What do all things have in common?

Big Data

Is Mathematics Or Philosophy More Fundamental?

fly-by

Is Mathematics Or Philosophy More Fundamental?

Answer: Philosophy is more fundamental than mathematics.

This is changing, but mathematics is incapable at this time of comprehensively describing epistemology, whereas, philosophy can.

Hence; mathematics is restrained to pure ontology. It does not reach far enough into the universe to distinguish anything other than ontologies. This will change soon. I am working on exactly this problem. See http://mathematica-universalis.com for more information on my work. (I’m not selling anything on this site.)

Also, mathematics cannot be done without expressing some kind of philosophy to underlie any axioms which it needs to function.

PROOF:

Implication is a ‘given’ in mathematics. It assumes a relation which we call implication. Mathematics certainly ‘consumes’ them as a means to create inferences, but the inference form, the antecedent, and the consequent are implicit axioms based upon an underlying metaphysics.

Ergo: philosophy is more general and universal than mathematics.

Often epistemology is considered separate from metaphysics, but that is incorrect, because you cannot answer questions as to ‘How do we know?” without an underlying metaphysical framework within which such a question and answer can be considered.


HUD Fly-by Test

vlcsnap-2016-08-21-22h18m14s161

Link to video.

Don’t take this as an actual knowledge representation; rather, simply a simulation of one. I’m working out the colour, transparent/translucent, camera movements, and other technical issues.
In any case you may find it interesting.
The real representations are coming soon.


A New Kind of Knowledge Representation Is Coming to Be!

VideoImage

Link to video

The project is now coming to conclusion (finally). In this video I show an example knowledge molecule being ‘examined’ by the knowledge representation.

I’ve hidden the other actors in this demonstration and have simplified the instrumentation to preserve my priority on my work.

Be patient! It won’t be long now… I have the theoretical underpinnings already behind me. Now it’s only about the representation of that work.


Obfuscation In A ‘Nut’ Shell

Obfuscation In A ‘Nut’ Shell
Distinctions that are no differences, are incomplete, or are in discord.

In knowledge representation these ‘impurities’ (artificiality) and their influence are made easy to see.

 

In groks you will see them as obfuscation fields. That means darkening and/or inversion dynamics. The term refers to the visual representation of an obfuscated field, and can also be represented as dark and/or inverted movements of a field or group. I concentrate more on the dark versions here and will consider the inversions (examples of lying) in a future post.

They bring dynamics that are manipulative, artificial, or non-relevant into the knowledge representation. Their dynamic signatures make them stand out out like a sore thumb.

Cymatic images reveal these dynamics too. There are multiple vortexes, each with their own semantic contribution to the overall meaning to a knowledge molecule or group.

Here is an example of a snow flake (seen below) https://www.flickr.com/photos/13084997@N03/12642300973/in/album-72157625678493236/
From Linden Gledhill.

Note that not all vortexes are continuous through the ‘bodies’ of the molecules they participate in. Also, in order to correctly visualize what I’m saying, one must realize that the cymatic images are split expressions. That means to see the relationship, you must add the missing elements which are hinted at by the image.

Every cymatic image is a cut through the dynamics it represents.
We are in effect seeing portions of something whole. Whole parts are dissected necessarily, because the surface of expression is limited to a ‘slice’ through the complete molecule.

(Only the two images marked ‘heurist.com’ are my own! The other images are only meant as approximations to aid in the understanding of my work.)


Men And Their Semantics – Turning Meaning into Legos

language

Semantically speaking: Does meaning structure unite languages?

This work is a dead end waiting to happen. Of course it will attract much interest, money, and perhaps even yield new insights into the commonality of language, but there’s better ways to get there.

What’s even more sad is that they, who should know better, will see my intentions in making this clear as destructive criticism instead of a siren warning regarding research governed/originating through a false paradigm. These people cannot see or overlook the costs humanity pays for the misunderstandings research like this causes and is based upon.

It’s even worse in the field of genetic engineering with their chimera research. The people wasting public money funding this research need to be gotten under control again.

I don’t want to criticize the researcher’s intentions. It’s their framing and methodology that I see as primitive, naive, and incomplete.

I’m not judging who they are nor their ends; rather, their means of getting there.

“Quantification” is exactly the wrong way to ‘measure/compare semantics; not to mention “partitioning” them!

1) The value in this investigation that they propose is to extrapolate and interpolate ontology. Semantics are more than ontology. They possess a complete metaphysics which includes their epistemology.

2) You cannot quantify qualities, because you reduce the investigation to measurement; which itself imposes meaning upon the meaning you wish to measure. Semantics, in their true form, are relations and are non-physical and non-reducible.

3) Notice also, partitioning is imposed upon the semantics (to make them ‘measurable/comparable’). If you compare semantics in such a way then you only get answers in terms of your investigation/ontology.

4) The better way is to leave the semantics as they are! Don’t classify them! Learn how they are related. Then you will know how they are compared.

There’s more to say, but I think you get the idea… ask me if you want clarification…


Typical Knowledge Acquisitions Node

Knowledge Representation

A typical knowledge acquisition node showing two layers of abstraction. Note how some of the acquisition field detection moves with the observer’s perspective. You can tell, due to the varying visual aspects of the fields and their conjunctions that it has already been primed and in use.

This node may be one of thousands/millions/billions which form when acquiring the semantics of any particular signal set.

Their purpose is to encode a waveform of meaning.

Basically it is these ‘guys’ which do the work of ‘digesting’ the knowledge contained within any given signal; sort of like what enzymes do in our cells.

The size, colour (although not here represented), orientation, quantity, sequence, and other attributes of the constituent field representations all contribute to a unique representation of those semantics the given node has encountered along its travel through any particular set of signal. The knowledge representation (not seen here) is comprised of the results of what these nodes do.

This node represents a unique cumulative ‘imprint’ or signature derived from the group of knowledge molecules it has processed during its life time in the collation similar to what a checksum does in a more or less primitive fashion for numerical values in IT applications.

I have randomized/obfuscated a bit here (in a few different ways), as usual, so that I can protect my work and release it in a prescribed and measured way over time.

In April I will be entering the 7th year of working on this phase of my work. I didn’t intentionally plan it this way, but the number 7 does seem to be a ‘number of completion’ for me as well.

The shape of the model was not intended in itself. It ‘acquired’ this shape during the course of its work. It could have just as well been of a different type (which I’m going to show here soon).

Important is the ‘complementarity’ of the two shapes as they are capable of encoding differing levels of abstraction. The inner model is more influenced by the observer than the outer one, for example. The outer shape contains a sort of ‘summary’ of what the inner shape has processed.


Ontology: Compelling and ‘Rich’

Ontology (Water)

Ontologies are surfaces… even if ‘rich’. (link)

Ontology: Compelling and ‘Rich’
They are only surfaces, but they seem to provide you with depth.

This exquisite video shows how the representation of knowledge is ripe for a revolution. I’ve written about this in depth in other places so I won’t bore you with the details here unless you ask me in the comments below.

Stay tuned! I’m behind in my schedule (work load), but I’m getting very close just the same. I will publish here and elsewhere.
I’m going to use this video (and others like it) to explain why ontologies are not sufficient to represent knowledge.

Soon everyone will acknowledge this fact and claim they’ve been saying it all along! (In spite of the many thousands of papers and books obsessively claiming the opposite!!!) They do not know that how dangerous that claim is going to be. Our future will be equipped with the ability to determine if such claims are true or not. That’s some of the reason I do what I do.

#KnowledgeRepresentation #BigData #Semantics #Metaphysics   #Ontology
#Knowledge #Wisdom #Understanding #Insight #Learning #MathesisUniversalis #MathesisGeneralis #PhilosophiaUniversalis #PhilosophiaGeneralis #ScientiaUniversalis #ScientiaGeneralis