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Understanding ai

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  • 1. Understanding AI By Salam Ali Ahasan ECSE-09010043 NUB
  • 2. Today’s topics
    • Today I will tell you about Artificial Intelligence, Knowledge and expert system.
    • Making this slide I got help from Internet, some books, class mates and specially helpful sir.
  • 3. What is Artificial Intelligence?
    • Artificial intelligence  ( AI ) is the intelligence of machines and the branch of computer science that aims to create it. AI textbooks define the field as "the study and design of intelligent agents" where an intelligent agent is a system that perceives its environment and takes actions that maximize its chances of success.
    • John McCarthy, who coined the term in 1956,defines it as "the science and engineering of making intelligent machines”.
  • 4. Dictionary: Intelligence
    • 1.(a) The capacity to acquire and apply knowledge.
    • (b) The faculty of thought and reason.
    • (c) Superior powers of mind.
    • 2. An intelligent, incorporeal being, especially an angel.
    • 3. Information; news.
    • 4. (a) Secret information, especially about an actual or potential enemy.
    • (b) An agency, staff, or office employed in gathering such information.
    • (c) Espionage agents, organizations, and activities considered as a group.
  • 5. What is intelligence then?
    • Fast thinking?
    • Knowledge?
    • Ability to pass as a human?
    • Ability to reason logically?
    • Ability to learn?
    • Ability to perceive and act upon one’s environment?
    • Ability to play chess at grand-master’s level?
  • 6. artificial intelligence: research areas
    • Knowledge Representation
    • Programming Languages
    • Natural Language (e.g., Story) Understanding
    • Speech Understanding
    • Vision
    • Robotics
    • Machine Learning
    • Expert Systems
    • Qualitative Simulation
    • Planning
  • 7. Different Types of Artificial Intelligence
    • 1.Modeling exactly how humans actually think
    • 2.Modeling exactly how humans actually act
    • 3.Modeling how ideal agents “should think”
    • 4.Modeling how ideal agents “should act”
      • =>Modern AI focuses on the last definition
      • -we will also focus on this “engineering” approach
      • -success is judged by how well the agent performs
  • 8. Operational Definition of AI Systems that act like humans Turing test. Systems that think like humans Cognitive Science Systems that think rationally Logic-based AI Systems that act rationally Rational Agents
  • 9. What is AI?
    • Turing Test(1950)
    • =>The computer is interrogated by a human via a teletype.
    • =>It passes if the human cannot tell if there is a computer or human at the other end.
  • 10. The ability to solve problem
    • Search: Efficient trial-and-error
    • =>Enormous computational complexity.
    • =>Space time trades offs.
    • =>Use of domain knowledge-heuristics.
    • *During 1985-1995 computation become free.
  • 11. Knowledge and Deduction
    • How to store and retrieve knowledge.
    • How to interpret facts and rules, and be able to deduce?
    • The gap between knowledge and realization.
    • Logics of knowledge.
    • *Between 1990-2000 storage become free.
  • 12. But What About Knowledge?
    • Why do we need it?
    Find me stuff about dogs who save people’s lives. Around midnight, two beagles spotted a fire in the house next door. Their barking alerted their owners, who call for help. =>How can we represent it and use it? =>How can we acquire it?
  • 13. Representing Knowledge - Logic 1958 McCarthy’s paper, “Programs with Common Sense” at (I, car)  can (go (home, airport, driving)) at (I, desk)  can (go (desk, car, walking)) 1965 Resolution theorem proving invented
  • 14. Representing Knowledge- Semantic Nets 1961
  • 15. Representing Knowledge – Capturing Experience Representing Experience with Scripts, Frames, and Cases 1977 Scripts Rony went to a restaurant. Rony ordered a hamburger. When the hamburger came, it was burnt to a crisp. Rony stormed out without paying. The restaurant script: Did Rony eat anything?
  • 16. Understanding in Expert Systems? Understanding requires: *Representation and manipulation of Domain Knowledge *Perceive analogies *Learn Pragmatic view: *Intentional intelligence is not required *Programs will work the better if more human domain knowledge is encoded in them Normative Descriptive Limitive Definition of Expert System: An Expert System is a computer program that represents and reasons with knowledge of some specialist subject with a view to solving problems or giving advice Expert Systems are a subfield of Artificial Intelligence. Term: Knowledge Based System
  • 17. Characteristics of Expert Systems:
    • Expert systems: Knowledge Based Systems
    • Separation of facts, knowledge, and inference knowledge is explicit, not hidden in algorithm
    • Simulates Human reasoning Built from approach of Human Expert
    • Uses approximate or heuristic search
    • Not the only approach to solving AI problems! (Music classification, chess…)
    • Use: Legal, medical, scientific, tech support, language, …
  • 18. Main challenges in Expert Systems field
    • Acquiring knowledge Expert is unaware, uncommunicative, busy, unwilling
    • Representing knowledge Facts, Relations, Conclusions, Meta-knowledge
    • Controlling reasoning Selection between alternatives is guided by higher order knowledge (meta rules)
    • Explanation
      • Sequence of reasoning steps?
      • Interpretation at higher level
      • Why were other steps NOT chosen?
    • Quality evaluation ; acceptance
  • 19. Why AI?
    • "AI can have two purposes. One is to use the power of computers to augment human thinking, just as we use motors to augment human or horse power. Robotics and expert systems are major branches of that. The other is to use a computer's artificial intelligence to understand how humans think. In a humanoid way. If you test your programs not merely by what they can accomplish, but how they accomplish it, they you're really doing cognitive science; you're using AI to understand the human mind."
    • - Herb Simon