Artificial Intelligence Artificial Intelligence (AI) conjures up visions of robots that can mix dry martinis while beating a grand master at chess; and to some, will one day be able to look, act, think and react just like a real person. I would like to explore the concept of AI as it relates to the business world, and its possible many other applications. I believe that true AI is a dream worth pursuing. Like me, there are many who, just like those of the early 1960’s, thought that putting a man on the moon seemed to be an extremely difficult, but not an impossible task, believing the achievement of true AI to come is just a matter of time. To remain competitive, companies must continue to improve by doing better and doing more; all the while using fewer and fewer resources, especially, manpower.
Greater numbers of the world’s companies are turning to systems, which they feel offer the best means of attaining these goals. A group, or suite of tools that can help accomplish this pursuit of doing less with more is generally known as Decision Support Systems. This broad category usually consists of computer software and hardware, which includes Intelligent Decision Support Systems, Expert Systems and Artificial Intelligence.
Do these systems really provide a valuable contribution to those who use them, and just how much faith can be put into them? Strategic decision making concerns itself with determining where and how to deploy present resources to gain competitive advantages with the expectation of achieving some future reward. This simple, but powerful idea, permeates the planning process of large and small companies.Decisions related to how resources should be deployed consider specific measures necessary to compete effectively and efficiently; while strategic decisions are made with the expectation of improving future corporate profitability. Decision support systems are important additions in developing long term strategic plans, and thus long range profitability measures. Definition ARTIFICIAL INTELLIGENCE Before we can explore the possibilities and implications of AI, we must carefully define exactly what attributes make something “intelligent”. The most common way to define intelligence in through the term “consciousness”. A term such as this has no fixed definition; rather, it is a family of related concepts that tie together to form a picture of consciousness. Self-awareness, rationality, the ability for abstract thinking, and strategic thinking characterize consciousness.
From this definition or description of intelligence we can gather that to exhibit true intelligence, there must be a conscious state, in other words, a state or condition of self-awareness.AI is broadly defined as anything that a computer does that we normally consider to be a human trait. AI is the part of computer science concerned with designing intelligent computer systems, that is systems that exhibit the characteristics we associate with intelligence in human behavior – understanding language, learning, reasoning, solving problems and so on. Today’s AI sprung from the discipline commonly referred to Decision Support Systems, and as such, a true look at AI can not be conducted without first taking a look at its predecessors. Why Have Decision Support Systems Decision Support Systems provide a valuable data repository of lessons learned. By maintaining this data and providing real time updates, managers can help support their business choices by looking at a history of similar decisions made by others in their positions. This does not mean that every decision made, based on this data, will be good. However, it does help lower the probability of a bad one.
This function alone can save a company from making a small error to making errors, which could threaten its ability to remain viable. What types of Decision Support Systems are there? Before we can understand the ramifications of these systems we must first explore the types and some of their features and functions. However, to understand them first we need to know what they are. Intelligent Decision Support Systems: This is a new paradigm for the DSS area.
These extend the applicability and functionality beyond those traditionally covered by DSS applications and utilize a range of advanced technologies.The main role of an Intelligent Decision Support System in an organization is as an enabler for knowledge processing with communications capabilities. The approach, unlike traditional approaches in DSS, is that it does not focus merely on managerial decision-making, but attempts to reflect organizational realities. These systems usually consist of database which has a software interface designed to aid the researcher/decision maker with the information required to make an informed decision based on past events and experiences. Expert Systems: This is a research system that does just that, research. These systems use current information to make logical guesses and extrapolations about something unknown. These first appeared in the engineering field and other physical sciences, these computer systems dramatically decrease the time required to take a product or idea from concept to execution by running simulations within itself, locating problems, refining the model, and repeating these steps, gradually working the “bugs” out of the system.
Expert Systems are computer programs designed to review a set of facts (market conditions) and apply a set of rules (knowledge base) to arrive at the same conclusion that a team of experts would make if presented with the same facts. Artificial Intelligence: Generally Artificial Intelligence (AI) is the discipline of building intelligence into computers. The term, AI, refers to a machine’s capability of processing data and responding with humanlike intelligence. They are essentially the Expert System taken to its next logical level of evolution. Artificial Intelligence “AI is having a Trojan renaissance,” says Nick Cassimatis, an AI researcher at the Massachusetts Institute of Technology’s (MIT) Media Lab, in Cambridge, Mass. Vendors are quietly building AI technologies into practical software applications that do everything from recommend music for Web shoppers to direct airplanes at airports.
Because of AI’s tarnished reputation, vendors aren’t promoting their products as being AI-based. However, understanding the advanced AI technologies behind the products can help technology managers determine a product’s value and consider the potential of AI solving related business problems. In business some of the most successful applications have been constructed by building substantial domain knowledge into computer programs. These systems are often referred to as knowledge base systems. Typically, these system use decision and process rules presented from experts to summarize that knowledge.
Other systems use representations of cases from past experience to generate solutions for current situations, “case-based reasoning” (CBR). Law and other domains where reasoning is based on cases, find this approach very useful. Other approaches include so-called data mining and machine learning where knowledge is generated from an analysis of data. That knowledge is then summarized and used to make inferences. Case-based reasoning is an approach to AI where a system stores case studies, responds to a problem by finding similar cases in its memory, and adapts the solution that worked in the past to the current situation. CBR sprang from cognitive science research, which was begun, in the early 1980’s by Roger Schank at Yale University’s AI lab, in New Haven, Conn.
Automated Customer-Support systems are an important business use of CBR. This is growing rapidly as companies look to reduce product support costs by encouraging customers to find their own answers on a web site instead, of calling expensive or toll free numbers. An additional technology that has sprung from AI research and is finding a new home on the web, is rule-based expert systems.
These systems, unlike collaborative filtering …