Epstein Secrets

ative Architecture for Embodied Artificial General Intelligence, http://wiki.opencog.org/w/CogPrime_Overview • Goertzel, Ben, Cassio Pennachin and Nil Geisweiller (2013). Engineering General Intelligence, vol. 1 and 2. Atlantis Press. • Hunt, Gavin R. (January 1996). "Manufacture and use of hook-tools by New

Role Time Dedication Annual Cost Dr. Ben Goertzel Team leader 75% $75K Cosmo Harrigan Lead programmer, OpenCog code expert 75% $60K Dr. Nil Geisweiller MOSES/PLN specialist 50% $40K Dr. Joscha Bach Expert in motivated behavior 20% -- Dr. Matthew lkle' ECAN/ PLN specialist 45% $40K Juni

. PLN, a probabilistic logic engine; MOSES, an evolutionary program learning algorithm; ECAN, a nonlinear-dynamical attention allocation framework; DeSTIN, a deep learning perception algorithm) within a human-like cognitive architecture. The integration of these multiple learning algorithms is achieved

ative Architecture for Embodied Artificial General Intelligence, http://wiki.opencog.org/w/CogPrime_Overview • Goertzel, Ben, Cassio Pennachin and Nil Geisweiller (2013). Engineering General Intelligence, vol. 1 and 2. Atlantis Press. • Hunt, Gavin R. (January 1996). "Manufacture and use of hook-tools by New

Role Time Dedication Annual Cost Dr. Ben Goertzel Team leader 75% $75K Cosmo Harrigan Lead programmer, OpenCog code expert 75% $60K Dr. Nil Geisweiller MOSES/PLN specialist 50% $401( Dr. Joscha Bach Expert in motivated behavior 20% -- Dr. Matthew lkle' ECAN/ PLN specialist 45% $40K Jun

. PLN, a probabilistic logic engine; MOSES, an evolutionary program learning algorithm; ECAN, a nonlinear-dynamical attention allocation framework; DeSTIN, a deep learning perception algorithm) within a human-like cognitive architecture. The integration of these multiple learning algorithms is achieved

primary author, Ben Goertzel. Ben will thus begin by expressing his thanks to his primary co-authors, Cassio Pennachin (collaborator since 1998) and Nil Geisweiller (collaborator since 2005). Without outstandingly insightful, deep-thinking colleagues like you, the ideas presented here - let alone the book itsel

overlapping communities of people over a long period of time. The vast bulk of the writing here was done by Ben Goertzel; but Cassio Pennachin and Nil Geisweiller made sufficient writing, thinking and editing contributions over the years to more than merit their inclusion of co-authors. Further, many of the c

spatiotemporally hier- archical deep learning system is an effective way to handle representation and learning of low-level sensorimotor knowledge. DeSTIN is one example of a deep learning system of this nature that can be effective in this context. i. One effective way to handle goals is to represent

primary author, Ben Goertzel. Ben will thus begin by expressing his thanks to his primary co-authors, Cassio Pennachin (collaborator since 1998) and Nil Geisweiller (collaborator since 2005). Without outstandingly insightful, deep-thinking colleagues like you, the ideas presented here — let alone the book itself—

le overlapping communities of people over a long period of time. The vast bulk of the writing here was done by Ben Goertzel; but Cassio Pennachin and Nil Geisweiller made sufficient writing, thinking and editing contributions over the years to more than merit their inclusion of co-authors. Further, many of the cha

a spatiotemporally hier- archical deep learning system is an effective way to handle representation and learning of low-level sensorimotor knowledge. DeSTIN is one example of a deep learning system of this nature that can be effective in this context. i. One effective way to handle goals is to represent

oertzel, the overall work and underlying ideas have been very much a team effort, with major input from the sec- ondary authors Cassio Pennachin and Nil Geisweiller, and large contributions from various other contributors as well. Nlany chapters have specifically indicated coauthors; but the contributions from

g Patterns from DeSTIN States 175 29.3.2 Probabilistic Inference on Mined Hypergraphs 176 29.3.3 Insertion of OpenCog-Learned Predicates into DeSTIN's Pattern Library 177 29.4 Multisensory Integration, and Perception-Action Integration 178 29.4.1 Perception-Action Integration 179 29.4.2 Th

itecture for embodied artificial general intelligence. In Proceedings of IEEE Symposium on Human-Level Al, Singapore, 2013. CCC+ 11. Ben Goertzel, Nil Geisweiller, Lucio Coelho, Predrag Janicic, and Cassio Pennachin. Real World Reasoning. Atlantis, 2011. CH11. N Garg and J Henderson. Temporal restricted bolt

d tool. However, if one represents low-level procedures like this using another method, e.g. learned cell assemblies in a hierarchical network like DeSTIN, then it's very feasible to make Combo programs that invoke these low-level procedures, and encode higher-level actions like "pick up the cup in fro

n second life. In Proc.of the First Conf. on AC1. IOS Press, 2008. Cer99. Michael Gershon. The Second Brain. Harper, 1999. CCC+11. Ben Goertzel, Nil Geisweiller, Lucio Coelho, Predrag Janicic, and Cassio Pennachin. Real World Reasoning. Atlantis, 2011. CCK02. T. Cilovich, D. Griffin, and D. Kahneman. Heuri

tiple layers, involving feedfor- ward and feedback dynamics, and adaptation of the links between the elements. An example deep learning algorithm is DeSTIN, which is being integrated with OpenCog for perception processing. • Defrosting: Restoring, into the RAM portion of an Atomspace, an Atom (or set t

s in second life. In Proc.of the First Conf. on AGI. IOS Press, 2008. Ger99. Michael Gershon. The Second Brain. Harper, 1999. GGCr11. Ben Goertzel, Nil Geisweiller, Lucio Coelho, Predrag Janicic, and Cassio Pennachin. Real World Reasoning. Atlantis, 2011. GGKO02. T. Gilovich, D. Griffin, and D. Kahneman. Heuris

ltiple layers, involving feedfor- ward and feedback dynamics, and adaptation of the links between the elements. An example deep learning algorithm is DeSTIN, which is being integrated with OpenCog for perception processing. Defrosting: Restoring, into the RAM portion of an Atomspace, an Atom (or set ther