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This chapter discusses various information systems and artificial intelligence (AI) technologies crucial for decision making and operational efficiency. It covers different types of information systems such as Transaction Processing Systems, Management Information Systems, and Expert Systems. The text elaborates on components of expert systems including the knowledge base and inference engine. Furthermore, it explores applications of AI like Natural Language Processing, intelligent agents, and robotics, differentiating between weak and strong AI, and addresses ethical considerations in AI development.
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CSCE101 –Chapter 8 (continued) Tuesday, December 5, 2006
Information Systems • Office Information Systems • Transaction Processing Systems • Management Information Systems • Decision Support Systems • Executive Support Systems • Expert Systems
Experts System Components • End user, problem domain expert, knowledge engineer • Components of an expert system • Knowledge Base • Inference Engine • User Interface
Inference • Given that certain premises are true, one can deduce a conclusion that is also true. Example #1: All men are mortal Socrates is a man ------------------ Therefore Socrates is mortal. Example #2: Given the output from two queries against a personnel database: • How many women are in Department X: Result: 1 2. What is the average salary of the women in Department x? Result: $60,000 Conclusion: One now knows the exact salary of the only woman in Department X
Other Uses of AI • Natural language processing • Intelligent agents • Pattern recognition • Fuzzy logic • Virtual reality & simulation devices • Robotics
Natural Language Processing Brute force processing = Generating all possible answers + selection of best answer Ex. #1: Word substitution until a meaningful sentence occurs: English: “The spirit is willing, but the flesh is weak.” Russian: “The wine is agreeable, but the meat is spoiled.” Ex. #2: Computers that play chess Ex. #3: Decryptors
Intelligent Agents • Act autonomously on behalf of the user (ex.: bots, crawlers, spiders). • Data mining capabilities • Learning and adapting
Pattern Recognition • Recognition of some kind of pattern in multimedia data or text data. Ex. #1: Face recognition software Ex. #2: Data mining • AI winters • Pattern recognition software became an important research area after 9/11
Fuzzy Logic • Predicate logic vs. fuzzy logic • Degrees of participation in a set Ex. #1 – In which room are you standing? Ex. #2 – Programming elevators in order to optimize traffic flow
Virtual Reality and Simulation Devices • Computer-generated sensory data • Virtual reality programs create output that simulates some aspect of reality. Can be used for entertainment or training purposes. • Simulators are specifically designed to train a response into the user (ex.: surgeons, pilots)
Robots • Robots perform physical tasks that would normally be done by a human
Weak AI vs. Strong AI • Weak AI – Conventional AI • May include brute-force calculations • Finite reasoning capability • Strong AI – Computational Intelligence • Computer can “learn” • Chinese Room thought experiment • Edsgar Dijkstra – • “Debating as to whether a computer can actually think is about as relevant as debating whether a submarine is really swimming”
Weak AI vs. Strong AI (continued) • Strong AI – Computational Intelligence • Attempts at implementing Strong AI • Neural networks • Genetic algorithms • Cyborgs • Turing test • Captchas • Ethics in AI – AI can’t be value free because it is built by humans • AI run amok is standard fare for science fiction. • Many ideas for strong AI come from the discipline of epistemology.
Weak AI vs. Strong AI (continued) • A branch of philosophy known as ontology is also studied by AI researchers • General purpose AI applications vs. specific purpose AI applications
