Artificial Intelligence


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Artificial Intelligence

  1. 1. A PRESENTATION BY-- Biswajit Mondal, Academy of Technology, Electronics and Communication Engineering. Roll No. 071690103019 Reg. No. 071690103101019 Date: 18th February, 2010
  2. 2. What is Artificial Intelligence?  It is the study of how to make computers do things which, at the moment, people do better.  In other words, it can be defined as the study of making of computer with the ability to mimic or duplicate the human brain functions.  John McCarthy coined the term in 1956, at Massachusetts Institute of Technology, defines it as "the science and engineering of making intelligent machines”
  3. 3. Crossbreeding of a lot of fields: Philosophy Logic, methods of reasoning, mind as physical system, foundations of learning, language, rationality. Mathematics Formal representation and proof, algorithms, computation, (un)decidability, (in)tractability Statistics Modeling uncertainty, learning from data Economics Utility, decision theory, rational economic agents Neuroscience Neurons as information processing units Psychology / How do people behave, perceive, process cognitive Neuroscience information, represent knowledge Computer Building fast computers Engineering Control Theory Design systems that maximize an objective function over time Linguistics Knowledge representation, grammars
  4. 4. History of Artificial Intelligence  1943 McCulloch & Pitts: Boolean circuit model of brain  1950 Turing's "Computing Machinery and Intelligence"  1956 Dartmouth meeting: "Artificial Intelligence" adopted  1950s Early AI programs, including Samuel's checkers program, Newell & Simon's Logic Theorist, Gelernter's Geometry Engine  1965 Robinson's complete algorithm for logical reasoning  1966—73 AI discovers computational complexity Neural network research almost disappears  1969—79 Early development of knowledge-based systems  1980-- AI becomes an industry  1986-- Neural networks return to popularity  1987-- AI becomes a science  1995-- The emergence of intelligent agents
  5. 5. The Protagonists The various fields of AI have in common is the creation of machines that can think. In order to classify machines as thinking, it is to be intelligence. Perhaps the best way to gauge the intelligence of a machine is British scientist Alan Turing’s test. He stated that a computer would deserve to be called intelligent if it could deceive a human into believing that it was human. Alan Turing
  6. 6. The Protagonists The beginning of AI reach back before electronics, to philosophers and mathematicians such as Boole and other theorizing on principle that were used as the foundation of AI logic. AI really begun to intrigue researchers with the invention of computer in 1943. The technology was finally available, or so it seemed, to simulate intelligent behaviour. George Boole
  7. 7. The Protagonists Although the computer provided the technology necessary for AI, It was not until the early 1950’s that the link between human intelligence and machines was really observed. Norbert Wiener was one of the first Americans to make observations on the principle of feedback theory. He theorized that all intelligent behaviour was the result of feedback mechanism. Norbert Wiener
  8. 8. The Protagonists In 1956 John McCarthy organized a conference to draw the talent and expertise of others interested in machine intelligence for a month of brainstorming. He invited them to Vermont for “The Dartmouth summer research project on Artificial Intelligence” which brought together the founders in AI, and served to lay the groundwork John McCarthy for the future of AI.
  9. 9. Intelligent behaviour 1: Learn from experience 2: Apply knowledge acquired from experience 3: Handle complex situations 4: Solve problems when important information is missing 5: Determine what is important 6: React quickly and correctly to a new situation 7: Understand visual images 8: Process and manipulate symbols 9: Be creative and imaginative 10:Use heuristics
  10. 10. Artificial intelligence Vision Learning systems systems Robotics Expert systems Neural networks Natural language processing
  11. 11. Branches of Artificial Intelligence  Perceptive system • A system that approximates the way a human sees, hears, and feels objects  Vision system • Capture, store, and manipulate visual images and pictures  Robotics • Mechanical and computer devices that perform tedious tasks with high precision  Expert system • Stores knowledge and makes inferences
  12. 12. Branches of Artificial Intelligence  Learning system • Computer changes how it functions or reacts to situations based on feedback  Natural language processing • Computers understand and react to statements and commands made in a “natural” language, such as English  Neural network • Computer system that can act like or simulate the functioning of the human
  13. 13. Fields of Artificial Intelligence: 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
  14. 14. Perceptive system in game playing Currently, no computer exhibits full artificial intelligence (i.e., is able to simulate human behavior). 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. Deep Blue
  15. 15. Expert systems In the early 1980s, expert systems were believed to represent the future of artificial intelligence and of computers in general. To date, however, they have not lived up to expectations. 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.
  16. 16. Natural language: Natural-language processing offers the greatest potential rewards because it would allow people to interact with computers without needing any specialized knowledge. Unfortunately, programming computers to understand natural languages has proved to be more difficult than originally thought. Some rudimentary translation systems that translate from one human language to another are in existence. There are also voice recognition systems that can convert spoken sounds into written words. Even these systems are quite limited -- you must speak slowly and distinctly.
  17. 17. Neural network: 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.
  18. 18. Robotics: 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. Sony AIBO
  19. 19. Beowulf + robot = “Beobot”
  20. 20. State of the Artificial Intelligence  Deep Blue defeated the reigning world chess champion Garry Kasparov in 1997  Proved a mathematical conjecture (Robbins conjecture) unsolved for decades  No hands across America (driving autonomously 98% of the time from Pittsburgh to San Diego)  During the 1991 Gulf War, US forces deployed an AI logistics planning and scheduling program that involved up to 50,000 vehicles, cargo, and people  NASA's on-board autonomous planning program controlled the scheduling of operations for a spacecraft  Proverb solves crossword puzzles better than most humans
  21. 21. Examples of applications of AI AUSDA: Examines the software to see if it is capable of handling the task you need performed. EGRESS: Program studying the human reaction to accidents. It is trying to make a model how people’s reaction in panic moments save life. VOICE RECOGNIZATION: You tell the computer to do what you want without it having to learn your voice. SCRIPT RECOGNIZATION: With the pen accompanied by silicon notepad you can write a little note to yourself which magically changes into computer text.
  22. 22. Examples of applications of AI The General Problem Solver: The GPS has successfully solved a variety of problems including Deductive reasoning, Hanoi Tower. SAM: SAM is a program from Yale’s AI lab which is able to read between the lines, and assume certain facts. ELIZA: An earlier AI program that simulated the behavior of a Rogerian therapist. ELIZA’s knowledge about English and Psychology was coded in a set of simple rule based on Complex and Approximate Matching, Conflict Reasoning.
  23. 23. Examples of applications of AI CYRUS: It is a MOP(memory organization packets) based program which contains episodes taken from the life of a particular individual. It can answer questions that require significant amounts of memory reconstruction. IPP: This program accepts stories about terrorist attack and stores them in an episodic memory. MOPTRANS: This program uses a MOP based memory to understand sentences in one language and translates into another.
  24. 24. Examples of applications of AI PROSPECTOR: This is a program that provides advices on mineral exploration. DESIGN ADVISOR: It is a system that critiques chip design. It gives advice to the chip designer who decides to accept or reject the advice. NEUROGAMMON: This program is based on neural network that learns from experience. This is one of the few game playing program which relies heavily on automatic learning. There are lots of example of AI application on different aspects.
  25. 25. References “ARTIFICIAL INTRLLIGENCE” by Elaine Rich & Kevin Knight