1. Artificial intelligence 4 Expert systems 4 Neural nets 4 Data base mining

2. Expert systems 4 Use data base of knowledge to draw conclusions –Data base is If, Then knowledge Rules of thumb provided by expert individuals 4 Can handle routine situations with great accuracy; leaves complex situations to humans 4 Can explain why it reached conclusion: loan denied because

3. When expert systems should be used: 4 task does not require common sense 4 experts can articulate their methods 4 genuine experts exist 4 experts agree on solution –can’t handle conflicting solutions 4 task is narrowly focused –Loan credit scoring; fraud detection; HP printer support

4. Expert systems are also used when: 4 task solution has a high payoff 4 human expertise scarce 4 expertise needed in many locations 4 expertise needed in hostile environments 4 human expertise being lost

5. Neural nets 4 Numerous connected processors like neurons of brain 4 Learn by trial and error, experts don’t know the answer 4 Looks for patterns that work –May use genetic algorithms where only the best solutions “survive”

6. Neural nets are used when 4 There are not “hard and fast” solutions such as pattern recognition and forecasting –ANNs are well suited to problems that people are good at solving, but for which computers are not.

7. Data base mining: automated extraction of predictive information from large databases 4 What customer will respond to a catalog of bird feed and houses? 4 Deep Blue used data mining to calculate all possible moves and selected best move 4 Confirming or discovering patterns in huge data bases –Software can either: Pattern validation: Confirm existence of expected patterns. Top down data mining –May not ask about the right patterns. –Some patterns occur by chance. »Money manager found that the best predictor of the U.S. stock market was butter production in Bangladesh. Pattern discovery –75% of the purchasers o f low-fat ice cream buy bottled water