Epstein Secrets

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

han using a Handle based notation which Atoms would be referred to as 1, 3433322, etc.; but sometimes we will use the Handle notation as well. Some ConceptNodes and conceptual PredicateNode or Schemallodes may correspond with human-language words or phrases like cat, bite, and so forth. This will be the mino

e we describe in detail the strategy that would be used to integrate 129 EFTA00624276 130 26 Perceptual and Motor Hierarchies CogPrime with the DeSTIN framework for AGI perception/action (which was described in some detail in Chapter 4 of Part 1). In terms of the integrative cognitive architecture

MemberLinks. This is in accordance with the notion of glocal memory, in the language of which the HPAN attractors are the maps and the corresponding ConceptNodes are the keys. Put simply, when a HPAN attractor is recognized, MemberLinks are created between the HPAN nodes comprising the main body of the attra

front of the robot, via pinching it between two fingers and then lifting it. In this case, • The visual scene, including the block, is perceived by DeSTIN; and appropriate patterns in various DeSTIN nodes are formed • Predicates corresponding to the distribution of patterns among DeSTIN nodes are acti

usual, but making use of Metallodes as well as ordinary CT nodes. The modeling of Metallodes is quite similar to the modeling of Nodes representing ConceptNodes and PredicateNodes using embedding vectors. In this way, one can use standard, small decision tree models to model fairly large portions of CTs (be

nderstanding or generation. A CogPrime approach to speech would be quite feasible to develop, for instance using neural-symbolic hybridization with DeSTIN or a similar perceptual-motor hierarchy. However, this potential aspect of CogPrime has not been pursued in detail yet, and we won't devote space to

s of word sense creation. Suppose we have a WordNode W that has ReferenceLinks to a number of different Con- ceptNodes. A common case is that these ConceptNodes fall into clusters, each one denoting a "sense" of the word. The clusters are defined by the following relationships: 1. ConceptNodes within a clus

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

at contains all required packages and tools to test and develop OpenCog. • Concept Creation: A general term for cognitive processes that create new ConceptNodes, PredicateNodes or concept maps representing new concepts. • Conceptual Blending: A process of creating new concepts via judiciously combining pie

c, cognitive and linguistic functions. We will achieve this via the creation and real-time utilization of links between the nodes in CogPrime's and DeSTIN's internal networks (a topic to be explored in more depth Inter in this chapter). 6.4 Memory Types and Associated Cognitive Processes in CogPrime

Knowledge in the Atomspace. A simple example of implicit knowledge in the Atomspace. The "chicken" and "food" concepts are represented by "maps" of ConceptNodes interconnected by HebbianLinks, where the latter tend to form between Con- ceptNodes that are often simultaneously important. The bundle of links bet

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

at contains all required packages and tools to test and develop OpenCog. • Concept Creation: A general term for cognitive processes that create new ConceptNodes, PredicateNodes or concept maps representing new concepts. • Conceptual Blending: A process of creating new concepts via judiciously combining pie

ric, cognitive and linguistic functions. We will achieve this via the creation and real-time utilization of links between the nodes in CogPrime’s and DeSTIN’s internal networks (a topic to be explored in more depth later in this chapter). 6.4 Memory Types and Associated Cognitive Processes in CogPrime N

t Knowledge in the Atomspace. A simple example of implicit knowledge in the Atomspace. The "chicken" and "food" concepts are represented by "maps" of ConceptNodes interconnected by HebbianLinks, where the latter tend to form between Con- ceptNodes that are often simultaneously important. The bundle of links bet

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

hat contains all required packages and tools to test and develop OpenCog. e Concept Creation: A general term for cognitive processes that create new ConceptNodes, PredicateNodes or concept maps representing new concepts. e Conceptual Blending: A process of creating new concepts via judiciously combining piece