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Extraction Summary

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People
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Organizations
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Locations
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Events
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Relationships
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Quotes

Document Information

Type: Scientific paper / academic book excerpt
File Size: 1.92 MB
Summary

This document is page 115 of a technical scientific text describing the 'CogPrime' Artificial Intelligence architecture. It details how PLN (Probabilistic Logic Networks) inference utilizes declarative, episodic, and procedural knowledge to estimate probabilities within a cognitive schematic. The text uses hypothetical examples involving a 'virtual dog' and characters named Bob, Jim, Jack, and Jill to illustrate logical implications (C ∧ P → G). The document bears a 'HOUSE_OVERSIGHT' Bates stamp, indicating it was part of a document production for a Congressional investigation, likely related to Jeffrey Epstein's funding of or interest in AI research.

People (4)

Name Role Context
Bob Hypothetical Example
Used in an AI logic example regarding a 'virtual dog' asking for food.
Jim Hypothetical Example
Used in an AI logic example regarding feature similarity.
Jack Hypothetical Example
Used in an AI logic example regarding concept creation (children playing).
Jill Hypothetical Example
Used in an AI logic example regarding concept creation (children playing).

Organizations (2)

Name Type Context
CogPrime
The AI architecture/design discussed in the text.
House Oversight Committee
Implied by the footer stamp 'HOUSE_OVERSIGHT'.

Key Quotes (2)

"PLN inference, acting on declarative knowledge, is used for estimating the probability of the implication in the cognitive schematic, given fixed C, P and G."
Source
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Quote #1
"That is, it requires the sort of cognitive synergy built into the CogPrime design."
Source
HOUSE_OVERSIGHT_013031.jpg
Quote #2

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