Introduction to Artificial Intelligence.pptx

Introduction to Artificial Intelligence

1. Introduction to Artificial Intelligence From : Laurence Nash’s published work. Link: https://slideplayer.com/slide/7967511/ Accessed on: 8/6/2022

2. What is AI? B ranch of computer science Born at a conference held at Dartmouth, USA, in 1956 The scientists attending that conference represented several different disciplines: mathematics , neurology , psychology , electrical engineering, etc. Commonality: They all were trying to use the recently developed computers to simulate various aspects of human intelligence

3. Definition of AI Artificial Intelligence may be defined as The branch of computer science that is concerned with the automation of intelligent behavior An exact definition of intelligence is not easy to formulate. However, there are some general abilities which are universally considered as intelligent.

4. General Abilities of Intelligent Behavior According to Douglas Hofstadter, these are: - To respond to situations very flexibly. If the same response is exhibited each time, the behavior is called mechanical. To survive in changing environments, one need to exhibit innovative behavior (e.g. art of begging) To make sense out of ambiguous or contradictory messages We understand such messages because our knowledge and experience allows us to place them in context. (e.g. time flies like an arrow, buy this washing powder versus buy that washing powder)

5. To recognize the relative importance of different elements of a situation (e.g. quality versus price of a commodity) To find similarities between situations despite differences which may separate them (e.g. chairs in two different pictures) To draw distinctions between situations despite similarities which may link them (e.g. differences in two cars) These abilities are largely due to knowledge and experience, which allows you to place an information in its wider context

6. Another definition of Intelligence It is the ability to - perceive inter-relationship of facts - learn and understand from experience - acquire and retain knowledge - respond quickly and successfully to a new situation

7. Turing Test Proposed in 1950. It is a test to decide whether or not a particular machine is intelligent. Predicted that by 2000, a machine might have a 30% chance of fooling a lay person for 5 minutes

8. Turing Test - Continued Contact only through monitor and keyboard Machine tries to pose as a human If the player cannot distinguish between human and machine, then machine is considered intelligent Revised Turing Test: A human converses with an unseen respondent and attempts to determine whether it is a man or machine. If the computer fools you into thinking that it is a human, than that machine is intelligent

9. Sometimes it is possible to program computers to carry on shallow conversations, in limited areas, and thus fool unsuspecting humans into believing that they are addressing other humans.

10. Example: Program ELIZA simulating a psychiatrist. Person: I miss my children ELIZA: “Why do you miss your children?” or “ Tell me more about your family” ELIZA is programmed to ask pre-determined questions and parrot segments of your responses back to you. Hence Turing test may not be such a good judge of machine intelligence after all This leads us to the issue of “ understanding ” Even though a machine may be exhibiting intelligent behavior, it does not “ understands ” what it is doing

11. Major AI Areas

12. Expert Systems: An ES is a computer program designed to act as an expert in a particular domain (area of expertise). It typically includes a sizeable knowledge base, consisting of facts about the domain and rules for application to those facts. Medical (e.g. PXDES, MYCIN) and Agriculture (e.g. AGREX)

13. 2. Natural Language Processing Goal is to enable people and computers to communicate in ordinary or natural English. - Comprehension of natural language: Keyboard input (e.g. MS Word Processor), speech recognition (e.g. IBM VoiceType Dictation, BBN corporation: voice activated browsers, speaker identification for security and Operetta™) - Generation of natural language.

14. 3. Machine learning Field of study that gives computer the ability to learn without being explicitly programmed (Arthur Samuel, 1956) ML learning provides best methods for developing particular kinds of software, in applications where: Application is too much complex for people to manually design the algorithm. For instance, soft- wares for sensor-based prediction tasks such as speech recognition and computer vision. Applications require that the software customize to its operational environment after it is fielded. For example, speech recognition system that customize to the user who purchase the software or recommenders

15. 4. Robotics and Computer Vision Factory automation Autonomous vehicles Robots: Electromechanical devices programmed to perform manual tasks. Not all robots are intelligent. Some are pre-programmed by conventional techniques and are dumb. An intelligent robot usually includes some kind of sensory apparatus that allows it to respond to changes in its environment. Computer Vision: it is field that include methods for acquiring, processing, analyzing and understanding images in order to produce numerical and symbolic information. For example, medical image processing is an application of computer vision