Artificial Intelligence: Concepts and Future in Business - Coursework Example

Running head: ARTIFICIAL INTELLIGENCE CONCEPTS AND FUTURE Artificial Intelligence Affiliation December 2008 In this paper I will investigate Artificial intelligence tools and techniques for the business and the future trends those are emerging from the implementation of AI in the decision making, resource management, targeting market and several other aspects of business. New technology also gives the opportunity to E-commerce to enhance the business. E-commerce is considered as the back bone for business. So I am also going to investigate the implementation of the AI in the e-commerce and new emerging trends. It is universal truth that “Knowledge is power”: Modern business settings are not that simple as before. Enterprises are experiencing tough and vast competitions in their relevant fields. Customers demand for product features and services. Corporate expenses are constantly mounting. To survive in this harsh situation, high-level management needs business intelligent information to efficiently administer corporate operations and support their decisions. Support-level staffs should have knowledge and information to endow with superior customer services for gaining satisfaction and retaining loyalty. Vast operating data is staggered into a range of corporate databases and needs consolidating (Vitt et al, 2002). It has become more important than ever to access and to produce expensive knowledge and share information among endorsed users surrounded by a corporation or business partners. Thus, a system of integrating knowledge, management and decision support processes is in great demand (Moss et al, 2003). In this section I will discuss the major tools and fields of AI those are involved in the business and their contribution for the business handling. Expert System and Business Expert systems have been profitably functional to some financial decision making tasks; there are many other systems that are beyond the scope of expert systems technology. Knowledge base from human experts is converted into the IF-THEN rules upon which the system depend, and the powerlessness of an expert system to make use of inductive learning and inference to get used to the rule base to changing situations. These problems may be particularly causing trouble in financial analysis and management environments (Vitt et al, 2002). Neural network and Business Many of these problems could be triumph over with another product of artificial intelligence research i.e. the artificial neural system, also acknowledged as an artificial neural network, electronic neural network or merely neural net. Neural networks endeavor to mock-up human intuition by simulating the physical process upon which perception is based that is, by simulating the progression of adaptive biological learning. A neural network is theoretically accomplished for producing an appropriate reaction to a given problem even when the information is noisy or incomplete, or when there is no set practice for solving the problem (Gray et al, 1994). Data Mining and Business AI to data mining techniques is producing amorphous data emails, blogs, business records, manufacturer warranties, and other kinds of text more functional to companies. This efficacy develops from computers’ capability to automatically understand and systematize text so that they can slice and dice it (Moss et al, 2003). Machine learning, natural language processing, hidden Markov models, and rules-based model matching methods parse text for gist, break it into bits, tag it, and reschedule it. The recently structured text can expose such information as workflows, instances of indecency or authorized incompliance, cases and causes of problems, and market trends. The fusion tools proffer companies speed and complication in their search for valuable and previously hidden business information (Moss et al, 2003). The Decision Environment Decision support systems are aimed to help out the decision-maker devoid of constraining the decision method. Expert systems endeavor to model the decision-making capability of human experts (Knapik et al, 1997). The intuition or knowledge base of the experts must be programmed into an expert system; however, the conception of the knowledge base is a long and exclusive method. Expert systems are thus merely not cost-effective for one-time decision problems. Furthermore, it is not often apparent what an experts knowledge base takes in and what heuristics must be used to model it (Liu et al, 1999). Why we need artificial intelligence in the business? In the business we need a lot of knowledge and expertise for the financial management. One of the major confronts that the majority security investment institutions experience was the lack of an intelligent data mining system to hold up investment researches decisions (Collins et al, 2007). The problems that we encountered through the project management and finical decision making are as: (Gray et al, 1994) (I) lack of effectiveness in running vast financial data, (ii) Lack of communication and knowledge giving out among analysts, (iii) Lack of a mechanism to determine synchronization problems when numerous users are accessing data, (iv) Lack of a mechanism to proficiently administer generated research experiments, (v) Lack of an automation to bring out its reports to clients by the use of its web sites or email. As the securities become more intricate, the problems of data management and knowledge innovation has become more complicated. Think about a security group; the portfolio construction is a method of quantitative analysis over massive amounts of data. The data cube and ad hoc analysis techniques are an indistinguishable resolution to carry this process. A system that efficiently supports financial application thus would provide support for: (Vitt et al, 2002) (i) temporal objects, (ii) Object management, which is the proficient storage and exploitation of intricate data, (iii) Knowledge finding, the competence of extracting information as rules for decision making. The (i) and (ii) result in financial data modeling by means of object-oriented techniques, while (iii) is merely data mining techniques. Amalgamation of decision support and intelligent knowledge management course: This is a triumphant intelligent knowledge management system which enhance the mode people work together, enables knowledge workers and partners to contribute to information easily so they can build on each other’s thoughts and work more effectively (Vitt et al, 2002). The purpose is to congregate company proprietary knowledge in order to come up with the preeminent decision-making, or to swiftly take hold of the proposal with innovative ideas. To optimize how the information could be interface from one knowledge-worker-to another knowledge-worker , so that company can always make better trading or investment decisions, the specific group of data and modeling consequences should be administered and appropriately shared (Moss et al, 2003). Future knowledge can be generated by incarcerating existing knowledge via altering, storing, retrieving and disseminating explicit knowledge and by creating and testing new knowledge (Collins et al, 2007). The future of Business and Business As a field of artificial intelligence data mining has the capability to support the future prediction regarding the marketing and business decisions. Data mining is a way to take out the functional information for the already present data. By Appling data mining we can have the predictions regarding our future plans and decisions (Knapik et al, 1997). Data mining application is to corporate connection classification. Bond rating is a system whereby rating agencies investigate and assess the security of principal repayments and interest payments for individual bonds, and stand for the level of issuer credit worthiness. Rating agencies present this investment information as a service to investors (Moss et al, 2003). The bond rating characterizes the evasion risk for the investor and affects the borrowing cost for the issuer. Rating agencies such as the Standard & Poor’s and the Moody’s does agree on the ratings from an assortment of financial data, which is not publicly disclosed (Gray et al, 1994). E-Commerce Now I will discuss the artificial intelligence application to e-commerce business. The 21st century is the time of internet and World Wide Web. The Web revolutionizes the way we congregate, practice, and make use of information. At the same time, it also redefines the meanings and processes of business, commerce, marketing, finance, publishing, education, research, development, as well as other facets of our daily life. The revolution is being taking place in modern life and technology. Although individual Web-based information systems are constantly being deployed, sophisticated issues and techniques for developing and form having benefits from web intelligence at a standstill remains to be systematically studied. Web Intelligence by giving an entire picture of WI related topics for systematic study on advanced (Liu et al, 1999). Web technology and developing Web-based intelligent information systems: It is roughly supposed that, WI exploits AI and highly developed information technology on the web and internet. It is the solution and the most urgent research field of IT for business intelligence. Here I will discuss the WI and artificial intelligence application to ecommerce and different traits of the AI application regarding the business management. Related Topics In the section below I will discuss the Up Coming or we can say future trends of the AI in the E-commerce. This section will discuss the new AI techniques and emerging tools which are helping the Ecommerce and customers to have more automated and user-friendly background. Web Information Management: Nowadays data superiority management, information transformation, internet and web-based data management, multi-dimensional are the most important functions for the AI in the business. AI is getting more involved in these areas. Web databases and OLAP (on-line analytical processing), multimedia information management, new-fangled data models for the web are object oriented. Web information management, personalized information management, semi-structured data management (Liu et al, 1999). Web Information Retrieval: AI is utilized and anticipated by the businesses which are involved in the web based business. The fields on which AI can have critical impacts are approximate retrieval, conceptual information extraction, image repossession, multi-linguistic information retrieval, multimedia retrieval, new retrieval models, ontology-based information retrieval, as well as automatic web content cataloging and indexing (Liu et al, 1999). Web Agents: AI biggest accomplishment is the web agents which are intelligent software and have the ability to investigate and retrieve users specificitions are elated information sources, e-mail filtering, email semi-automatic reply, comprehensive information collecting, information filtering, navigation guides, suggested systems, remembrance agents, reputation mechanisms, resource intermediary and organization mechanisms, as well as Web-based obliging problem solving (Liu et al, 1999). Web Mining and Farming: As I have discussed earlier that data mining is now increasingly mounting the business. But its implementation in the E-commerce is for the knowledge innovation, hypertext analysis and transformation, laming user profiles, multimedia data mining, regularities in web surfing and internet congestion, text mining, web-based ontology engineering, web-based turn around engineering, web farming, web-log mining, and web warehousing.1 Intelligent Agent Stakes for E-Commerce (Liu et al, 1999) Intelligent agents: Intelligent agent is the software, which is different from the usual one as it is made to order, enduringly in succession and semi-autonomous. Intelligent agent is personalized as it is intelligent to change performance and assortment of services according to user requirements. Intelligent agent operates everlastingly until the orders are given by users. It is satisfactorily autonomous to carry out particular tasks with little or no human supervision. Nowadays, there are three fundamental types of agents existing on the e-commerce market: shopping, pricing, auctioning robots (Galuszewska et al, 2003). Shopping robot is a software agent helping the buyers. It is intelligent to query multiple servers on the network in the place of user) to get together the information about prices and other service characteristics, like service superiority and anticipated waiting time. Buyers give the relevant information to the agents for the accountability for searching, choosing the basis of defined criteria and presenting available offer (Galuszewska et al, 2003). Pricing robot is a software agent which is attached to a seller and having the capability to vigorously vary the price of product or service to exploit the provider’s anticipated profit. The foremost duty of pricing robot is to look around eternally the websites of contending sellers and relevantly change prices of products. Vendors can apply more sophisticated pricing strategies, such as self-motivated pricing. The market illustration of a pricebot is Books.com. It queries the catalogues of three huge online booksellers in the US: those are Amazon, Borders.com and BarnesandNoble.com and slightly undercuts the lowest of them (Bolloju et al, 2002). Auctioning robot is software helping both, buyers or sellers. They take part in auctions on behalf of their users discussing the most expedient price concerning with set user-specified constraints. The constraints relay to such parameters as: preliminary asking price, the lowest or highest adequate price, a date by which to complete the transaction and how to change the price over time. Future of agent-based electronic commerce: Here I have discussed the wide application/range of AI implementations to the market and their advantages. Now we will have a discussion on the future of the artificial intelligent ways of doing business. From the economic point of view, the mechanism that spreads information makes the market communicate directly to idealized market since it enlarges its effectiveness. Thus, the more intelligent agents will be presented in the market, the more resourceful it would be. We cannot anticipate accomplishing the rank of wonderful information market as there are still many reasons why customers behave irrationally while rational behavior of the customer is the basic premise of perfect information market theory. Beyond this, also vendors can illustrate lack of respect to information market principles by directing information. They can have an enticement to present some data in the way that proves the highest quality of their product. It is over and over again the case when internet has a possibility to communicate their opinions on diverse products. Nevertheless, the imminent period of agent-based market model will be the greatest test for all economic theories ever elaborated. It will concern the whole microeconomic setting, leading consequently to changes to macroeconomics. Future information civilization, have to reach a higher level of maturity triumph over the sellers fear of market transparency as well as reversing customers conviction about shopbots business model (Liu et al, 1999). Conclusion In this paper I have discussed the lots of AI applications and tools for the business and their future scope. Nowadays it has become apparent to many organizations that they need to administer knowledge-related activities in a organized and explicit way in order to advance their competitive positions. Valuable human and knowledge resources will be wasted unless management frankly accepts and supports efforts to get together, sort, make over, record and share knowledge. Knowledge management seems to be able to present to decision makers superior quality of support in the direction of real-time adaptive active decision support. References 1. Bolloju, N., Khalifa, M., & Turban, E. (2002). Integrating knowledge management into enterprise environments for the next generation decision support. Decision Support Systems, 33, 163–176. 2. Collins, K. Goodnight, J. Hagström, M. Davis, J., (2007). The Future of Business Intelligence: Four Questions, Four Views, SASCOM, First quarter. 3. Galuszewska, M. Sahut, J. M. (2003). Intelligent agent stakes for e-commerce. Applications and the Internet Workshops, 2003. Proceedings. 2003 Symposium on. 4. Gray, T. Prez, E. Pinard, D. Abu-hakima, D. Daz, A., and Ferguson, I. (1994). A multi-agent architecture for enterprise applications. InW. Hamscher, editor, Working Notes of the AAAI- 94 Workshop on Artificial Intelligence in Business Process Reengineering. 5. Knapik, M., Johnson, J., (1997). Developing Intelligent Agents for Distributed Systems: Exploring Architecture, Technologies and Applications, McGraw-Hill, New York. 6. Liu. J., and Zhong, N. (1999). Intelligent Agent Technology. $wems, World Scientific. 7. Moss, L.T., and Atre, S. (2003). Business Intelligence Roadmap, Addison Wesley. 8. Vitt, E. Luckevich, M., and Misner, S. (2002). Business Intelligence: Making Better Decisions Faster, Microsoft Press. Read More