artificial intelligence

Views:
 
Category: Education
     
 

Presentation Description

No description available.

Comments

Presentation Transcript

Slide 1: 

Artificial Intelligence Presented By: Kapil Dev Pathak Afzal Khan

Slide 2: 

The activity of providing such machines as computers with the ability to display behavior that would be regarded as intelligent if it were observed in humans.

Slide 3: 

Expert systems AI hardware Robotics Perceptive systems (vision, hearing) Neural networks Natural language Learning Artificial Intelligence

Slide 4: 

An expert system is a computer program that is designed to hold the accumulated knowledge of one or more domain experts

Applications of Expert Systems : 

Applications of Expert Systems PROSPECTOR: Used by geologists to identify sites for drilling or mining PUFF: Medical system for diagnosis of respiratory conditions

Applications of Expert Systems : 

Applications of Expert Systems DENDRAL: Used to identify the structure of chemical compounds. First used in 1965 LITHIAN: Gives advice to archaeologists examining stone tools

Slide 7: 

The knowledge base is the collection of facts and rules which describe all the knowledge about the problem domain The inference engine is the part of the system that chooses which facts and rules to apply when trying to solve the user’s query The user interface is the part of the system which takes in the user’s query in a readable form and passes it to the inference engine. It then displays the results to the user.

Slide 8: 

Know- ledge base User User interface Instructions & information Solutions & explanations Knowledge Inference engine Problem Domain Expert and knowledge engineer Development engine Expert system

Slide 9: 

Two basic approaches to using rules 1. Forward reasoning (data driven) 2. Reverse reasoning (goal driven)

Slide 10: 

Experts are not always available. An expert system can be used anywhere, any time. Human experts are not 100% reliable or consistent Experts may not be good at explaining decisions Cost effective

Slide 11: 

Limited domain Systems are not always up to date, and don’t learn No “common sense” Experts needed to setup and maintain system

Slide 12: 

Who is responsible if the advice is wrong? The user? The domain expert? The knowledge engineer? The programmer of the expert system shell? The company selling the software?

Slide 14: 

Natural Language Processing (NLP) Computers use (analyze, understand, generate) natural language A somewhat applied field Computational Linguistics (CL) Computational aspects of the human language faculty More theoretical

Slide 15: 

speech processing: get flight information or book a hotel over the phone information extraction: discover names of people and events they participate in, from a document machine translation: translate a document from one human language into another question answering: find answers to natural language questions in a text collection or database

Slide 16: 

Spoken Input Identify words and phonemes in speech Generate text for recognized word parts Concatenate text elements Perform spelling, grammar and context checking Output results Research question: How can speech recognition assist a deaf student taking notes in class? VUST – Villanova University Speech Transcriber (http://www.csc.villanova.edu/~tway/publications/wayAT08.pdf)

Slide 17: 

slides from Xin Li lecture notes, West Virginia Univ.

Slide 18: 

The first picture of moon by US spacecraft Ranger 7 on July 31, 1964 at 9:09AM EDT Digitization Compression Error Recovery Sir Godfrey N. Housefield and Prof. Allan M. Cormack shared 1979 Nobel Prize in Medicine for the invention of CT Enhancement Edges, Contrast, Brightness, etc.

Slide 19: 

Knowledge management (KM) comprises a range of strategies and practices used in an organization to identify, create, represent, distribute, and enable adoption of insights and experiences. Such insights and experiences comprise knowledge, either embodied in individuals or embedded in organizational process or practice.

Slide 21: 

Neural networks, as used in artificial intelligence, have traditionally been viewed as simplified models of neural processing in the brain, even though the relation between this model and brain.

Slide 23: 

We want: To automatically solve a problem We need: A representation of the problem Algorithms that use some strategy to solve the problem defined in that representation

Slide 24: 

General: State space: a problem is divided into a set of resolution steps from the initial state to the goal state Reduction to sub-problems: a problem is arranged into a hierarchy of sub-problems Specific: Game resolution Constraints satisfaction

authorStream Live Help