Model of an intelligent knowledge extraction in organizations #extraction #definition
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Chris Collison
I’m with you Fred.
...and from my perspective (which not everyone shares!), I would add that explicit knowledge = information.
Cheers,
Chris
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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
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@...
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
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. |
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HI Chris
I agree with explicit = information
Bill From: main@SIKM.groups.io <main@SIKM.groups.io> On Behalf Of
Chris Collison via groups.io
Sent: Friday, January 15, 2021 12:03 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
Get Outlook for iOS From:
main@SIKM.groups.io <main@SIKM.groups.io> on behalf of
bill@... <bill@...>
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
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@...
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
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. |
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Fred Nickols
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
Get Outlook for iOS From: main@SIKM.groups.io <main@SIKM.groups.io> on behalf of bill@... <bill@...>
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
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@...
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
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. |
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Fred Nickols
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
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@...
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
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. |
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Tim Powell
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
New York City, USA | DIRECT/MOBILE +1.212.243.1200 | ZOOM 212-243-1200 SITE www.KnowledgeAgency.com | BLOG www.KnowledgeValueChain.com
From: <main@SIKM.groups.io> on behalf of Fred Nickols <fred@...>
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
Get Outlook for iOS From:
main@SIKM.groups.io <main@SIKM.groups.io> on behalf of
bill@... <bill@...>
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
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@...
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
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. |
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Mila Malekolkalami
How is explicit knowledge information? We have information. But if we don't put it in a proper process and use it, it is not knowledge. It is useless. The information which is not used can't be knowledge, can it? I think these 2 are related to each other. If there is no information, so there isn't any knowledge.
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Tim Powell
Good question. As I see it, once knowledge is elicited/codified, it is (by definition) information (e.g., in a database, report, etc.). It can then be converted back into (someone else’s) knowledge – by consulting the database, reading the report, and so on. This is a very noisy and inexact process (first described but Claude Shannon) – but it’s what we have. I call it the “K-I-K translation,” others call it “writing and reading.”
Knowledge usage (in making decisions, formulating and taking actions, and ultimately producing value) I see as separate from, and subsequent to, the production of knowledge. And usage is the only path to value.
Said another way, yes it is (unfortunately) possible to have knowledge that produces little or no value. It happens all the time, in fact – and identifying and fixing that condition is what has over time become a focus (obsession?) of mine.
tp
New York City, USA | DIRECT/MOBILE +1.212.243.1200 | ZOOM 212-243-1200 SITE www.KnowledgeAgency.com | BLOG www.KnowledgeValueChain.com
From: <main@SIKM.groups.io> on behalf of "Mila Malekolkalami via groups.io" <Mila_malek_1365@...>
How is explicit knowledge information? We have information. But if we don't put it in a proper process and use it, it is not knowledge. It is useless. The information which is not used can't be knowledge, can it? I think these 2 are related to each other. If there is no information, so there isn't any knowledge.
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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@...
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
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@...
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
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. |
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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
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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
Get Outlook for iOS
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.
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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
Get Outlook for iOS
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.
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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
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
Get Outlook for iOS
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.
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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
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Mila Malekolkalami
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Eli Miron
Hi Bill,
From: main@SIKM.groups.io <main@SIKM.groups.io> On Behalf Of Murray Jennex via groups.io
but to play devil's advocate, once you video your below examples does that turn it into information? -----Original Message----- 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
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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
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----- 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
I’m with you Fred. ...and from my perspective (which not everyone shares!), I would add that explicit knowledge = information. Cheers, Chris
Get Outlook for iOS From: main@SIKM.groups.io <main@SIKM.groups.io> on behalf of bill@... <bill@...>
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
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@...
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
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. |
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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:
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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:
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 Bounds Executive, Information Management Cordelta E: stephen.bounds@... M: 0401 829 096 ==================================== On 16/01/2021 6:02 am, Chris Collison
wrote:
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I have always considered categories convenient and efficient, but an oversimplification when it comes to real life matters such as knowledge, relationships and emotions.
toggle quoted message
Show quoted text
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:
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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:
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.
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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
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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.
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