Epstein Secrets

g used as the basis for an open-source software project called OpenCog, which is being worked on by software developers around the world. Right now OpenCog embodies only a percentage of the overall CogPrime design as described here. But if OpenCog continues to attract sufficient funding or volunteer in

the ideas presented here are not mere armchair speculation - they are currently being used as the basis for an open-source software project called OpenCog, which is being worked on by software developers around the world. Right now OpenCog embodies only a percentage of the overall CogPrime design as d

validated or refuted via practice. (As a related note: here and there in this book, we will refer to the "current" CogPrime implementation (in the OpenCog framework); in all cases this refers to OpenCog as of late 2013.) To state one believes one knows a workable path to creating a human-level (and po

268 13.6.4 Neural-Symbolic Glocality in CogPrime 269 14 Representing Implicit Knowledge via Hypergraphs 271 14.1 Introduction 271 14.2 Key Vertex and Edge Types 271 14.3 Derived Hypergraphs 272 14.3.1 SMEPH Vertices 272 14.3.2 SMEPH Edges 273 14.4 Implications of Patternist Philoso

ing used as the basis for an open-source software project called OpenCog, which is being worked on by software developers around the world. Right now OpenCog embodies only a percentage of the overall CogPrime design as described here. But if OpenCog continues to attract sufficient funding or volunteer inte

at the ideas presented here are not mere armchair speculation — they are currently being used as the basis for an open-source software project called OpenCog, which is being worked on by software developers around the world. Right now OpenCog embodies only a percentage of the overall CogPrime design as des

be validated or refuted via practice. (As a related note: here and there in this book, we will refer to the "current" CogPrime implementation (in the OpenCog framework); in all cases this refers to OpenCog as of late 2013.) To state one believes one knows a workable path to creating a human-level (and pot

0000.0 269 14 Representing Implicit Knowledge via Hypergraphs ....................... 271 14.1 Introduction .......00..000 000 tte teen ee 271 14.2: Key Vertex atid Edge Typé$ casas cuine cs ce ees ee ses ems es emo ws Meee MERE OE 271 14.3 Derived Hypergraphs .......... 0.000. c cece tenet eae 272 14.3.1 SM

us to attractor neural nets. The OpenCog design also contains a mechanism called Psynese (not yet implemented), in- tended to make it easier for one OpenCog instance to translate its personal thoughts into the mental language of another OpenCog instance. This translation process may be quite subtle, sin

representation, with an explicit (uncertain) logical aspect as well as an explicit subsymbolic aspect more analogous to attractor neural nets. The OpenCog design also contains a mechanism called Psynese (not yet implemented), in- tended to make it easier for one OpenCog instance to translate its persona

ergent" hypergraphs of the sort described here using the acronym SMEPH, which stands for Self-Modifying, Evolving Probabilistic Hy- pergraphs. 14.2 Key Vertex and Edge Types We begin by introducing a particular collection of Vertex and Edge types, to be used in modeling the internal structures of intellig

us to attractor neural nets. The OpenCog design also contains a mechanism called Psynese (not yet implemented), in- tended to make it easier for one OpenCog instance to translate its personal thoughts into the mental language of another OpenCog instance. This translation process may be quite subtle, since

d representation, with an explicit (uncertain) logical aspect as well as an explicit subsymbolic aspect more analogous to attractor neural nets. The OpenCog design also contains a mechanism called Psynese (not yet implemented), in- tended to make it easier for one OpenCog instance to translate its persona

mergent" hypergraphs of the sort described here using the acronym SMEPH, which stands for Self-Modifying, Evolving Probabilistic Hy- pergraphs. 14.2 Key Vertex and Edge Types We begin by introducing a particular collection of Vertex and Edge types, to be used in modeling the internal structures of intellige