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Knowledge in Individuals Knowledge in Individuals
Prof. Andrew Basden. Prof. Andrew [email protected]@basden.demon.co.uk
with thanks towith thanks to
Prof. Prof. Elaine Ferneley Elaine Ferneley
Prof Elaine Ferneley 2
From tacit to articulate knowledge From tacit to articulate knowledge
““We know more than we can tell.” We know more than we can tell.”
Michael Polanyi, 1966Michael Polanyi, 1966
TacitArticulated
High Low
MANUALHow to
play soccer
Codifiability
Prof Elaine Ferneley 33
Knowledge is experience, Knowledge is experience, everything else is just everything else is just
information.information.-Albert Einstein-Albert Einstein
““We know more than we can tell.”We know more than we can tell.”
Prof Elaine Ferneley
Explicit KnowledgeExplicit Knowledge
Mend a
broken legCalculate
tax
Make a cake
Raise a
n
invoiceBuild anengine
Service a boiler
Formal and systematic: easily communicated &
shared in product specifications, scientific formula or as computer programs;
Management of explicit knowledge: management of
processes and information
Are the activities to the right information or knowledge dependent ?
Prof Elaine Ferneley
Tacit Knowledge ExamplesTacit Knowledge Examples
Work in
team
Get 100%in an
assignmentCo-ordinate colours
Ride a
bikeDesign apresentation
Arrange furniture
Highly personal: hard to formalise; difficult (but not
impossible)to articulate; often in the form of know
how.
Management of tacit knowledge is the management of people: how do you extract and
disseminate tacit knowledge.
Prof Elaine Ferneley
Knowledge As An Attribute of ExpertiseKnowledge As An Attribute of Expertise
An expert in a specialized area masters the requisite knowledge
The unique performance of a knowledgeable expert is clearly noticeable in decision-making quality
Experts are more selective in the information they acquire: they know what is important
Experts are beneficiaries of the knowledge that comes from experience
Prof Elaine Ferneley
Expertise, Experience & UnderstandingExpertise, Experience & Understanding
Experience – rules of thumb: What e.g. gardener might have
Understanding – general knowledge:What a biology graduate might have
Expertise – E + U in harmonyWhat an expert has
Prof Elaine Ferneley
Definitions: Definitions: DataData, , Information, Knowledge, Information, Knowledge,
UnderstandingUnderstanding and Wisdom and Wisdom
The appreciation of why The difference between learning and memorising
If you understand you can take existing knowledge and creating new knowledge, build upon currently held information and knowledge and develop new information and knowledge
In computing terms AI systems possess understanding in the sense that they are able to infer new information and knowledge from previously stored information and knowledge
Prof Elaine Ferneley
Definitions: Definitions: DataData, , Information, Knowledge, Information, Knowledge,
Understanding and Understanding and WisdomWisdom
Evaluated understanding Essence of philosophical probing
Critically questions, particularly from a human perspective of morals and ethics
discerning what is right or wrong, good or bad A mix of experience, values, contextual
information, insight In computing terms may be unachievable
– can a computer have a soul??
Prof Elaine Ferneley
Illustrations of the Different Types of Illustrations of the Different Types of Knowledge Knowledge
Know ‘that’
Know ‘how’
Prof Elaine Ferneley
ReasoningReasoningandand
ThinkingThinkingandand
Generating KnowledgeGenerating Knowledge
Prof Elaine Ferneley
Expert’s Reasoning MethodsExpert’s Reasoning Methods
Reasoning by analogy: relating one concept to another Formal reasoning: using deductive or inductive methods (see next slide) Case-based reasoning: reasoning from relevant past cases
Prof Elaine Ferneley
Deductive and inductive reasoningDeductive and inductive reasoning
Deductive reasoning: exact reasoning. It deals with exact facts and exact facts and exact conclusionsexact conclusions
Inductive reasoning: reasoning from a set of facts or individual cases to a general general conclusionconclusion
Prof Elaine Ferneley
LearningLearning
Learning by experience: a function of time and talent
Learning by example: more efficient than learning by experience
Learning by sharing, education.
Learning by discovery: explore a problem area.