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Artificial intelligence

Jan 27, 2015




  • 1. Artificial Intelligence

2. Intelligence is the ability to learn about, to learn from, to understand about and interact with ones environment. Intelligence is the faculty of understanding. Make sense out of ambiguous or contradictory messages. Respond quickly and successfully to new situations. Use reasoning to solve problems. Intelligence is not to make no mistakes but quickly to understand how to make them good (German Poet) 3. A branch of computer science whose goal is the design of machines that have attributes associated with human intelligence, such as learning, reasoning, vision, understanding speech, and, ultimately, consciousness. Computer software that can mimic the learning capability of a human. The use of programs to enable machines to perform tasks which humans perform using their intelligence.The term was coined in 1956 by John McCarthy at the Massachusetts Institute of Technology. 4. Examples: Defeating the besthuman chess players. Driving hundreds of miles through the desert unaided. Chatting in internet chat-rooms. Examining x-rays for tumors. 5. Currently, no computers exhibit full artificial intelligence (that is, are able to simulate human behaviour). The greatest advances have occurred in the field of games playing. The best computer chess programs are now capable of beating humans. In May, 1997, an IBM super-computer called Deep Blue defeated world chess champion Gary Kasparov in a chess match. In the area of robotics, computers are now widely used in assembly plants, but they are capable only of very limited tasks. Robots have great difficulty identifying objects based on appearance or feel, and they still move and handle objects clumsily. Natural-language processing offers the greatest potential rewards because it would allow people to interact with computers without needing any specialized knowledge. You could simply walk up to a computer and talk to it. Unfortunately, programming computers to understand natural languages has proved to be more difficult than originally thought. 6. There are also voice recognition systems that can convert spoken sounds into written words, but they do not understand what they are writing; they simply take dictation. Even these systems are quite limited -you must speak slowly and distinctly. In the early 1980s, expert systems were believed to represent the future of artificial intelligence and of computers in general. Many expert systems help human experts in such fields as medicine and engineering, but they are very expensive to produce and are helpful only in special situations. Today, the hottest area of artificial intelligence is neural networks, which are proving successful in a number of disciplines such as voice recognition and natural-language processing. There are several programming languages that are known as AI languages because they are used almost exclusively for AI applications. The two most common are LISP and Prolog. 7. Foundation of AI is based on Mathematics Neuroscience Control Theory Linguistics 8. More formal logical methods Boolean logic Fuzzy logicUncertainty The basis for most modern approaches to handle uncertainty in AI applications can be handled by Probability theory Modal and Temporal logics 9. How do the brain works? Early studies (1824) relied on injured and abnormalpeople to understand what parts of brain work More recent studies use accurate sensors to correlate brain activity to human thought By monitoring individual neurons, monkeys can now control a computer mouse using thought alone Moores law states that computers will have as manygates as humans have neurons in 2020 How close are we to have a mechanical brain? Parallel computation, remapping, interconnections,. 10. Machines can modify their behavior in responseto the environment (sense/action loop) Water-flow regulator, governor, thermostatsteamengine The theory of stable feedback systems (1894) Build systems that transition from initial state to goal state with minimum energy In 1950, control theory could only describe linear systems and AI largely rose as a response to this shortcoming 11. Speech demonstrates so much of human intelligence Analysis of human language reveals thought takingplace in ways not understood in other settings Children can create sentences they have never heard before Language and thought are believed to be tightly intertwined 12. More permanent Ease of duplication and dissemination Less expensive Consistent and thorough Can be documented Can execute certain tasks much faster than a human can Can perform certain tasks better than many or even most people 13. Natural intelligence is creative People use sensory experience directly Can use a wide context of experience in different situations AI - Very Narrow Focus 14. Artificial intelligenceVision systemsLearning systemsRoboticsExpert systemsNeural networks Natural language processing 15. Artificial intelligence includes : Games playing: programming computers to play games such as chess and checkers. Expert systems : programming computers to make decisions in real-life situations. (for example, some expert systems help doctors diagnose diseases based on symptoms) Natural language : programming computers to understand natural human languages. Neural networks : Systems that simulate intelligence by attempting to reproduce the types of physical connections that occur in animal brains. Robotics : programming computers to see and hear and react to other sensory stimuli. 16. Alan Turing (1912 - 1954) Proposed a test - TuringsInterrogatorImitation Game Tests the intelligence of the computer. Phase 1: Man and woman separated from an interrogator. The interrogator types in a question to either party. By observing responses, the interrogators goal was to identify which was the man and which was the woman. Honest WomanLying Man 17. Phase 2 of the Turings test:Interrogator The man was replacedby the computer. If the computer could fool the interrogator as often as the person did, it could be said that the computer had displayed intelligence. Honest WomanComputer 18. According to Darlington: An expert system is a program that attempts to mimic human expertise by applying inference methods to a specific body of knowledge. The term expert system is used in a seminal paper by Alan Turing in 1937 related to a study in AI. An Expert System (ES) is a computer program that reasons using knowledge to solve complex problems. Traditionally, computers solve complex problems by arithmetic calculations; and the knowledge to solve the problem is only known by the human programmer. 19. ES's are: 1. Open to inspection, both in presenting intermediate steps and in answering questions about the solution process. 2. Easily modified, both in adding and deleting skills from the knowledge base. 3. Heuristic, in using knowledge to obtain solutions Development of Expert Systems will allow us not only to provide very powerful technical capabilities but also to further nurture our own understanding of human thought process. 20. An ES will normally have two aspects: A development environment A consultation environmentThe former is used by the system builder to modify the system. The later is used by the non-expert to obtain knowledge or advice. It is the latter which is thought of as an ES. 21. An ES is a program with various components: 1. 2. 3. 4. 5. 6. 7.Knowledge acquisition subsystem Knowledge base Inference engine User interface Explanation subsystem Blackboard Knowledge refinement subsystem 22. Explanation facilityKnowledge baseInference engineKnowledge base acquisition facilityExpertsUser interfaceUser 23. An ES may obtain input from an online data source (database, text file, web page, etc). An ES may be used to monitor a physical system, in which case input may come directly from sensing devices. An ES may be used to control a physical system, in which case output will be signals to the system. When interacting with humans, standard HCI (Human-Computer Interaction) concerns apply. 24. The power of problem solving is primarily the consequence of the knowledge base and secondarily on the inference method employed. A storehouse of knowledge primitives. The design of knowledge representation scheme impacts the design of the inference engine, the knowledge updating process, the explanation process and the overall efficiency of the system. Therefore the selection of the knowledge representation scheme is one of the most critical decision in ES design. Knowledge update is done either : 1. Manual by the knowledge engineer domain expert2. Machine learning 25. The inference engine controls the reasoning involved when the system is run. It has its own mechanism for interpreting the stored knowledge (in the appropriate form), and for sequencing the steps involved in reaching conclusions. Inference here means any of the methods by which the system reaches conclusions. Facts: All animals breathe oxygen. All dogs are animals. Infer: All dogs breathe oxygen. 26. If the user is to have confidence in the output from an ES, it will be important for the ES to have ways of explaining how its conclusions were arrived at. It will be useful to allow the user to ask. In response to a question from the ES: WHY (did you ask that question)? After a conclusion has been presented:HOW (did you reach that conclusion)? 27. This just means a place where temporary working may be stored, where it is accessible to various component parts of a large ES. This may include, for example, a (dynamic) agenda --- a list of tasks to be done (by the ES). It may also include a list of intermediate conclusions, or results of searches, in order to avoid duplication of effort.Not all ES will use (or need) a blackboard. 28. Knowledge refinement means analyzing experience and adjusting the body of stored knowledge as a result. People do th