Epstein Secrets

nd seem relevant to AGI, especially advanced AGI systems capable of strong self-modification. • PLN: short for Probabilistic Logic Networks • PLN, First-Order: See First-Order hnference • PLN, Higher-Order: See Higher-Order Inference • PLN Rules: A PLN Rule takes as input one or more Atoms (the "premises"

, Ruiting Lien, Hugo de Gar's, Shuo Chen, and hamar Arel. World survey of artificial brains, part ii: Biologically inspired cognitive architectures. Neurocomputing, April 2010. GM11108. B. Goertzel, I. Coertzel M. lkle, and A. Heljakka. Probabilistic Logic Networks. Springer, 2008. GN02. Alfonso Gerevini and

nd seem relevant to AGI, especially advanced AGI systems capable of strong self-modification. • PLN: short for Probabilistic Logic Networks • PLN, First-Order: See First-Order hnference • PLN, Higher-Order: See Higher-Order Inference • PLN Rules: A PLN Rule takes as input one or more Atoms (the "premises"

. CP1+10. Ben Goertzel, Joel Pitt, Matthew Ikle, Casein Pennachin, and Rui Liu. Glocal memory: a design principle for artificial brains and minds. Neurocomputing, April 2010. CPPG06. Ben Goertzel, Hugo Pinto, easel° Pennachin, and Izabela Freire Goertzel. Using dependency pars- ing and probabilistic inferenc

et and seem relevant to AGI, especially advanced AGI systems capable of strong self-modification. PLN: short for Probabilistic Logic Networks PLN, First-Order: See First-Order Inference PLN, Higher-Order: See Higher-Order Inference PLN Rules: A PLN Rule takes as input one or more Atoms (the “premises”, us

1. GPIt 10. Ben Goertzel, Joel Pitt, Matthew Ikle, Cassio Pennachin, and Rui Liu. Glocal memory: a design principle for artificial brains and minds. Neurocomputing, April 2010. GPPGO06. Ben Goertzel, Hugo Pinto, Cassio Pennachin, and Izabela Freire Goertzel. Using dependency pars- ing and probabilistic inferenc