Capturing the knowledge of individuals - Assignment Example

Capturing the knowledge of individuals One of the main challenges with the expert system is their inability to make decisions which are based on situations which are have not been in existence before. Even the most advanced modern artificial intelligence software are unable to make dynamic decisions and are therefore not able to make dynamic decisions in spontaneous situations. These systems are only able to make decisions which are supported by the facts and rules that are readily available to them in their inference engine and their knowledge base (Leondes, 2002). They are not able to be spontaneous like human beings would in situations which require him or her to make a decision which he or she has never made before. This one challenge is the one that makes the modern expert systems to be unable to operate like humans do. Although these systems are not entirely perfect for delivering decisions like human beings, they do have a number of useful elements that make it worth investing in them. These knowledge systems can be used to help the organization on knowledge management. According to Malhotra (2000) one of the biggest advantages with these systems is that, unlike human experts, they don’t leave the firm and they don’t die. Knowledge and expertise held by these systems can stay in the organization forever as long as it is protected from physical damage. They therefore provide the firm with consistence knowledge over time and are able to make sure that this knowledge is always available. It is prudent for any firm to invest in this system because even if they cannot replace the experts completely, they are very useful in knowledge management. Expert systems are knowledge systems which helps the organization to not only store information but to also utilize it. They make it possible for the organization to store useful expert knowledge which can then be used by other experts and aids in decision making. Capturing the knowledge of individuals The main importance of expert systems is that when correctly used, they gather the information from the experts of the organization and store this information in the form of knowledge. These systems don’t just gather information and data but rather they gather knowledge and expertise from the experts working in an organization. This means that when an organization hires an expert for a certain period of time and then the expert leaves the firm, the organization will still be able to retain eh expertise that the expert brought to the firm. This is unlike the early days where when an employee left a firm, they left with all the knowledge. The use of expert systems in the modern business environment has mean that businesses can be able to maintain their knowledge even after the experts leave the firm due to any reason. This is important because firms stand to lose a lot of knowledge when their employees, retire, resign, area sucked or die. By using the expert systems, the firm is able to retain this knowledge and expertise indefinitely. In this regard, it is most advisable from any firm to procure and use expert systems in order to guarantee knowledge preservation in the firm. Question 2 The major importance of neural networks is their ability to act like a human brain. Neural networks are designed to be able to imitate the human brain using the principle of fuzzy logic (Abe, 2012). This means that the systems don’t have to deal with absolute variables but deal with estimate variables. By using this kind of logic, they are able to achieve one important thing which is to make decisions where there are not straight answers. This ability also makes the systems to be able to make decisions in dynamic situations where there is no prior knowledge. One of the biggest problems with most decisions making systems is that they are only able to make decisions where the system has prior knowledge in a specific problem. This issue is not there with artificial neural networks in that these systems are able to learn and create their own knowledge thus making is possible for the systems to create knowledge. These systems are also able to tolerate faults which lead to more stable systems which are able to reduce inconsistencies. However, there are various disadvantages with the artificial neural networks. The main one being that they require a lot of coaching before they can be used to perform actions like a human brain. These systems need to be exposed to so many example situations before they are now able to apply the general rules to other situations. They also require large storage space and computational power to be able to function. Question 3 Collective intelligence has its advantages as well as its disadvantages. To be able to harness the advantages of collective intelligence would mean that the firm would have to understand the weaknesses or issues associated with using the collective intelligence and plan how to overcome. One of the major weaknesses is that loss of control of the decision making. This as Bonabeau (2009) says, can manifest itself in various ways. One such way is through the fact that the collective decision making may lead to the wrong patterns of decisions making. Collective intelligence also places the firm in a situation the firm will have to decide between expertise and diversity. In decision making, the firm can either depend on the collective intelligence of many people, or the expert knowledge of one or a few experts. In this regard, Nucor Steel should be able to know how to implement the collective intelligence without losing control of the decision-making process or without losing the expert resources they have. One of the biggest issues with collective decision making the fact that sometimes the decisions take a long time to be made since the decision makers may have a disagreement that is not solvable. In implementing collective intelligence, Nucor Steel should be careful to make sure that these factors have been considered that there are proper systems to avoid a situation where the collective intelligence leads to less effective decision making. References: Abe, S. (2012). Neural Networks and Fuzzy Systems: Theory and Applications. London, UK: Springer London, Limited,. Bonabeau, E. (2009). Decisions 2.0: The Power of Collective Intelligence. MIT Sloan Management revew, Vol 50 (2) , 1-10. Leondes, T. (2002). Expert systems: the technology of knowledge management and decision making for the 21st century, Volume 6. Waltham, MA: Academic Press. Malhotra, Y. (2000). Knowledge Management and Virtual Organizations. New York City, NY: Idea Group Inc. Read More