Epstein Secrets

sentation EFTA00624019 EFTA00624020 Chapter 13 Local, Global and Glocal Knowledge Representation Co-authored with Matthew Ikle, Joel Pitt and Rui Liu 13.1 Introduction One of the most powerful metaphors we've found for understanding minds is to view them as networks - i.e. collections of interre

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

OVERSIGHT_013159 HOUSE_OVERSIGHT_013160 Chapter 13 Local, Global and Glocal Knowledge Representation Co-authored with Matthew Ikle, Joel Pitt and Rui Liu 13.1 Introduction One of the most powerful metaphors we’ve found for understanding minds is to view them as networks — i.e. collections of interrel

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

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 issue, we could just have simpl

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

the specific examples that gave rise to it. EFTA00624582 EFTA00624583 Chapter 46 Natural Language Generation Co-authored with Ruiting Lian and Rui Liu 46.1 Introduction Language generation, unsurprisingly, shares most of the key features of language comprehension discussed in chapter 44 - after a

icitly. In general, entropy maximization principles provide the foundation for learning systems such as (hidden) Markov models, Markov networks and Hopfield neural networks, and they connect indirectly with Bayesian probability based analyses. However, the actual task of maximizing the entropy is an NP-h