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Artificial Intelligence

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Artificial Intelligence

  1. 1. An Introduction to Artificial Intelligence Dr Iman Ardekani
  2. 2. Understanding intelligence Imitating intelligence Artificial intelligence - AI Branches of AI Content
  3. 3. Undrstanding Intelligence Many great philosopher over the ages attempted to explain the process of thought and understanding. Intelligence Plato 428–348 BC Aristotle 384–322 BC Philosophy Math Nature and Universe Human knowledge Intelligence *
  4. 4. Undrstanding Intelligence Plato 428–348 BC Aristotle 384–322 BC Copernic 1473-1543 Galileo 1564-1642 Philosophy & Natural Science Math Intelligence Nature and Universe Human knowledge The real key that started the quest for the simulation of inteligence did not occure until … *
  5. 5. Undrstanding Intelligence Philosophy & Natural Science Math Intelligence Nature and Universe Human knowledge Thomas Hobbes (English Philosopher) put forth an interesting concept that thinking consists of symbolic operations and that everything in the life can be represented mathematically. Hobbes 1588-1679 *
  6. 6. Undrstanding Intelligence Philosophy & Natural Science Math Intelligence Nature and Universe Human knowledge Thomas Hobbes (English Philosopher) put forth an interesting concept that thinking consists of symbolic operations and that everything in the life can be represented mathematically. Hobbes 1588-1679 *
  7. 7. Hobbes (British Philosopher): Thinking consists of symbolic operations! Based on this logic, a machine capable of caring out mathematical operations on symbols could imitate human thinking. Undrstanding Intelligence Hobbes 1588-1679 What is a symbolic operation? • Numeric operation (2+3)2 = 25 • Symbolic operation (a+b)2 = a2 + b2 + 2ab
  8. 8. Rene Descartes (French Philosopher and Mathematician): He believed that the mind and the real world are in parallel planes. The physical word (i.e. machines) cannot imitate the mind because there is no common reference point. Undrstanding Intelligence Descartes 1596-1650
  9. 9. Charles Babbage (British Mathematician): In Babbage's time, numerical tables were calculated by humans who were called 'computers’. He saw the high error-rate of this human- driven process and started work of trying to calculate the tables mechanically. He created a “difference engine” to compute values of polynomial functions. Imitating Inteligence Babbage 1791-1871 A part of Babbage's difference engine He also introduced the idea of “Analytical Machine”, but he could never realize this idea.
  10. 10. George Boole (British Mathematician): Boole formulated the “Laws of Thought” that set up rules of logic for representing thoughts (symbolic logic). This was the birth of digital logic, a key component of AI. In the early 1900s, Alfred Whitehead and Bertrand Russell extended Boole’s logic to include mathematical operations. This led to the formulation of digital computers. Also, this made possible one of the first ties between computers and thought process. Imitating Inteligence Boole 1815-1864 Russell 1872-1970 Whitehead 1861-1947
  11. 11. Design a digital computer using logical operations to compute y=x1+x2 where x1 and x2 are 4-digit binary numbers (4-bit adder). Design a digital computer using logical operations to compute y=x1.x2 where x1 and x2 are 4-digit binary numbers (4-bit multiplier). Design a digital computer using logical operations to compute y=ex where x1 and x2 are 4-digit binary numbers (ex=1+x+x2/2+x3/6+…). Imitating Inteligence
  12. 12. Claude Shannon (American Electrical Engineer): He wrote his master’s thesis demonstrating that electrical applications of Boolean algebra could construct and resolve any logical, numerical relationship. It has been claimed that this was the most important master's thesis of all time. His PhD these was on mathematical relationships of genetics. He is known as the father of Information Technology. Imitating Inteligence Shannon 1916-2001
  13. 13. John Neumann (American Mathematician) He suggested that the computers  should be general purpose logic machines.  could react intelligently to the results of their calculations  could choose among alternatives, and even play checker and chess This represented something unheard of at that time: a machine with built-in intelligence, able to operate on internal instructions. Before introducing this concept, even the most complex mechanical devices had always been controlled from the outsides, by knobs and dials. He didn't’ invent the computer but what he introduced was equally significant: computing by use of computer programs. Imitating Inteligence Neumann 1903-1957
  14. 14. John Mauchly (American Electrical Engineer): John Mauchly designed and built the first general purpose digital computer in 1946 at the University of Pennsylvania: ENIAC (Electronic Numerical Integrator and Computer) Weight = 30 Tons Floor Space = 1500 Square Feet Shannon’s idea  Hardware Neumann’s idea  Software Imitating Inteligence Mauchly 1907-1980
  15. 15. Alan Turing (British Mathematician): He introduced “Universal Machine Concept” that describe a machine for solving all problems based on variable instructions. Turing’s universal machine concept, along with Neumann’s concept of computing using programs led to programmable computers. Operational machines were now being realized. The question was “Are they intelligent?” and “in what extend?”. Turing also designed Turing’s test for determining the intelligence of a system. Imitating Inteligence Turing 1912-1954
  16. 16. Turing Test – Step 1 (man/woman) A is a man and B is a woman and C is of either sex. C is unable to see either A or B, and can communicate with them only through online computer chat. By asking questions of A and B, C tries to determine which of the two is the man and which is the woman. A's role is to trick C into making the wrong decision, while B attempts to assist C in making the right one. Imitating Inteligence
  17. 17. Turing Test – Step 2 (human/computer) Substitute a computer for A. By asking questions of Computer and B, C tries to determine which of the two is the computer. Computer's role is to trick C into making the wrong decision, while B attempts to assist C in making the right one. If the C’s success rate in human/computer game is not better than his success rate in the man/woman game Imitating Inteligence
  18. 18. Turing Test If the C’s success rate in human/computer game is not better than his success rate in the man/woman game, then the computer can be said to be “thinking”. Imitating Inteligence
  19. 19. There was now a need for a high-level programming language. Logic Theorist was written in 1955 by A. Newell, H. A. Simon and J. C. Shaw. It was the first program deliberately engineered to mimic the problem solving skills of a human being and is called "the first artificial intelligence program.” It would eventually prove 38 of the first 52 theorems of Whitehead and Russell, and find new and more elegant proofs for some.[2] Imitating Inteligence
  20. 20. John McCarthy (American Computer Scientist) He coined the term “Artificial Intelligence” in the first conference on machine intelligence, 1956. He also developed LISP (List Processing) programming language, which has become a standard tool for AI development. LISP distinctions:  Memory organization – in a tree fashion  Control structure – instead of working from perquisites to a goal, it starts with the goal and works backward to determine what perquisites are required to achieve the goal. Artificial Intelligence McCarthy 1927-2011
  21. 21. GPS (General Problem Solver) was another AI programming language that introduced in 1959. It was capable of solving theorems, playing chess, or doing puzzles. Its core was based on the use of means-end analysis, which involves comparing a present state with a goal state. The difference between the two state is determined and a search is done to find a method to reduce this difference. This process is continued until there is no difference between the current state and the goal state. It was capable of backtracking to an earlier state to correct its mistakes. It was also able to define sub-goals. GPS did a good job of imitating the human subjects. Artificial Intelligence
  22. 22. ELIZA was the first intelligent computer program that was enable of interacting in a two-way conversation. It could sustain very realistic conversations by very smart techniques. For example, ELIZA used a pattern matching method that would scan for keywords like “I”, “You”, “Like” and so on. If one of these words was found, it would execute rules associated with it. If no match was found, it would request for more information. Artificial Intelligence Link to ELIZA
  23. 23. The various attempts at formally defining the use of machines to simulate human intelligence let to several AI branches 1. Natural Language Processing (NLP) 2. Computer Vision 3. Robotics 4. Problem-solving and planning 5. Learning 6. Expert Systems Branches of AI
  24. 24. Branches of AI NLP ComputerVision ExpertSystems ProblemSolving Robotics Learning Artificial Intelligence Human-like artificial creatures Other artificial creatures Special robots/machines with higher capabilities
  25. 25. How successful we have been in creating human-like artificial creatures? Branches of AI
  26. 26. Natural Language Processing (NLP) NLP understands, and generates languages that humans use naturally so that eventually you will be able to address your computer as though you were addressing another person (e.g. ELIZA) Branches of AI Speech NLP Knowledge
  27. 27. Natural Language Processing (NLP) NLP Categories: 1- Phonology: modeling the pronunciation of words (chair, car, cell) 2- Morphology: identifying the structure of words (dog, dogs, hot dogs) 3- Syntax (identifying grammars) 4- Semantics (understanding and representing the meaning) Applications: automatic text indexing, grammar and style analyser, automatic text generation, machine translation, optical character recognition (OCR) and etc. Branches of AI
  28. 28. Computer Vision Computer vision is a field that includes methods for acquiring, processing, analysing, and understanding images and, in general, high-dimensional data from the real world in order to produce numerical or symbolic information, e.g., in the forms of decisions. Branches of AI Images Computer Vision Knowledge
  29. 29. Branches of AI Computer Vision US Deference Advance Research Projects Agency (DARPA)
  30. 30. Computer Vision Applications: 1. Recognize objects (e.g. people we know and things we own) 2. Locate objects in space (to pick them up?) 3. Track objects in motion (catching a baseball, avoiding collisions with cars on the road) 4. Recognize actions (e.g. walking, running, pushing) Branches of AI
  31. 31. Robotics Robotics involves the control of actuators on robots to move, manipulate or grasp objects, locomotion of independent machines and use of sensory input to guide actions. Branches of AI
  32. 32. Problem-solving and Planning This technology involves application such s refinement of high-level goals into lower-level ones, determination of actions to achieve goals, revision of plans based on intermediate results, and focused search of important goals. A good example is chess players software. Branches of AI
  33. 33. Learning Learning deals with research into various forms of learning including rote learning, learning through advise, learning by example, learning by task performance, and learning by following concepts. Branches of AI
  34. 34. Expert Systems Expert systems deal with the processing of knowledge as opposed to processing of data. It involves the development of computer software to solve complex decision problems. In fact, an expert system is a computer system that make decisions on behalf of human. Branches of AI Link to ANNA Android Doctor

Notes de l'éditeur

  • https://www.youtube.com/watch?v=MaTfzYDZG8c
  • Phonology: Modelling the pronunciation of a word as a string of symbols (chair, car, cell,…)
    Morphology: Identification of the structure of words (dog, dogs, hot dog, ….)
    Syntax: Study of grammars
    Semantics: Understanding and representing the meaning
  • Phonology: Modelling the pronunciation of a word as a string of symbols (chair, car, cell,…)
    Morphology: Identification of the structure of words (dog, dogs, hot dog, ….)
    Syntax: Study of grammars
    Semantics: Understanding and representing the meaning
  • https://www.youtube.com/watch?v=RU_Ed9mA_jE

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