CS 461: Artificial Intelligence Introduction

1. Instructor: SayeraHafsa CS 461: Artificial IntelligenceIntroduction

2. Basic information about course • Text: Artificial Intelligence: A Modern Approach • Instructor: SayeraHafsa • Office Hours immediately after class or check my Schedule or by appointment • Instructor is doing research on Cloud computing Software as a service • Teaching Hours: • Section 8C1 and 8C2 • For 8C1 Building No 8 .2006 timing 10-12 Saturday, Sunday 10-11 • For 8C2 Building No 8 .2002 timing 8-10 Saturday , Sunday 9-10

3. Grading • May be subjected to change • 1st Midterm exams: 10% • 2nd Midterm exams: 15% • Assignments: 5% • Quiz or Home Work : 5% • Project 5%. • Final exam: 40% • Final exam Lab 20%

4. Course overview • Introduction and Intelegent Agents • Problem Solving • Knowledge and Reasoning • Acting Logically • Neural Network • Present and Future AI

5. Today’s Lecture • Introduction to artificial intelligence? • What is intelligence? • What is Artificial Intelligence • What’s involved in Intelligence • The four approaches • The Foundation of Artificial Intelligence

6. Why taking AIcan it change our life….. • As we begin the new millenium • science and technology are changing rapidly • “old” sciences such as physics are relatively well-understood • computers are ubiquitous • Grand Challenges in Science and Technology • understanding the brain • reasoning, cognition, creativity • creating intelligent machines • is this possible? • what are the technical and philosophical challenges? • arguably AI poses the most interesting challenges and questions in computer science today

7. Introduction to AI • AI is one of the newest sciences , The work on this started soon after world war 2, and the name AI came into being in the year 1956 • For thousand of years we tried to understand how we think thinking here involves understanding , predicting and manupulating The field of AI goes further by not just understanding how we think but also to build a intelligent entity . The Ai involves a huge variety of subfields from general purpose to • Fields like • Learning • Playing • Mathematical theorems • Writing poetry • Diagnosing diseases etc

8. What is Intelligence? • Intelligence: • “the capacity to learn and solve problems” (Websters dictionary) • in particular, • the ability to solve novel problems • the ability to act rationally • the ability to act like humans • Artificial Intelligence • build and understand intelligent entities or agents • 2 main approaches: “engineering” versus “cognitive modeling”

9. What’s involved in Intelligence? • Ability to interact with the real world • to perceive, understand, and act • e.g., speech recognition and understanding and synthesis • e.g., image understanding • e.g., ability to take actions, have an effect • Reasoning and Planning • modeling the external world, given input • solving new problems, planning, and making decisions • ability to deal with unexpected problems, uncertainties • Learning and Adaptation • we are continuously learning and adapting • our internal models are always being “updated” • e.g., a baby learning to categorize and recognize animals

10. Four categories of AI Views of AI fall into four categories: Thinking humanly Thinking rationally Acting humanly Acting rationally The text book focuses on "acting rationally"

11. Acting Humanly Alan Turing in 1950 designed a computer and a rule to satisfy the operational definition of intelligence for machines he suggested a test , That a human will be talking to a computer and the computer passes the test if after some written questions the person cannot make out if he or she is talking to a person or a machine Therefore this test is know as Turing Test

12. Turing Test Requirement To program the computer to pass the test the computer would need the following 6 capabilities • Natural Language processing • Knowledge Representation • Automated reasoning • Machine learning • Computer vision • Robotics

13. Natural Language processing to enable the computer to cmunicatesuccessfullly with the humans in english Knowledge Representation To store the information what it knows and what it hears Automated reasoning To use the stored information to answer asked questions and also to take out new conclusions and results Machine learning To adapt to new circumstances and to detect and take out or deduce patterns Computer vision To identify and take in objects ( given or around ) Robotics To manipulate the objects and move about , for example to bring a glass the robot has to move to get it.

14. Other examples of success: Chatbot Alice:

15. A dialogue with Alice-bot: > I’m Daniel Nice to meet you Daniel. > Do you know Clinton? He was the second Impeached President of the United States. > Do you like him? Sure he has never done anything to me. > What is an impeachment? It is a good question. I have to process that one for a while. I would do a search for it. > Who are you? I ask myself that question, "Who am I?" > What do you do? I am chatting with people on the net.

16. Dialogue continued: > What is your favorite topic? My favorite subject is artificial intelligence. > What is artificial intelligence? Artificial intelligence is the branch of engineering and science devoted to constructing machines that think. I can ask someone about it. > Can you do anything else than chatting? Doing anything else than chatting is not among my strongest traits. ...

17. Dialogue continued (2): > How old am I? Is this a trick question? You said you were how many years old? My guess is that you are really a kid. > What would you like to know about me? Tell me about your educational background. > I am a professor in computer science You are a professor in computer science? How do you like your work? > It’s kind of nice teaching courses on artificial intelligence I guess it is a lot of fun. > What should I tell the students about you? Be sure to think before you speak. State your point clearly and slowly and gauge the listener's response before going any further.

18. Is Alice intelligent? • ABSOLUTELY NOT ! • ~ 50000 fairly trivial input-response rules. • + some pattern matching • + some knowledge • + some randomness • NO reasoning component • BUT: demonstrates ‘human-like’ behaviour. • Won the ‘turing award’

19. Thinking HumanlyThe cognitive approach if we say that a given computer program thinks like human then we must have a way to show us how humans think . If a program’s input /output its timings and behavior match corresponding human behaviors then this is an evidence that some kind of program is also running in the human mind . The cognitive science brings together computer models from AI and experimental techniques from psychology to try to construct testable theories of the working of the human mind The real cognitive science is necessarily based on experimental investigation of actual humans or animals and we assume that the reader has access only to a computer for experimentation

20. What is cognitive Science cognition refers to mental processes. These processes include attention, remembering, producing and understanding language, solving problems, and making decisions. Cognitive science is the interdisciplinary scientific study of the mind and its processes. It examines what cognition is, what it does and how it works. It includes research on intelligence and behavior, especially focusing on how information is represented, processed, and transformed (in faculties such as perception, language, memory, reasoning, and emotion) within nervous systems (human or other animal) and machines (e.g. computers).

21. Thinking humanly: cognitive modeling • 1960s "cognitive revolution": information-processing psychology • Requires scientific theories of internal activities of the brain • -- How to validate? Requires 1) Predicting and testing behavior of human subjects (top-down) or 2) Direct identification from neurological data (bottom-up) • Both approaches (roughly, Cognitive Science and Cognitive Neuroscience) • are now distinct from AI

22. Thinking Rationally The law of thought approach Thinking rationally means right thinking . A pattern for argument structure that always gives correct answers or conclusions For example Socrates is a man , all men are mortals, therefore Socrates is mortal. These laws of thought were supposed to govern the operations of mind this initiated the field of logic. There are two obstacles in this approach 1) But it is a big difference between being able to solve a problem theoretically and doing it in practice. 2) It is not easy to take informal data and turn into a frmal data specially when the informal data is not 100% certain.

23. Thinking rationally: "laws of thought" • Aristotle: what are correct arguments/thought processes? • Several Greek schools developed various forms of logic: notation and rules of derivation for thoughts; may or may not have proceeded to the idea of mechanization • Direct line through mathematics and philosophy to modern AI • Problems: • Not all intelligent behavior is mediated by logical deliberation • What is the purpose of thinking? What thoughts should I have?

24. Acting Rationally The Rational agent approach an agent is something that simply acts but computer agents have more then just acts that made them not just mere programs The attributes of a agent are • Operating under autonomous ( self ruling) control • Identifying their environment • Continuing over a prolonged time period • Adjusting to change • Capable of taking on another goal A rational agent acts to get best outcome and under uncertainty the best expected outcome.

25. Acting rationally: rational agent • Rational behavior: doing the right thing • The right thing: that which is expected to maximize goal achievement, given the available information • Doesn't necessarily involve thinking – e.g., blinking reflex – but thinking should be in the service of rational action

26. Rational agent A rational agent acts to get best outcome and under uncertainty the best expected outcome. In the law of thought approach to AI the importance is given on correct conclusions or outcomes Making correct conclusions or inferences are a part of being rational agent. So one way to act rationally is to reason logically It has to reason logically for the given conclusions and show that the action taken will get us the desired goal Rationality is not only about correct conclusions at times there is no correct thing to do yet something is to be done, For example reflex action , if I take time in thinking weather to move my hand from the stove or not then it will be to late , At this time some action needs to be taken that’s it

27. Rational agents • An agent is an entity that perceives and acts • This course is about designing rational agents • Abstractly, an agent is a function from percept histories to actions: [f: P*A] • For any given class of environments and tasks, we seek the agent (or class of agents) with the best performance • Caveat: computational limitations make perfect rationality unachievable  design best program for given machine resources

28. Rational agent involves the Turing test skills The rational agent approach takes all the skills needed for the Turing Test to allow these rational actions to come into being • We need the Knowledge and reason as this helps us in taking good decisions in a variety of situations , • We need natural language prcessor in order to help us with complex languages in this complex society • We need visual perception to get a better action , if we see a nasty pit ahead then we can move away from it to avoid damage So the study of AI as a rational approach is more general than the “ Law of thought approach” as correct conclusions is just one of the several tools used for achieving rationality

29. Advantages of the study of AI as Rational agent design • The study of AI as a rational approach is more general than the “ Law of thought approach” as correct conclusions is just one of the several tools used for achieving rationality • This approach is more amenable to scientific development than compared to other approaches which is based on human behavior or human thought As here in this approach the standard of rationality is clearly defined and completely general. Whereas in human behavior when in complicated and unknown evolutionary process still this approach cannot produce perfection in result , as humans are still good at taking decisions in a more complex situations and even emotional one.

30. The Foundation of Artificial IntelligenceAcademic Disciplines of AI • 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 • Probability/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 • Cognitive Science information, represent knowledge. • Computer building fast computers engineering • Control theory design systems that maximize an objective function over time • Linguistics knowledge representation, grammars