1 Lecture 33 Introduction to Artificial Intelligence (AI) Overview Lecture Objectives. Introduction to AI. The Turing Test for Intelligence. Main.
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1 Lecture 33 Introduction to Artificial Intelligence (AI) Overview Lecture Objectives. Introduction to AI. The Turing Test for Intelligence. Main Research Areas of AI. Preview: Knowledge Representation & Expert Systems.
2 Lecture 33 Lecture Objectives Motivate and outline the goals of AI research. Give brief historical account on the development of AI. Introduce the main research areas of AI.
3 Lecture 33 Introduction to AI (cont’d) What is Artificial Intelligence? Artificial Intelligence can be defined as the study of making machines behave as if they had human intelligence. That is, making computers simulate human intelligence. What is intelligence? Understanding, thinking, reasoning, creating Intelligence: If something can emulate or imitate a human, then it is intelligent.
4 Lecture 33 Introduction to AI (cont’d) In order to understand AI, lets study the things that humans can do. Computational Tasks Arithmetic Sorting Searching Calculating,.. Recognition Recognize faces ( vision) Recognize spoken language (Natural Language Processing) Reasoning Planning Taking decisions Ability to behave under new situations Learning from old experiences. HOW GOOD ARE HUMANS AND MACHINE IN THESE TASKS?
6 Lecture 33 Turing Test for Intelligence As early as 1950, Alan Turing addressed the question: “can computers think?” He proposed the Turing Test as a way to determine the intelligence of computers… Turing Test: Can a program convince a person that s/he is conversing with a real person? The test uses a human, an AI program, and a judge. If the Judge couldn’t differentiate which is the machine and which is the human within a certain amount of time only by asking questions, the machine is intelligent Judge
7 Lecture 33 Artificial Intelligence Approach Real AI research rarely attempts to “pass” the Turing Test of global intelligence. Instead it tries to perform “well” on tasks which are traditionally difficult for computers, but easy for people. For example: vision - recognize shapes natural language - understand and speak compose musical tunes make good guesses using incomplete data AI doesn’t deal with tasks such as adding up numbers, sorting, compiling code into machine language, which are all relatively easy for computers.
8 Lecture 33 Brief History of AI The gestation of A.I. (1943-1956) Early enthusiasm, great expectations (1952-1969) A dose of reality (1966-1974) NP-completeness theory and computational intractability Knowledge-based systems (1969-1979) Expert systems (MYCIN) A.I. becomes an industry (1980-present) The return of neural networks (1986-present)
9 Lecture 33 Areas of AI Research Main areas of Artificial Intelligence Research include : 1. Knowledge Representation and Logic 2. Expert Systems 3. Natural Language 4. Robotics 5. Computer vision 6. Neural Network 7. Machine Learning We’ll highlight each of these in the next two lectures.