2. Transmission of knowledge
Knowledge has to be sharable and has to be understandable. The knowledge in a brilliant brain that does not communicate with anyone else is not useful (for all practical purposes it is non-existent). Similarly, a knowledge stored in data centres that is never accessed is completely useless. Accessing knowledge is not enough, it needs to be understandable. The ancient Egyptian knowledge faded away in the first centuries of the last millennium as humanity lost the capability to understand hieroglyphs. The Rhind papyrus shares with us the ancient Egyptian knowledge on math (at least part of it) but this sharing was possible only because Champollion provided the key to decipher the hieroglyphs in 1824.
Today English is the tool most people use to share knowledge in the scientific domain, although important pockets of knowledge are represented in other languages, Chinese being the second one after English. The barriers created by languages are fading away thanks to artificial intelligence. Today I read articles written in Chinese, courtesy of Google translator. Of course I have to trust the translation and I do not have, in practice, any way to be sure it is correct. Let’s say that I am contented if I feel what I am reading makes sense and if it gives me food for thought.
In a way we can say that the language barrier to knowledge access is fading away.
What I often do is to look at graphics. I like the ones created by WEF – World Economic Forum, by Gartner, by McKinsey … Notice that all these companies are in the business of making knowledge meaningful and easily understandable. Since an image is worth a thousand words they often publish their meaning extraction in form of graphics.
One of the problem in these images is that they “hide” the precise knowledge on which they are based. Often you have access to that but most of the time you are happy to look at the graphic. A caption is usually explaining how to interpret the graphics and that is what ensures knowledge sharing and understanding.
Another way of using graphical representation is to have knowledge emerging from it. You might have a table with hundreds of data, even many more, that would be basically impossible to digest. By transforming it into a graphic you often succeed in having knowledge emerging. Graphics can be both a shorthand notation for knowledge representation and a way to stimulate thinking thus leading to “creation” of knowledge.
What you see on the left hand side, a circle diagram, is an example. I created it from a table reporting correlations (in the form of numbers up to the 8th decimal point). This can be much more effective than trying to understand thousands of numbers.
However, and this is the interesting part, when I shared the resulting diagram, a discussion started among some of the group participant on “what is the meaning” of the graphic, what knowledge can we derive from it.
I am pointing this out because if it is sure that a graphic representation can be easier to grasp, hence more meaningful, it is also bound to generate “different” interpretations, hence to create different, subjectives knowledge spaces.
It is true that you should always be able to go the the source data and try to understand what the true meaning is, but in practice we never do that.
All this rambling is to point to a new wave of knowledge transfer, based on the computation of raw data. This is what we will be seeing more and more, as knowledge will be accessed “through” machines that will be in charge of rendering the raw data in ways that can be understood and can provide a “meaning”.
The rendering requires intelligence, and artificial intelligence is often, and more so in the future, a crucial component of knowledge transfer, and in a way of knowledge creation.
Virtual Reality and Augmented Reality are just two technologies that we will be using to access knowledge. As discussed they will also create knowledge and this creations is bound to be biased. That is a discussion for the next post.
Following this discussion we can state that:
- knowledge represented by “raw” data is becoming impossible to digest for the sheer volume of data
- there is value in the rendering of data, as it can be done using AI, AR, VR. In the future a significant chunk of the perceived value will be derived from these three technologies
- the very act of rendering is crucial and it is subject to potential bias and errors. Trust in the rendering (i.e. in the organisation/software that perform the rendering) will be an essential component of the perceived value.
The corollary is that IEEE will need to move from being the trusted repository of knowledge to become a transfer knowledge service provider.