HOUSE_OVERSIGHT_013111.jpg
2.24 MB
Extraction Summary
0
People
2
Organizations
0
Locations
0
Events
0
Relationships
3
Quotes
Document Information
Type:
Academic book page / technical report (evidence document)
File Size:
2.24 MB
Summary
This document is a page from a technical academic text focusing on Artificial General Intelligence (AGI), specifically discussing 'CogPrime' and the 'Novamente Cognition Engine' in relation to Piaget's developmental stages and uncertain inference. It outlines the 'Infantile Stage' of AI development, comparing automated inference to human intelligence. The page bears the footer 'HOUSE_OVERSIGHT_013111', indicating it was collected as evidence by the House Oversight Committee, likely in relation to investigations into Jeffrey Epstein's funding of scientific research and his connections to AI theorists.
Key Quotes (3)
"Only a system capable of dealing with constructs of arbitrary (or at least very high) complexity will have any potential of leading to human-level, human-like intelligence."Source
HOUSE_OVERSIGHT_013111.jpg
Quote #1
"The lack of effective, context-sensitive inference control heuristics is why the general ability of current automated theorem provers is considerably weaker than that of a mediocre university mathematics major."Source
HOUSE_OVERSIGHT_013111.jpg
Quote #2
"We now review the Piagetan developmental stages from the perspective of AGI systems heavily based on uncertain inference."Source
HOUSE_OVERSIGHT_013111.jpg
Quote #3
Full Extracted Text
Complete text extracted from the document (3,623 characters)
11.4 Piaget’s Stages in the Context of Uncertain Inference 195
Broadly speaking, examples of content representation schemes are predicate logic and term
logic [ES00]. Examples of uncertainty representation schemes are fuzzy logic [Zad78], imprecise
probability theory [Goo86, FC86], Dempster-Shafer theory [Sha76, Kyb97], Bayesian probability
theory [Kyb97], NARS [Wan95], and the Atom representation used in CogPrime, briefly alluded
to in Chapter 6 above and described in depth in later chapters.
logic [ES00]. Examples of uncertainty representation schemes are fuzzy logic [Zad78], imprecise
probability theory [Goo86, FC86], Dempster-Shafer theory [Sha76, Kyb97], Bayesian probability
theory [Kyb97], NARS [Wan95], and the Atom representation used in CogPrime, briefly alluded
to in Chapter 6 above and described in depth in later chapters.
Many, but not all, approaches to uncertain inference involve only a limited, weak set of in-
ference rules (e.g. not dealing with complex quantified expressions). CogPrime’s PLN inference
framework, like NARS and some other uncertain inference frameworks, contains uncertain in-
ference rules that apply to logical constructs of arbitrary complexity. Only a system capable of
dealing with constructs of arbitrary (or at least very high) complexity will have any potential
of leading to human-level, human-like intelligence.
ference rules (e.g. not dealing with complex quantified expressions). CogPrime’s PLN inference
framework, like NARS and some other uncertain inference frameworks, contains uncertain in-
ference rules that apply to logical constructs of arbitrary complexity. Only a system capable of
dealing with constructs of arbitrary (or at least very high) complexity will have any potential
of leading to human-level, human-like intelligence.
The subtlest part of uncertain inference is inference control: the choice of which inferences
to do, in what order. Inference control is the primary area in which human inference currently
exceeds automated inference. Humans are not very efficient or accurate at carrying out inference
rules, with or without uncertainty, but we are very good at determining which inferences to do
and in what order, in any given context. The lack of effective, context-sensitive inference control
heuristics is why the general ability of current automated theorem provers is considerably weaker
than that of a mediocre university mathematics major [Mac95].
to do, in what order. Inference control is the primary area in which human inference currently
exceeds automated inference. Humans are not very efficient or accurate at carrying out inference
rules, with or without uncertainty, but we are very good at determining which inferences to do
and in what order, in any given context. The lack of effective, context-sensitive inference control
heuristics is why the general ability of current automated theorem provers is considerably weaker
than that of a mediocre university mathematics major [Mac95].
We now review the Piagetan developmental stages from the perspective of AGI systems
heavily based on uncertain inference.
heavily based on uncertain inference.
11.4.1 The Infantile Stage
In this initial stage, the mind is able to recognize patterns in and conduct inferences about
the world, but only using simplistic hard-wired (not experientially learned) inference control
schema, along with pre-heuristic pattern mining of experiential data.
the world, but only using simplistic hard-wired (not experientially learned) inference control
schema, along with pre-heuristic pattern mining of experiential data.
In the infantile stage an entity is able to recognize patterns in and conduct inferences about
its sensory surround context (i.e., it’s “world”), but only using simplistic, hard-wired (not expe-
rientially learned) inference control schemata. Preheuristic pattern-mining of experiential data
is performed in order to build future heuristics about analysis of and interaction with the world.
s tasks include:
its sensory surround context (i.e., it’s “world”), but only using simplistic, hard-wired (not expe-
rientially learned) inference control schemata. Preheuristic pattern-mining of experiential data
is performed in order to build future heuristics about analysis of and interaction with the world.
s tasks include:
1. Exploratory behavior in which useful and useless / dangerous behavior is differentiated by
both trial and error observation, and by parental guidance.
2. Development of “habits” – i.e. Repeating tasks which were successful once to determine if
they always / usually are so.
3. Simple goal-oriented behavior such as “find out what cat hair tastes like” in which one must
plan and take several sequentially dependent steps in order to achieve the goal.
both trial and error observation, and by parental guidance.
2. Development of “habits” – i.e. Repeating tasks which were successful once to determine if
they always / usually are so.
3. Simple goal-oriented behavior such as “find out what cat hair tastes like” in which one must
plan and take several sequentially dependent steps in order to achieve the goal.
Inference control is very simple during the infantile stage (Figure 11.4), as it is the stage
during which both the most basic knowledge of the world is acquired, and the most basic of
cognition and inference control structures are developed as the building block upon which will
be built the next stages of both knowledge and inference control.
during which both the most basic knowledge of the world is acquired, and the most basic of
cognition and inference control structures are developed as the building block upon which will
be built the next stages of both knowledge and inference control.
Another example of a cognitive task at the borderline between infantile and concrete cog-
nition is learning object permanence, a problem discussed in the context of CogPrime’s prede-
cessor "Novamente Cognition Engine" system in [GPSL03]. Another example is the learning of
nition is learning object permanence, a problem discussed in the context of CogPrime’s prede-
cessor "Novamente Cognition Engine" system in [GPSL03]. Another example is the learning of
HOUSE_OVERSIGHT_013111
Discussion 0
No comments yet
Be the first to share your thoughts on this epstein document