Showing posts with label Big Data. Show all posts
Showing posts with label Big Data. Show all posts

Friday, January 15, 2016

 
'Trust' For Sale
More of Google's attempt to become the 'clearing house' of truthful, 'trustful', and important facts and therewith create a 'truthful tribe'. I thought we wanted to rid ourselves of tribalism?

So many talented people will never be known, because they work 'under the radar' or for being ignored (exiled) as 'heretics'.

Here is a question: how can even truth, not to mention trust, be systematized when we cannot know all of it as well as its many sources of origination?

Google is creating its own demise with this. It will go down or cause a vast migration of awakened (and non-evangelists) to move to, create, or participate in other search engines.

True research must make it's own decisions upon what is truthful, trustworthy, and valuable. If we allow a corporation to manage these values, we will enter an age of 'privatized credibility'.

They will be able to keep people out of the debate (social discourse) by making them non-authoritative. If they can establish metrics then everyone must conform to them.

It's like believing Marx, Engels, and Lenin were philosophers when they were really children playing with snake-oil in order to sell the idea that a tyranny of Communism was the solution to humanity's problems.

Monday, January 4, 2016

Typical Knowledge Acquisitions Node

Link to video...

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.

Monday, August 31, 2015

A Holon's Topology, Morphology, and Dynamics (2a)

Holons Topology, Morphology, and Dynamics (2a)

This is the second video of a large series and the very first video in a mini-series about holons. In this series I will be building the vocabulary of holons which in turn will be used in my knowledge representations.
The video following this one will go into greater detail describing what you see here and will be adding more to the vocabulary.

This is the second video of a large series and the very first video in a mini-series about holons. In this series I will be building the vocabulary of holons which in turn will be used in my knowledge representations.

#Knowledge #Wisdom #Understanding #Insight #Learning #MathesisUniversalis #ScientiaUniversalis #Holons   #BigData  

Friday, May 29, 2015

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

Tuesday, May 26, 2015

Coming Soon To A World Near You
The time is coming when we will exchange massive amounts of knowledge between us without any corporation standing in between.
My life's work is dedicated to this vision and I'm actually carrying it out right in front of you!

We will not only create and share our books, documents, web sites, search results, and media with each other - we will be sharing their conceptual landscapes.

3D Scientific Visualization with Blender
It's a book everyone in knowledge representation should at least know about. It has great tips and clarifications inside.

Unfortunately it is also based solely on ontologies so it provides only limited value for what I'm doing, but it is a valuable resource for understanding and creating visualizations just the same.

Video - Rendering a data cube:
https://www.youtube.com/watch?v=3GvTTVEeEmk
Video - Colliding galaxies:
https://www.youtube.com/watch?v=CPuVfiWLlHI



Tuesday, May 5, 2015

Lynda.com - Overview of Data Visualization
(Lynda.com - Overview of Data Visualization)

Information Visualization Is Not Knowledge Representation

This great video from Lynda.com shows how the processing language/interpreter is great for modeling information.
With such a multitude of interesting ways to model data, we find it hard to resist the temptation to call this knowledge, but it's not!

All of the wonderful representations here still require us to interpret their meaning!
What if there were a way to present knowledge in which our own understanding is not required to interpret them? What if our understanding of what we have presented to us becomes part of the presentation itself, and in fact, influences what we take from that representation?

We obviously need knowledge representation that can provide their meaning on their own for only they can provide a true understanding of their inherent structure and dynamics.
You see real understanding is the personalization of knowledge into your own mind. If your mind cannot dialog with that knowledge, it's not really yours and if your mind does all the work, it's only information.