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About Quasi Artifactual Intelligence
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About ∞ White paper ∞ QuasiMatrix Code ∞ QAT Code ∞ Data Examples ∞ Scatter-Gather Explained ∞ The Plow Share ∞
This is about
a synchronicity unwinding
A man with primitive IT skills is opportunistically tutored advanced computer science skills. This was during the 'dawning' of artificial intelligence deep research —circa 2000. The tutor retreated into the clandestine world of the intelligence community.
Nearly a quarter century later, the dawning computer science of machine intelligence arrived to the general populous.
Things happen. Visions ignite. An energetic Summer ensued. And the QAT was ushered into modern light.
This providential ignition is about extending the concept of the QAT human-in-the-loop with machine-intelligence-in-the-loop, during categorical analysis creating visual abstraction patterns. To paraphrase the Soviet cyber-warfare scientist of the late 90-s "...to find the unknown and unanticipated."
An overview of the Quasi Axiomatic Theory
The Quasi Axiomatic Theory (QAT) is a big-data tool developed for analyzing significant events in the expanding world-corpus.
QAT uses category theory to isolate category trends and waves of change in large data samples.
The paradigm includes categorical abstraction and eventChemistry algorithms for exploring categories and reverse-searching causative determinants.
Machine learning can be integrated into the QAT framework to provide a perception of relational dynamics.
The architecture of the QAT algorithms involves visual abstraction and mapping cross-category coincidence-frequency dynamics.
The QuasiAI system controller is a visual abstraction of multiple synchronous measurements, striated by frequency of simultaneity.
The parameters of the system components provide the causal model for training machine intelligence.