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| {{headerQuasiAI}}
| | == Summary == |
| =THIS DOCUMENT IS IN SEVERE EDIT= | | [[Category:AI art]] |
| | | [[Category:DALL-E]] |
| 911 Era category theory applied as a front-end ontologizer for machine learning and control<br/><br/>Developing a perceptive envelope of reckoning for an artifactual intelligence —quasiAI: [[the perceptor]]
| | [[Category:Quantum Emergence Comics]] |
| | | {{qe.footer}} |
| ==Abstract==
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| 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. But that is not entirely correct. The simultaneity is stratified into proportions, and abstracted as a diffraction grating, so to speak. By reflecting random pairs against the diffraction of dynamic motion in a scatter map representing the population of categories in the dual channels of analysis is afforded.
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| == Implementation Philosophy ==
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| 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.
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| By structuring the data-sample stream, or some sparse selection of samples over time, into a structure including all members in an accessible structure, the tree-list composite becomes a rudimentary ontology of the data-stream structured by value-comparisons against a data-relationship.
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| Additionally, as a time-series log, there is implicitly a time-line addressable ontology.
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| Two measurement channels provide one QAT dimension of analysis<sup>[I-RIB]</sup> 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.
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| Picture visual abstraction at work as a point cloud animated by the relationships between two data streams.
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| <small>
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| '''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<br/> http://www.bcngroup.org/beadgames/TaosDiscussion/referentialBases.htm<br/>"URLs for Orbs and Ribs are scattered in the OntologyStream Inc and BCNGroup Inc web sites, in particular:<br/>http://www.bcngroup.org/area1/2005beads/GIF/RoadMap.htm and<br/>http://www.ontologystream.com/OS/clientTechnology.htm
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| </small>
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| <div style="float:left; margin-right:2em;">
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| __TOC__
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| </div>
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| ==Introduction==
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| The Quasi Axiomatic Theory was developed as a big-data tool to significant events in the live-edge of the expanding world-corpus.
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| [ Historical discussion, old limitations, new affordances with 'key-less tables' (re-branding of a binary-tree) as a solution to the has-table key which relies on a hash-table much larger than the data. The quadrabytes of data within the digital world domain simply needed a so called 'destructive' technology against acceptable-use practices, that works at the scale of planetary data volumes. ]
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| == The making of ontological access ==
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| In this notational paradigm of measurement and category frequency trending, the data is a recording and remains immutable.
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| 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.
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| In the category theory implementation, the data may be sparsely sampled with the same results, within limits.
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| Should a data-access excursion on the tree-list structure get lengthy (as an unbalanced binary-tree) in any certain data sample, it is simply rejected as part of the un-sampled, dis-obviating any need for binary tree balancing.
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| 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.
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| 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, i.e., the magntitude of the access-index is a relative age value in a time-line sampling.
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| 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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| ==Theory for Applications==
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| ===Observation 1===
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| *Section
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| **Subsection
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| ===Observation 2===
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| *Section
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| **Subsection
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| **Bullets
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| ==Theory of Applications==
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| ===Research, Analytics & Development===
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| ====Deep sense reality augmentation====
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| == Conclusion==
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| ===The affordances of the QAT===
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| * Sparse sampling, which provides similar results with any sampling rate above scarce.
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| :* As an examination of categories, groupings of recurring category values in a sample are gathered on a abstraction-map. These groupings occur as a process of time (many micro-motions), and the summation of micro-motions of the map elements eventually converge on the map into groupings of like categories in respective regions on the abstraction-map.
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| :* When a sparser selection of measurements across a time line is analyzed, the grouping of members occurs faster, but category values occurrences will begin to drop-out of the sampling set, for the lower frequency categories, dropping out first.
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| :* Binary-tree based access affords a lean-memory model —each measured event requires a
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| ==Technical Development==
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| ==Future Work==
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| ;* Hyper-accelerating quasiAI with massive parallel circuits on FPGA configurations
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