Chapter 13 Intelligent information systems (2)
Intelligent agents
Machine learning
Fuzzy logic
Natural language processing
Contextual computing
AI and decision support systems
Genetic algorithms
Follows a rules-based approach
Characteristics:
-Adaptable
-Autonomous
-Collaborative
-Human-like interference
-Mobile (between platforms)
-Reactive
Appears to be able to reason
Examples:
-Shopping agents - Pricechack.co.za
-Personal agents - gmails suggested responses to emails
-Data-mining agents
-Siri / Alexa / Google Assist
Bots / Virtual agents / Intelligent virtual agents
Fuzzy logic work on degrees of membership
Appears intelligent
There are ranges and ambiguities
Variety of complexities included in decision or result
Life isnt binary
Computer teaches itself based on ecperience
Artificial neutral networks works on pattern recognition
Knowledge is gained through experience
Examples:
-Fraud detection
-Opportunity identification
-Speech recognition
-Disease identification
Form of artificial intelligence
Concepts of survival of the fittest and mutation
Used to find solutions to optimization and search problems
Examples:
-Siri
-Alexa
But much wider than that:
-Analyzing text
-Creating documentation or reports (e.g. newspaper articles)
-Asking natural questions (e.g. who had the most sales in December) instead of manually doing the filters and reports
-Real time translation or transcription
Communicating with computers in human language
Improved decisions
Identify new opportunities and relationships
Decision support systems (DSS's) benefit from different forms of AI
More natural interaction through natural language processing
User context is used to provide services and information:
-Location based
-Reminders of events
Set to keep on growing:
-Personalized experiences