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A.I. is a branch of computer science that studies the computational requirements for tasks such as perception, reasoning and learning and develop systems to perform those tasks The field of Artificial intelligence strives to understand and build intelligent entities A.I. Strong A.I. M/C can think and act like human Weak A.I. Some thinking like features can be added to M/C Slide 4: INTRODUCTION TURING TEST * Intelligence is defined as the ability to achieve human level performance in all cognitive tests, sufficient to fool a human interrogator. * The test was devised in response to the question,” Can a computer think ?”. * Result was +ve if interrogator can not tell if responses are coming from the M/C or Human. * Proposed by Alan Turing(1950), a British Computer Scientist. Slide 5: INTRODUCTION TURING TEST One person sits at a computer and types the questions. The computer is connected to two other hidden computers At one computer, Human reads and responds to questions. At the other end, computer with no Human aid runs the program to provide responses. Slide 6: INTRODUCTION DEFINITIONS * AI is a branch of computer science dealing with symbolic, nonalgorithmic methods of problem solving * AI is a branch of computer science that deals with ways of knowledge using symbols rather than numbers and with Heuristics, method for processing information. * AI works with pattern matching methods which attempt to describe objects , events or processes in terms of their qualitative features and logical and computational Relationship. Slide 7: INTRODUCTION What is Intelligence ? To respond to situations very flexibly. To make sense out of ambiguous or contradictory messages. To recognize the relative importance of different elements of situations To find similarities between situations despite difference To draw distinctions between situations despite similarities which may link them. Slide 8: HISTORY 1943 – McCulloh and Pitts, Boolean circuit model of brain. 1950 – Turing’s computing machine and intelligence. 1950’s – Early AI programs including Samuel’s checker program, Newell and Simon’s logic theorist, Gelisnters geometry engine 1956 – Dartmouth conference. Slide 9: HISTORY 1952-69 – “Look, Ma, no hands!” era. 1958 – McCarthy moves to MIT, LISP was born. 1965 – Robinson’s complete algorithm for logical reasoning. 1966-74 – AI discovers computational complex. Neural network research almost disappears. 1969-79 - Early development in knowledge based systems. Slide 10: HISTORY 1980-88 : Expert system industry booms. 1988-93 : Expert system industry busts. 1985-88 : Neural networks return to popularity. 1995 : Agents… Agents… Agents. (present) BRANCHES : BRANCHES Logical AI What a program knows about the world in general the facts of the specific situation in which it must act and it’s goal are all represented by sentences of some mathematical logical language. Pattern Recognition When a program makes observation of some kind, it is often programmed to compare what it sees with already stored patterns. BRANCHES : BRANCHES Representation Facts about the world have to be represented in some way. Usually languages of mathematical logic are used. Common Sense, Knowledge and Reasoning This is an era in which AI is farthest from human level. While there has been considerable progress, e.g. in development systems of non monotonic reasoning and theories of action BRANCHES : BRANCHES Planning Planning programs start with general facts about the world. They generate a strategy for achieving the goal, the strategy is just a sequence of action. Epistemology This is a study of the kinds of knowledge that are required for solving problems in the world. Ontology It is the study of kinds of things that exist. In AI, things deal with various kinds of object. BRANCHES : BRANCHES Heuristics Heuristics is a way of trying to discover something or an idea embedded in a program. It predicates that compare two nodes in a search tree to see if one is better than other, e.I. constitutes an advance towards the goal, may be more useful. Genetic Engineering It is a technique for getting programs to solve a task by mating random LISP programs and selecting fittest in millions of generations. APPLICATIONS OF A.I. : APPLICATIONS OF A.I. Expert systems. Natural Language Processing (NLP). Speech recognition. Computer vision. Robotics. Automatic Programming. APPLICATIONS : APPLICATIONS EXPERT SYSTEMS An Expert System is a computer program designed to act as an expert in a particular domain (area of expertise). Expert systems currently are designed to assist experts, not to replace them, They have been used in medical diagnosis, chemical analysis, geological explorations etc. Domain of E.S. Knowledge base Facts Heuristics Phases in Expert System APPLICATIONS : APPLICATIONS Speech Recognition The primary interactive method of communication used by humans is not reading and writing, it is speech. The goal of speech recognition research is to allow computers to understand human speech. So that they can hear our voices and recognize the words we are speaking. It simplifies the process of interactive communication between people and computers, thus it advances the goal of NLP. APPLICATIONS : APPLICATIONS Natural Language Processing The goal of NLP is to enable people and computers to communicate in a natural (humanly) language(such as, English) rather than in a computer language. The field of NLP is divided in 2 categories— Natural Language understanding. Natural Language generation. APPLICATIONS : APPLICATIONS Computer Vision People generally use vision as their primary means of sensing their environment, we generally see more than we hear, feel or smell or taste. The goal of computer vision research is to give computers this same powerful facility for understanding their surrounding. Here AI helps computer to understand what they see through attached cameras. APPLICATIONS : APPLICATIONS Robotics A Robot is a electro-mechanical device that can by programmed to perform manual tasks or a reprogrammable multi functional manipulator designed to move materials, parts, tools, or specialized devices through variable programmed motions for performance of variety of tasks. An ‘intelligent’ robot includes some kind of sensory apparatus that allows it to respond to change in it’s environment. Slide 21: APPLICATIONS Robotics APPLICATIONS : APPLICATIONS Automatic Programming Programming is a process of telling a computer exactly what you want it to do.Writing a program is a tedious job. It must be designed, written, tested, debugged and evaluated. The goal of automatic planning is to create special programs that act intelligent tools to assist programmers and expedite each phase of programming process. Ultimate aim is computer itself should develop a program in accordance with specifications of programmer. FUTURE : FUTURE The day is not far when you will just sit back in your cozy little beds and just command your personal Robot's to entirely do your ruts . He will be a perfect companion for you. Just enjoy the Technology. FUTURE : FUTURE But wait, don’t be happy. It may end in other way too. Some day there will be a knock to your door. As you open it, you see a large number of Robots marching into your house destroying everything you own and looting you. This is because ever since there is an advantage in the Technology, it attracts anti-social elements. This is true for Robots too. Because now they will have full power to think as human, even as of anti-social elements. So think trice before giving them power of Cognition. CONCLUSION : CONCLUSION In it’s short existence, AI has increased understanding of the nature of intelligence and provided an impressive array of application in a wide range of areas. It has sharpened understanding of human reasoning, and of the nature of intelligence in general. At the same time, it has revealed the complexity of modeling human reasoning providing new areas and rich challenges for the future. Slide 26: THANK YOU You do not have the permission to view this presentation. In order to view it, please contact the author of the presentation.