Epstein Secrets

ing. Neural Computing, 10:251-276, 1998. ANOO. Shun-ichi Amari and Hiroshi Nagaoka. Methods of information geometry. ANIS, 2000. ARC09a. I. Aral, D. Rose, and R. Coop. Destin: A scalable deep learning architecture with application to high-dimensional robust pattern recognition. Proc. AAA! Workshop on

ue to their involvement in the agent's activities, is because they had their LTI boosted at some point in the past. And after these Atoms leave the AttentionalFocus and their STI reduces, they will have boosted LTI and hence likely remain in RAM for a long while, to be involved in "background thought", and in c

Daniel J. Amit. Modeling brain function - the world of attractor neural networks. Cambridge University Press, New York, USA, 1989. ARC09. I. Arel, D. Rose, and R. Coop. Destin: A scalable deep learning architecture with application to high-dimensional robust pattern recognition. Proc. AAA! Workshop on

r than simply assumed a priori. • Consciousness: CogPrime is not predicated on any particular conceptual theory of con- sciousness. Informally, the AttentionalFocus is sometimes referred to as the "conscious" mind of a CogPrime system, with the rest of the Atomspace as "unconscious" but this is just an informal

. Daniel J. Amit. Modeling brain function — the world of attractor neural networks. Cambridge University Press, New York, USA, 1989. ARCO9. I. Arel, D. Rose, and R. Coop. Destin: A scalable deep learning architecture with application to high-dimensional robust pattern recognition. Proc. AAAI Workshop on B

ther than simply assumed a priori. Consciousness: CogPrime is not predicated on any particular conceptual theory of con- sciousness. Informally, the AttentionalFocus is sometimes referred to as the “conscious” mind of a CogPrime system, with the rest of the Atomspace as “unconscious” but this is just an informal u