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| <center><b><big>DRAFT DOCUMENT</big></b></center>
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| ;This document discusses the development of the 911 era category theory as a quasi-relational perceptive envelope for machine learning and control.
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| ==Introduction==
| | Find out <b><big>[[About Quasi Artifactual Intelligence]]</big></b> |
| 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.
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| 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.
| | [[S100.cover.front | Exploring UAP temporal technology]] |
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| The QAT allows for a perception of relational dynamics. It can enable AI in training to witness the quasi-artifact of measured reality.
| | [[:category:wiki | Wiki top categories]] |
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| ===The Architecture of the QAT Algorithms===
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| 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.
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| 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.
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| ===QAT Theory in principle of emergent processing of causal artifact within chaos===
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| 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.
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| ===Implementation of QAT Data Structures===
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| The QAT uses three lists to support its 'gather method':
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| '''*UniqueList-A:''' A list of the unique values within the sample block from a data stream of channel-A.
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| '''*UniqueList-B:''' A similar list for channel-B.
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| '''*ExpandedList-A:''' A list of all the combinations that can be expanded from the UniqueList-A.
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| Two additional lists are used in the 'eventChemistry methods':
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| '''4. Link Index-List by Atom:''' An index per each unique category value within the channel-A data sample block.
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| '''5. Atom Index-list by Link:''' An identical index for Channel-B.
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| In 2001, a human operated these systems. However, in 2024, machine intelligence can operate them.
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| ===Simplification===
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| 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.
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| The array-based binary-tree / linked-list hybrid
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| 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.
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| 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.
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| ===Conclusion===
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| 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.
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| '''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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| ;A ChatGPT 4.0 simplification of grammar
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| [[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
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| ;A Dascient — groupKOS Collaborative
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| '''QuasiArtifactualIntelligence: Perception from the Edge'''
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| __TOC__
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| ===Introduction===
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| 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.
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| ===The Architecture of the QAT Algorithms===
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| 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.
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| ===QAT Theory in Principle of Emergent Processing of Causal Artifact within Chaos===
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| 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.
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| ===Implementation of QAT Data Structures===
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| 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.
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| ===Simplification===
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| 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.
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| ===Future Development Notes===
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| 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.
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| '''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.
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