INFSY 540 Information Resources in Management Lesson 11

INFSY 540 Information Resources in Management Lesson 11 ECommerce

Finalizing Artificial Intelligence

Some AI Technologies Expert Systems: Diagnose, respond & act like a human expert Neural Networks: Use data to predict outputs or interpret inputs Genetic Algorithms: Use data to find “optimal” solutions Fuzzy Logic: Facilitate solutions to human vagueness problems Robotics: Mimic physical human processes Natural-Language Processing: Mimic human communication Intelligent Tutorials: Facilitate human learning Computer Vision: Mimic human sensory(visual) process Virtual Reality: Mimic human reality inside a computer Game Playing: Beat humans in games, e. g. chess Slide 3

Cognitive vs Biological AI Cognitive-based Artificial Intelligence Top Down approach l Attempts to model psychological processes l Concentrates on what the brain gets done l Biological-based Artificial Intelligence Bottom Up approach l Attempts to model biological processes l Concentrates on how the brain works l Slide 4

Cognitive vs Biological AI Cognitive AI Tools: l l l l Expert Systems Natural Language Fuzzy Logic Intelligent Agents Intelligent Tutorials Planning Systems Virtual Reality Biological AI Tools l l l l Neural Networks Speech Recognition Computer Vision Genetic Algorithms Evolutionary Programming Machine Learning Robotics Slide 5

Neural Networks vs Expert Systems Neural Nets is to Expert Systems. . l As Recognition is to Thought Process Some problems can use either one l How do the experts solve it? Logical step-by-step fashion? … Expert System l Recognizing the big picture? … Neural Network l l Is enough historical data present? Yes. … Neural Network l No. … Expert System l Slide 6

Neural Networks vs. Expert Systems Can we use both together? YES! l Output of neural net used as a fact in expert system: l l Vehicle suspension system diagnostics. Neural net classifies the behavior pattern of the shock absorber (shock is worn, ok, etc. ) Expert system uses result to perform diagnosis of the whole system. Expert System output as input to neural network: l l l Different expert systems can perform interpretation of individual events (ex. terrorist activities) Interpretation can serve as input to neural network Network identifies likelihood of perpetrator or commonalities among events Slide 7

Genetic Algorithms vs Neural Networks: l Build models of the real world l Use models to make predictions Genetic Algorithms: l Typically uses an existing model (Fitness Function) l Searches for a good (or optimal) solution to the model. Slide 8

Difference between Prediction and Optimization Prediction: What is the nutrition content of a Mc. Donald’s Happy Meal? Optimization: What is the most nutritious meal at Mc. Donald’s? Solving optimization problems typically requires solving many iterations of smaller prediction problems. Slide 9

Genetic Algorithms with Expert Systems & Neural Nets • GA can use ES to test feasibility of a chromosome. • Constraints often easy to express in rules. . . • GA can use trained NN as the Fitness Function. GA Fitness Value Is it feasible? ES NN How good is it? Slide 10

Genetic Algorithms with Expert Systems & Neural Nets If infeasible, return an extremely bad Fitness GA Fitness Value ES NN If it is a feasible solution, send to Neural Network Slide 11

Questions about Artificial Intelligence? Slide 12

ECommerce Learning Objectives Identify advantages of e-commerce Outline how e-commerce works Identify challenges companies must overcome to succeed in e-commerce Identify the major issues that threaten the continued growth of e-commerce Slide 13

Learning Objectives List the key technology components that must be in place for successful e-commerce Discuss key features of electronic payments systems needed for e-commerce Identify some e-commerce applications Outline key components of a successful ecommerce strategy Slide 14

An Introduction to Electronic Commerce

Fig 8. 1 Slide 16

E-Commerce Challenges Define strategy Change distribution systems & work processes Integrate web-based order processing with traditional systems Slide 17

Can you find examples of community, content & commerce on www. drugstore. com? Slide 18

Fig 8. 3 Slide 19

Fig 8. 4 Slide 20

Forms of E-Commerce Business to Business (B 2 B) Business to Consumer (B 2 C) Slide 21

E-Commerce Applications

Retail and Wholesale E-tailing: electronic retailing Cybermalls Wholesale e-commerce: B 2 B Slide 23

Fig 8. 5 Slide 24

Marketing Double. Click Slide 25

Table 8. 1 Slide 26

Table 8. 2 Slide 27

Priceline Slide 28

Technology Infrastructure

Fig 8. 6 Slide 30

Web Server Hardware Server platform Hardware l Operating system l Website hosting Capital investment l Technical staff l Must run 24 -7 -365 to avoid disrupting business & losing customers Slide 31

Web Server Software Security & identification Encryption Retrieving & sending web pages Web site tracking Slide 32

E-Commerce Software Catalog management Product configuration Shopping cart Transaction processing Traffic data analysis Slide 33

Network Selection Cost Availability Reliability Security Redundancy Slide 34

Electronic Payment Systems

Payment Security Authentication Digital certificate l Certificate authority (CA) l Encryption l Secure Sockets Layer (SSL) Slide 36

Payment Mechanisms Electronic cash Identified electronic cash l Anonymous electronic cash (digital cash) l Electronic wallets Smart, credit, charge & debit cards Slide 37

Threats to E-Commerce

Threats to E-Commerce Security Slide 39

Threats to E-Commerce Intellectual property Fraud On-line auctions l Spam l Pyramid schemes l Investment fraud l Stock scams l Slide 40

Threats to E-Commerce Privacy Online profiling l Clickstream data l Slide 41

Fig 8. 8 TRUSTe Seal Slide 42

Fig 8. 9 BBB Online Privacy Seal Slide 43

How to Protect Your Privacy While On-Line Table 8. 3 Slide 44

Strategies for Successful E-Commerce

Developing an Effective Web Presence Obtain information Learn about products or services Buy products or services Check order status Provide feedback or complaints Slide 46

Putting Up a Web Site In-house development Web site hosting companies Storefront brokers Slide 47

Driving Traffic to Your Web Site Domain names Meta tags Traffic logs Slide 48