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

ive development of humans and human-like AGI systems, but in Table 11.1 we present them in a slightly more general context, using two examples: the Piagetan example of the human (or humanlike) mind as it develops from infancy to maturity; and also the example of the "origin of life" and the development

refining inference control. EFTA00623977 202 11 Stages of Cognitive Development A problem commonly used to illustrate the difference between the Piagetan concrete opera- tional and formal stages is that of figuring out the rules for making pendulums swing quickly versus slowly pins'. If you ask a chil

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

itive development of humans and human-like AGI systems, but in Table 11.1 we present them in a slightly more general context, using two examples: the Piagetan example of the human (or humanlike) mind as it develops from infancy to maturity; and also the example of the “origin of life’ and the development of

inference control. HOUSE_OVERSIGHT_013117 202 11 Stages of Cognitive Development A problem commonly used to illustrate the difference between the Piagetan concrete opera- tional and formal stages is that of figuring out the rules for making pendulums swing quickly versus slowly [P58]. If you ask a child

FTA00623877 102 5 A Generic Architecture of Human-Like Cognition touch and smell (the latter being better modeled as something like an asymmetric Hopfield net, prone to frequent chaotic dynamics ILIAV*051) - these may also cross-connect with each other and with the more hierarchical perceptual subnetw

led "cognitive complete- ness" is necessary and sufficient for efficient general intelligence at the level of an inteligent adult human (e.g. at the Piagetan formal level IP ia531). Of course, the above considerations are very, far from a rigorous mathematical proof (or even precise formulation) of this

RSIGHT_013017 102 5 A Generic Architecture of Human-Like Cognition touch and smell (the latter being better modeled as something like an asymmetric Hopfield net, prone to frequent chaotic dynamics [LLW~05]) — these may also cross-connect with each other and with the more hierarchical perceptual subnetwork

lled “cognitive complete- ness” is necessary and sufficient for efficient general intelligence at the level of an inteligent adult human (e.g. at the Piagetan formal level [Pia53]). Of course, the above considerations are very far from a rigorous mathematical proof (or even precise formulation) of this hyp