| --- |
| title: "House Oversight: Estate Documents (Nov 12) (HOUSE_OVERSIGHT_013027)" |
| source: "House Oversight: Estate Documents (Nov 12)" |
| sourceUrl: "https://www.justice.gov/epstein" |
| date: "2026-01-01" |
| category: "House Oversight" |
| eftaNumber: "HOUSE_OVERSIGHT_013027" |
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| --- |
| |
| 6.4 Memory Types and Associated Cognitive Processes in CogPrime |
| 111 |
| CogPrime's memory types are the declarative, procedural, sensory, and episodic memory |
| types that are widely discussed in cognitive neuroscience [TC05], plus attentional memory for |
| allocating system resources generically, and intentional memory for allocating system resources |
| in a goal-directed way. Table 6.2 overviews these memory types, giving key references and indi- |
| cating the corresponding cognitive processes, and also indicating which of the generic patternist |
| cognitive dynamics each cognitive process corresponds to (pattern creation, association, etc.). |
| Figure 6.7 illustrates the relationships between several of the key memory types in the context |
| of a simple situation involving an OpenCogPrime-controlled agent in a virtual world. |
| In terms of patternist cognitive theory, the multiple types of memory in CogPrime should be |
| considered as specialized ways of storing particular types of patterns, optimized for spacetime |
| efficiency. The cognitive processes associated with a certain type of memory deal with creating |
| and recognizing patterns of the type for which the memory is specialized. While in principle all |
| the different sorts of pattern could be handled in a unified memory and processing architecture, |
| the sort of specialization used in CogPrime is necessary in order to achieve acceptable efficient |
| general intelligence using currently available computational resources. And as we have argued |
| in detail in Chapter 7, efficiency is not a side-issue but rather the essence of real-world AGI |
| (since as Hutter has shown, if one casts efficiency aside, arbitrary levels of general intelligence |
| can be achieved via a trivially simple program). |
| The essence of the CogPrime design lies in the way the structures and processes associated |
| with each type of memory are designed to work together in a closely coupled way, yielding coop- |
| erative intelligence going beyond what could be achieved by an architecture merely containing |
| the same structures and processes in separate "black boxes." |
| The inter-cognitive-process interactions in OpenCog are designed so that |
| • conversion between different types of memory is possible, though sometimes computation- |
| ally costly (e.g. an item of declarative knowledge may with some effort be interpreted |
| • when a learning process concerned centrally with one type of memory encounters a situation |
| where it learns very slowly, it can often resolve the issue by converting some of the relevant |
| knowledge into a different type of memory: i.e. cognitive synergy |
| 6.4.1 Cognitive Synergy in PLN |
| To put a little meat on the bones of the "cognitive synergy" idea, discussed repeatedly in prior |
| chapters and more extensively in latter chapters, we now elaborate a little on the role it plays |
| in the interaction between procedural and declarative learning. |
| While MOSES handles much of CogPrime's procedural learning, and CogPrime's internal |
| simulation engine handles most episodic knowledge, CogPrime's primary tool for handling |
| declarative knowledge is an uncertain inference framework called Probabilistic Logic Networks |
| (PLN). The complexities of PLN are the topic of a lengthy technical monograph [GMIH08], and |
| are summarized in Chapter 34; here we will eschew most details and focus mainly on pointing |
| out how PLN seeks to achieve efficient inference control via integration with other cognitive |
| As a logic, PLN is broadly integrative: it combines certain term logic rules with more standard |
| predicate logic rules, and utilizes both fuzzy truth values and a variant of imprecise probabilities |
| called indefinite probabilities. PLN mathematics tells how these uncertain truth values propagate |
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