Artificial Intelligence Lecture 2 By Waqas Haider Bangyal
Artificial Intelligence Lecture 2 By: Waqas Haider Bangyal
What can AI systems do Here are some example applications Computer vision: face recognition from a large set Robotics: autonomous (mostly) automobile Natural language processing: simple machine translation Expert systems: medical diagnosis in a narrow domain Spoken language systems: ~1000 word continuous speech Planning and scheduling: Hubble Telescope experiments Learning: text categorization into ~1000 topics User modeling: Bayesian reasoning in Windows help (the infamous paper clip…) Games: Grand Master level in chess (world champion), checkers, etc.
What can’t AI systems do yet? Understand natural language robustly (e. g. , read and understand articles in a newspaper) Surf the web Interpret an arbitrary visual scene Learn a natural language Play Go well Construct plans in dynamic real-time domains Refocus attention in complex environments Perform life-long learning
AI Objectives Make machines smarter (primary goal) Make machines more useful (entrepreneurial purpose) (Winston and Prendergast [1984])
Signs of Intelligence Learn or understand from experience Make sense out of ambiguous or contradictory messages Respond Use quickly and successfully to new situations reasoning to solve problems
More Signs of Intelligence Deal with perplexing (confuse or trouble with uncertainty) situations Understand infer in ordinary, rational ways Apply knowledge to manipulate the environment Think and reason Recognize the relative importance of different elements in a situation
Pattern Matching – Attempt to describe objects, events, or processes in terms of their qualitative features and logical and computational relationships
Knowledge Processing – Given facts or other representations Knowledge Bases – Where knowledge is stored Using the Knowledge Base in AI Programs - Inference
Using the Knowledge Base Computer Inputs Knowledge Base Inferencing Capability Outputs
AI Computing Based on symbolic representation and manipulation A symbol is a letter, word, or number representing objects, processes, and their relationships Objects can be people, things, ideas, concepts, events, or statements of fact Creates a symbolic knowledge base
AI Computing (cont’d) Manipulates symbols to generate advice AI reasons or infers with the knowledge base by search and pattern matching Hunts for answers (via algorithms)
Major AI Areas Expert Systems Natural Language Processing Speech Understanding Robotics and Sensory Systems Computer Vision and Scene Recognition Intelligent Computer-Aided Instruction Neural Computing
Additional AI Areas News Summarization Language Fuzzy Translation Logic Genetic Algorithms Intelligent Software Agents
AI Transparent in Commercial Products Anti-lock Braking Systems Video CAM corders Appliances Washers Toasters Stoves Data Mining Software Help Desk Software Subway Control
- Slides: 14