Date   

Re: Model of an intelligent knowledge extraction in organizations #extraction #definition

Arthur Shelley
 

Thanks for the support on this Murray,

 

You make some very good points that illustrate this point well.

The mainstream education systems is very quantitative, which lends itself to categories and “explicit” elements. This is mainly, I think, because these are more easily assessed in objective ways (to be “fair”). The more intangible aspects of developing a capable human is often lost in the formal education and people are left to learn these critical capabilities outside the system - through their experiences of interacting with others.

 

The human “real world” is far more about relationships, perspectives and interpretation than quantitative “proof” and our education system (mostly) does not adequately prepare us for this subjective world. The subjective aspects of how we interact are spread across a wide spectrum (like knowledge in its own continuum). A truth for one person is seen as a false statement for another. Whilst there are some mathematical and chemical aspects of the world that can be “proven and repeated” through science, many things in the human world can’t. The ISO30401 states knowledge is a characteristic of humans – that is, it exists in their heads. Each person will have their own interpretation from subjective observations and these can all be true to varying degrees. Our beliefs (what we know to be true) are based on many things.  An absolute truth for many can be utter nonsense for others (with each side having a set of “evidence” to support their view). The amount of love in a relationship can be felt and observed, but not measured. We have the knowledge that it is there and that it is important. However, we can’t easily define it in a tangible way (and if we did it would not fully reflect the importance and impact it has).

 

I can’t prove any of this of course, because it is based on my experiences and reflections over 6 decades of interacting with many, reading extensively and engaging in many conversations and arguments. I am personally comfortable in knowing what I know and completely comfortable knowing that others see that same thing differently. I continuously challenge my “knowledge” and remain open to adjust it when new evidence influences me to believe a new understating can be justified (based on a mix of tangible and intangible evidence).

 

I “know” some knowledge is more tangible than others - even a given element can shift over time. There are many things in human history that shift in and out of acceptance over time.  One thing is for sure, when you are up for promotion, the outcome is unlikely to be determined on absolute quantitative data (just like the grading of an assignment worth doing - IE, one that reflects your ability to solve issues in the real world).

Your effectiveness as a leader will be determined by your EQ and soft skills (intangible knowledge) more than your IQ and technical expertise (Tangible knowledge/capabilities). Perhaps I am stretching this too far, but it is worthy of thought/reflection, perhaps even conversation.

 

This is my perspective of the reality of the human world we live in – one in which “knowledge” is a “shape-shifter” that acts in curious and unpredictable ways. We should not try to over categorise our most valuable assets - our knowledge, relationships, trust, social capital etc. We should accept they are a bit of a mystery and enjoy them (ensuring that we invest in them, knowing they are more important than “measurables”).

 

Regards

Arthur Shelley

Producer: Creative Melbourne

Author: KNOWledge SUCCESSion  Sustained performance and capability growth through knowledge projects

Earlier Books: The Organizational Zoo (2007) & Being a Successful Knowledge Leader (2009)

Principal: www.IntelligentAnswers.com.au 

Founder: Organizational Zoo Ambassadors Network

Mb. +61 413 047 408  Skype: Arthur.Shelley  Twitter: @Metaphorage

LinkedIn: https://www.linkedin.com/in/arthurshelley/

Free behavioural profiles: www.organizationalzoo.com

Blog: www.organizationalzoo.com/blog

Creative-Melbourne-Banner_2018_Final_Smaller

 

 

From: main@SIKM.groups.io <main@SIKM.groups.io> On Behalf Of Murray Jennex via groups.io
Sent: Sunday, 17 January 2021 6:37 PM
To: arthur@...; main@sikm.groups.io
Cc: arthur@...
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations

 

To support what I think Arthur is saying I have believed for many years and have seen the academic literature evolve to understand that knowledge is neither tacit nor explicit but instead is a mixture of both and that the mix varies between users.  To illustrate: an expert may see their knowledge as mostly explicit and can explain it when asked, however other users who aren't expert see that same knowledge as mostly tacit.  I personally believe that there is no purely tacit knowledge and that most all explicit knowledge has a small degree of tacitness, for example, I also teach systems analysis and design and while I can teach architecture concepts and discuss coding techniques and make them quite explicit with rules and heuristics, I still find that there is a tacitness to this as there are concepts I understand from 40 years ago that aren't taught today and haven't been for several years.  So when I talk about a piece of code that I developed 30+ years ago to do nuclear containment leak rate testing I can explain all the coding aspects but still find students don't really understand it as I developed that code using compiled basic that required me to define my graphics pixel by pixel, communication protocols had to be expressed explicitly, and data/memory management had to work in a 64k environment.  Students don't need to do this now and so while I can explain it exactly, I find that I have to go into much greater detail because while I consider the knowledge explicit, students don't.  Same for paper and chapter on why we can't go to the moon, the need for KM.  So while many have said that explicit knowledge is really information, in reality it is not always so.  So to agree with Arthur, knowledge exists on a continuum with the end points being tacit and explicit and knowledge is a mixture of both.  It is why I also said in an earlier post that I group knowledge by that which is harder to extract and that which is easier.

 

And as a side note, when my nuclear code was used a couple of years ago we had to go purchase older designed computers from eastern Europe to run the code (new computers won't and no one wants to invest the money to upgrade the code and recertify it), another aspect of tacitness that we hadn't anticipated......murray jennex

-----Original Message-----
From: Arthur Shelley <arthur@...>
To: main@sikm.groups.io
Cc: Arthur Shelley <arthur@...>
Sent: Sat, Jan 16, 2021 10:39 pm
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations

I have always considered categories convenient and efficient, but an oversimplification when it comes to real life matters such as knowledge, relationships and emotions.

 

I see such things as a mixed "This snd that" continuum rather than either "This or that". In the case of knowledge, this means a gradually shifting balance of tangibles (one bookend of the continuum) through to pure intangible (the other bookend). Most aspects of knowledge & related artefacts have elements of both tangible (explicit if you like) characteristics and intangibles (tacit). Its like light behaving as both a wave and a particle - we come to understand different insights depending on which perspective we adopt.

 

When I proposed such an insight be part of the ISO30401 KM Standard, it generated considerable dialogue. Although not included in the standard as such, it gets a mention in the appendix. I suggest it is useful to see knowledge as a complex thing that can be in different forms that merge into each other to differing degrees. These aspects are interdependent and  are challenging to separate out, but it can be useful to consider the knowledge if interest from a range of perspectives when looking for solutions.

 

Hope this helps

Arthur Shelley

Founder, Intelligent Answers

Producer Creative Melbourne

@Metaphorage

+61 413 047 408



On 16 Jan 2021, at 23:12, Stephen Bounds <km@...> wrote:



Hi Chris & all,

I am not a fan of the tacit/explicit dualism because I think it is too reductive and privileges a human-centric view of knowledge. I agree with Nick that it is perfectly fine for something to be both information and knowledge.

Here are just a few scenarios which I think challenge traditional views of tacit vs explicit, and explicit knowledge = information:

  • Ant scent trails provide an active, contextual history and guide for the behaviour of the hive. By any reasonable metric this is a form of "knowledge" despite being entirely "external" to the ants.
  • Mandatory procedural instructions (PIs) very distinctively corral the efforts and choices of an organisation. From the perspective of an outsider, while the emergent problem solving rhythms and algorithms are observable, the organisational knowledge encoded through multiple overlapping documents and put into practice by staff is quite opaque to the client and definitely not directly extractable as knowledge.
  • The contents of the text messages on my phone are parsed and actioned as if they were a form of external memory, quite distinct from the information processing involved in reading a newspaper article or book. (For example, think about the rich visual, audible, and emotional memories that that may be triggered from the act of reading a text.)

Rather than tacit vs explicit, I believe that the key transition occurs as we cross a system threshold. Inside the system threshold, it is meaningful to talk about its knowledge; outside, we must talk about transmitting information. New information can only be accepted as knowledge into that system once it achieves a certain trust threshold.

<piglkbginohplahg.png>

Thus, a written process sent through by head office represents information received by a staff member, but can be knowledge once incorporated into the execution of their role's systemic practices.

The switch in language between a role and a person is important since it represents an enlargement of system scope; employees are often asked to undertake a role "performance" which includes a broad scaffolding of policies, processes, technology, and person-to-person relationships.

Cheers,
Stephen.

====================================
Stephen Bounds
Executive, Information Management
Cordelta
E: stephen.bounds@...
M: 0401 829 096
====================================

On 16/01/2021 6:02 am, Chris Collison wrote:

I’m with you Fred.

...and from my perspective (which not everyone shares!), I would add that explicit knowledge = information.

Cheers,

Chris

 


From: main@SIKM.groups.io <main@SIKM.groups.io> on behalf of bill@... <bill@...>
Sent: Friday, January 15, 2021 7:57:21 PM
To: main@SIKM.groups.io <main@SIKM.groups.io>
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations

 

Hi Fred

 

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.

 

Best

 

Bill

 

From: main@SIKM.groups.io <main@SIKM.groups.io> On Behalf Of Fred Nickols via groups.io
Sent: Friday, January 15, 2021 11:22
To: main@SIKM.groups.io
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations

 

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.

 

For more, see a piece I wrote for the KM Yearbook (2000-2001).  https://www.nickols.us/knowledge_in_KM.pdf

 

Regards,

 

 

Fred Nickols, Consultant

 

My Objective is to Help You Achieve Yours

 

 

 

 

From: main@SIKM.groups.io <main@SIKM.groups.io> On Behalf Of bill@...
Sent: Friday, January 15, 2021 10:41 AM
To: main@SIKM.groups.io
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations

 

Dear Colleagues

 

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.” 

Best

 

Bill

 

 

<image002.png>

  

 

 

Learn more about the solutions and value we provide at www.workingknowledge-csp.com

 

 

 

 

From: main@SIKM.groups.io <main@SIKM.groups.io> On Behalf Of Sam Yip via groups.io
Sent: Thursday, January 14, 2021 21:41
To: main@SIKM.groups.io
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations

 

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 sense. 


Re: Model of an intelligent knowledge extraction in organizations #extraction #definition

Stephen Bounds
 

Hi Chris,

Agreed, and I would also agree with Prusak's characterisation of information. However if you haven't read up about Vigo information theory (best seen as an extension to Shannon information theory), I highly recommend it.

Vigo talks about information being a "rate of change in signal complexity". It is a cumbersome phrase but one that distinguishes between the quantity of a signal and the utility of a signal.

Here is a tangible example:

  • A traditional IM lens would consider a weekly project status report to contain a large amount of information, and this may well be true the first time someone reads one of these reports, since all the information is novel.

  • In Vigo's information model, we consider these reports as a continuous stream of signals from the project to the manager. And after the first report, the "change in complexity" generated by these reports is very low. Same format, frequently repeated content, and removal of lots of granularity (how "green" is a "green" project anyway?).

  • From this we can conclude that the "Vigo information" contained in lots of project communications is minimal, which explains why they are often an ineffective management tool.

Personally I find adoption of Vigo's model to be the most useful way to delineate discussions of information (which derive from signals) from knowledge (as a systemic source of action).

Cheers,
Stephen.

====================================
Stephen Bounds
Executive, Information Management
Cordelta
E: stephen.bounds@...
M: 0401 829 096
====================================
On 18/01/2021 9:28 am, Chris Collison wrote:

 

I’ve always been drawn to the way Larry Prusak describes information – as ‘a message with a sender, a receiver and an intent to inform’.  I’d happily include within Larry’s definition, any messages explicitly encoded chemically as ant trails, digitally as content, or through the medium of dance by a waggling honey bee )

 

I think this is one of those areas which we’ll all take different stances on, and all tailor where and whether we draw the boundary line – or overlap zone -  for the needs of specific clients.   It’s (as Nick M once wrote) probably a bit of a cul-de-sac conversation for KM enthusiasts – but it’s been really interesting to read the different perspectives in the safe-space which is SIKM! 

 

 

From: <main@SIKM.groups.io> on behalf of Stephen Bounds <km@...>
Reply to: "main@SIKM.groups.io" <main@SIKM.groups.io>
Date: Saturday, 16 January 2021 at 12:12
To: "main@SIKM.groups.io" <main@SIKM.groups.io>
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations

 

Hi Chris & all,

I am not a fan of the tacit/explicit dualism because I think it is too reductive and privileges a human-centric view of knowledge. I agree with Nick that it is perfectly fine for something to be both information and knowledge.

Here are just a few scenarios which I think challenge traditional views of tacit vs explicit, and explicit knowledge = information:

  • Ant scent trails provide an active, contextual history and guide for the behaviour of the hive. By any reasonable metric this is a form of "knowledge" despite being entirely "external" to the ants.
  • Mandatory procedural instructions (PIs) very distinctively corral the efforts and choices of an organisation. From the perspective of an outsider, while the emergent problem solving rhythms and algorithms are observable, the organisational knowledge encoded through multiple overlapping documents and put into practice by staff is quite opaque to the client and definitely not directly extractable as knowledge.
  • The contents of the text messages on my phone are parsed and actioned as if they were a form of external memory, quite distinct from the information processing involved in reading a newspaper article or book. (For example, think about the rich visual, audible, and emotional memories that that may be triggered from the act of reading a text.)

Rather than tacit vs explicit, I believe that the key transition occurs as we cross a system threshold. Inside the system threshold, it is meaningful to talk about its knowledge; outside, we must talk about transmitting information. New information can only be accepted as knowledge into that system once it achieves a certain trust threshold.

Thus, a written process sent through by head office represents information received by a staff member, but can be knowledge once incorporated into the execution of their role's systemic practices.

The switch in language between a role and a person is important since it represents an enlargement of system scope; employees are often asked to undertake a role "performance" which includes a broad scaffolding of policies, processes, technology, and person-to-person relationships.

Cheers,
Stephen.

====================================
Stephen Bounds
Executive, Information Management
Cordelta
E: stephen.bounds@...
M: 0401 829 096
====================================

On 16/01/2021 6:02 am, Chris Collison wrote:

I’m with you Fred.

...and from my perspective (which not everyone shares!), I would add that explicit knowledge = information.

Cheers,

Chris

 


From: main@SIKM.groups.io <main@SIKM.groups.io> on behalf of bill@... <bill@...>
Sent: Friday, January 15, 2021 7:57:21 PM
To: main@SIKM.groups.io <main@SIKM.groups.io>
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations

 

Hi Fred

 

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.

 

Best

 

Bill

 

From: main@SIKM.groups.io <main@SIKM.groups.io> On Behalf Of Fred Nickols via groups.io
Sent: Friday, January 15, 2021 11:22
To: main@SIKM.groups.io
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations

 

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.

 

For more, see a piece I wrote for the KM Yearbook (2000-2001).  https://www.nickols.us/knowledge_in_KM.pdf

 

Regards,

 

 

Fred Nickols, Consultant

 

My Objective is to Help You Achieve Yours

 

 

 

 

From: main@SIKM.groups.io <main@SIKM.groups.io> On Behalf Of bill@...
Sent: Friday, January 15, 2021 10:41 AM
To: main@SIKM.groups.io
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations

 

Dear Colleagues

 

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.” 

Best

 

Bill

 

 

  

 

Learn more about the solutions and value we provide at www.workingknowledge-csp.com

 

 

 

 

From: main@SIKM.groups.io <main@SIKM.groups.io> On Behalf Of Sam Yip via groups.io
Sent: Thursday, January 14, 2021 21:41
To: main@SIKM.groups.io
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations

 

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 sense. 


Re: Model of an intelligent knowledge extraction in organizations #extraction #definition

Chris Collison
 

 

I’ve always been drawn to the way Larry Prusak describes information – as ‘a message with a sender, a receiver and an intent to inform’.  I’d happily include within Larry’s definition, any messages explicitly encoded chemically as ant trails, digitally as content, or through the medium of dance by a waggling honey bee )

 

I think this is one of those areas which we’ll all take different stances on, and all tailor where and whether we draw the boundary line – or overlap zone -  for the needs of specific clients.   It’s (as Nick M once wrote) probably a bit of a cul-de-sac conversation for KM enthusiasts – but it’s been really interesting to read the different perspectives in the safe-space which is SIKM! 

 

 

From: <main@SIKM.groups.io> on behalf of Stephen Bounds <km@...>
Reply to: "main@SIKM.groups.io" <main@SIKM.groups.io>
Date: Saturday, 16 January 2021 at 12:12
To: "main@SIKM.groups.io" <main@SIKM.groups.io>
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations

 

Hi Chris & all,

I am not a fan of the tacit/explicit dualism because I think it is too reductive and privileges a human-centric view of knowledge. I agree with Nick that it is perfectly fine for something to be both information and knowledge.

Here are just a few scenarios which I think challenge traditional views of tacit vs explicit, and explicit knowledge = information:

  • Ant scent trails provide an active, contextual history and guide for the behaviour of the hive. By any reasonable metric this is a form of "knowledge" despite being entirely "external" to the ants.
  • Mandatory procedural instructions (PIs) very distinctively corral the efforts and choices of an organisation. From the perspective of an outsider, while the emergent problem solving rhythms and algorithms are observable, the organisational knowledge encoded through multiple overlapping documents and put into practice by staff is quite opaque to the client and definitely not directly extractable as knowledge.
  • The contents of the text messages on my phone are parsed and actioned as if they were a form of external memory, quite distinct from the information processing involved in reading a newspaper article or book. (For example, think about the rich visual, audible, and emotional memories that that may be triggered from the act of reading a text.)

Rather than tacit vs explicit, I believe that the key transition occurs as we cross a system threshold. Inside the system threshold, it is meaningful to talk about its knowledge; outside, we must talk about transmitting information. New information can only be accepted as knowledge into that system once it achieves a certain trust threshold.

Thus, a written process sent through by head office represents information received by a staff member, but can be knowledge once incorporated into the execution of their role's systemic practices.

The switch in language between a role and a person is important since it represents an enlargement of system scope; employees are often asked to undertake a role "performance" which includes a broad scaffolding of policies, processes, technology, and person-to-person relationships.

Cheers,
Stephen.

====================================
Stephen Bounds
Executive, Information Management
Cordelta
E: stephen.bounds@...
M: 0401 829 096
====================================

On 16/01/2021 6:02 am, Chris Collison wrote:

I’m with you Fred.

...and from my perspective (which not everyone shares!), I would add that explicit knowledge = information.

Cheers,

Chris

 


From: main@SIKM.groups.io <main@SIKM.groups.io> on behalf of bill@... <bill@...>
Sent: Friday, January 15, 2021 7:57:21 PM
To: main@SIKM.groups.io <main@SIKM.groups.io>
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations

 

Hi Fred

 

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.

 

Best

 

Bill

 

From: main@SIKM.groups.io <main@SIKM.groups.io> On Behalf Of Fred Nickols via groups.io
Sent: Friday, January 15, 2021 11:22
To: main@SIKM.groups.io
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations

 

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.

 

For more, see a piece I wrote for the KM Yearbook (2000-2001).  https://www.nickols.us/knowledge_in_KM.pdf

 

Regards,

 

 

Fred Nickols, Consultant

 

My Objective is to Help You Achieve Yours

 

 

 

 

From: main@SIKM.groups.io <main@SIKM.groups.io> On Behalf Of bill@...
Sent: Friday, January 15, 2021 10:41 AM
To: main@SIKM.groups.io
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations

 

Dear Colleagues

 

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.” 

Best

 

Bill

 

 

  

 

Learn more about the solutions and value we provide at www.workingknowledge-csp.com

 

 

 

 

From: main@SIKM.groups.io <main@SIKM.groups.io> On Behalf Of Sam Yip via groups.io
Sent: Thursday, January 14, 2021 21:41
To: main@SIKM.groups.io
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations

 

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 sense. 


Lessons Learned Storage & Access #lessons-learned

Beatriz Benezra
 

Hello everyone!

Sorry!  Probably I´m late with that topic.

Lessons Learned Complete Cycle is a methodology is used in Brazil and in some Latin America countries.

This methodology considers that the  real value of lessons learned is about correcting failures and institutionalizing good practices. It is also possible to rank the lessons learned according to predefined criteria, so that LL that really add value to the organization are treated. The methodology is based on Knowledge Management, Change Management and Project Management practices and tools. Its application is very simple.  So, who executes the methodology doesn’t need to know about these disciplines.  Applying  the methodology,  the user is  able to develop action plans to implement changes and fill in  knowledge gaps. The storage  is done by the use of  a knowledge tree and a context description. When the user wants to recover  a LL  he can  use the tree and inform the context of the activity that he will execute. A matching with the context of the Lesson Learned will show which one  can add more valuable knowledge. I still don't have the English version, only  Portuguese and Spanish.  I´m sending a brief material attached to this email. More material is available at  the website: www.2l2cmet.com

I hope it helps!  😊

 

Regards,

 

 

 

 

 

De: main@SIKM.groups.io <main@SIKM.groups.io> Em nome de David Graffagna
Enviada em: terça-feira, 12 de janeiro de 2021 18:13
Para: main@SIKM.groups.io
Assunto: Re: [SIKM] Lessons Learned Storage & Access #lessonslearned

 

Gabi ... I'll be happy to share our working set of questions we use to help project leads tackle gathering lessons learned their teams. I will clean them up (e.g., remove any corporate-specific references) and share a copy in this forum. 

Frankly, I have borrowed liberally from other resources to frame how we ask for lessons learned so these may be similar to things you have seen elsewhere, but as I said I'm happy to share ours!

Best,
David


Re: SIKM Peer Assist - Lessons Learned Delivery - January 25, 2021 #lessons-learned #peer-assist

Tom Barfield
 

As of Jan 17 here are the planned attendees of the Jan 25 peer assist scheduled for 3 PM CST. 

Name                                                 Response

Thomas Barfield                                Accepted

Kate Pugh                                          Accepted

Tom Short                                          Tentative

Graffagna, David                              Accepted

ainglett@...                          Accepted

mleftwich82@...              Accepted

randhir.rp@...                   Accepted

Evgeny Victorov                                 Accepted

sugandhaworks@...       Accepted

jimleesrkm@...                 Accepted

pekadad@...                     Tentative

jamie@...          Accepted

sswasu@...                        Accepted

danieleranta@...             Accepted

 


Re: SIKM Peer Assist - Lessons Learned Delivery - January 25, 2021 #lessons-learned #peer-assist

Andrew Trickett
 

Thanks for organising this Tom I do plan to attend from the UK (21:00 UK time). I've posted on this earlier and I think my contribution will be between 4-5. 


Re: Model of an intelligent knowledge extraction in organizations #extraction #definition

Murray Jennex
 

To support what I think Arthur is saying I have believed for many years and have seen the academic literature evolve to understand that knowledge is neither tacit nor explicit but instead is a mixture of both and that the mix varies between users.  To illustrate: an expert may see their knowledge as mostly explicit and can explain it when asked, however other users who aren't expert see that same knowledge as mostly tacit.  I personally believe that there is no purely tacit knowledge and that most all explicit knowledge has a small degree of tacitness, for example, I also teach systems analysis and design and while I can teach architecture concepts and discuss coding techniques and make them quite explicit with rules and heuristics, I still find that there is a tacitness to this as there are concepts I understand from 40 years ago that aren't taught today and haven't been for several years.  So when I talk about a piece of code that I developed 30+ years ago to do nuclear containment leak rate testing I can explain all the coding aspects but still find students don't really understand it as I developed that code using compiled basic that required me to define my graphics pixel by pixel, communication protocols had to be expressed explicitly, and data/memory management had to work in a 64k environment.  Students don't need to do this now and so while I can explain it exactly, I find that I have to go into much greater detail because while I consider the knowledge explicit, students don't.  Same for paper and chapter on why we can't go to the moon, the need for KM.  So while many have said that explicit knowledge is really information, in reality it is not always so.  So to agree with Arthur, knowledge exists on a continuum with the end points being tacit and explicit and knowledge is a mixture of both.  It is why I also said in an earlier post that I group knowledge by that which is harder to extract and that which is easier.

And as a side note, when my nuclear code was used a couple of years ago we had to go purchase older designed computers from eastern Europe to run the code (new computers won't and no one wants to invest the money to upgrade the code and recertify it), another aspect of tacitness that we hadn't anticipated......murray jennex


-----Original Message-----
From: Arthur Shelley <arthur@...>
To: main@sikm.groups.io
Cc: Arthur Shelley <arthur@...>
Sent: Sat, Jan 16, 2021 10:39 pm
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations

I have always considered categories convenient and efficient, but an oversimplification when it comes to real life matters such as knowledge, relationships and emotions.

I see such things as a mixed "This snd that" continuum rather than either "This or that". In the case of knowledge, this means a gradually shifting balance of tangibles (one bookend of the continuum) through to pure intangible (the other bookend). Most aspects of knowledge & related artefacts have elements of both tangible (explicit if you like) characteristics and intangibles (tacit). Its like light behaving as both a wave and a particle - we come to understand different insights depending on which perspective we adopt.

When I proposed such an insight be part of the ISO30401 KM Standard, it generated considerable dialogue. Although not included in the standard as such, it gets a mention in the appendix. I suggest it is useful to see knowledge as a complex thing that can be in different forms that merge into each other to differing degrees. These aspects are interdependent and  are challenging to separate out, but it can be useful to consider the knowledge if interest from a range of perspectives when looking for solutions.

Hope this helps

Arthur Shelley
Founder, Intelligent Answers
Producer Creative Melbourne
www.OrganizationalZoo.com
@Metaphorage
+61 413 047 408
https://au.linkedin.com/pub/arthur-shelley/1/4bb/528 

On 16 Jan 2021, at 23:12, Stephen Bounds <km@...> wrote:


Hi Chris & all,
I am not a fan of the tacit/explicit dualism because I think it is too reductive and privileges a human-centric view of knowledge. I agree with Nick that it is perfectly fine for something to be both information and knowledge.
Here are just a few scenarios which I think challenge traditional views of tacit vs explicit, and explicit knowledge = information:
  • Ant scent trails provide an active, contextual history and guide for the behaviour of the hive. By any reasonable metric this is a form of "knowledge" despite being entirely "external" to the ants.

  • Mandatory procedural instructions (PIs) very distinctively corral the efforts and choices of an organisation. From the perspective of an outsider, while the emergent problem solving rhythms and algorithms are observable, the organisational knowledge encoded through multiple overlapping documents and put into practice by staff is quite opaque to the client and definitely not directly extractable as knowledge.

  • The contents of the text messages on my phone are parsed and actioned as if they were a form of external memory, quite distinct from the information processing involved in reading a newspaper article or book. (For example, think about the rich visual, audible, and emotional memories that that may be triggered from the act of reading a text.)
Rather than tacit vs explicit, I believe that the key transition occurs as we cross a system threshold. Inside the system threshold, it is meaningful to talk about its knowledge; outside, we must talk about transmitting information. New information can only be accepted as knowledge into that system once it achieves a certain trust threshold.
<piglkbginohplahg.png>
Thus, a written process sent through by head office represents information received by a staff member, but can be knowledge once incorporated into the execution of their role's systemic practices.
The switch in language between a role and a person is important since it represents an enlargement of system scope; employees are often asked to undertake a role "performance" which includes a broad scaffolding of policies, processes, technology, and person-to-person relationships.
Cheers,
Stephen.
====================================
Stephen Bounds
Executive, Information Management
Cordelta
E: stephen.bounds@...
M: 0401 829 096
====================================
On 16/01/2021 6:02 am, Chris Collison wrote:
I’m with you Fred.
...and from my perspective (which not everyone shares!), I would add that explicit knowledge = information.
Cheers,
Chris


From: main@SIKM.groups.io <main@SIKM.groups.io> on behalf of bill@... <bill@...>
Sent: Friday, January 15, 2021 7:57:21 PM
To: main@SIKM.groups.io <main@SIKM.groups.io>
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations
 
Hi Fred
 
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.
 
Best
 
Bill
 
From: main@SIKM.groups.io <main@SIKM.groups.io> On Behalf Of Fred Nickols via groups.io
Sent: Friday, January 15, 2021 11:22
To: main@SIKM.groups.io
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations
 
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.
 
For more, see a piece I wrote for the KM Yearbook (2000-2001).  https://www.nickols.us/knowledge_in_KM.pdf
 
Regards,
 
 
Fred Nickols, Consultant
 
My Objective is to Help You Achieve Yours
 
 
 
 
From: main@SIKM.groups.io <main@SIKM.groups.io> On Behalf Of bill@...
Sent: Friday, January 15, 2021 10:41 AM
To: main@SIKM.groups.io
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations
 
Dear Colleagues
 
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.” 
Best
 
Bill
 
 
<image002.png>
  
<image004.png>
 
Learn more about the solutions and value we provide at www.workingknowledge-csp.com
 
 
 
 
From: main@SIKM.groups.io <main@SIKM.groups.io> On Behalf Of Sam Yip via groups.io
Sent: Thursday, January 14, 2021 21:41
To: main@SIKM.groups.io
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations
 
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 sense. 


Re: Model of an intelligent knowledge extraction in organizations #extraction #definition

Arthur Shelley
 

I have always considered categories convenient and efficient, but an oversimplification when it comes to real life matters such as knowledge, relationships and emotions.

I see such things as a mixed "This snd that" continuum rather than either "This or that". In the case of knowledge, this means a gradually shifting balance of tangibles (one bookend of the continuum) through to pure intangible (the other bookend). Most aspects of knowledge & related artefacts have elements of both tangible (explicit if you like) characteristics and intangibles (tacit). Its like light behaving as both a wave and a particle - we come to understand different insights depending on which perspective we adopt.

When I proposed such an insight be part of the ISO30401 KM Standard, it generated considerable dialogue. Although not included in the standard as such, it gets a mention in the appendix. I suggest it is useful to see knowledge as a complex thing that can be in different forms that merge into each other to differing degrees. These aspects are interdependent and  are challenging to separate out, but it can be useful to consider the knowledge if interest from a range of perspectives when looking for solutions.

Hope this helps

Arthur Shelley
Founder, Intelligent Answers
Producer Creative Melbourne
www.OrganizationalZoo.com
@Metaphorage
+61 413 047 408
https://au.linkedin.com/pub/arthur-shelley/1/4bb/528 

On 16 Jan 2021, at 23:12, Stephen Bounds <km@...> wrote:



Hi Chris & all,

I am not a fan of the tacit/explicit dualism because I think it is too reductive and privileges a human-centric view of knowledge. I agree with Nick that it is perfectly fine for something to be both information and knowledge.

Here are just a few scenarios which I think challenge traditional views of tacit vs explicit, and explicit knowledge = information:

  • Ant scent trails provide an active, contextual history and guide for the behaviour of the hive. By any reasonable metric this is a form of "knowledge" despite being entirely "external" to the ants.

  • Mandatory procedural instructions (PIs) very distinctively corral the efforts and choices of an organisation. From the perspective of an outsider, while the emergent problem solving rhythms and algorithms are observable, the organisational knowledge encoded through multiple overlapping documents and put into practice by staff is quite opaque to the client and definitely not directly extractable as knowledge.

  • The contents of the text messages on my phone are parsed and actioned as if they were a form of external memory, quite distinct from the information processing involved in reading a newspaper article or book. (For example, think about the rich visual, audible, and emotional memories that that may be triggered from the act of reading a text.)

Rather than tacit vs explicit, I believe that the key transition occurs as we cross a system threshold. Inside the system threshold, it is meaningful to talk about its knowledge; outside, we must talk about transmitting information. New information can only be accepted as knowledge into that system once it achieves a certain trust threshold.

<piglkbginohplahg.png>

Thus, a written process sent through by head office represents information received by a staff member, but can be knowledge once incorporated into the execution of their role's systemic practices.

The switch in language between a role and a person is important since it represents an enlargement of system scope; employees are often asked to undertake a role "performance" which includes a broad scaffolding of policies, processes, technology, and person-to-person relationships.

Cheers,
Stephen.

====================================
Stephen Bounds
Executive, Information Management
Cordelta
E: stephen.bounds@...
M: 0401 829 096
====================================
On 16/01/2021 6:02 am, Chris Collison wrote:
I’m with you Fred.
...and from my perspective (which not everyone shares!), I would add that explicit knowledge = information.
Cheers,
Chris


From: main@SIKM.groups.io <main@SIKM.groups.io> on behalf of bill@... <bill@...>
Sent: Friday, January 15, 2021 7:57:21 PM
To: main@SIKM.groups.io <main@SIKM.groups.io>
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations
 

Hi Fred

 

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.

 

Best

 

Bill

 

From: main@SIKM.groups.io <main@SIKM.groups.io> On Behalf Of Fred Nickols via groups.io
Sent: Friday, January 15, 2021 11:22
To: main@SIKM.groups.io
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations

 

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.

 

For more, see a piece I wrote for the KM Yearbook (2000-2001).  https://www.nickols.us/knowledge_in_KM.pdf

 

Regards,

 

 

Fred Nickols, Consultant

 

My Objective is to Help You Achieve Yours

 

 

 

 

From: main@SIKM.groups.io <main@SIKM.groups.io> On Behalf Of bill@...
Sent: Friday, January 15, 2021 10:41 AM
To: main@SIKM.groups.io
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations

 

Dear Colleagues

 

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.” 

Best

 

Bill

 

 

<image002.png>

  
<image004.png>

 

Learn more about the solutions and value we provide at www.workingknowledge-csp.com

 

 

 

 

From: main@SIKM.groups.io <main@SIKM.groups.io> On Behalf Of Sam Yip via groups.io
Sent: Thursday, January 14, 2021 21:41
To: main@SIKM.groups.io
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations

 

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 sense. 


Re: Model of an intelligent knowledge extraction in organizations #extraction #definition

Stephen Bounds
 

Hi Chris & all,

I am not a fan of the tacit/explicit dualism because I think it is too reductive and privileges a human-centric view of knowledge. I agree with Nick that it is perfectly fine for something to be both information and knowledge.

Here are just a few scenarios which I think challenge traditional views of tacit vs explicit, and explicit knowledge = information:

  • Ant scent trails provide an active, contextual history and guide for the behaviour of the hive. By any reasonable metric this is a form of "knowledge" despite being entirely "external" to the ants.

  • Mandatory procedural instructions (PIs) very distinctively corral the efforts and choices of an organisation. From the perspective of an outsider, while the emergent problem solving rhythms and algorithms are observable, the organisational knowledge encoded through multiple overlapping documents and put into practice by staff is quite opaque to the client and definitely not directly extractable as knowledge.

  • The contents of the text messages on my phone are parsed and actioned as if they were a form of external memory, quite distinct from the information processing involved in reading a newspaper article or book. (For example, think about the rich visual, audible, and emotional memories that that may be triggered from the act of reading a text.)

Rather than tacit vs explicit, I believe that the key transition occurs as we cross a system threshold. Inside the system threshold, it is meaningful to talk about its knowledge; outside, we must talk about transmitting information. New information can only be accepted as knowledge into that system once it achieves a certain trust threshold.

Thus, a written process sent through by head office represents information received by a staff member, but can be knowledge once incorporated into the execution of their role's systemic practices.

The switch in language between a role and a person is important since it represents an enlargement of system scope; employees are often asked to undertake a role "performance" which includes a broad scaffolding of policies, processes, technology, and person-to-person relationships.

Cheers,
Stephen.

====================================
Stephen Bounds
Executive, Information Management
Cordelta
E: stephen.bounds@...
M: 0401 829 096
====================================
On 16/01/2021 6:02 am, Chris Collison wrote:

I’m with you Fred.
...and from my perspective (which not everyone shares!), I would add that explicit knowledge = information.
Cheers,
Chris


From: main@SIKM.groups.io <main@SIKM.groups.io> on behalf of bill@... <bill@...>
Sent: Friday, January 15, 2021 7:57:21 PM
To: main@SIKM.groups.io <main@SIKM.groups.io>
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations
 

Hi Fred

 

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.

 

Best

 

Bill

 

From: main@SIKM.groups.io <main@SIKM.groups.io> On Behalf Of Fred Nickols via groups.io
Sent: Friday, January 15, 2021 11:22
To: main@SIKM.groups.io
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations

 

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.

 

For more, see a piece I wrote for the KM Yearbook (2000-2001).  https://www.nickols.us/knowledge_in_KM.pdf

 

Regards,

 

 

Fred Nickols, Consultant

 

My Objective is to Help You Achieve Yours

 

 

 

 

From: main@SIKM.groups.io <main@SIKM.groups.io> On Behalf Of bill@...
Sent: Friday, January 15, 2021 10:41 AM
To: main@SIKM.groups.io
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations

 

Dear Colleagues

 

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.” 

Best

 

Bill

 

 

  

 

Learn more about the solutions and value we provide at www.workingknowledge-csp.com

 

 

 

 

From: main@SIKM.groups.io <main@SIKM.groups.io> On Behalf Of Sam Yip via groups.io
Sent: Thursday, January 14, 2021 21:41
To: main@SIKM.groups.io
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations

 

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 sense. 


Re: Model of an intelligent knowledge extraction in organizations #extraction #definition

John Carney
 

I have only recently joined this Forum - after a number of years absence in formally working in the KM field - but seeing a name I recognise on the post (Hi Nick ;-)) has motivated me to contribute.  Re the information vs (explicit) knowledge language debate it's an intractable issue for me and there will never be a right or wrong response, I think what's important is clarity of understanding in dealing with non expert 'clients' for want of a better term and speaking personally my observation is that the ambiguity is unhelpful. I have seen many strategy documents in Government that use the terms interchangeably that then confuses what folk are practically meant to do. I prefer to talk about IM and people/ social learning approaches - the latter being my predominant interest. I do accept that for most KM has now become synonymous with IM 

Recognising that having only just joined this party it feels a tad  disingenuous of me to challenge the very phrase that brings us together ;-) but I have always found the term Knowledge Management largely unhelpful to our cause - indeed I squirm with embarrassment when I have to introduce myself as KM Lead for Dstl as it doesn't really convey either what I think I am trying to do or what is attractive to the audience 
I think some of the early commentators like Peter Drucker got it right when they spoke about the importance of managing people . In my context ( I respect that others might be different) the KM challenge remains a leadership one IMO. 

I look forward to future interactions - in particular the peer assist discussion on learning lessons (thank you)  - another example where there are multiple interpretations of the phrase 

Kind Regards (and Happy New Year) John 


On 16 Jan 2021, at 09:21, Nick Milton <nick.milton@...> wrote:

In my view, codified knowledge can be seen as BOTH knowledge AND information as far as our management systems are concerned.

 

It is Information in as much as it is a document, video or other file which can be handled within information management systems, and which therefore falls underneath the umbrella of an information management system.

 

It is knowledge in as much as it can convey understanding, know-how and the ability to make effective decisions, from one person to another, and therefore falls underneath the umbrella of a knowledge management system (using the term “system” to mean “system of management” rather than IT system).

 

I know many people see “information” and “knowledge” as two mutual exclusive descriptors, but there is no reason why this should be the case. We are used to thinking this way, but there is no logical basis for it that I can see.

 

I would therefore submit that the category “codified knowledge” can be seen as a clear example of something that is both categories.

 

More on the idea here

http://www.nickmilton.com/2017/09/why-some-knowledge-is-also-information.html

http://www.nickmilton.com/2018/06/a-new-way-to-look-at-knowledge-and.html

 

Nick Milton
Knoco Ltd

 

 

From: main@SIKM.groups.io <main@SIKM.groups.io> On Behalf Of Murray Jennex via groups.io
Sent: 16 January 2021 01:36
To: fred@...; main@SIKM.groups.io
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations

 

at the risk of sounding obnoxious, I have to ask why codified knowledge is information?  its still knowledge, just well understood knowledge.  For example, just because we well understand the knowledge of how fire works and causes burns does not make that information, it is still knowledge...murray

-----Original Message-----
From: Fred Nickols <fred@...>
To: main@SIKM.groups.io
Sent: Fri, Jan 15, 2021 12:24 pm
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations

I agree, Chris.  Explicit knowledge is knowledge that has been codified.  It is indeed information.

 

Fred Nickols

 

From: main@SIKM.groups.io <main@SIKM.groups.io> On Behalf Of Chris Collison
Sent: Friday, January 15, 2021 3:03 PM
To: main@SIKM.groups.io
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations

 

I’m with you Fred.

...and from my perspective (which not everyone shares!), I would add that explicit knowledge = information.

Cheers,

Chris

 


From: main@SIKM.groups.io <main@SIKM.groups.io> on behalf of bill@... <bill@...>
Sent: Friday, January 15, 2021 7:57:21 PM
To: main@SIKM.groups.io <main@SIKM.groups.io>
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations

 

Hi Fred

 

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.

 

Best

 

Bill

 

From: main@SIKM.groups.io <main@SIKM.groups.io> On Behalf Of Fred Nickols via groups.io
Sent: Friday, January 15, 2021 11:22
To: main@SIKM.groups.io
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations

 

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.

 

For more, see a piece I wrote for the KM Yearbook (2000-2001).  https://www.nickols.us/knowledge_in_KM.pdf

 

Regards,

 

 

Fred Nickols, Consultant

 

My Objective is to Help You Achieve Yours

 

 

 

 

From: main@SIKM.groups.io <main@SIKM.groups.io> On Behalf Of bill@...
Sent: Friday, January 15, 2021 10:41 AM
To: main@SIKM.groups.io
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations

 

Dear Colleagues

 

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.” 

Best

 

Bill

 

 

<image002.png>

 

Learn more about the solutions and value we provide at www.workingknowledge-csp.com

 

 

 

 

From: main@SIKM.groups.io <main@SIKM.groups.io> On Behalf Of Sam Yip via groups.io
Sent: Thursday, January 14, 2021 21:41
To: main@SIKM.groups.io
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations

 

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 sense. 


Re: Model of an intelligent knowledge extraction in organizations #extraction #definition

Nick Milton
 

In my view, codified knowledge can be seen as BOTH knowledge AND information as far as our management systems are concerned.

 

It is Information in as much as it is a document, video or other file which can be handled within information management systems, and which therefore falls underneath the umbrella of an information management system.

 

It is knowledge in as much as it can convey understanding, know-how and the ability to make effective decisions, from one person to another, and therefore falls underneath the umbrella of a knowledge management system (using the term “system” to mean “system of management” rather than IT system).

 

I know many people see “information” and “knowledge” as two mutual exclusive descriptors, but there is no reason why this should be the case. We are used to thinking this way, but there is no logical basis for it that I can see.

 

I would therefore submit that the category “codified knowledge” can be seen as a clear example of something that is both categories.

 

More on the idea here

http://www.nickmilton.com/2017/09/why-some-knowledge-is-also-information.html

http://www.nickmilton.com/2018/06/a-new-way-to-look-at-knowledge-and.html

 

Nick Milton
Knoco Ltd

 

 

From: main@SIKM.groups.io <main@SIKM.groups.io> On Behalf Of Murray Jennex via groups.io
Sent: 16 January 2021 01:36
To: fred@...; main@SIKM.groups.io
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations

 

at the risk of sounding obnoxious, I have to ask why codified knowledge is information?  its still knowledge, just well understood knowledge.  For example, just because we well understand the knowledge of how fire works and causes burns does not make that information, it is still knowledge...murray

-----Original Message-----
From: Fred Nickols <fred@...>
To: main@SIKM.groups.io
Sent: Fri, Jan 15, 2021 12:24 pm
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations

I agree, Chris.  Explicit knowledge is knowledge that has been codified.  It is indeed information.

 

Fred Nickols

 

From: main@SIKM.groups.io <main@SIKM.groups.io> On Behalf Of Chris Collison
Sent: Friday, January 15, 2021 3:03 PM
To: main@SIKM.groups.io
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations

 

I’m with you Fred.

...and from my perspective (which not everyone shares!), I would add that explicit knowledge = information.

Cheers,

Chris

 


From: main@SIKM.groups.io <main@SIKM.groups.io> on behalf of bill@... <bill@...>
Sent: Friday, January 15, 2021 7:57:21 PM
To: main@SIKM.groups.io <main@SIKM.groups.io>
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations

 

Hi Fred

 

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.

 

Best

 

Bill

 

From: main@SIKM.groups.io <main@SIKM.groups.io> On Behalf Of Fred Nickols via groups.io
Sent: Friday, January 15, 2021 11:22
To: main@SIKM.groups.io
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations

 

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.

 

For more, see a piece I wrote for the KM Yearbook (2000-2001).  https://www.nickols.us/knowledge_in_KM.pdf

 

Regards,

 

 

Fred Nickols, Consultant

 

My Objective is to Help You Achieve Yours

 

 

 

 

From: main@SIKM.groups.io <main@SIKM.groups.io> On Behalf Of bill@...
Sent: Friday, January 15, 2021 10:41 AM
To: main@SIKM.groups.io
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations

 

Dear Colleagues

 

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.” 

Best

 

Bill

 

 

  

 

Learn more about the solutions and value we provide at www.workingknowledge-csp.com

 

 

 

 

From: main@SIKM.groups.io <main@SIKM.groups.io> On Behalf Of Sam Yip via groups.io
Sent: Thursday, January 14, 2021 21:41
To: main@SIKM.groups.io
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations

 

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 sense. 


Re: Model of an intelligent knowledge extraction in organizations #extraction #definition

Eli Miron
 

Hi Bill,


I suggest to modify your definition:  that “tacit” knowledge can be sometimes elicited, harvested, captured, distilled and codified, to make it searchable, findable, accessible and usable/reusable


Eli

 

From: main@SIKM.groups.io <main@SIKM.groups.io> On Behalf Of Murray Jennex via groups.io
Sent: Saturday, January 16, 2021 3:46 AM
To: nancydixon@...; main@SIKM.groups.io
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations

 

but to play devil's advocate, once you video your below examples does that turn it into information?

-----Original Message-----
From: Nancy Dixon <nancydixon@...>
To: main@sikm.groups.io <main@SIKM.groups.io>
Sent: Fri, Jan 15, 2021 5:05 pm
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations

I support what both Fred and Chris write about tacit and implicit knowledge. I think it is possible to transfer tacit knowledge through observation of the expert, followed by in-depth conversation/coaching. It is much in the same way that athletic coaches or musicians that run Master’s classes transfer their tacit knowledge.   Dorothy Leonard talks about this is in her book, Critical Knowledge Transfer.  

 

Nancy



On Jan 15, 2021, at 2:02 PM, Chris Collison via groups.io <chris.collison@...> wrote:

 

I’m with you Fred. 

...and from my perspective (which not everyone shares!), I would add that explicit knowledge = information.

Cheers,

Chris 

 


From: main@SIKM.groups.io <main@SIKM.groups.io> on behalf of bill@... <bill@...>
Sent: Friday, January 15, 2021 7:57:21 PM
To: main@SIKM.groups.io <main@SIKM.groups.io>
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations

 

Hi Fred

 

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.

 

Best

 

Bill

 

From: main@SIKM.groups.io <main@SIKM.groups.io> On Behalf Of Fred Nickols via groups.io
Sent: Friday, January 15, 2021 11:22
To: main@SIKM.groups.io
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations

 

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.

 

For more, see a piece I wrote for the KM Yearbook (2000-2001).  https://www.nickols.us/knowledge_in_KM.pdf

 

Regards,

 

 

Fred Nickols, Consultant

 

My Objective is to Help You Achieve Yours

 

 

 

 

From: main@SIKM.groups.io <main@SIKM.groups.io> On Behalf Of bill@...
Sent: Friday, January 15, 2021 10:41 AM
To: main@SIKM.groups.io
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations

 

Dear Colleagues

 

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.” 

Best

 

Bill

 

 

<image002.png>

 

Learn more about the solutions and value we provide at www.workingknowledge-csp.com

 

 

 

 

From: main@SIKM.groups.io <main@SIKM.groups.io> On Behalf Of Sam Yip via groups.io
Sent: Thursday, January 14, 2021 21:41
To: main@SIKM.groups.io
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations

 

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 sense. 

 


Re: Model of an intelligent knowledge extraction in organizations #extraction #definition

Mila Malekolkalami
 

Interesting! 👍
But even, the knowledge that produces little or no value can be useful for someone, somewhere.
Thank you😊




Re: Model of an intelligent knowledge extraction in organizations #extraction #definition

Murray Jennex
 

but to play devil's advocate, once you video your below examples does that turn it into information?


-----Original Message-----
From: Nancy Dixon <nancydixon@...>
To: main@sikm.groups.io <main@SIKM.groups.io>
Sent: Fri, Jan 15, 2021 5:05 pm
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations

I support what both Fred and Chris write about tacit and implicit knowledge. I think it is possible to transfer tacit knowledge through observation of the expert, followed by in-depth conversation/coaching. It is much in the same way that athletic coaches or musicians that run Master’s classes transfer their tacit knowledge.   Dorothy Leonard talks about this is in her book, Critical Knowledge Transfer.  

Nancy

On Jan 15, 2021, at 2:02 PM, Chris Collison via groups.io <chris.collison@...> wrote:

I’m with you Fred. 
...and from my perspective (which not everyone shares!), I would add that explicit knowledge = information.
Cheers,
Chris 


From: main@SIKM.groups.io <main@SIKM.groups.io> on behalf of bill@... <bill@...>
Sent: Friday, January 15, 2021 7:57:21 PM
To: main@SIKM.groups.io <main@SIKM.groups.io>
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations
 
Hi Fred
 
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.
 
Best
 
Bill
 
From: main@SIKM.groups.io <main@SIKM.groups.io> On Behalf Of Fred Nickols via groups.io
Sent: Friday, January 15, 2021 11:22
To: main@SIKM.groups.io
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations
 
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.
 
For more, see a piece I wrote for the KM Yearbook (2000-2001).  https://www.nickols.us/knowledge_in_KM.pdf
 
Regards,
 
 
Fred Nickols, Consultant
 
My Objective is to Help You Achieve Yours
 
 
 
 
From: main@SIKM.groups.io <main@SIKM.groups.io> On Behalf Of bill@...
Sent: Friday, January 15, 2021 10:41 AM
To: main@SIKM.groups.io
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations
 
Dear Colleagues
 
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.” 
Best
 
Bill
 
 
<image002.png>
 
Learn more about the solutions and value we provide at www.workingknowledge-csp.com
 
 
 
 
From: main@SIKM.groups.io <main@SIKM.groups.io> On Behalf Of Sam Yip via groups.io
Sent: Thursday, January 14, 2021 21:41
To: main@SIKM.groups.io
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations
 
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 sense. 


Re: Model of an intelligent knowledge extraction in organizations #extraction #definition

Murray Jennex
 

information are related facts but don't necessarily have an understanding of why or how something happens, knowledge is the how or why something happens, doesn't matter if you can write it down or not, this has been the basis for the taxonomy of the knowledge pyramid.  As a trained physicist/engineer I am very leery of trying to say that once we can write knowledge down it becomes information.  We wrote down the Bohr model of the atom and it wasn't information but led to further development and generation of more knowledge.  If what you'all are saying is true then why does anyone argue with the Bible or Koran?  They are written down so now are information that all should just use.  But that is not the case...murray


-----Original Message-----
From: Tim Powell <tim.powell@...>
To: main@SIKM.groups.io <main@SIKM.groups.io>
Sent: Fri, Jan 15, 2021 1:08 pm
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations

I’m so glad, Bill, Chris, and Fred that you’ve mentioned that “explicit knowledge” is actually information.  When I wrote this in my latest book, I predicted that it would be received in some quarters as heresy — but am reassured that others have independently arrived at a similar conclusion.
 
The failure to distinguish between knowledge and information is, in my experience, one of the two biggest stumbling blocks to the effective managing of knowledge.  Both K and I are critically important — but are very different for reasons I go into. 
 
In short, information about knowledge — essentially, that which we are seeking to elicit and/or codify — is crucial as an index or trace of the human knowledge it represents.  It must not, however, be conflated with the knowledge itself -- which remains (as Drucker first described in 1964) perpetually captive within human brains.  “The map is not the territory.”
 
My opinions,
 
Tim

TIM WOOD POWELL 
| President, The Knowledge Agency® Author, The Value of Knowledge
New York City, USA | DIRECT/MOBILE +1.212.243.1200 | ZOOM 212-243-1200
 
 
From: <main@SIKM.groups.io> on behalf of Fred Nickols <fred@...>
Reply-To: "main@SIKM.groups.io" <main@SIKM.groups.io>
Date: Friday, January 15, 2021 at 3:24 PM
To: "main@SIKM.groups.io" <main@SIKM.groups.io>
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations
 
I agree, Chris.  Explicit knowledge is knowledge that has been codified.  It is indeed information.
 
Fred Nickols
 
From: main@SIKM.groups.io <main@SIKM.groups.io> On Behalf Of Chris Collison
Sent: Friday, January 15, 2021 3:03 PM
To: main@SIKM.groups.io
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations
 
I’m with you Fred.
...and from my perspective (which not everyone shares!), I would add that explicit knowledge = information.
Cheers,
Chris
 

From: main@SIKM.groups.io <main@SIKM.groups.io> on behalf of bill@... <bill@...>
Sent: Friday, January 15, 2021 7:57:21 PM
To: main@SIKM.groups.io <main@SIKM.groups.io>
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations
 
Hi Fred
 
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.
 
Best
 
Bill
 
From: main@SIKM.groups.io <main@SIKM.groups.io> On Behalf Of Fred Nickols via groups.io
Sent: Friday, January 15, 2021 11:22
To: main@SIKM.groups.io
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations
 
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.
 
For more, see a piece I wrote for the KM Yearbook (2000-2001).  https://www.nickols.us/knowledge_in_KM.pdf
 
Regards,
 
 
Fred Nickols, Consultant
 
My Objective is to Help You Achieve Yours
 
 
 
 
From: main@SIKM.groups.io <main@SIKM.groups.io> On Behalf Of bill@...
Sent: Friday, January 15, 2021 10:41 AM
To: main@SIKM.groups.io
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations
 
Dear Colleagues
 
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.” 
Best
 
Bill
 
 
  
 
Learn more about the solutions and value we provide at www.workingknowledge-csp.com
 
 
 
 
From: main@SIKM.groups.io <main@SIKM.groups.io> On Behalf Of Sam Yip via groups.io
Sent: Thursday, January 14, 2021 21:41
To: main@SIKM.groups.io
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations
 
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 sense. 


Re: Model of an intelligent knowledge extraction in organizations #extraction #definition

Murray Jennex
 

at the risk of sounding obnoxious, I have to ask why codified knowledge is information?  its still knowledge, just well understood knowledge.  For example, just because we well understand the knowledge of how fire works and causes burns does not make that information, it is still knowledge...murray


-----Original Message-----
From: Fred Nickols <fred@...>
To: main@SIKM.groups.io
Sent: Fri, Jan 15, 2021 12:24 pm
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations

I agree, Chris.  Explicit knowledge is knowledge that has been codified.  It is indeed information.
 
Fred Nickols
 
From: main@SIKM.groups.io <main@SIKM.groups.io> On Behalf Of Chris Collison
Sent: Friday, January 15, 2021 3:03 PM
To: main@SIKM.groups.io
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations
 
I’m with you Fred.
...and from my perspective (which not everyone shares!), I would add that explicit knowledge = information.
Cheers,
Chris
 

From: main@SIKM.groups.io <main@SIKM.groups.io> on behalf of bill@... <bill@...>
Sent: Friday, January 15, 2021 7:57:21 PM
To: main@SIKM.groups.io <main@SIKM.groups.io>
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations
 
Hi Fred
 
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.
 
Best
 
Bill
 
From: main@SIKM.groups.io <main@SIKM.groups.io> On Behalf Of Fred Nickols via groups.io
Sent: Friday, January 15, 2021 11:22
To: main@SIKM.groups.io
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations
 
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.
 
For more, see a piece I wrote for the KM Yearbook (2000-2001).  https://www.nickols.us/knowledge_in_KM.pdf
 
Regards,
 
 
Fred Nickols, Consultant
 
My Objective is to Help You Achieve Yours
 
 
 
 
From: main@SIKM.groups.io <main@SIKM.groups.io> On Behalf Of bill@...
Sent: Friday, January 15, 2021 10:41 AM
To: main@SIKM.groups.io
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations
 
Dear Colleagues
 
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.” 
Best
 
Bill
 
 
  
 
Learn more about the solutions and value we provide at www.workingknowledge-csp.com
 
 
 
 
From: main@SIKM.groups.io <main@SIKM.groups.io> On Behalf Of Sam Yip via groups.io
Sent: Thursday, January 14, 2021 21:41
To: main@SIKM.groups.io
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations
 
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 sense. 


Re: Model of an intelligent knowledge extraction in organizations #extraction #definition

Murray Jennex
 

this is why I didn't answer the initial question of whether smart knowledge discovery/extraction was addressing tacit and/or explicit knowledge.  Frankly, given that we can't agree on what is tacit and explicit and implicit knowledge I don't use any of those terms, I simply use knowledge since as a editor in chief if I can't point to a solid definition of all these terms it is hard to get people to use them.  Also, from a practical point, I don't really care if knowledge is tacit or explicit or implicit; I just consider knowledge easier or harder to extract and codify.  Also, at the risk of sounding a heretic, I don't think knowledge that can't be stated is knowledge at all, it may be instinct or luck, but if you can't state it in some way (now here I'm fairly liberal as it doesn't have to be in words or documents, it can be visual, or pantomime, or some other explication) it isn't useful, it becomes mystical or metaphysical and I don't see that makes it very useful....murray jennex, eic International Journal of Knowledge Management


-----Original Message-----
From: Chris Collison <chris.collison@...>
To: main@SIKM.groups.io <main@SIKM.groups.io>
Sent: Fri, Jan 15, 2021 12:02 pm
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations

I’m with you Fred.
...and from my perspective (which not everyone shares!), I would add that explicit knowledge = information.
Cheers,
Chris


From: main@SIKM.groups.io <main@SIKM.groups.io> on behalf of bill@... <bill@...>
Sent: Friday, January 15, 2021 7:57:21 PM
To: main@SIKM.groups.io <main@SIKM.groups.io>
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations
 
Hi Fred
 
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.
 
Best
 
Bill
 
From: main@SIKM.groups.io <main@SIKM.groups.io> On Behalf Of Fred Nickols via groups.io
Sent: Friday, January 15, 2021 11:22
To: main@SIKM.groups.io
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations
 
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.
 
For more, see a piece I wrote for the KM Yearbook (2000-2001).  https://www.nickols.us/knowledge_in_KM.pdf
 
Regards,
 
 
Fred Nickols, Consultant
 
My Objective is to Help You Achieve Yours
 
 
 
 
From: main@SIKM.groups.io <main@SIKM.groups.io> On Behalf Of bill@...
Sent: Friday, January 15, 2021 10:41 AM
To: main@SIKM.groups.io
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations
 
Dear Colleagues
 
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.” 
Best
 
Bill
 
 
  
 
Learn more about the solutions and value we provide at www.workingknowledge-csp.com
 
 
 
 
From: main@SIKM.groups.io <main@SIKM.groups.io> On Behalf Of Sam Yip via groups.io
Sent: Thursday, January 14, 2021 21:41
To: main@SIKM.groups.io
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations
 
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 sense. 


Re: Model of an intelligent knowledge extraction in organizations #extraction #definition

Nancy Dixon
 

I support what both Fred and Chris write about tacit and implicit knowledge. I think it is possible to transfer tacit knowledge through observation of the expert, followed by in-depth conversation/coaching. It is much in the same way that athletic coaches or musicians that run Master’s classes transfer their tacit knowledge.   Dorothy Leonard talks about this is in her book, Critical Knowledge Transfer.  

Nancy

On Jan 15, 2021, at 2:02 PM, Chris Collison via groups.io <chris.collison@...> wrote:

I’m with you Fred. 
...and from my perspective (which not everyone shares!), I would add that explicit knowledge = information.
Cheers,
Chris 


From: main@SIKM.groups.io <main@SIKM.groups.io> on behalf of bill@... <bill@...>
Sent: Friday, January 15, 2021 7:57:21 PM
To: main@SIKM.groups.io <main@SIKM.groups.io>
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations
 
Hi Fred

 

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.

 

Best

 

Bill

 

From: main@SIKM.groups.io <main@SIKM.groups.io> On Behalf Of Fred Nickols via groups.io
Sent: Friday, January 15, 2021 11:22
To: main@SIKM.groups.io
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations

 

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.

 

For more, see a piece I wrote for the KM Yearbook (2000-2001).  https://www.nickols.us/knowledge_in_KM.pdf

 

Regards,

 

 

Fred Nickols, Consultant

 

My Objective is to Help You Achieve Yours

 

 

 

 

From: main@SIKM.groups.io <main@SIKM.groups.io> On Behalf Of bill@...
Sent: Friday, January 15, 2021 10:41 AM
To: main@SIKM.groups.io
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations

 

Dear Colleagues

 

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.” 

Best

 

Bill

 

 

<image002.png>

 

Learn more about the solutions and value we provide at www.workingknowledge-csp.com

 

 

 

 

From: main@SIKM.groups.io <main@SIKM.groups.io> On Behalf Of Sam Yip via groups.io
Sent: Thursday, January 14, 2021 21:41
To: main@SIKM.groups.io
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations

 

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 sense. 



Re: Model of an intelligent knowledge extraction in organizations #extraction #definition

 

OK Fred…that’s fine.

 

Best

 

Bill

 

From: main@SIKM.groups.io <main@SIKM.groups.io> On Behalf Of Fred Nickols via groups.io
Sent: Friday, January 15, 2021 12:24
To: main@SIKM.groups.io
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations

 

I understand, Bill.  I simply don’t agree with you.

 

Fred Nickols

 

From: main@SIKM.groups.io <main@SIKM.groups.io> On Behalf Of bill@...
Sent: Friday, January 15, 2021 2:57 PM
To: main@SIKM.groups.io
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations

 

Hi Fred

 

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.

 

Best

 

Bill

 

From: main@SIKM.groups.io <main@SIKM.groups.io> On Behalf Of Fred Nickols via groups.io
Sent: Friday, January 15, 2021 11:22
To: main@SIKM.groups.io
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations

 

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.

 

For more, see a piece I wrote for the KM Yearbook (2000-2001).  https://www.nickols.us/knowledge_in_KM.pdf

 

Regards,

 

 

Fred Nickols, Consultant

 

My Objective is to Help You Achieve Yours

 

 

 

 

From: main@SIKM.groups.io <main@SIKM.groups.io> On Behalf Of bill@...
Sent: Friday, January 15, 2021 10:41 AM
To: main@SIKM.groups.io
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations

 

Dear Colleagues

 

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.” 

Best

 

Bill

 

 

  

 

Learn more about the solutions and value we provide at www.workingknowledge-csp.com

 

 

 

 

From: main@SIKM.groups.io <main@SIKM.groups.io> On Behalf Of Sam Yip via groups.io
Sent: Thursday, January 14, 2021 21:41
To: main@SIKM.groups.io
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations

 

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 sense. 


SIKM Peer Assist - Lessons Learned Delivery - January 25, 2021 #lessons-learned #peer-assist

Tom Barfield
 
Edited

The SIKM Community will host a peer assist call to help David Graffagna in the challenge he recently posted - Lessons Learned Storage & Access. It will be held on Monday, Jan 25 from 3-4:30 PM CST.  The community has already responded with a treasure trove of thoughts and insights. This felt like a topic that would benefit from live conversation.  I encourage you to read David's post to better understand the problem.  Here is a taste from the end of David's post:

"Here's one of my biggest challenges … if we have hundreds of lessons learned what’s the best way of capturing and sharing (e.g., making them accessible) those without overwhelming our audiences … while making it easy for them to find the right lesson in the right context? So, bottom line … would love to know good, effective approaches you’ve seen around capturing and sharing those lessons. What have you seen around lessons learned from broad-ranging projects?"

If you can attend, please use the attached calendar file to add it to your calendar and to respond.  Also please send me a brief note (thomas.m.barfield@...) to let me know on a scale of 1-5 how active do you believe you will be on the call (1 - Plan to listen and learn TO 5 - You have experiences to share).  This will help me plan the discussion.

Tom

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