From tacit to articulate knowledge

1. Knowledge in Individuals Prof. Andrew Basden.km@basden.demon.co.ukwith thanks to Prof. Elaine Ferneley

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

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

4. Calculate tax Mend a broken leg Make a cake Raise an invoice Build an engine Service a boiler Explicit Knowledge • 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

5. Get 100% in an assignment Work in team Co-ordinate colours Ride a bike Design a presentation Arrange furniture Tacit Knowledge Examples • 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

6. 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 Knowledge As An Attribute of Expertise Prof Elaine Ferneley

7. Expertise, 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

8. Definitions: Data, Information, Knowledge, Understanding 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

9. Definitions: Data, Information, Knowledge, Understanding and Wisdom • 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

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

11. ReasoningandThinkingandGenerating Knowledge Prof Elaine Ferneley

12. 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 Expert’s Reasoning Methods Prof Elaine Ferneley

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

14. 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. Learning Prof Elaine Ferneley