1. 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. 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. 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. 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?

5. 5 Lecture 33 Humans versus Computers Computational RecognitionReasoning Ability Computers Humans

6. 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. 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. 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. 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.