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< | <div style="margin:0 2em; background:azure; border:1px outset azure; padding:0 1em; max-width:888px; min-width:62%; margin:0 auto;"> | ||
< | <center><b><big>DRAFT DOCUMENT</big></b></center> | ||
< | |||
</ | |||
< | |||
;This document discusses the development of the 911 era category theory as a quasi-relational perceptive envelope for machine learning and control. | |||
==Introduction== | |||
The Quasi Axiomatic Theory (QAT) was developed as a big-data tool to discover significant events in cyberspace. By using a sampling of the world corpus, the QAT can isolate category trends and waves of change in large data samples by examining internet router logs. | |||
This process was implemented as an in-memory data engine which enabled the discovery of category-frequency-trends. From this, the cause of the change-wave may be reverse-searched from category to data-event. | |||
The QAT allows for a perception of relational dynamics. It can enable AI in training to witness the quasi-artifact of measured reality. | |||
===The Architecture of the QAT Algorithms=== | |||
The QAT algorithms use a method called '''Categorical abstraction''', which produces a visual map of data trends. This is achieved by taking two points and moving them slightly closer together on the map. Over time, this produces a clustering of categories on the map. | |||
A second technique used by the QAT is '''Event Chemistry'''. This method allows the user to explore interesting groups of categories within the data-stream that have been isolated by Category Abstraction mapping. | |||
===QAT Theory in principle of emergent processing of causal artifact within chaos=== | |||
The QAT is a map maker. It uses a method called 'tweening' to group categories over time on a category-map. This is a form of emergent computing. | |||
===Implementation of QAT Data Structures=== | |||
The QAT uses three lists to support its 'gather method': | |||
'''*UniqueList-A:''' A list of the unique values within the sample block from a data stream of channel-A. | |||
'''*UniqueList-B:''' A similar list for channel-B. | |||
< | '''*ExpandedList-A:''' A list of all the combinations that can be expanded from the <b><big>UniqueList-A</big></b>. | ||
< | |||
Two additional lists are used in the 'eventChemistry methods': | |||
'''4. Link Index List by Atom:''' An index per each unique category value within the channel-A data sample block. | |||
'''5. Atom Index list by Link:''' An identical index treatment, but for Channel-B. | |||
In 2001, a human operated these systems. However, in 2024, machine intelligence can operate them. | |||
</ | ===Simplification=== | ||
</ | An optimal solution for big data information access is for the data to be loaded once, never moved, indexed in place, and structured in place for binary-tree access. The QAT foundation lists of Atoms and Links are afforded by the binary tree, which is a linking-structure of categories of values, as sampled from a data stream. | ||
The array-based binary-tree / linked-list hybrid | |||
This structure is supported by an array of integers, with one integer pointing to the data, and four more integers pointing to data elements earlier in the data sample. | |||
By using this hybrid structure, all the tables of the formal QAT solution are accessible directly, or from a cached table directly built from this fast, hybrid solution. | |||
===Conclusion=== | |||
Two measurement channels provide one QAT dimension of analysis, providing a visual abstraction of relationships in the cross-comparison. By randomly scattering points representing all existing pairs within dual channels of measurement on a visual map, we can animate the category mapping and gain insights into the dynamics of the data. | |||
'''Note:''' this simplified version doesn't include all of the in-depth technical details, templates, images, or styling present in the original wikitext document, but it should give you a general understanding of the content. If you need a detailed analysis of a specific part, please let me know! | |||
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<div style="margin:0 2em; background:cornsilk; border:1px outset azure; padding:0 1em; max-width:888px; min-width:62%; margin:0 auto;"> | |||
</div> | |||
<div style="margin:0 2em; background:azure; border:1px outset azure; padding:0 1em; max-width:888px; min-width:62%; margin:0 auto;"> | |||
;A ChatGPT 4.0 simplification of grammar | |||
[[User:XenoEngineer|XenoEngineer]] ([[User talk:XenoEngineer|talk]]) 18:19, 25 July 2023 (UTC) | |||
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;D R A F T D O C U M E N T | |||
;A Dascient — groupKOS Collaborative | |||
'''QuasiArtifactualIntelligence: Perception from the Edge''' | |||
<div style="float:right;"> | |||
__TOC__ | |||
</div> | |||
===Introduction=== | |||
The Quasi Axiomatic Theory (QAT) was conceived as a big-data tool designed to discern significant events on the ever expanding edge of the world-corpus: Cyberspace: 2000. It functions as a category theory, a means of systematically organizing and classifying data, that can isolate trends and detect waves of change in large data samples. | |||
===The Architecture of the QAT Algorithms=== | |||
The QAT uses algorithms to offer a form of 'categorical abstraction,' visual maps of data, and 'eventChemistry,' a method of exploring categories. With these algorithms, machine learning can be integrated into the quasi-axiomatic analysis loop, enabling an AI-in-training to witness the quasi-artifact of measured reality. | |||
===QAT Theory in Principle of Emergent Processing of Causal Artifact within Chaos=== | |||
QAT is essentially a map-maker. It uses a group-motion on two random points on the map in each iteration. This 'grouping' is the 'emergence' in this implementation of emergent computing. | |||
===Implementation of QAT Data Structures=== | |||
The data structures needed to produce emergent processing of the QAT are relatively simple and include three lists: UniqueList-A, UniqueList-B, and ExpandedList-A. In addition, the eventChemistry methods operate with two more lists: Link Index-List by Atom and Atom Index-list by Link. | |||
===Simplification=== | |||
In an optimal solution for big data information access, the data is loaded once, never moved, indexed in place, and structured in place for binary-tree access. This solution slows down very little even with vast data sampling sets, making it highly scalable. | |||
===Future Development Notes=== | |||
Two measurement channels provide one QAT dimension of analysis: QAT.I-RIB, offering a visual abstraction of relationships in the cross-comparison. This happens by randomly scattering onto a visual map the set of points representing all existing pairs within dual channels of measurement in the data ontology widget. | |||
'''Note:''' This draft focused on improving grammar, style, and readability. As a technical document, it would also benefit from including diagrams or figures to help illustrate complex concepts or processes. | |||
</div> | |||
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Latest revision as of 17:28, 26 July 2023
- This document discusses the development of the 911 era category theory as a quasi-relational perceptive envelope for machine learning and control.
Introduction
The Quasi Axiomatic Theory (QAT) was developed as a big-data tool to discover significant events in cyberspace. By using a sampling of the world corpus, the QAT can isolate category trends and waves of change in large data samples by examining internet router logs.
This process was implemented as an in-memory data engine which enabled the discovery of category-frequency-trends. From this, the cause of the change-wave may be reverse-searched from category to data-event.
The QAT allows for a perception of relational dynamics. It can enable AI in training to witness the quasi-artifact of measured reality.
The Architecture of the QAT Algorithms
The QAT algorithms use a method called Categorical abstraction, which produces a visual map of data trends. This is achieved by taking two points and moving them slightly closer together on the map. Over time, this produces a clustering of categories on the map.
A second technique used by the QAT is Event Chemistry. This method allows the user to explore interesting groups of categories within the data-stream that have been isolated by Category Abstraction mapping.
QAT Theory in principle of emergent processing of causal artifact within chaos
The QAT is a map maker. It uses a method called 'tweening' to group categories over time on a category-map. This is a form of emergent computing.
Implementation of QAT Data Structures
The QAT uses three lists to support its 'gather method':
*UniqueList-A: A list of the unique values within the sample block from a data stream of channel-A.
*UniqueList-B: A similar list for channel-B.
*ExpandedList-A: A list of all the combinations that can be expanded from the UniqueList-A.
Two additional lists are used in the 'eventChemistry methods':
4. Link Index List by Atom: An index per each unique category value within the channel-A data sample block.
5. Atom Index list by Link: An identical index treatment, but for Channel-B.
In 2001, a human operated these systems. However, in 2024, machine intelligence can operate them.
Simplification
An optimal solution for big data information access is for the data to be loaded once, never moved, indexed in place, and structured in place for binary-tree access. The QAT foundation lists of Atoms and Links are afforded by the binary tree, which is a linking-structure of categories of values, as sampled from a data stream.
The array-based binary-tree / linked-list hybrid This structure is supported by an array of integers, with one integer pointing to the data, and four more integers pointing to data elements earlier in the data sample.
By using this hybrid structure, all the tables of the formal QAT solution are accessible directly, or from a cached table directly built from this fast, hybrid solution.
Conclusion
Two measurement channels provide one QAT dimension of analysis, providing a visual abstraction of relationships in the cross-comparison. By randomly scattering points representing all existing pairs within dual channels of measurement on a visual map, we can animate the category mapping and gain insights into the dynamics of the data.
Note: this simplified version doesn't include all of the in-depth technical details, templates, images, or styling present in the original wikitext document, but it should give you a general understanding of the content. If you need a detailed analysis of a specific part, please let me know!
- A ChatGPT 4.0 simplification of grammar
XenoEngineer (talk) 18:19, 25 July 2023 (UTC)
- D R A F T D O C U M E N T
- A Dascient — groupKOS Collaborative
QuasiArtifactualIntelligence: Perception from the Edge
Introduction
The Quasi Axiomatic Theory (QAT) was conceived as a big-data tool designed to discern significant events on the ever expanding edge of the world-corpus: Cyberspace: 2000. It functions as a category theory, a means of systematically organizing and classifying data, that can isolate trends and detect waves of change in large data samples.
The Architecture of the QAT Algorithms
The QAT uses algorithms to offer a form of 'categorical abstraction,' visual maps of data, and 'eventChemistry,' a method of exploring categories. With these algorithms, machine learning can be integrated into the quasi-axiomatic analysis loop, enabling an AI-in-training to witness the quasi-artifact of measured reality.
QAT Theory in Principle of Emergent Processing of Causal Artifact within Chaos
QAT is essentially a map-maker. It uses a group-motion on two random points on the map in each iteration. This 'grouping' is the 'emergence' in this implementation of emergent computing.
Implementation of QAT Data Structures
The data structures needed to produce emergent processing of the QAT are relatively simple and include three lists: UniqueList-A, UniqueList-B, and ExpandedList-A. In addition, the eventChemistry methods operate with two more lists: Link Index-List by Atom and Atom Index-list by Link.
Simplification
In an optimal solution for big data information access, the data is loaded once, never moved, indexed in place, and structured in place for binary-tree access. This solution slows down very little even with vast data sampling sets, making it highly scalable.
Future Development Notes
Two measurement channels provide one QAT dimension of analysis: QAT.I-RIB, offering a visual abstraction of relationships in the cross-comparison. This happens by randomly scattering onto a visual map the set of points representing all existing pairs within dual channels of measurement in the data ontology widget.
Note: This draft focused on improving grammar, style, and readability. As a technical document, it would also benefit from including diagrams or figures to help illustrate complex concepts or processes.