Talk:White paper on Quasi Artifactual Intelligence - Revision history

XenoEngineer: /* Implementation of QAT Data Structures */

2023-07-26T17:28:39Z

Implementation of QAT Data Structures

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	'''*UniqueList-A:''' A list of the unique values within the sample block from a data stream of channel-A.		'''*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.		'''*UniqueList-B:''' A similar list for channel-B.
	'''*ExpandedList-A:''' A list of all the combinations that can be expanded from the UniqueList-A.
			'''*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':		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.		'''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 for Channel-B.		'''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.		In 2001, a human operated these systems. However, in 2024, machine intelligence can operate them.

XenoEngineer: /* The Architecture of the QAT Algorithms */

2023-07-26T17:27:02Z

The Architecture of the QAT Algorithms

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	===The Architecture of the QAT Algorithms===		===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.		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.		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===		===QAT Theory in principle of emergent processing of causal artifact within chaos===

XenoEngineer at 17:25, 26 July 2023

2023-07-26T17:25:45Z

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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;">		<div style="margin:0 2em; background:azure; border:1px outset azure; padding:0 1em; max-width:888px; min-width:62%; margin:0 auto;">
	DRAFT DOCUMENT		<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		;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.		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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	The QAT allows for a perception of relational dynamics. It can enable AI in training to witness the quasi-artifact of measured reality.		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 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.		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.		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		===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.		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.

Don: /* Conclusion */

2023-07-25T23:17:12Z

Conclusion

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	===Conclusion===		===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.		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!

			'''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!

	</div>		</div>
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	</div>		</div>

	===Introduction===		===Introduction===

XenoEngineer at 18:41, 25 July 2023

2023-07-25T18:41:15Z

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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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XenoEngineer: /* Introduction */

2023-07-25T18:25:06Z

Introduction

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	===Introduction===		===Introduction===

	The Quasi Axiomatic Theory (QAT) was conceived as a big-data tool designed to discern significant events on the ~~<s>cutting</s>~~ 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~~, potentially as vast as planet-scale~~.		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 Architecture of the QAT Algorithms===

XenoEngineer: /* Introduction */

2023-07-25T18:22:48Z

Introduction

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	===Introduction===		===Introduction===

	The Quasi Axiomatic Theory (QAT) was conceived as a big-data tool designed to discern significant events on the cutting edge of the ~~ever-expanding~~ 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, potentially as vast as planet-scale.		The Quasi Axiomatic Theory (QAT) was conceived as a big-data tool designed to discern significant events on the <s>cutting</s> 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, potentially as vast as planet-scale.

	===The Architecture of the QAT Algorithms===		===The Architecture of the QAT Algorithms===

XenoEngineer at 18:20, 25 July 2023

2023-07-25T18:20:47Z

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	;D R A F T    D O C U M E N T		;D R A F T    D O C U M E N T

	A Dascient — groupKOS Collaborative		;A Dascient — groupKOS Collaborative
	~~QuasiArtifactualIntelligence: Perception from the Edge~~

	~~Table of Contents~~:		'''QuasiArtifactualIntelligence: Perception from the Edge'''

			<div style="float:right;">
	__TOC__		__TOC__

			</div>
	===Introduction===		===Introduction===

XenoEngineer at 18:19, 25 July 2023

2023-07-25T18:19:18Z

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		;A ChatGPT 4.0 simplification of grammar
		[[User:XenoEngineer\|XenoEngineer]] ([[User talk: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

		Table of Contents:
		__TOC__

		===Introduction===

		The Quasi Axiomatic Theory (QAT) was conceived as a big-data tool designed to discern significant events on the cutting edge of the ever-expanding 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, potentially as vast as planet-scale.

		===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>

XenoEngineer: Blanked the page

2023-07-24T15:28:07Z

Blanked the page

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	~~<html>~~
	~~<head>~~
	~~<title>Updating QAT to 2023 Standards</title>~~
	~~</head>~~
	~~<body>~~

	~~<h1>Updating Quasi Axiomatic Theory to 2023 Standards</h1>~~
	~~<p>Author: Don@groupKOS.com</p>~~
	~~<p>Copyright © 2023 Don@groupKOS.com, All rights reserved under appropriate open-source licenses</p>~~

	~~<h2>Introduction</h2>~~
	~~<p>The Quasi Axiomatic Theory was developed as a big-data tool to significant events in the live-edge of the expanding world-corpus.</p>~~

	~~<h2>Abstract and Theoretical Framework</h2>~~
	<p>The QAT is based on a simultaneity of relational events in dual channels of measurement. The simultaneity is registered not at a process thread in an algorithmic moment, but as registered as simultaneous within the entire set of dual measurements.</p>

	~~<h3>Implementation Philosophy</h3>~~
	<p>In this paradigm of measurement and category-frequency stratification, the data is a recording and remains immutable. A binary tree implementation for node access had no need to balance an immutable log. A delete function for a tree node isn't needed. In the category theory implementation, the data may be sparsely sampled with the same results.</p>

	~~<h3>The Quasi Axiomatic Theory (QAT) and its Components</h3>~~
	~~<p>The QAT facilitates the display of cross-channel relationships through two key components: Category Abstraction (cA) and Event Chemistry (eC).</p>~~

	~~<h4>Category Abstraction (cA)</h4>~~
	~~<p>Once interesting groups of categories within the data-stream are isolated by category abstraction mapping, groupings of interest are selected from the clusters on the cA mapping.</p>~~

	~~<h4>Event Chemistry (eC)</h4>~~
	~~<p>Isolation of small groups allows event chemistry abstraction to sleuth the chemistry of categorical interactions as an interactive classical graph of circles connected with lines.</p>~~

	~~<h2>The Role and Evolution of QAT</h2>~~

	~~<h3>The QAT in the 911 Response Era</h3>~~
	<p>With contributions nationally, during the 911 response era, aiding in the global cause were the data structures of the QAT visual abstraction, navigable with event chemistry, providing access back to the source-moment of any categorical trend, and as such was the purpose of domestic surveillance.</p>

	~~<h3>The Creation of Ontological Access</h3>~~
	~~<p>Data access methods to access the values of a data stream by value-category by natural order in a linking-sequence visiting the data-solution at Big(O) efficiency.</p>~~

	~~<h3>Visual Abstraction of Relationships</h3>~~
	<p>Two measurement channels provide one QAT dimension of analysis providing visual abstraction of relationships in the cross-comparison. This occurs 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. The gather process accesses the proportionality map to animate the category-mapping- wherein this is a visual abstraction of dynamic relationships translated to a stratification of motions within the category-scatter-map.</p>

	~~<h2>Applications and Future Directions of QAT</h2>~~

	~~<h3>Existing and Potential Applications</h3>~~
	<p>The outcome of the paradigm rendered to notation is a binary-tree/linked-list data widget where all members of a category value are linked as a chain, as category-member-chains attached to each binary tree node.</p>

	~~<h3>Conclusion</h3>~~
	<p>The binary-tree nodes each have a chain of equivalent values indexed in natural order as a linked list. The fully structured data widget affords any data by relational-path-access, including lower_category, higher_category, next_member, previous_member, with all index values anywhere within the structure also indicating relative age by natural order.</p>

	~~<h3>Future Work</h3>~~
	<p>With structured historical data access of a measured channel, a second structure is referenced holding a second channel of measurements. These two structures provide the prismatic affordance of the proportionality map of their implicit relational dynamics across a sampling of events within a time line.</p>

	~~</body>~~
	~~</html>~~