Re: The role of the AI KR Strategist:

Thank you Carl!!
I have always cited Tom Gruber who wrote about it before the date of the
article
http://www-ksl.stanford.edu/kst/what-is-an-ontology.html


On Sat, Jun 13, 2020 at 10:49 PM carl mattocks <carlmattocks@gmail.com>
wrote:

>
> Regarding AIKR object -
>
> Object is a resource as per the https://www.w3.org/RDF/  Resource
> Description Framework
> <https://en.wikipedia.org/wiki/Resource_Description_Framework> (RDF)
> which provides a basic capability to define classes, subclasses, and
> properties of objects..Other technologies, like OWL
> <https://www.w3.org/2001/sw/wiki/OWL> or SKOS
> <http://www.w3.org/2004/02/skos/wiki/Main_Page>, build on RDF and provide
> language for defining structured, Web-based ontologies
>
> Acknowledging that definition of  AI and KR  continue to evolve .. and
> some challenges persist .. as described in the 1993 paper (attached)
> titled   What Is a Knowledge Representation?  which argued that a knowledge
> representation plays five distinct roles, each important to the nature of
> representation and its basic tasks ..AND ..   suggests that combining
> representations is a task that should be driven by insights about how to
> combine their theories of intelligent reasoning, not their implementation
> mechanisms
>
> https://en.wikipedia.org/wiki/Knowledge_representation_and_reasoning#cite_note-12
>
>
> First, a knowledge representation is most fundamentally a surrogate, a
> substitute for the thing itself, that is used to *enable an entity to
> determine consequences* by thinking rather than acting, that is, by
> reasoning about the world rather than taking action in it.
> Second, it is a set of *ontological commitments,* that is, an answer to
> the question, In what terms should I think about the world?
> Third, it is a *fragmentary theory* of intelligent reasoning expressed in
> terms of three components: (1) the representation’s fundamental conception
> of intelligent reasoning, (2) the set of inferences that the representation
> sanctions, and (3) the set of inferences that it recommends.
> Fourth, it is a medium for pragmatically efficient computation, that is,
> the *computational environment in which thinking is accomplished*. One
> contribution to this pragmatic efficiency is supplied by the guidance that
> a representation provides for organizing information to facilitate making
> the recommended inferences.
> Fifth, it is *a medium of human expression*, that is, a language in which
> we say things about the world.
> enjoy
>
> Carl
>
> It was a pleasure to clarify
>
>
> On Fri, Jun 12, 2020 at 8:25 AM carl mattocks <carlmattocks@gmail.com>
> wrote:
>
>>
>>
>> We will be addressing Evaluation in the discussions around use of
>> (StratMl part 2) KPIs which are essential for measuring success of a
>> strategy.
>>
>> Agreed - KR activities can start only after the goal and scope of the AI
>> system have been defined
>>
>>
>>    - Document the vision, values, goals, objectives for one or more *AIKR
>>    objects*
>>    - Employ ontological statements when explaining *AIKR object* audit
>>    data, veracity facts and (human, social and technology) risk mitigation
>>
>>
>>
>>
>> It was a pleasure to clarify
>>
>>
>> On Thu, Jun 11, 2020 at 8:46 PM Paola Di Maio <paola.dimaio@gmail.com>
>> wrote:
>>
>>>
>>> However, we can develop KR say ontologies, which are implementation
>>> independent,
>>> Perhaps look at the role of the ontologist, to get a sense of the role
>>> for the AI KR strategist?
>>>
>>> I stress that I dont think of this as a strategy but adopting this
>>> construct as it is part of the stratml vocab and can be useful way of of
>>> looking at it
>>> When it comes to ML, as per your table (ML Evaluation) it is definitely
>>> not the role of the AI KR strategist to evaluate machine learning imho
>>> Track (monitor) and evaluate are part of the same task imho
>>>
>>> It could be the role of the AI strategist to decide whether ML is needed
>>> in the AI, and to evaluate the alternatives
>>> The role of the AI KR strategies would be to devise and implement the KR
>>> according to the type of AI design/methods being used by the system.
>>>
>>> The evaluation of a ML techniques irequires different approaches from
>>> the evaluation of NL systems, for example
>>>
>>> On Fri, Jun 12, 2020 at 8:23 AM Paola Di Maio <paola.dimaio@gmail.com>
>>> wrote:
>>>
>>>> Thank you for clarifying. The table is neat and a good starting point
>>>> for elaboration. It is a bit scattered but that can be sorted.
>>>>
>>>>  I dont see KR  though
>>>>
>>>> 1. Can you please point out which of these items fall under KR? (say,
>>>> ontology?)
>>>>
>>>> my experience, the AI KR development work starts after the case for the
>>>> AI system has already been  made, , ie, when the need for the AI system has
>>>> already been identified, ( the first two points).  The system design is in
>>>> place schedules and resources to be in place,
>>>> KR activities are not AI general system development activities.  What I
>>>> mean is that KR activities can start only after the goal and scope of the
>>>> AI system have been defined.  (not sure whose role is to define them,
>>>> perhaps the system developer or the AI strategist)
>>>>
>>>> On Thu, Jun 11, 2020 at 10:44 PM carl mattocks <carlmattocks@gmail.com>
>>>> wrote:
>>>>
>>>>>
>>>>> Please peruse this GOAL Dependency approach to sequencing
>>>>>
>>>>> *Goal Activities*
>>>>>
>>>>> *Title AI Strategist Goal*
>>>>>
>>>>> *Goal Dependency*
>>>>>
>>>>> *AIKR Strategist Protocol*
>>>>>
>>>>> Give an understanding of the possible applications of AI to
>>>>> conversations/decisions about business strategy
>>>>>
>>>>>
>>>>>
>>>>> Business Strategy
>>>>>
>>>>>
>>>>>
>>>>> Document, Requirements, Quality, Robust, Ontological Statements,
>>>>> Ethics, Lawful, Machine Learning Evaluation, Track
>>>>>
>>>>> yes
>>>>>
>>>>> Identify which areas of the requirements warrant AI solutions versus
>>>>> which can be achieved with other types of solutions
>>>>>
>>>>>
>>>>>
>>>>> Requirements
>>>>>
>>>>>
>>>>>
>>>>> Business Strategy, Document, Quality, Robust, Ontological Statements,
>>>>> Ethics, Lawful, Machine Learning Evaluation, Track
>>>>>
>>>>> yes
>>>>>
>>>>> Document the vision, values, goals, objectives for one or more AIKR
>>>>> objects
>>>>>
>>>>>
>>>>>
>>>>> Document
>>>>>
>>>>>
>>>>>
>>>>> Business Strategy, Requirements, Quality, Robust, Ontological
>>>>> Statements, Ethics, Lawful, Machine Learning Evaluation, Track
>>>>>
>>>>> yes
>>>>>
>>>>> Define the limits of quality. If a product has limits of
>>>>> quality/action, then these should be stated
>>>>>
>>>>> Quality
>>>>>
>>>>>
>>>>>
>>>>> Business Strategy, Document, Requirements, Robust, Ontological
>>>>> Statements, Ethics, Lawful, Machine Learning Evaluation, Track
>>>>>
>>>>> yes
>>>>>
>>>>> Evaluate machine learning models
>>>>>
>>>>>
>>>>>
>>>>> Machine Learning Evaluation
>>>>>
>>>>>
>>>>>
>>>>> Business Strategy, Document, Requirements, Quality, Robust,
>>>>> Ontological Statements, Ethics, Lawful, Track
>>>>>
>>>>> yes
>>>>>
>>>>> Track AIKR object performance outcome via KPI (Key Performance
>>>>> Indicator) based on supervised learning models measurements
>>>>>
>>>>>
>>>>>
>>>>> Track
>>>>>
>>>>>
>>>>>
>>>>> Document, Requirements, Quality, Robust, Ontological Statements,
>>>>>  Machine Learning Evaluation
>>>>>
>>>>> yes
>>>>>
>>>>> Ensure AI Systems are designed to handle uncertainty and tolerate
>>>>> perturbation from a likely threat perspective, such as, design
>>>>> considerations incorporate human, social and technology risk factors
>>>>>
>>>>>
>>>>>
>>>>> Robust
>>>>>
>>>>>
>>>>>
>>>>> Document, Requirements, Quality, Ontological Statements, Ethics,
>>>>> Lawful, Machine Learning Evaluation, Track
>>>>>
>>>>> yes
>>>>>
>>>>> Employ ontological statements when explaining AIKR object audit data,
>>>>> veracity facts and (human, social and technology) risk mitigation
>>>>>
>>>>> Ontological Statements
>>>>>
>>>>>
>>>>>
>>>>> Business Strategy, Document, Requirements, Quality, Robust, Ethics,
>>>>> Lawful, Machine Learning Evaluation, Track
>>>>>
>>>>> yes
>>>>>
>>>>> Ensure AI Systems adhere to principles of ethics
>>>>>
>>>>>
>>>>>
>>>>> Ethics
>>>>>
>>>>>
>>>>>
>>>>> Document, Requirements, Quality, Robust, Ontological Statements,
>>>>> Lawful, Machine Learning Evaluation, Track
>>>>>
>>>>> yes
>>>>>
>>>>> Ensure AI Systems comply with all applicable laws and regulations,
>>>>> such as, provision audit data defined by a governance operating model
>>>>>
>>>>>
>>>>>
>>>>> Lawful
>>>>>
>>>>>
>>>>>
>>>>> Document, Requirements, Quality, Robust, Ontological Statements,
>>>>> Ethics, Machine Learning Evaluation, Track
>>>>>
>>>>> yes
>>>>>
>>>>> Carl Mattocks
>>>>>
>>>>> It was a pleasure to clarify
>>>>>
>>>>>
>>>>> On Wed, Jun 10, 2020 at 8:46 AM carl mattocks <carlmattocks@gmail.com>
>>>>> wrote:
>>>>>
>>>>>>
>>>>>> Please find attached (summary extract below)  Strategic Plan - The
>>>>>> role of the AI KR Strategist (A strategist is a person with
>>>>>> responsibility for the formulation and implementation of a strategy).
>>>>>>
>>>>>> To help validate the list of goals we invite proposals for a
>>>>>> sequencing (of the goals) that could be the foundation of an AI Strategy
>>>>>> Protocol.
>>>>>>
>>>>>> Please provide your proposals as a reply to this post.. intent is to
>>>>>> apply them to the StratMl version at 9:00 am (NY time) June 23 meeting.
>>>>>>
>>>>>>
>>>>>>
>>>>>> thanks
>>>>>>
>>>>>> Carl Mattocks
>>>>>> AIKRCG co-Chair
>>>>>>
>>>>>>
>>>>>> Strategic Plan
>>>>>>
>>>>>> *The role of the AI KR Strategist*
>>>>>>
>>>>>> For: *Artificial Intelligence Knowledge Representation Community
>>>>>> Group (AIKR CG)*
>>>>>>
>>>>>> Submitted By: *Carl Mattocks*
>>>>>>
>>>>>> y *CarlMattocks@WellnessIntelligence.Institute*
>>>>>>
>>>>>> *Overview*
>>>>>>
>>>>>> This plan defines the role of the AI KR Strategist.
>>>>>>
>>>>>> Contents
>>>>>>
>>>>>> *No table of contents entries found.*
>>>>>> 1.    Articulation 1.1.    Vision
>>>>>>
>>>>>> For all AI systems to have clearly and transparently documented goals
>>>>>> and performance data showing that they are being achieved.
>>>>>> 1.2.    Mission
>>>>>>
>>>>>> The mission of an AI Strategist is to define the purpose and goals of
>>>>>> AI systems, as well as the KPIs by which we can determine if the system is
>>>>>> meeting its goals.
>>>>>> 1.3.    Scorecard
>>>>>>
>>>>>>
>>>>>>
>>>>>> *Goals*
>>>>>>
>>>>>> *Objectives*
>>>>>>
>>>>>> *Performance Indicators*
>>>>>>
>>>>>> *Commentary*
>>>>>>
>>>>>> *Goals with no perspective*
>>>>>>
>>>>>> Quality (see pp1)
>>>>>>
>>>>>> Adherence to Environmental Impacts (see pp1)
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>> Efficiency (see pp1)
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>> Ethics (see pp1)
>>>>>>
>>>>>> Accountability (see pp1)
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>> Autonomy (see pp1)
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>> Confidentiality (see pp1)
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>> Veracity (see pp1)
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>> Machine Learning Evaluation (see pp1)
>>>>>>
>>>>>> Track (see pp1)
>>>>>>
>>>>>> Precision Recall
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>> Accuracy
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>> Confusion Matrix
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>> Per-class accuracy
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>> Log-Loss
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>> AUC-ROC Curve
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>> F-measure
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>> NDCG
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>> Regression Analysis
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>> Quantiles of Errors
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>> "Almost correct" predictions
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>> Trustworthy (see pp1)
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>> Lawful (see pp1)
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>> Ontological Statements (see pp1)
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>> Track (see pp1)
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>> Business Strategy (see pp1)
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>> Requirements (see pp1)
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>> Document (see pp1)
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>> Robust (see pp1)
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>> 1.4.    Goals....
>>>>>>
>>>>>> It was a pleasure to clarify
>>>>>>
>>>>>>
>>>>>> On Tue, Jun 9, 2020 at 11:56 PM Paola Di Maio <paola.dimaio@gmail.com>
>>>>>> wrote:
>>>>>>
>>>>>>> oops-
>>>>>>> Thanks Carl Owen and Chris
>>>>>>> for capturing and sharing.
>>>>>>> I repeat here my comment shared with participants after the session
>>>>>>> - also pasted below
>>>>>>>
>>>>>>> if the plan we worked on yesterday is to describe the role of an AI
>>>>>>> Strategist
>>>>>>> then we need to make explicit what the goal of the role and
>>>>>>> associated activities  is/are
>>>>>>>
>>>>>>> AI systems /software development can be very sophisticated and well
>>>>>>> understood practice
>>>>>>> in systems/software engineering . It is not a strategy role to do
>>>>>>> requirements analysis and write
>>>>>>> technical systems specifications, especially not technically
>>>>>>> advanced ones
>>>>>>>  So we should review and devise an AI strategist/strategy goals
>>>>>>> accordingly, taking into account the technical system specifications.
>>>>>>> or viceversa?
>>>>>>> A strategist could identify the need for a technical system based on
>>>>>>> a business problem/need, but then pass the development
>>>>>>> to a specialist (a systems developer/engineer) who has the skills to
>>>>>>> do so
>>>>>>> So upon further reflection, I dont think requirements belong to an
>>>>>>> ai strategy. unless better specified
>>>>>>>
>>>>>>> pdm
>>>>>>> paola wrote:
>>>>>>> Thanks for starting this plan
>>>>>>> As I read what had been drafted I commented with my software/system
>>>>>>> developer hat on
>>>>>>> The system lifecycle is considered a process, rather than a
>>>>>>> strategy, and consists of
>>>>>>> identifying requirements, specifications and documentation and all
>>>>>>> Its well understood and documented and there are several models, but
>>>>>>> the lifecycle itself
>>>>>>> is fairly standard, it applies to AI systems as to any system
>>>>>>> So before I have a go at reorganising and perhaps streamlining a bit
>>>>>>> those goals
>>>>>>> I think we should define better what is the role of the AI strategist
>>>>>>> does it overlap the role of a ai system analyst and designer (which
>>>>>>> is what I have in mind)
>>>>>>> ie  identify what needs to be done
>>>>>>> or is it more a role to decide HOW these goals need to be achieved in
>>>>>>> terms of resources, schedules, policies, coordination with
>>>>>>> management etc
>>>>>>> Also, as I mentioned, this may depend on what other roles the AI
>>>>>>> strategist works with/depends on-
>>>>>>> p
>>>>>>>
>>>>>>>
>>>>>>> On Wed, Jun 10, 2020 at 11:45 AM Paola Di Maio <
>>>>>>> paola.dimaio@gmail.com> wrote:
>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>> On Wed, Jun 10, 2020 at 12:38 AM carl mattocks <
>>>>>>>> carlmattocks@gmail.com> wrote:
>>>>>>>>
>>>>>>>>> Role of Data Scientist has a process that is described in the
>>>>>>>>> links below :
>>>>>>>>>
>>>>>>>>> *Cross-industry standard process for data mining*, known as
>>>>>>>>> *CRISP-DM*,[1]
>>>>>>>>> <https://en.wikipedia.org/wiki/Cross-industry_standard_process_for_data_mining#cite_note-Shearer00-1> is
>>>>>>>>> an open standard <https://en.wikipedia.org/wiki/Open_standard> process
>>>>>>>>> model that describes common approaches used by data mining
>>>>>>>>> <https://en.wikipedia.org/wiki/Data_mining> experts. It is the
>>>>>>>>> most widely-used analytics
>>>>>>>>> <https://en.wikipedia.org/wiki/Analytics> model
>>>>>>>>>
>>>>>>>>> https://en.wikipedia.org/wiki/Cross-industry_standard_process_for_data_mining
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> these lecture notes provide good background notes
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> https://wiwi.hs-duesseldorf.de/personen/thomas.zeutschler/Documents/HSD_W_ITAiBA_Zeutschler_SS2016_Lecture2_CRSIP_DM.pdf
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> this is an IBM presentation on the  The Data Science Process
>>>>>>>>>
>>>>>>>>> https://www-01.ibm.com/events/wwe/grp/grp304.nsf/vLookupPDFs/Polong%20Lin%20Presentation/$file/Polong%20Lin%20Presentation.pdf
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> enjoy
>>>>>>>>> Carl
>>>>>>>>>
>>>>>>>>> It was a pleasure to clarify
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> On Tue, Jun 9, 2020 at 12:13 PM Owen Ambur <Owen.Ambur@verizon.net>
>>>>>>>>> wrote:
>>>>>>>>>
>>>>>>>>>> Carl, thanks for leading another productive editing session on
>>>>>>>>>> the AIKR CG televideo conference today.  I found myself agreeing with much
>>>>>>>>>> of what Paola was saying, and I continue to be impressed by Chris'
>>>>>>>>>> capability to comprehend and capture the essence of our dialog in his
>>>>>>>>>> StratNavApp.  For the benefit of those who were unable to join us, I am
>>>>>>>>>> providing a link here
>>>>>>>>>> <https://www.stratnavapp.com/StratML/Part1/861566c8-e9be-4642-b52f-f673fa499f4e/Styled>
>>>>>>>>>> so that they can view our draft plan as it currently exists.
>>>>>>>>>>
>>>>>>>>>> I'm sorry that Justin was not able to participate to ensure that
>>>>>>>>>> we are capturing the knowledge he has gained in considering the roles of AI
>>>>>>>>>> strategists.  However, it would be great if he and others could contribute
>>>>>>>>>> their comments and edits on our draft in Chris' app
>>>>>>>>>> <https://www.stratnavapp.com/> and perhaps participate in our
>>>>>>>>>> text televideo conference on June 23.
>>>>>>>>>> While the StratML collection does not yet contain a model
>>>>>>>>>> performance plan for Data Scientists, it does include one for Chief Data
>>>>>>>>>> Officers (CDOs), at https://stratml.us/drybridge/index.htm#PP4CDO
>>>>>>>>>>
>>>>>>>>>> Unless someone has a better source, I will soon convert the
>>>>>>>>>> contents of this one to StratML format:
>>>>>>>>>> https://www.sas.com/en_us/insights/analytics/what-is-a-data-scientist.html
>>>>>>>>>>
>>>>>>>>>> BTW, Carl, your reference to OASIS's Business Centric Methodology
>>>>>>>>>> (BCM) prompted me to convert its content to StratML format at
>>>>>>>>>> https://stratml.us/drybridge/index.htm#BCM
>>>>>>>>>>
>>>>>>>>>> These elements of the BCM are closely related to the purposes of
>>>>>>>>>> the StratML standard:
>>>>>>>>>>
>>>>>>>>>> Objective 1.1: Goals
>>>>>>>>>> <https://stratml.us/carmel/iso/BCMwStyle.xml#_6b2c0b98-aa65-11ea-9885-42f42983ea00>
>>>>>>>>>> - Determine the business goals.
>>>>>>>>>> Objective 1.3: Participants
>>>>>>>>>> <https://stratml.us/carmel/iso/BCMwStyle.xml#_6b2c0d32-aa65-11ea-9885-42f42983ea00>
>>>>>>>>>> - Identify the project participants.
>>>>>>>>>> Objective 1.4: COIs
>>>>>>>>>> <https://stratml.us/carmel/iso/BCMwStyle.xml#_6b2c0e40-aa65-11ea-9885-42f42983ea00>
>>>>>>>>>> - Identify the Community of Interest.
>>>>>>>>>> Goal 2: Goals
>>>>>>>>>> <https://stratml.us/carmel/iso/BCMwStyle.xml#_6b2c1106-aa65-11ea-9885-42f42983ea00>
>>>>>>>>>> - Understand the business goals.
>>>>>>>>>> Objective 3.3: Standards Bodies
>>>>>>>>>> <https://stratml.us/carmel/iso/BCMwStyle.xml#_6b2c1750-aa65-11ea-9885-42f42983ea00>
>>>>>>>>>> - Map interoperability requirements to standard bodies.
>>>>>>>>>> Objective 3.4: Formats
>>>>>>>>>> <https://stratml.us/carmel/iso/BCMwStyle.xml#_6b2c182c-aa65-11ea-9885-42f42983ea00>
>>>>>>>>>> - Map interoperability requirements to internal legacy system formats.
>>>>>>>>>> Objective 3.5: Templates
>>>>>>>>>> <https://stratml.us/carmel/iso/BCMwStyle.xml#_6b2c191c-aa65-11ea-9885-42f42983ea00>
>>>>>>>>>> - Capture information in a way that can be reused over time and among
>>>>>>>>>> participating organizations.
>>>>>>>>>> Objective 3.5.1: Strategies
>>>>>>>>>> <https://stratml.us/carmel/iso/BCMwStyle.xml#_6b2c19f8-aa65-11ea-9885-42f42983ea00>
>>>>>>>>>> - Implement strategies for interoperability.
>>>>>>>>>> Objective 3.5.5: Standards
>>>>>>>>>> <https://stratml.us/carmel/iso/BCMwStyle.xml#_6b2c1da4-aa65-11ea-9885-42f42983ea00>
>>>>>>>>>> - Apply standards for interoperability and contracts.
>>>>>>>>>> Objective 3.5.7: Performance
>>>>>>>>>> <https://stratml.us/carmel/iso/BCMwStyle.xml#_6b2c206a-aa65-11ea-9885-42f42983ea00>
>>>>>>>>>> - Monitor and manage performance.
>>>>>>>>>> Objective 4.1: Standards
>>>>>>>>>> <https://stratml.us/carmel/iso/BCMwStyle.xml#_6b2c2b82-aa65-11ea-9885-42f42983ea00>
>>>>>>>>>> - Use existing standards.
>>>>>>>>>>
>>>>>>>>>> It would be great if BCM practitioners were to use the StratML
>>>>>>>>>> standard (ISO 17469-1).  Anyone who may wish to apply the BCM template as a
>>>>>>>>>> performance plan for their organization in StratML Part 2 format could do
>>>>>>>>>> so by clicking on the link provided here.
>>>>>>>>>>
>>>>>>>>>> Owen
>>>>>>>>>>
>>>>>>>>>> -------- Forwarded Message --------
>>>>>>>>>> Subject: Re: ai strategist role
>>>>>>>>>> Date: Tue, 9 Jun 2020 11:22:53 -0400
>>>>>>>>>> From: carl mattocks <carlmattocks@gmail.com>
>>>>>>>>>> <carlmattocks@gmail.com>
>>>>>>>>>> To: Paola Di Maio <paoladimaio10@googlemail.com>
>>>>>>>>>> <paoladimaio10@googlemail.com>
>>>>>>>>>> CC: Owen Ambur <Owen.Ambur@verizon.net> <Owen.Ambur@verizon.net>,
>>>>>>>>>> Chris Fox <chris@chriscfox.com> <chris@chriscfox.com>, Paul
>>>>>>>>>> Alagna <pjalagna@gmail.com> <pjalagna@gmail.com>
>>>>>>>>>>
>>>>>>>>>> Data Scientist is a complimentary role.. which is supported by an
>>>>>>>>>> open source method that has strong emphasis on machine learning and is Plan
>>>>>>>>>> Do Check Act oriented.
>>>>>>>>>>
>>>>>>>>>> Carl
>>>>>>>>>>
>>>>>>>>>> On Tue, Jun 9, 2020, 11:12 AM Paola Di Maio <
>>>>>>>>>> paola.dimaio@gmail.com> wrote:
>>>>>>>>>>
>>>>>>>>>>> Thanks for starting this plan
>>>>>>>>>>> As I read what had been drafted I commented with my
>>>>>>>>>>> software/system developer hat on
>>>>>>>>>>> The system lifecycle is considered a process, rather than a
>>>>>>>>>>> strategy, and consists of
>>>>>>>>>>> identifying requirements, specifications and documentation and
>>>>>>>>>>> all
>>>>>>>>>>> Its well understood and documented and there are several models,
>>>>>>>>>>> but the lifecycle itself
>>>>>>>>>>> is fairly standard, it applies to AI systems as to any system
>>>>>>>>>>> So before I have a go at reorganising and perhaps streamlining a
>>>>>>>>>>> bit those goals
>>>>>>>>>>> I think we should define better what is the role of the AI
>>>>>>>>>>> strategist
>>>>>>>>>>> does it overlap the role of a ai system analyst and designer
>>>>>>>>>>> (which is what I have in mind)
>>>>>>>>>>> ie  identify what needs to be done
>>>>>>>>>>> or is it more a role to decide HOW these goals need to be
>>>>>>>>>>> achieved in
>>>>>>>>>>> terms of resources, schedules, policies, coordination with
>>>>>>>>>>> management etc
>>>>>>>>>>> Also, as I mentioned, this may depend on what other roles the AI
>>>>>>>>>>> strategist works with/depends on-
>>>>>>>>>>> p
>>>>>>>>>>>
>>>>>>>>>>