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Scientific/technical document (page from a book or paper)
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Summary
This document is page 150 from a technical paper or book chapter titled 'Cognitive Synergy.' It contains two matrices detailing the synergistic relationships between various cognitive processes such as 'Uncertain inference,' 'Supervised procedure learning,' 'Attention allocation,' and 'Concept creation.' The text references specific AI architectures like OCP (OpenCog Prime) and MOSES. It bears a 'HOUSE_OVERSIGHT' Bates stamp, indicating it was produced as part of a congressional investigation, likely related to Jeffrey Epstein's funding of or interest in artificial intelligence research (specifically OpenCog/Ben Goertzel).
Organizations (3)
| Name | Type | Context |
|---|---|---|
| OCP |
Mentioned in table regarding procedure learning: '(e.g. in OCP...)' (Likely refers to OpenCog Prime)
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| MOSES |
Mentioned in table regarding procedure learning: '...phases of MOSES)' (Meta-Optimizing Semantic Evolutionary Search)
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| House Oversight Committee |
Source of the document stamp (HOUSE_OVERSIGHT_013066)
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Key Quotes (3)
"Cognitive Synergy"Source
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Quote #1
"Inference can be used to allow prior experience to guide each instance of procedure learning."Source
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Quote #2
"Importance levels may be used to bias choices made in the course of procedure learning (e.g. in OCP, in the fitness evaluation and representation-building phases of MOSES)"Source
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Quote #3
Full Extracted Text
Complete text extracted from the document (3,006 characters)
150
8 Cognitive Synergy
8 Cognitive Synergy
[Table 1]
How ... -> Helps | \/ | Uncertain inference | Supervised procedure learning | Attention allocation | Concept creation
How ... -> Helps | \/ | Uncertain inference | Supervised procedure learning | Attention allocation | Concept creation
Uncertain inference | NA | When inference gets stuck in an inference trail, it can ask procedure learning to learn new patterns regarding concepts in the inference trail (if there is adequate data regarding the concepts) | Importance levels allow pruning of inference trees | Provides new concepts, allowing briefer useful inference trails
Supervised procedure learning | Inference can be used to allow prior experience to guide each instance of procedure learning. | NA | Importance levels may be used to bias choices made in the course of procedure learning (e.g. in OCP, in the fitness evaluation and representation-building phases of MOSES) | Provides new concepts, allowing compacter programs using new concepts in various roles
Attention allocation | Enables inference of new HebbianLinks and HebbianPredicates from existing ones | Procedure learning can recognize patterns in historical system activity, which are then used to build concepts and relationships guiding attention allocation | NA | Combination of concepts formed via map formation, may lead to new concepts that even better direct attention
Concept creation | Allows inferential assessment of the value of new concepts | Procedure learning can be used to search for high-quality blends of existing concepts (using e.g. inferential and attentional knowledge in the fitness functions) | Allows assessment of the value of new concepts based on historical attentional knowledge | NA
[Table 2]
How ... -> Helps | \/ | Uncertain inference | Supervised procedure learning | Attention allocation | Concept creation
How ... -> Helps | \/ | Uncertain inference | Supervised procedure learning | Attention allocation | Concept creation
Map formation | Speculative inference can help map formation guess which maps to hunt for | Procedure learning can be used to search for maps that are more complex than mere "co-occurrence" | Attention allocation provides the raw data for map formation | No significant direct synergy
Goal system | Inference can carry out goal refinement | No significant direct synergy | Flow of importance among subgoals determines which subgoals get used, versus being forgotten | Concept creation can be used to provide raw data for goal refinement (e.g. a new subgoal that blends two others)
Simulation | In order to provide data for setting up simulations, inference will often be needed | No significant direct synergy | Attention allocation tells which portions of a simulation need to be run in more detail | No significant direct synergy
Sensorimotor pattern recognition | Speculative inference helps fill in gaps in sensory data | Procedure learning can be used to find subtle patterns in sensorimotor data | Attention allocation guides pattern recognition via indicating which sensorimotor stimuli and patterns tend to be associatively linked | New concepts may be created that then are found to serve as significant patterns in sensorimotor data
HOUSE_OVERSIGHT_013066
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