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<h1>Updating Quasi Axiomatic Theory to 2023 Standards</h1>
;July 10, 2023
<p>Author: Don@groupKOS.com</p>
;Communique about '''''White_paper_on_Quasi_Artifactual_Intelligence'''''
<p>Copyright © 2023 Don@groupKOS.com, All rights reserved under appropriate open-source licenses</p>
----
CC: Stephen J. Roberts (working the project space with me --nuanced on the wiki as psiCommander, me as XenoEngineer the promptrix)


Hi Don D. M. Tadaya!
<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>


Your question [ "What is an I-RIB?" ] is answered by Prueitt here: 
<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>


http://www.bcngroup.org/beadgames/TaosDiscussion/referentialBases.htm
<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>


I-RIB: In-memory Referential Information Base. A term coined by P. S. Prueitt. Foundational Paper on Referential Bases, Paul S Prueitt Ph.D, October 31, 2006 
<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>


I'm pouring through Prueitt's web site, and validating my lingo against logic.  
<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>


The I-RIB, or In-memory Referential Information Base is a binary-tree data-access structure that preserves access to any and all certain events comprising a categorical trend.
<h2>The Role and Evolution of QAT</h2>


I'm working my edits here, at our think-tank wiki (just installed this wiki a few days ago, and it will host a comic book that extends the concepts of a [[Alienesque Psience of Mind|coalescent school of thought]]: ...
<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>


http://groupkos.com/dev/index.php/White_paper_on_Quasi_Artifactual_Intelligence#THIS_DOCUMENT_IS_IN_SEVERE_EDIT
<h3>The Creation of Ontological Access</h3>
<p>In this notational paradigm of measurement and category frequency trending, the data is a recording and remains immutable. A binary tree implementation for data access has no need to balance an immutable log. A delete function for a tree node isn't needed.</p>


Prueitt's I-RIB, or ORB, as Ontological Reference Base, are exactly reduced a pair of the same data widget, and one table holding the Cartesian product of category pairs within the two widgets.  This creates the 'proportionality map' of the categories of two measurement data channels within the same period of time.
<h3>Visual Abstraction of Relationships</h3>
To see the visual abstraction of the time-block of measurements, one is essentially visually observing a sorting process of categories from a data stream, but sorted by using random-selections, and randomly moving pairs closer together, one can observe motion and grouping behaviors.
<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>
For future discussion: I'm naïve enough to assume there will be 'something' about the patterns over time of the motions of categories that reflect category frequency as a 'training data' for ML AI.


The above discussion is within Prueitt's concepts of categorical abstraction. ( cA )
<h2>Applications and Future Directions of QAT</h2>
Prueitt's 'event chemistry' (eC) shows (I think properly stated as) linking between measurement columns Each category is represented as a circle, and links represent edges of a classic graph showing how any one category links (within the time-line sample block) with any other category.  Those linkings can have interlinking between categories several categories, which form the visual kinship to chemical compounds; an event chemistry.
This is another discussion topic once I get more smarter with this refreshing refocus on yesteryear!


Cheers!
<h3>Existing and Potential Applications</h3>
DonEM
<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>


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<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>


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<h3>Future Work</h3>
The text appears to be an abstract for a technical paper on applying category theory in the context of machine learning and control, more specifically in relation to the development of an AI perception envelope. The language used is quite complex and requires a reasonable understanding of the subject matter to grasp the full meaning. Here are the potential issues and clarifications:
<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>


Use of technical jargon: The text uses quite a few specialized terms and jargon. Terms like "QAT," "perceptor," "perceptive envelope," "dual channels of measurement," "artifactual intelligence," and "diffraction grating" are not explained and could potentially cause confusion. If you're writing for an audience familiar with these terms, this might not be an issue. However, if the audience might not have the requisite knowledge, it would be best to clarify these terms or provide a brief explanation for each.
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Ambiguous semantics: The sentence "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" could benefit from some rewording for clarity. I'm not exactly sure what is meant by an "algorithmic moment," and the use of "simultaneous" and "simultaneity" in the same sentence creates a tautology that doesn't add clarity.
 
Sentence structure: Some sentences are quite long and complex, making them harder to parse. Breaking them down into simpler sentences might help with readability and comprehension.
 
Lack of context: If "911 Era" refers to a specific method or period in the field of category theory, that isn't clear from the text. Providing some background or additional context could be helpful.
 
Proper Citation: The term "[I-RIB]: In-memory Referential Information Base" is attributed to P. S. Prueitt. If this is a key term, the author may want to cite the original work where it was defined.
 
Diagram or Illustrations: Given the complexity of the topic and the abstract nature of the described methodology, including some diagrams or illustrations could be beneficial for visual learners and could help to clarify some of the concepts.
 
Overall, while the text shows a deep understanding of a complex subject matter, it could benefit from more clarity and a simplification of some of the complex sentences. It's also important to ensure that all specialized terms are clearly explained or defined.
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Revision as of 02:12, 11 July 2023

Updating QAT to 2023 Standards

Updating Quasi Axiomatic Theory to 2023 Standards

Author: Don@groupKOS.com

Copyright © 2023 Don@groupKOS.com, All rights reserved under appropriate open-source licenses

Introduction

The Quasi Axiomatic Theory was developed as a big-data tool to significant events in the live-edge of the expanding world-corpus.

Abstract and Theoretical Framework

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.

Implementation Philosophy

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.

The Quasi Axiomatic Theory (QAT) and its Components

The QAT facilitates the display of cross-channel relationships through two key components: Category Abstraction (cA) and Event Chemistry (eC).

Category Abstraction (cA)

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.

Event Chemistry (eC)

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.

The Role and Evolution of QAT

The QAT in the 911 Response Era

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.

The Creation of Ontological Access

In this notational paradigm of measurement and category frequency trending, the data is a recording and remains immutable. A binary tree implementation for data access has no need to balance an immutable log. A delete function for a tree node isn't needed.

Visual Abstraction of Relationships

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.

Applications and Future Directions of QAT

Existing and Potential Applications

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.

Conclusion

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.

Future Work

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.

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