-linkages, more at

d Development of Ethics..........................005. 205 121 JPOAWMEtIOH, «205 sa2ms emees Cees ENNeS LE SMES MEME OEMS SERS EME!s dees em; 205 12.2 Review of Current Thinking on the Risks of AGI ................ 00.0002 eee 206 12.3 The Value of an Explicit Goal System. ....... 0.0... eee ee ee 209 12.4 Ethical Synergy ...... 0.00. eee eee te