Epstein Secrets

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

hich data and procedure are explicitly identical, and there is just one new active data structure in place of the distinction between AtomSpace and MindAgents Where on this continuum does the "mere learning" end and the "real self-modification" start? In this chapter we consider some mechanisms for "adva

ilation 319 schema learning the learning of schemata carrying out cognitive processes in more specialized, context-dependent ways than the general MindAgents do. Eventually, once a CogPrime instance becomes sufficiently complex and advanced, these cognitive schema may replace the MindAgents altogether, l

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

mpilation 319 schema learning the learning of schemata carrying out cognitive processes in more specialized, context-dependent ways than the general MindAgents do. Eventually, once a CogPrime instance becomes sufficiently complex and advanced, these cognitive schema may replace the MindAgents altogether, lea

which data and procedure are explicitly identical, and there is just one new active data structure in place of the distinction between AtomSpace and MindAgents Where on this continuum does the "mere learning" end and the "real self-modification" start? In this chapter we consider some mechanisms for "advan

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

Agent migration, and machines joining or leaving the CogPrime cluster. So, at the Unit level, attention allocation in CogPrime has two aspects: how MindAgents and Tasks receive attention from CogPrime, and how Atoms receive attention from different MindAgents and Tasks. The topic of this Section is the fo

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

idual cognitive processes. The CogPrime design gravitates more toward the latter approach. though also with some specific mechanisms within various MindAgents; and efforts have been made to have these specific mechanisms modulated by the generic attention allocation structures and dynamics wherever possibl

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

to Atoms with more impor- tance, and hence creates an evolutionary, dynamic with importance as the fitness criterion and the whole constellation of MindAgents as the novelty-generation mechanism. However, MOSES explicitly embodies evolutionary dynamics for the learning of new patterns and procedures that

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

sambigua- tion that needs to be done to get the structure of the resultant Atom-set correct. Any further disambiguation is left to be done later, by MindAgents acting on the Atom-sets after they've already been placed in the AtomSpace. • In the old approach, the RelEx2Frame rules attempted, in many cases,

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

d effort management frameworks attempt to enforce this, via minimizing the amount of effort spent by the system in getting to a certain conclusion. MindAgents operating primarily on one kind of knowledge (e.g. MOSES, PLN) may for a time seek to follow the shortest paths within their particular correspondin

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

gh STI., which rapidly decreases in meet cases local workspaces bubbles of interlinked Atoms with moderate impor- tance, focused on by a subset of MindAgents (defined in Chapter 19 of Part 2) for a period of time perceptual associative memory HebbianLinks in the AT sensory memory spaceserver/timeserve

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

high STI, which rapidly decreases in most cases ocal workspaces bubbles of interlinked Atoms with moderate impor- ance, focused on by a subset of MindAgents (defined in Chapter 19 of Part 2) for a period of time perceptual associative memory |HebbianLinks in the AT sensory memory spaceserver /timeserver