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y different yet both appear cognitively necessary. The argument for using probability in an AGI system is assailed by some AGI researchers such as Pei Wang, but we are swayed by the theoretical arguments in favor of probability theory's mathematically fundamental nature, as well as the massive demonstra
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
achines. Oxford University Press, 2010. Wan95. P. Wang. Non-Axiomatic Reasoning System. PhD Thesis, Indiana University. Bloomington, 1995. Wan06. Pei Wang. Rigid Flexibility: The Logic of Intelligence. Springer. 2006. Was09. Mark Weser. Ethics for self-improving machines. In ACf-09, 2009. http://vimeo
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
Machines. Oxford University Press, 2010. Wan95. P. Wang. Non-Aziomatic Reasoning System. PhD Thesis, Indiana University. Bloomington, 1995. Wan06. Pei Wang. Rigid Flexibility: The Logic of Intelligence. Springer, 2006. Was09. Mark Waser. Ethics for self-improving machines. In AGI-09, 2009. http: //vimeo
. 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
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