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INTELLIGENT SYSTEMS ARTIFICIAL INTELLIGENCE APPLICATIONS IN BUSINESS LEARNING OBJECTIVES Describe decision models and the benefits of computer supported decision making and experimentation Describe artificial intelligence (AI) Compare capabilities for natural (human) intelligence versus artificial intelligence Define an expert system and identify its components Discuss intelligent system examples that illustrate various forms of problem representation and reasoning Identify intelligent systems applications in business functional areas DECISION MODELS Data + models = decisions Models are representations of problems that vary by degree of abstraction Examples include: Iconic (scale) models – least abstract Analog models Linear programs, statistical models Mental models – most abstract Organizational charts, blueprints Mathematical (quantitative) models Car or house scale model Consumer behavior models Other examples include visualization methods, geographic information systems, and virtual reality BENEFITS OF COMPUTER SUPPORTED DECISION SYSTEMS Cost of virtual experimentation is lower Compresses time Manipulations are easier Cost of mistakes is lower Can evaluate risk and uncertainty Can compare a large number of alternatives Can be used for training INTELLIGENT SYSTEMS Intelligent systems is a term that best describes the various commercial applications of artificial intelligence Artificial intelligence (AI) is a subfield of computer science that is concerned with studying the thought processes of humans and re-creating the effects of those processes via machines, such as computers and robots AI’s ultimate goal is to build machines that will mimic human intelligence An interesting test to determine whether a computer exhibits intelligent behavior was designed by Alan Turing (the Turing test) COMPARISON OF THE CAPABILITIES OF NATURAL VERSUS ARTIFICIAL INTELLIGENCE Capabilities Natural Intelligence Artificial Intelligence Preservation of knowledge Perishable Permanent Duplication and sharing of knowledge Difficult, expensive, takes time Easy, fast, and cheap when in the right format Total cost of knowledge Can be erratic and inconsistent Consistent and thorough Documentability of knowledge Difficult, expensive Fairly easy, inexpensive Creativity Can be very high Low, uninspired Use of sensory experiences Direct and rich in possibilities Limited Recognizing patterns and relationships Fast, easy to explain Getting better, but not as good as humans Reasoning Makes use of a wide range of experiences Good only in narrow, focused, stable domains EXPERT SYSTEMS When an organization has a complex decision to make or problem to solve, it often turns to experts for advice Expert systems (ESs) are computer systems that attempt to mimic human experts by applying expertise in a specific domain The transfer of expertise from an expert to a computer and then to the user involves four activities: Knowledge acquisition Knowledge representation Knowledge inferencing Knowledge transfer EXPERT SYSTEM STRUCTURE QUESTION? What makes a system “intelligent”? ANSWER Intelligent systems include one, or more, of the following capabilities: Reasoning Deductive Inductive Analogical (extremely difficult to implement in AI systems) Rationality Efficient search for answers Learning Incorporate knowledge learned from past experience to improve decision making over time INTELLIGENT SYSTEM EXAMPLES Machine (concept) learning Case-based reasoning Decision trees Other examples: Rule-based expert systems Natural language processing (NLP) For example, a bird classification expert system NLP involves the largest knowledge base and most complex inference processes How could intelligent systems be used in: Accounting? Marketing? Manufacturing? Computer network management?