Chapter 1

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Chapter 1

Introduction to Artificial Intelligence

King Saud UniversityCollege of Computer and Information Sciences Information Technology DepartmentIT422 - Intelligent systems

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Objectives

• Artificial Intelligence subject to be worthy of study.

• What is exactly Artificial Intelligence.

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Introduction to Artificial Intelligence

• Artificial Intelligence is one of the newest sciences which emerged after the world war II. • AI represents a big and open field.• The name Artificial Intelligence was adopted for the first time in 1956. (Computational

Intelligence)• Artificial Intelligence can be viewed as a universal field: How to automate intellectual

tasks?

• Goal of Artificial Intelligence: Not only to understand how does mind work? but also how to build intelligent entities?

– Engineering point of view:• Solve real-world problems using knowledge and reasoning• Develop concepts, theory and practice of building intelligent entities• Emphasis on system building

– Scientific point of view:• Use computers as a platform for studying intelligence itself• Emphasis on understanding intelligent behavior.

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Introduction to Artificial Intelligence

• What is artificial Intelligence?– Several definitions are available in the literature.

Thinking vs. BehaviorModel humans vs. Ideal standard (Rationality)

• Rational System = system which does the “right thing” given what it knows.• Definitions fall into four categories:Human models Rationality

Thinking Thinking like humans:The exciting new effort to make computers think…machines with minds, in the full and literal sense. (Haugeland, 1985)

Thinking rationally:-The study of mental faculties through the use of computational models. (Charniak and Mcdermott, 1985)-The study of the computations that make it possible to perceive, reason and act. (Winston 1992)

Acting Acting Humanly:-The art of creating machines that perform functions that require intelligence when performed by people. (Kurzweil, 1990)-The study of how to make computers do things at which, at the moment, people are better. (Rich and Knight, 1991)

Acting rationally:- Computational intelligence is the study of the design of intelligent agents. (Poole et al.,1998).- AI…is concerned with intelligent behavior in artifacts. (Nilsson, 1998)

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Introduction to Artificial Intelligence

• What is artificial Intelligence?

• Keep this one: Science of making machines do things that would require intelligence if done by humans. - Marvin Minsky

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• Turing TestAlan Turing 1950 «Computing Machinery and Intelligence»

• Test: The computer passes the “test of intelligence” if a human, after posing some written questions, cannot tell whether the responses were from a person or not.

• Provides an operational definition of AI.

• Still being used today within the AI community in annual competitions – the Loebner Prize ($100,000 and a true 18K gold medal)

Acting Humanly

Is A the human or is B?

A B

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Acting Humanly• Turing Test (Cont.)• To give an answer, the computer would need to posses some capabilities:

– Natural language processing: To communicate successfully.– Knowledge representation: To store what it knows or hears.– Automated reasoning: to answer questions and draw conclusions using stored

information.– Machine learning: To adapt to new circumstances and to detect and extrapolate

patterns.– Computer vision: To perceive objects.– Robotics to manipulate objects and move.

• Example: ELIZA program.

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Acting Humanly

• Example: ELIZA program. Some extracts from the test:– Patient: Men are all alike.– ELIZA: In what way?– Patient: They are always bugging about something or the

other.– ELIZA: Can you think of a specific example?– Patient: Well, my boyfriend made me come here.– ELIZA: Your boyfriend made you come here?– Patient: He says I’m depressed much of the time.– ELIZA: I am sorry you are depressed.

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Thinking humanly

• Program think like human → How humans think?

• Requires Scientific theories of internal activities of the brain (cognitive science and cognitive neuroscience).

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Thinking and acting rationally• Thinking rationally• Based on the Laws of

Thought (rules explaining how a human make conclusions from facts)

• Example, “Socrates is a man; all men are mortal, therefore Socrates is mortal.” The laws of thought initiated the field of logic.

• Acting rationally• Modern AI can be

characterized as the engineering of rational agents.

• An agent is simply an entity that perceives and acts. A rational agent is an entity that perceives, reasons and acts rationally (correctly).

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Introduction to Artificial Intelligence

• Foundations:An interdisciplinary subject found on:

– Philosophy,– mathematics, – economics, – neuroscience, – psychology, – computer engineering, – linguistics, and so on

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History of Artificial Intelligence

Expectations and Initial enthusiasm

(1952 – 1969)

Birth of AI (1956)

Early days (1943-1955)

Big DreamReality Check (1966 – 1973)

Resurgence (1969 – 1979)

AI becomes an industry

(1980 – present)

Renewing with connectionism and AI

becomes a science (1986 – present)

• Ultimately, we are dealing with the question: “What are we (human beings) doing when we are thinking?”

• Thought processes in the human mind are computational in nature. There are mechanistic procedures for generating these thoughts.

• Such computations can be simulated and implemented by a Turing machine. Therefore, it can be programmed.

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History of Artificial Intelligence

Expectations and Initial enthusiasm

(1952 – 1969)

Birth of AI (1956)

Early days (1943-1955)

Big DreamReality Check (1966 – 1973)

Resurgence (1969 – 1979)

AI becomes an industry

(1980 – present)

Renewing with connectionism and AI

becomes a science (1986 – present)

1943: first piece of AI work: Warren McCulloch and Walter PittsModel of artificial neurons.Mathematical learnable functions that generate “on/off” depending on inputs (logic gates)Any computable function can be computed by a network of connected neurons.Suitably defined networks can learn.

1949: Hebbian learningA mechanism for updating the connection strength of a neuron.Today, neurologists have confirmed that something similar to Hebbian learning indeed is going on in our brain when we are learning.

1950: Turing test complete vision of AI in “computing machinery and Intelligence”1951: first neural network computer

Implemented by M. Minsky and D. Edmonds

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History of Artificial Intelligence

• Mcculloch and Pitts artificial neuron

Human neuron (brain) Artificial neuron

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History of Artificial Intelligence

Expectations and Initial enthusiasm

(1952 – 1969)

Birth of AI (1956)

Early days (1943-1955)

Big DreamReality Check (1966 – 1973)

Resurgence (1969 – 1979)

AI becomes an industry

(1980 – present)

Renewing with connectionism and AI

becomes a science (1986 – present)

1956: Dartmouth ConferenceOrganized by John McCarthy and colleagues for starting a new area in studying computation and intelligence.John McCarthy introduced the term “artificial intelligence” in the conference.

The next 20 years witnessed steady growth of the field led by the pioneers appeared in the Dartmouth conference.

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History of Artificial Intelligence

Expectations and Initial enthusiasm

(1952 – 1969)

Birth of AI (1956)

Early days (1943-1955)

Big DreamReality Check (1966 – 1973)

Resurgence (1969 – 1979)

AI becomes an industry

(1980 – present)

Renewing with connectionism and AI

becomes a science (1986 – present)

1963: Thomas Evan’s program ANALOG Solved analogy problems in an IQ test.1965: ELIZA

Simulates a dialog with a computer in English on any topic.Became popular when programmed to simulate a psychotherapist (Fedora’s Emacs).

1967: Dendral program (developed at Stanford)- First successful program for scientific reasoning – one of the earlier rule based expert systems. - A program that can infer molecular structures given the information provided by a mass spectrometer

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History of Artificial Intelligence

Expectations and Initial enthusiasm

(1952 – 1969)

Birth of AI (1956)

Early days (1943-1955)

Big DreamReality Check (1966 – 1973)

Resurgence (1969 – 1979)

AI becomes an industry

(1980 – present)

Renewing with connectionism and AI

becomes a science (1986 – present)

Series of disappointments and frustrationsAI was poured little buckets of “reality cold water”

Problems:- Most early systems contain little or no knowledge of their subject matter

Example: Poor performance of earlier machine translation system (Russian ⇔English): “the spirit is willing but the flesh is weak” was translated to “the vodka is good but the meat is rotten”.

- Computational Intractability of AI problemsTheory of computational complexity was not developed. Polynomial solvable problems, NP-completeness, etcPeople thought a faster machine could solve any hard problem.

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History of Artificial Intelligence

Expectations and Initial enthusiasm

(1952 – 1969)

Birth of AI (1956)

Early days (1943-1955)

Big DreamReality Check (1966 – 1973)

Resurgence (1969 – 1979)

AI becomes an industry

(1980 – present)

Renewing with connectionism and AI

becomes a science (1986 – present)

- 1971: T. Winograd’s Ph.D. thesis (MIT) demonstrated a system that can understand English in a micro-domain (the block world).

- 1972: PROLOG was developed- 1974: MYCIN was developed by Ted Shortliffe

Expert system for medical diagnosis. Sometimes called the first expert system.- And many other works…

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History of Artificial Intelligence

Expectations and Initial enthusiasm

(1952 – 1969)

Birth of AI (1956)

Early days (1943-1955)

Big DreamReality Check (1966 – 1973)

Resurgence (1969 – 1979)

AI becomes an industry

(1980 – present)

Renewing with connectionism and AI

becomes a science (1986 – present)

AI started to become industrially and commercially beneficial- 1982: R1 was deployed at DEC – an expert system that saved the company around $40M / year- Du Pont had 100 in use and an estimated 500 in development at late 90’s to early 21st century

At an international level, AI was considered a part of a country’s technological developments

- Japan: “First Generation” project (10 year plan to build intelligence machines running in Prolog)- USA: Microelectronics and Computer Technology Corporation (MCC) was formed in response- Britain: Funding for AI was reinstated

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History of Artificial Intelligence

Expectations and Initial enthusiasm

(1952 – 1969)

Birth of AI (1956)

Early days (1943-1955)

Big DreamReality Check (1966 – 1973)

Resurgence (1969 – 1979)

AI becomes an industry

(1980 – present)

Renewing with connectionism and AI

becomes a science (1986 – present)

- Work of the physicist John Hopfield (1982) on using techniques from statistical mechanics.- Connectionist models of intelligent systems competitor to the symbolic models (Newell and Simon) and logicist approach (McCarthy). (complementary approaches in fact). - Several revolutions in many fields: pattern recognition, computer vision, robotics…- Emergence of intelligent agents.

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Examples of AI applications (1)

• Game Playing– TDGammon • The world champion

backgammon player, built by Gerry Tesauro of IBM research (1992)

– Deep Blue • Chess program that beat

world champion Gary Kasparov (1997)

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Examples of AI applications (2)

• Natural Language Understanding– Spell/Grammar checkers– AI translators

• Alta Vista’s translation of web pages– Text summarization– Question answering– PROVERB (Littman 1999):

• Automated crossword solver• Competed in the American Crossword Puzzle Tournament crossword puzzles

– START system• Accesses raw data tables, and then can carry on a

dialogue (English Conversation)

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Examples of AI applications (3)• Expert systems

– In geology• prospector expert system carries evaluation of mineral potential of geological

site or region– Diagnostic Systems

• Pathfinder (medical diagnosis system) developed by Heckerman and other Microsoft research

• Microsoft Office Assistant in Office (provides customized help)• MYCIN system (diagnosing bacterial infections of the blood and suggesting

treatments)– System Configuration

• "XCON" (for custom hardware configuration): a production-rule-based to assist in the ordering of DEC's VAX computer systems by automatically selecting the computer system components based on the customer's requirements.

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Examples of AI applications (4)

• Robotics– Robotics becoming increasing

important in various areas like: • Games• To handle hazardous conditions • To do tedious jobs among other

things– Examples: automated cars,

ping pong player, mining, construction, robot assistant in microsurgery,…

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Examples of AI applications (5)

• Google’s Automated Cars (2010)– They use video cameras, radar sensors and a laser

range finder to "see" other traffic, detailed maps– The cars have already logged more than 140,000

miles.

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Summary• Intelligence is studied from many perspectives: Are you concerned with

thinking or behavior?

• AI can help us solve difficult, real-world problems, creating new opportunities in business, engineering, and many other application areas

• The history of AI has had cycles of success, misplaced optimism, and resulting cutbacks in enthusiasm and funding. There have also been cycles of introducing new creative approaches and systematically refining the best ones

• AI has advanced more rapidly in the past decade because of greater use of the scientific method in experimenting with and comparing approaches