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.)

See the animated versions of these images here:  http://wp.me/p2VGCk-gQ

Nobel Prize For Stating The Obvious

Nobel Prize For Stating The Obvious
You can get a Nobel for anything these days: Obama for peace(!) and Merkel soon for treason, for example.

You need only be 'connected' or have something to say or do that our controllers value.

In this one we have 3 points:
1) 'Huh' and its variants appear in 31 languages
2) People stop for clarification in conversation once every 90 seconds
3) People share the burden of fixing misunderstanding in conversation.

Now if you didn't know this already, then celebrate!

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.

Precursors Of Knowledge

Precursors Of Knowledge
Fractal fields provide a nice framework in which to think about knowledge. They are not all we need for precision, but they are helpful in a generic way. I'll be posting more on them as the knowledge representations are published, because there are many 'gaps to fill' to show how these relate to knowledge.
A Unifying Topology of Fractal Fields

More sources:
https://www.youtube.com/watch?v=2nTLI89vdzg
https://www.youtube.com/watch?v=1ZVNIZGw4X0
https://www.youtube.com/watch?v=Yp4ogF2w13M
https://www.youtube.com/watch?v=8UPD2_gEjvM
https://www.youtube.com/watch?v=ArZLXHVVV5I