Epstein Secrets

self-modification. EFTA00623979 EFTA00623980 Chapter 12 The Engineering and Development of Ethics Co-authored with Stephan Vladimir Bugaj and Joel Pitt 12.1 Introduction Most commonly, if a work on advanced AI mentions ethics at all, it occurs in a final summary chapter, discussing in broad terms

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

n. HOUSE_OVERSIGHT_013119 HOUSE_OVERSIGHT_013120 Chapter 12 The Engineering and Development of Ethics Co-authored with Stephan Vladimir Bugaj and Joel Pitt 12.1 Introduction Most commonly, if a work on advanced AI mentions ethics at all, it occurs in a final summary chapter, discussing in broad terms s

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

satisfaction whereas Western cultures are more short term oriented. EFTA00624228 EFTA00624229 Chapter 23 Attention Allocation Co-authored with Joel Pitt and Matt Ikle' and Rui Liu 23.1 Introduction The critical factor shaping real-world general intelligence is resource constraint. Without this issu

some of the properties that the above ECAN equations display when we use an ECAN defined by them as an associative memory network in the manner of a Hopfield network. We consider a situation where the ECAN is supplied with memories via a "training" phase in which one imprints it with a series of binary

orks Economic Attention Networks (ECANs) are dynamical systems based on the propagation of STI and LTI values. They are similar in many respects to Hopfield nets, but are based on a different conceptual foundation involving the propagation of amounts of (conserved) currency rather than neural-net activa

rbeck, Jeff Reed, Pejman Makhfi, Welter Silva, Lukasz Kaiser and more • All theme who have helped with the OpenCog system, including Linas Vepstas, Joel Pitt, Jared Wigmore / Jade O'Neill, Zhenhua Cal, Deheng Huang, Shujing Ke, Lake Watkins, Alex van der Peet, Samir Araujo, Fabricio Silva, Yang Ye, Shuo

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

Verbeck, Jeff Reed, Pejman Makhfi, Welter Silva, Lukasz Kaiser and more All those who have helped with the OpenCog system, including Linas Vepstas, Joel Pitt, Jared Wigmore / Jade O'Neill, Zhenhua Cai, Deheng Huang, Shujing Ke, Lake Watkins, Alex van der Peet, Samir Araujo, Fabricio Silva, Yang Ye, Shuo Ch

e eee eee 255 13.4 Knowledge Representation via Attractor Neural Networks ................... 256 HOUSE_OVERSIGHT_012913 xviii Contents 13.4.1 The Hopfield neural net model ..............0..0 022 e eee 256 13.4.2 Knowledge Representation via Cell Assemblies .................2.05- 257 13.5 Neural Foundati

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