Knowledge Engineer A knowledge engineer integrates knowledge into computer systems in order to solve complex problems normally requiring a high level of human expertise. Overview[edit] Often knowledge engineers are employed to translate the information elicited from domain experts into terms which cannot be easily communicated by the highly technalized domain expert (ESDG 2000). Knowledge engineers interpret and organize information on how to make systems decisions (Aylett & Doniat 2002). The term "Knowledge engineer" first appeared in the 1980s in the first wave of commercialization of AI – the purpose of the job is to work with a client who wants an expert system created for them or their business. Validation & verification with knowledge engineers[edit] Validation is the process of ensuring that something is correct or conforms to a certain standard. It is important that a knowledge engineer incorporates validation procedures into their systems within the program code. References[edit]
Zeno's paradoxes Zeno's arguments are perhaps the first examples of a method of proof called reductio ad absurdum also known as proof by contradiction. They are also credited as a source of the dialectic method used by Socrates.[3] Some mathematicians and historians, such as Carl Boyer, hold that Zeno's paradoxes are simply mathematical problems, for which modern calculus provides a mathematical solution.[4] Some philosophers, however, say that Zeno's paradoxes and their variations (see Thomson's lamp) remain relevant metaphysical problems.[5][6][7] The origins of the paradoxes are somewhat unclear. Diogenes Laertius, a fourth source for information about Zeno and his teachings, citing Favorinus, says that Zeno's teacher Parmenides was the first to introduce the Achilles and the tortoise paradox. Paradoxes of motion[edit] Achilles and the tortoise[edit] Distance vs. time, assuming the tortoise to run at Achilles' half speed Dichotomy paradox[edit] Suppose Homer wants to catch a stationary bus.
Knowledge Community A knowledge community is community construct, stemming from the convergence of knowledge management as a field of study and social exchange theory. Formerly known as a discourse community and having evolved from forums and web forums, knowledge communities are now often referred to as a community of practice or virtual community of practice. As with any field of study, there are various points of view on the motivations, organizing principles and subsequent structure of knowledge communities. Perspectives[edit] As a web or virtual construct, knowledge communities can be said to have evolved from bulletin board systems, web forums and online discourse communities through the 80s and 90s. Stemming from social exchange theory, a well-established perspective is to view knowledge communities as a type of exchange. Knowledge communities can also be viewed as a method by which to do organizational or process innovation. Organizational behavior and structure[edit] Pitfalls[edit] References[edit]
DIKW Pyramid The DIKW Pyramid, also known variously as the "DIKW Hierarchy", "Wisdom Hierarchy", the "Knowledge Hierarchy", the "Information Hierarchy", and the "Knowledge Pyramid",[1] refers loosely to a class of models[2] for representing purported structural and/or functional relationships between data, information, knowledge, and wisdom. "Typically information is defined in terms of data, knowledge in terms of information, and wisdom in terms of knowledge".[1] History[edit] "The presentation of the relationships among data, information, knowledge, and sometimes wisdom in a hierarchical arrangement has been part of the language of information science for many years. Although it is uncertain when and by whom those relationships were first presented, the ubiquity of the notion of a hierarchy is embedded in the use of the acronym DIKW as a shorthand representation for the data-to-information-to-knowledge-to-wisdom transformation. Data, Information, Knowledge, Wisdom[edit] Description[edit] Data[edit]
Knowledge Ecosystem The idea of a knowledge ecosystem is an approach to knowledge management which claims to foster the dynamic evolution of knowledge interactions between entities to improve decision-making and innovation through improved evolutionary networks of collaboration.[1][2] In contrast to purely directive management efforts that attempt either to manage or direct outcomes, knowledge ecosystems espouse that knowledge strategies should focus more on enabling self-organization in response to changing environments.[3] The suitability between knowledge and problems confronted defines the degree of "fitness" of a knowledge ecosystem. Articles discussing such ecological approaches typically incorporate elements of complex adaptive systems theory. Key Elements[edit] To understand knowledge ecology as a productive operation, it is helpful to focus on the knowledge ecosystem that lies at its core. Key elements of networked knowledge systems[6] include: 1. 2. 3. 4. See also[edit] Notes[edit] Clippinger, J.
The Problem with the Data-Information-Knowledge-Wisdom Hierarchy - David Weinberger by David Weinberger | 9:00 AM February 2, 2010 The data-information-knowledge-wisdom hierarchy seemed like a really great idea when it was first proposed. But its rapid acceptance was in fact a sign of how worried we were about the real value of the information systems we had built at such great expense. What looks like a logical progression is actually a desperate cry for help. The DIKW hierarchy (as it came to be known) was brought to prominence by Russell Ackoff in his address accepting the presidency of the International Society for General Systems Research in 1989. Where is the Life we have lost in living? Those lines come from the poem “The Rock” by T.S. The DIKW sequence made immediate sense because it extends what every Computer Science 101 class learns: information is a refinement of mere data. But, the info-to-knowledge move is far more problematic than the data-to-info one. So, what is “knowledge” in the DIKW pyramid? And humbug.
Knowledge Transfer In organizational theory, knowledge transfer is the practical problem of transferring knowledge from one part of the organization to another. Like knowledge management, knowledge transfer seeks to organize, create, capture or distribute knowledge and ensure its availability for future users. It is considered to be more than just a communication problem. Background[edit] Argote & Ingram (2000) define knowledge transfer as "the process through which one unit (e.g., group, department, or division) is affected by the experience of another"[1] (p. 151). Szulanski's doctoral dissertation ("Exploring internal stickiness: Impediments to the transfer of best practice within the firm") proposed that knowledge transfer within a firm is inhibited by factors other than a lack of incentive. Knowledge transfer includes, but encompasses more than, technology transfer. Knowledge transfer between public and private domains[edit] Knowledge transfer in landscape ecology[edit] Types of knowledge[edit]
Five Best Mind Mapping Tools Knowledge Sharing Knowledge Sharing is an activity through which knowledge (i.e., information, skills, or expertise) is exchanged among people, friends, families, communities (e.g., Wikipedia), or organizations.[1][2] Knowledge Flow[edit] Although knowledge is commonly treated as an object, Snowden has argued it is more appropriate to teach it as both a flow and a thing.[8] Knowledge as a flow can be related to the concept of tacit knowledge, discovered by Ludwik Hirszfeld[9] which was later further explicated by Nonaka.[10][11] While the difficulty of sharing knowledge is in transferring knowledge from one entity to another,[12][13] it may prove profitable for organizations to acknowledge the difficulties of knowledge transfer and its paradoxicality, adopting new knowledge management strategies accordingly.[8] Explicit Knowledge Sharing[edit] Tacit Knowledge Sharing[edit] Embedded Knowledge Sharing[edit] Importance of Knowledge Sharing in Organizations[edit] Challenges in Knowledge Sharing[edit] See also[edit]
GSM - Addresses and Identifiers GSM distinguishes explicitly between user and equipment and deals with them separately. Besides phone numbers and subscriber and equipment identifiers, several other identifiers have been defined; they are needed for the management of subscriber mobility and for addressing of all the remaining network elements. The most important addresses and identifiers are presented in the following: International Mobile Station Equipment Identity (IMEI): The international mobile station equipment identity (IMEI) uniquely identifies a mobile station internationally. There are following parts of an IMEI: Type Approval Code (TAC): 6 decimal places, centrally assigned.Final Assembly Code (FAC): 6 decimal places, assigned by the manufacturer.Serial Number (SNR): 6 decimal places, assigned by the manufacturer.Spare (SP): 1 decimal place. Thus, IMEI = TAC + FAC + SNR + SP. International Mobile Subscriber Identity ( IMSI): There are following parts of an IMSI: Mobile Subscriber ISDN Number ( MSISDN):
Legal Case Management The terms Legal case management (LCM) or matter management refer to a subset of law practice management and cover a range of approaches and technologies used by law firms and courts to leverage knowledge and methodologies for managing the life cycle of a case or matter more effectively.[1][2] Generally, the terms refer to the sophisticated information management and workflow practices that are tailored to meet the legal field's specific needs and requirements. As attorneys and law firms compete for clients they are routinely challenged to deliver services at lower costs with greater efficiency, thus firms develop practice-specific processes and utilize contemporary technologies to assist in meeting such challenges. Law practice management processes and technologies include case and matter management, time and billing, litigation support, research, communication and collaboration, data mining and modeling, and data security, storage, and archive accessibility. e-Discovery systems[edit]
Goethe on the Psychology of Color and Emotion Color is an essential part of how we experience the world, both biologically and culturally. One of the earliest formal explorations of color theory came from an unlikely source — the German poet, artist, and politician Johann Wolfgang von Goethe, who in 1810 published Theory of Colors (public library; public domain), his treatise on the nature, function, and psychology of colors. Though the work was dismissed by a large portion of the scientific community, it remained of intense interest to a cohort of prominent philosophers and physicists, including Arthur Schopenhauer, Kurt Gödel, and Ludwig Wittgenstein. One of Goethe’s most radical points was a refutation of Newton’s ideas about the color spectrum, suggesting instead that darkness is an active ingredient rather than the mere passive absence of light. YELLOWThis is the color nearest the light. Brain Pickings has a free weekly newsletter.