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With this work we introduce a system of idealogical metaphysics which is primarily born out of an epistemological interpretation of Quantum Mechanics (QM). Our interpretative stance follows a long line of consciousness (what we call mind) based interpretations, or explanations, for the of the so-called measurement problem in QM, a position held by...
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... This work originates out of ongoing research into the potential application of modern machine learning and artificial intelligence paradigms with respect to metaphysics. It follows on the heels of a paper on semantic geometry which studies the underlying data representation layer for most modern large language models (Word Vectors essentially) [1], which itself follows on the heels of research into the creation of metaphysical reference architecture that applies the principles of quantum probability theory to human decision making through ideological (intentional spelling) space [2]. This paper takes an additional step forward in that we develop a prototype conceptual hierarchical constructor which looks to generate conceptual maps, effectively directed graphs of conceptual nodes tied together by actions (subject-object pairs), to see what might be possible with respect to metaphysical inquiry when applying ontology learning techniques onto specific literary texts of cultural significance, taking the Bible for example. ...
... The problem of knowledge representation in AI however, manifests as something very specific, as the problem of developing rational agents that are able to successfully navigate their environment effectively, and while solutions to this problem most certainly borrow techniques and tools from what might be considered classical software development or software engineering disciplines, in AI it nonetheless presents unique challenges because the representation of knowledge in this case must allow for the management and evolution of rules or axioms that provide the boundaries and scope for AI behavior, loosely speaking. These rules or axioms also must be tractable in the sense that they can grow and evolve at run time in a real environment, and these rules must be structured in such a way 2 The research seminar at Dartmouth-Dartmouth Summer Research Project on Artificial Intelligence-that typically marks the origins of the field was held in 1956. See https://home.dartmouth.edu/about/artificial-intelligence-ai-coined-dartmouth that they lend themselves to both querying, in the sense of is this operation or this relationship possible or feasible, as well as reasoning, in the sense of what is the best possible action given the set of rules and axioms that govern the system in question as well as the data input it has from its current environment and context. ...
