08-30-2017, 07:31 PM
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A Paper Presentation on Artificial Intelligence & Global Risk
ABSTRACT
computer systems are becoming commonplace; indeed, they are almost ubiquitous. We find them central to the functioning of most business, governmental, military, environmental, and health-care organizations. They are also a part of many educational and training programs. But these computer systems, while increasingly affecting our lives, are rigid, complex and incapable of rapid change. To help us and our organizations cope with the unpredictable eventualities of an ever-more volatile world, these systems need capabilities that will enable them to adapt readily to change. They need to be intelligent. Our national competitiveness depends increasingly on capacities for accessing, processing, and analyzing information. The computer systems used for such purposes must also be intelligent. Health-care providers require easy access to information systems so they can track health-care delivery and identify the most recent and effective medical treatments for their patients' conditions. Crisis management teams must be able to explore alternative courses of action and support decision making. Educators need systems that adapt to a student's individual needs and abilities. Businesses require flexible manufacturing and software design aids to maintain their leadership position in information technology, and to regain it in manufacturing Software Risk Management is a proactive approach for minimizing the uncertainty and potential loss associated with a project. A risk is an event or condition that, if it occurs, has a positive or negative effect on a project s objectives. The three common characteristics of risk are (1) it represents a future event, (2) it has a probability of occurring of greater than 0%, but less than 100%, and (3) the consequence of the risk must be unexpected or unplanned for. Future events can be categorized as opportunity-focused (positive risk) if their consequences are favorable, or as threat-focused (negative risk) if their consequences are unfavorable.
INTRODUCTION (AI) is a field of study based on the premise that intelligent thought can be regarded as a form of computation one that can be formalized and ultimately mechanized. To achieve this, however, two major issues need to be addressed. The first issue is knowledge representation, and the second is knowledge manipulation. Within the intersection of these two issues lies mechanized intelligence History The study of artificial intelligence has a long history, dating back to the work of British mathematician Charles Babbage (1791 1871) who developed a special-purpose "Difference Engine" for mechanically computing the values of certain polynomial functions. Similar work was also done by German mathematician Gottfried Wilhem von Leibniz (1646 1716), who introduced the first system of formal logic and constructed machines for automating calculation. George Boole, Ada Byron King, Countess of Lovelace, Gottlob Frege, and Alfred Tarski have all significantly contributed to the advancement of the field of artificial intelligence. Knowledge representation It has long been recognized that the language and models used to represent reality profoundly impact one's understanding of reality itself. When humans think about a particular system, they form a mental model of that system and then proceed to discover truths about the system. These truths lead to the ability to make predictions or general statements about the system. However, when a model does not sufficiently match the actual problem, the discovery of truths and the ability to make predictions becomes exceedingly difficult. A classic example of this is the pre-Copernican model in which the Sun and planets revolved around the Earth. In such a model, it was prohibitively difficult to predict the position of planets. However, in the Copernican revolution this Earth-centric model was replaced with a model where the Earth and other planets revolved around the Sun. This new model dramatically increased the ability of astronomers to predict celestial events.Arithmetic with Roman numerals provides a second example of how knowledge representation can severely limit the ability to manipulate that knowledge. Both of these examples stress the important relationship between knowledge representation and thought.