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1.92 MB
Extraction Summary
4
People
2
Organizations
0
Locations
0
Events
0
Relationships
2
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
HOUSE_OVERSIGHT_013031.jpg
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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