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