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Attaining Knowledge: Why throwing a baseball would not be possible for a neural network.
Learning how to throw a baseball occurs in stages. First, someone, usually a parent instructs a young child on the basic process of throwing the ball, before the child is scheduled to participate in a game. The child is first shown the basic motion as to how to curve his or her body when tossing the baseball, to get the ball across the field with some measure of force. Then, when the child gains a sense of physical confidence in the simple act of throwing, the parent shows the child how to hold the ball in such a fashion that the ball travels farther, with a sharper degree of accuracy.
The physical motion is only the most rudimentary aspect of throwing a baseball. Gradually, over time, the child enters into a real game with similarly skilled players, usually in elementary school or a Little League game. Through experience and more skilled coaching, the child learns that throwing a ball from first base to the pitcher's mound, or actually pitching the ball to a batter requires a slightly different level of speed, skill, and aim. If one is a pitcher, one learns different kinds of pitches. As one grows even older and becomes personally familiar with different individuals in a particular league, one grows to know different player's strengths and weaknesses regarding certain pitches. A young...
One's own body develops simultaneously as one learns. One develops stronger muscles and bones, and a higher stature. This usually aids in throwing a ball with greater force and dexterity. Also, a developing baseball player gets to know his or her own body and have more physical confidence, the player's skill level similarly increases.
Neural networks in computing are based on the processing and memory abstraction of human information processing, paralleling the knowledge acquisition of the human brain. (Smith, 1996) In contrast to an expert system that merely uses preset notions of rules and data to produce a decision or recommendation, neural networks, on the other hand, attempt to simulate the human brain by collecting and processing data for the purpose of remembering or learning past actions. For instance, when one realizes through experience alone that a ball thrown over rather than underhand as a child has greater force, one engages in a dialogue with one's body and environment, judging the outer stimulus and modifying one's process in a…
Work Cited
Smith, Leslie. (1996) "An introduction to neural networks." Center for Cognitive Development. Updated 2001. Retrieved 21 Mar 2004 http://www.cs.stir.ac.uk/~lss/NNIntro/InvSlides.html#what
Knowledge Management Toolkit A data warehouse encompasses and provides access to all the company's information to whoever needs access to it. A warehouse literally means a storehouse, and the information within an organization may be distributed within one computer or with many computers, form one single warehouse. They may contain several databases and all types of information, and in a large variety of different formats. However, all the above information and
Knowledge Management (KM) A new knowledge-based economy of learning individuals, organizations and economies has evolved from the machine-based economy which dominated the developed world throughout the twentieth century. The emergence of a new type of firm is signaled by the familiar symptoms of corporate change such as devolution of managerial responsibilities, more flexibility and skill in the workforce, more recourse to outsourcing, and increased networking both inside and outside the firm
The shift in the market macroenvironment engages these function for the strategic advantage of the organization. The Zack KM cycle and the Bukowitz and Williams KM Cycle are complementary by the fact that the Zack KM cycle can provide the information (external) to be utilized by the Bukowitz and Williams KM Cycle. Q4 Knowledge capture strategies The knowledge possessed by experts and employees could be tacit (Cairns, 2010) and can be captured using
Knowledge Management Titan Industries' Knowledge Management Titan Industries' Best Practices in Knowledge Management The ability of Titan Industries to attain best practices so rapidly in their Knowledge Management (KM) implementation illustrates how effective this strategic initiative can be in unifying business managers and Information Technology processors to a common objective. Global outsourcing leader Tata Information Systems is the subsidiary that owns Titan, so it is understandable how a very high priority was
Knowledge Management Succession Plan Strategy KBR Australia Knowledge Management Succession Plan Strategy Kellogg, Brown Root (KBR) is an international company founded by Brown and Root in 1960 as Kinhill Engineers, and has gone through various changes over the years. A significant chapter in its history occurred in 2007, when it was split from Halliburton, to become a fully independent company. KBR's Australian division has offices in Melbourne, Adelaide, Canberra, Sydney, Brisane, Perth,
Use of single version of the truth and single information Balanced set of strategic metrics (Financial and non-financial). New methods of cost accounting (ABC, Target Costing). Internal vs. External Focus (Benchmarking and Self-Assessment). Process Management and Measures (value delivery). Stakeholder value measures Uniform set of measures Causal relationships between measures across all levels. Source: Lieberman; (1994; et.al.). Automotive Industry Analysis Entering 2007 it is clear that Japanese firms, lead by Toyota, will be at parity with and potentially surpass the