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Knowledge in Individuals Prof. Andrew Basden. with thanks to Prof. Elaine Ferneley

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Presentation on theme: "Knowledge in Individuals Prof. Andrew Basden. with thanks to Prof. Elaine Ferneley"— Presentation transcript:

1 Knowledge in Individuals Prof. Andrew Basden. with thanks to Prof. Elaine Ferneley

2 Prof Elaine Ferneley 2 From tacit to articulate knowledge “We know more than we can tell.” Michael Polanyi, 1966 TacitArticulated High Low MANUAL How to play soccer Codifiability

3 Prof Elaine Ferneley 3 Knowledge is experience, everything else is just information. -Albert Einstein “We know more than we can tell.”

4 Prof Elaine Ferneley Explicit Knowledge Mend a broken leg Calculate tax Make a cake Raise an invoice Build an engine Service a boiler n nFormal and systematic: u ueasily communicated & shared in product specifications, scientific formula or as computer programs; n nManagement of explicit knowledge: u umanagement of processes and information n nAre the activities to the right information or knowledge dependent ?

5 Prof Elaine Ferneley Tacit Knowledge Examples Work in team Get 100% in an assignment Co-ordinate colours Ride a bike Design a presentation Arrange furniture n nHighly personal: u uhard to formalise; u udifficult (but not impossible)to articulate; u uoften in the form of know how. n nManagement of tacit knowledge is the management of people: u uhow do you extract and disseminate tacit knowledge.

6 Prof Elaine Ferneley Knowledge As An Attribute of Expertise nAn expert in a specialized area masters the requisite knowledge nThe unique performance of a knowledgeable expert is clearly noticeable in decision-making quality nExperts are more selective in the information they acquire: they know what is important nExperts are beneficiaries of the knowledge that comes from experience

7 Prof Elaine Ferneley Expertise, Experience & Understanding nExperience – rules of thumb: What e.g. gardener might have nUnderstanding – general knowledge: What a biology graduate might have nExpertise – E + U in harmony What an expert has

8 Prof Elaine Ferneley Definitions: Data, Information, Knowledge, Understanding and Wisdom nThe appreciation of why uThe difference between learning and memorising nIf you understand you can take existing knowledge and creating new knowledge, build upon currently held information and knowledge and develop new information and knowledge nIn 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

9 Prof Elaine Ferneley Definitions: Data, Information, Knowledge, Understanding and Wisdom nEvaluated understanding nEssence of philosophical probing uCritically questions, particularly from a human perspective of morals and ethics udiscerning what is right or wrong, good or bad nA mix of experience, values, contextual information, insight nIn computing terms may be unachievable – can a computer have a soul??

10 Prof Elaine Ferneley Illustrations of the Different Types of Knowledge Know ‘that’ Know ‘how’

11 Prof Elaine Ferneley Types (Categorization) of Knowledge nShallow (readily recalled) and deep (acquired through years of experience) nExplicit (already codified) and tacit (embedded in the mind) nProcedural (repetitive, stepwise) versus Episodical (grouped by episodes) chunks nKnowledge exist in chunks

12 Prof Elaine Ferneley Reasoning and Thinking and Generating Knowledge

13 Prof Elaine Ferneley Expert’s Reasoning Methods nReasoning by analogy: relating one concept to another n Formal reasoning: using deductive or inductive methods (see next slide) n Case-based reasoning: reasoning from relevant past cases

14 Prof Elaine Ferneley Deductive and inductive reasoning exact facts and exact conclusions nDeductive reasoning: exact reasoning. It deals with exact facts and exact conclusions general conclusion nInductive reasoning: reasoning from a set of facts or individual cases to a general conclusion

15 Prof Elaine Ferneley Learning Learning nLearning by experience: a function of time and talent nLearning by example: more efficient than learning by experience nLearning by sharing, education. nLearning by discovery: explore a problem area.


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