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
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
For more, see a piece I wrote for the KM
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.”
more about the solutions and value we provide at
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
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 sense.