Applied Data Sciences In Unification’s Wake
If you are an information sciences student or professional you likely understand the distinctions between data, information, and knowledge. Artificial Intelligence (AI) might throw some folks off just a bit, especially if we add consciousness and sentience to that list. We probably ought to throw in Knowledge Management (KM) for good measure because so many confuse that with information databases, especially if managed by AI. The question though, is how do these domains of discourse and their constituent detail sets change In Unification’s Wake? Rhetorically we ask everyone to noodle on the distinctions between data and information. Simplistically we might say that context is added in order to provide greater detail on how that data is meant to be interpreted.
While you are noodling on that contemplate this as well. In that first sentence above the increasing levels of detail are essentially in order along that gradient. Right? Those details are what manifest context which is subsequently interpreted, correct? What happens systemically to all higher ordered considerations of such a gradient as applied to any given domain of discourse or constituent detail set if that fundamental context changes? The details surrounding how that context may change is one discussion. What if there are multiple sets of contexts which are all simultaneously true within a given set of circumstances? What critieria do you apply to sort all that out? Do correlations equal causality? The video below is Richard P Feynman discussing knowing vs understanding.
Strategically at issue here are the implications due to encapsulation of foundational interpretative models (EIMs) on which everything is epistemologically dependent. At the core of these issues is commission of Langer Epistemology Errors (LEEs). When we critically consider what constitutes data we must recognize that the reality that data represents is some abstraction or set of them. We may say they are highly reflective of reality but they are not actual real reality. That may be a fine line of distinction but it matters relative to the unified Universe epistemologically a function of the Encapsulated Interpretative Model (EIM) we employ perceiving it. The critical insight here is the systemic nature of these particular insights more than any other exactly because they are integrated in every other higher ordered construct. That means this data, different from other data, holds the potential to recast and redefine perception and that changes how that data is handled even if the logical manifestation of it relative to Paradigms Of Interest/Nature (POI/N) do not change in any significant way. Then we get to the fact that insights must be presented in cognizant context of the utility process, framework, and epistemology making them manifest or you will never be able to justify those insights to anyone not cognizant of how they were developed. Relative to all applied data science curricula courses these issues have very direct impact exactly because of the systemic nature of these issues. Unification requirements are given to us by the unified Universe. They are not derived by humans. We are lucky to have the intellect needed in order to discern them at all because there are many creatures on our planet which can not. We should never take that for granted.
We are so used to the term empirical data, but what exactly does that mean? First of all it means that humans everywhere can experience the same data in the same way, but what exactly does that mean? Empiricism is one of many different philosophies of knowledge. Elegant Reasonism philosophically joins their ranks. The distinction between the various epistemologies is essentially their source of truth. In Elegant Reasonism’s case the source of truth is the unified Universe. The term empirical, defined by wordnik as:
- Relying on or derived from observation or experiment.
- Verifiable or provable by means of observation or experiment.
- Guided by practical experience and not theory, especially in medicine.
While there is nothing directly incorrect with those defined interpretations, there is a problem nevertheless. The problem arises when we realize that all humans on Earth have the same physiology, but then how does that work? Susanne K Langer points out that means our senses via our Central Nervous Systems (CNS) manifest wit our brains necessary abstractions in order to relate to the real world around us and to each other. Langer also makes the case that mistaking abstractions for actual reality is epistemologically a fatal error. We consequently designated Langer Epistemology Errors (LEEs) to honor her work. LEEs constitute commission of mistaking abstractions for reality and such mistakes need to be eliminated from every investigation and Elegant Reasonism is designed with that objective in mind. Ok, what is an abstraction? Information sciences will report that an abstraction is essentially a label with a detailed characterization of the properties, phenomena, and behaviors relative to and respective of other abstractions. It is also important to recognize that abstractions have a tendency to insulate and isolate higher ordered ideas from lower ordered detail along the entanglement gradient from the smallest real object to the largest real objects. All real objects along the entanglement gradient may be investigated from both the emergent and convergent vectors. Elegant Reasonism integrates all other epistemologies but expects statistical weighting of their results relative to and respective of the unified Universe. Therefore Elegant Reasonism can be thought of as a superset epistemology where truth is a function of the unified Universe. Data interpreted through this lens is different than characterized in any other manner exactly because here, it is in full context of the unified Universe and not how human physiology senses it. The objective here is an attempt not to fall into LEEs Empiricism Trap.
Elegant Reasonism: the Utility Process, Framework, and the Path to Epistemology
The epistemological path for Elegant Reasonism ultimately creates an interesting situation relative to paradigms and how both the Central Nervous System (CNS) and the Brain (across all Brodmann Areas) change neural pathways. Those pathways do change necessarily or no one would ever learn anything. No one would ever change their behavior. Obviously we all do change and leverage those changes in order to alter our behavior. Though some find such changes more difficult than others. Such changes are due to something called neural plasticity. These phenomena are natural and intrinsic. What is interesting is that when we go to school and we make these changes because we learn the pathways are altered. So our behavior can take advantage of what it was we learned at school. Here’s where it gets really interesting. If what we learned was via Elegant Reasonism in alignment with the unified Universe then so to does our thinking become over time. That’s pretty powerful. If we take these actions consistently and with that intent then we are implementing something we call Neural Network Reconfiguration by Programming or NNRP.
To say that what we have done is epic does not come close to describing this fundamental benchmark in history. What we have done will never happen in history again. Gaining the precipice of unification for the first time is a monumental accomplishment and will be recored in the annals of history on our little world for all time. Forever will this change the nature of how we approach Applied Data Science because now our methods demand truth as a function of the unified Universe.
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