Information vs Knowledge – Part 1, The Problem!

Standard and conventional scholar textbooks and most academic institutions are no-longer suited to vehicle the knowledge inherited on a daily basis from a dynamic and rapidly evolving society such as the one in which we live today. This is simply due to the two following main factors :

• the almost-exponential growth of the field of knowledge beyond these institutions and
• the lack of integration of this growth within them (institutions) among other things.

Consequently, most disciplines are turning into research fields in order to keep up the pace of progress with up to date information.

Meanwhile researchers in those fields are poorly trained and at a disadvantage because of :

• the extensive knowledge-base created in the world to be documented, among other factors and
• the short period of time required to compile and process the academic repository,

yielding to the problem of ‘Information Credibility vs Information self-preservation’. Otherwise stated, the problem of:

1. How to stay current with the latest trends of information, and at the same time
2. Making sure that the information trending is accurate and credible.

On the other hand institutions too concerned in ensuring ‘Information Credibility and Accuracy’ usually loose pace of the exponential change of information to the point where the knowledge available on their systems is often no longer up to date, hence does very little help. Besides what’s the point in having credible information or knowledge if it’s obsolete, outdated or no longer applicable?

Furthermore, institutions who prioritize on information speed, are more susceptible to data corruption or information inaccuracy (aka incredible sources).

This raises the question of the need to review the way in which knowledge is acquired, transferred and the structure of academia in many aspects of our modern-day society. Inasmuch, achieving a more sustainable cycle of “knowledge-acquisition” and “transfer-of-knowledge”- would warrant: keeping up with the pace of progressive change. A crucial step in the process.

To this end, a thorough analysis of the subject matter, brings more questions on the topic such as for instance, the issue of the proper technology or means to deliver on the current aforementioned realities and problems. Besides, in each generation, and in every era where some knowledge had to be transferred and acquired, there had to be a suitable technology or process to make it possible.

It is therefore inadequate to expect or hope for a coherent future of knowledge transfer and acquisition today, without first solving the intrinsic particulates of the platform or underlining technology. A technology which could process our modern-day “information” into the “knowledge” we seek.

Moreover, it has become apparent that in this age of “data” – be it in the context of small-data or big-data, microprocessors which were only designed to process random primitive binary data initially, in the likes of dinosaurs, would be obsolete for the modern ecosystem today. Their data storage and processing technology, are ill-equipped for the task at end, simply in part because they were designed to process “forms” of information mainly as in “data format”, rather than the “content” and “meaning” or “value” of the data in itself, let alone with added context.

One can therefore draw such observations at first-sight, by looking at the complexities of basic human language-processing requirements, which still remain quite tedious a task for even the most powerful computers today. It appears the issue isn’t one of “horsepower” but rather one of “intelligence” or “precision” to say the least. Because in principle, all the horsepower in the “galaxy” cannot solve the most basic task of : “finding the linguistic value or meaning of a misplaced symbol in a data-set” without “intelligence”. For the non-scientist, this is equivalent to the problem of “translating the idea of a sentence in a given context, rather than its literal expression”; A similar analogy to the dilemma of “brains versus muscles”.

Consequently, the big- or small- data, with all the horsepower available today, still has no-value unless the human operator and analyst does the heavy duty workload!

In the spirit of keeping this first part concise, I will resume with one of my favorite personal metaphors and open quotations. The metaphor stems from the quote ‘Information is Knowledge without value’. Hence follows the question which is: how do we keep information valuable and current at the same time? In other words, how do we define and organize pieces of information into meaningful valuables called ‘knowledge’. Then keep this knowledge up to date while more pieces of ‘information’ are constantly and dynamically being processed via technology into more knowledge to be added (without compromise)?

The answer to such question and the technological construct or platform that supports it will define the structure of academia, expertise and the knowledge centers of the future of our society in perspective.

Henceforth, in the next part of this article, titled “Information vs Knowledge – Part 2, The Solution”, we will discuss efficient solutions from Don Sigma Corp. & The Sigma Legacy’s “Problem-solving science” or “Sigma Spiral”. Its successful application to a practical and complete solution of the problem posed in this article, and introduce the concepts with a suitable and adequate tech: The Sigma Macroprocessors and Geno-Macroware technology.