I say the following knowing the risk with which I am placing myself 😊
…never been a fan of “implicit” and so I am not a fan of Polanyi’s definition.
My point, based on years of practical consulting application, is that “tacit” knowledge can be elicited, harvested, captured and distilled to make it searchable, findable, accessible and usable/reusable. There is a skill and craft involved IMO. My definition
of knowledge is based on the concept that it consists of information and experience = knowledge
For practical, not academic or theoretical application, this simple, practical definition I find has meaning in a business or operational environment. This has been my experience.
FWIW, in relation to Bill’s use of “explicit” and “tacit,” I find a third category useful; namely, “implicit.” I define “implicit” as knowledge which hasn’t been articulated but can be. Tacit,
by Polanyi’s definition, can’t be articulated. And explicit, of course is knowledge that has been articulated.
When it comes to capturing implicit knowledge, lots of folks have been doing that for many years. You will find them in the training and human performance community.
Fred Nickols, Consultant
My Objective is to Help You Achieve Yours
Been enjoying this timeless discussion on K capture and the extended inference about tacit knowledge and the subtleties of human language in the sharing in the context of the below thread.
Believe it is generally recognized that technology cannot yet get what is one person’s mind and transfer it to another person’s mind. At the heart of the issue, I believe, is the skill set that is required to capture or better said, to elicit what someone knows
or can share, to distill and make sense of that knowledge whether it be explicit or tacit, and then to characterize it for reuse within an organization (fit for purpose and context relevant) and then to make is searchable, findable, downloadable, and usable/reusable.
I believe that this is a reasonable approach to as stated below “…to study KM processes, identify patterns and see if technology can address that in a narrow sense.”
We are still very far from seeing any AI technology that can intelligently extract human knowledge in a broad sense. A main challenge is that of data, and in particular textual data. Data has to be structured in a certain format to be consumed
by an AI machine, but in the case of textual data, the number of permutation and subtleties in human language makes this task tremendously difficult. But perhaps the biggest challenge is the limitation of computation power. The most powerful computer today
has roughly 1 billion neural connections/synapses, similar to the amount of synapses that a honey bee has vs 100-150 trillion synapses of a human brain. It has been suggested by computer scientists that we are actually just approaching bee-level intelligence,
and we are not even there yet.
That said, AI should be sophisticated enough for a focussed application, especially if it’s built around narrow use cases and trained for specific domains. e.g. the technology to recognise and extract names/entities from homogenous, unstructured
text is fairly advanced, and so is the ability for a machine to auto-index/classify documents and textual data. Information extracted as such, combined with data visualisation techniques, will go a long way in enhancing the comprehension of what’s contained
in an organization's textual data, and that is a possible KM angle you can look into.
Instead of looking at broad ways “to extract automatically all types of knowledge in an organization” (I don't think this exists), it might be worthwhile to study KM processes, identify patterns and see if technology can address that in a narrow