INFSY540 Information Resources in Management Lesson 11 ECommerce

Finalizing Artificial Intelligence

Slide 3 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 4 Cognitive vs Biological AI Cognitive-based Artificial Intelligence Top Down approach Attempts to model psychological processes Concentrates on what the brain gets done Biological-based Artificial Intelligence Bottom Up approach Attempts to model biological processes Concentrates on how the brain works

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

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

Slide 7 Neural Networks vs. Expert Systems Can we use both together? YES! Output of neural net used as a fact in expert system: 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: 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 8 Genetic Algorithms vs Neural Nets Neural Networks: Build models of the real world Use models to make predictions Genetic Algorithms: Typically uses an existing model (Fitness Function) Searches for a good (or optimal) solution to the model.

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

Slide 10 Genetic Algorithms with Expert Systems & Neural Nets GAES NN Is it feasible? 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. How good is it? Fitness Value

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

Slide 12 Questions about Artificial Intelligence?

Slide 13 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 14 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 e- commerce strategy

An Introduction to Electronic Commerce

Slide 16 Fig 8.1

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

Slide 18 Can you find examples of community, content & commerce on

Slide 19 Fig 8.3

Slide 20 Fig 8.4

Slide 21 Forms of E-Commerce Business to Business (B2B) Business to Consumer (B2C)

E-Commerce Applications

Slide 23 Retail and Wholesale E-tailing: electronic retailing Cybermalls Wholesale e-commerce: B2B

Slide 24 Fig 8.5

Slide 25 Marketing DoubleClick

Slide 26 Table 8.1

Slide 27 Table 8.2

Slide 28 Priceline

Technology Infrastructure

Slide 30 Fig 8.6

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

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

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

Slide 34 Network Selection Cost Availability Reliability Security Redundancy

Electronic Payment Systems

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

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

Threats to E-Commerce

Slide 39 Threats to E-Commerce Security

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

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

Slide 42 Fig 8.8 TRUSTe Seal

Slide 43 Fig 8.9 BBB Online Privacy Seal

Slide 44 Table 8.3 How to Protect Your Privacy While On-Line

Strategies for Successful E-Commerce

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

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

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

Slide 49 Questions?