Artificial Intelligence Lecture # 1 An Introduction

1. Artificial IntelligenceLecture # 1An Introduction

2. Objectives • Understand the definition of artificial intelligence (AI) • Understand the different faculties involved with intelligent behaviour • Examine the different ways of approaching AI • Look at some example systems that use AI • Trace briefly the history of AI -- ? • Have a fair idea of the types of problems that can be currently solved by computers and those that are as yet beyond its ability

3. Definition of AI • Artificial Intelligence is a branch of Computer Science concerned with the automation of intelligent behaviour • Artificial Intelligence is concerned with the design of intelligence in an artificial device. • The art of creating machines that perform functions that require intelligence when performed by people. • The term was coined by McCarthy in 1956. • There are two ideas in the definition. • 1. Intelligence • 2. artificial device

4. Is it something which characterize humans? - or is there an absolute standard of judgement?

5. What is Intelligence • Someone’s intelligence is their ability to understand and learn things • Intelligence is the ability to think and understand instead of doing things by instinct or automatically • Thinking is the activity of using your brain to consider a problem or to create an idea.

6. Can machines think? Capabilities required • Natural Language Processing – to enable successful communication in English • Knowledge Representation – to store what it knows or hears • Automated Reasoning – to use the stored information to answers questions • Machine Learning – to adapt to new circumstances and detect patterns • Computer Vision – to perceive objects • Robotics – to manipulate objects and move about

7. Can machines think? Accordingly there are two possibilities: – A system with intelligence is expected to behave as intelligently as a human – A system with intelligence is expected to behave in the best possible manner (Rationality) Secondly what type of behaviour are we talking about? – Are we looking at the thought process or reasoning ability of the system? – Or are we only interested in the final manifestations of the system in terms of its actions?

8. Goal of AI • Understanding and building intelligent entities • Four approaches – Systems that think like humans – Systems that think rationally – Systems that act like humans – Systems that act rationally

9. Acting humanly: • The Turing Test • Proposed by Alan Turing (1950)

10. The Turing Test • “The computer passes the test if a human interrogator, after posing some written questions cannot tell whether the written responses come from a person or not.” • To pass the Turing test, the machine has to fool the interrogator into believing that it is human.

11. Thinking Humanly:Cognitive Modelling • Method must not just exhibit behavior sufficient to fool a human judge but must do it in a way demonstrably analogous to human cognition. • This view involves trying to understand human thought and an effort to build machines that emulate the human thought process. • Requires to know how humans think? • This view is the cognitive science approach to AI.

12. Cognitive Modelling Approach • “Cognitive Science brings together computer models from AI and experimental techniques from psychology to try to construct precise testable theories of the working of the mind.”

13. Thinking rationally:Laws of thought Approach • Formalize “correct” reasoning using a mathematical model • Syllogisms provided patterns for argument structures that yield the correct conclusions when given correct premises. • Syllogisms like “Socrates is human; humans are mortal; so Socrates is mortal” ….. • These laws were supposed to govern the operation of the mind • Initiated the field of logic

14. Thinking rationally:Laws of thought • Logic and laws of thought deals with studies of ideal or rational thought process and inference. • The emphasis in this case is on the inferencing mechanism, and its properties – i.e. how the system arrives at a conclusion, or the reasoning behind its selection of actions is very important in this point of view. • The soundness and completeness of the inference mechanisms are important here.

15. Acting rationally:Rational agents • An agent is something that acts – comes from Latin agere, - to do • The rational agent is one that acts so as to achieve the best outcome or best expected outcome if there is uncertainty. • Making correct inferences is often part of being a rational agent, however sometimes there is no provably correct thing to do, but something still has to be done

16. Acting rationally:Rational agents • This view deals with building machines that act rationally. • The focus is on how the system acts and performs, and not so much on the reasoning process. • A rational agent is one that acts rationally, i.e. is in the best possible manner.

17. Typical AI problems An “intelligent entity” is expected to perform both • common-place tasks: (humans and animals can do easily) • Recognizing people, objects. • Communicating (through natural language). • Navigating around obstacles on the streets • Expert tasks: (requires expertise and more intelligence) • Medical diagnosis • Mathematical problem solving • Playing games like chess What If Machines are To Perform These Set of Tasks!

18. Intelligent Behaviour Capabilities required • Natural Language Processing – to enable successful communication in English • Knowledge Representation – to store what it knows or hears • Automated Reasoning – to use the stored information to answers questions • Machine Learning – to adapt to new circumstances and detect patterns • Computer Vision – to perceive objects • Robotics – to manipulate objects and move about

19. Practical Impact of AI AI systems are in everyday use: • for identifying credit card fraud • for advising doctors • for recognizing speech • helping in complex planning tasks • there are intelligent tutoring systems that provide students with personalized attention Thus AI has increased understanding of the nature of intelligence and found many applications. It has helped in the understanding of human reasoning, and of the nature of intelligence. It has also helped us understand the complexity of modeling human reasoning.

20. What can AI systems do Today’s AI systems have been able to achieve limited success in some of these tasks. • In Computer vision, the systems are capable of face recognition • In Robotics, we have been able to make vehicles that are mostly autonomous. • In Natural language processing, we have systems that are capable of simple machine translation. • Today’s Expert systems can carry out medical diagnosis in a narrow domain • Speech understanding systems are capable of recognizing several thousand words continuous speech • Planning and scheduling systems had been employed in scheduling experiments with the Hubble Telescope. • The Learning systems are capable of doing text categorization into about a 1000 topics • In Games, AI systems can play at the Grand Master level in chess (world champion), checkers, etc.

21. What can AI systems NOT do yet? • Understand natural language robustly (e.g., read and understand articles in a newspaper) • Surf the web • Interpret an arbitrary visual scene • Learn a natural language • Construct plans in dynamic real-time domains • Exhibit true autonomy and intelligence

22. Abridged history of AI • 1943 McCulloch & Pitts: Boolean circuit model of brain • 1950 Turing's "Computing Machinery and Intelligence" • 1956 Dartmouth meeting: "Artificial Intelligence" adopted • 1952—69 Look, Ma, no hands! • 1950s Early AI programs, including Samuel's checkers program, Newell & Simon's Logic Theorist, Gelernter's Geometry Engine • 1965 Robinson's complete algorithm for logical reasoning • 1966—73 AI discovers computational complexity Neural network research almost disappears • 1969—79 Early development of knowledge-based systems • 1980-- AI becomes an industry • 1986-- Neural networks return to popularity • 1987-- AI becomes a science • 1995-- The emergence of intelligent agents

23. Further Reading • Searle, J. R. 1980. "Minds, Brains, and Programs". Behavioral and Brain Sciences, Vol.3., 417-424. • Turing, A. M. 1950. "Computing Machinery and Intelligence." Mind, Vol. LIX. 433-460. Online: http://www.loebner.net/Prizef/TuringArticle.html. • Descartes, R. 1637. Discourse on Method. Translated in J.Cottingham, R.Stoothoff, and D.Murdoch, The Philosophical Writings of Descartes, Vol.1. Cambridge University Press: Cambridge (1985). 131-141 (Part 5).