artificial intelligence · comprehend ideas, to use language, and to learn. ... greatest successes....

ARTIFICIAL

INTELLIGENCE

AN INTRODUCTION

Artificial Intelligence 2012 Lecture 01 Delivered By Zahid Iqbal 1

Course Logistics

Course Description This course will introduce the basics of Artificial Intelligence(AI), its scope and

application domains and principles.

Course Material Required:

S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach 2nd Edition

Reference:

George F Luger, Artificial Intelligence: Structures and Strategies for Complex Problem-solving 5th Edition

Elaine Rich and Kevin Knight, Artificial Intelligence: 3rd Edition

Prerequisite • Data Structures


Course Logistics

Instructor

Zahid Iqbal

Room # B211 ([email protected])

BS (CS) Punjab University (Gold Medalist)

MS (CS) FAST University Islamabad


mailto:[email protected]

Course Logistics

• Grading Policy

• Sessional 25%

• Mid Term 25%

• Final 50%


Course Contents Outline

Introduction to Artificial Intelligence

Knowledge Representation

Propositional Logic

Predicate Calculus

Search Methods

Learning

Languages for AI (Prolog, LISP)

Natural Language Processing

Automated Reasoning

Knowledge Based Systems

. . .


INTRODUCTION


Intelligence

• What is Intelligence?


Intelligence (Attempted Definitions)

• Intelligence is an umbrella term used to describe a property of the

mind that encompasses many related abilities, such as the capacities

to reason, to plan, to solve problems, to think abstractly, to

comprehend ideas, to use language, and to learn.

• The ability to learn, reason, and problem solving. Debate revolves

around the nature of intelligence as to whether it is an innate quality

or something that is developed as a result of interacting with the

environment. Many researchers believe that it is a combination of the

two.


Intelligence (Attempted Definitions)

• Intelligence refers to the various verbal and nonverbal skills and

aptitudes one uses to cope with, interact with, and manipulate the

outside world.

• Intelligence is effectively perceiving, interpreting and responding to

the environment. It is also taken to mean the ability of an organization

to survive and meet desired goals and objectives.


Artificial Intelligence

• AI may be defined as the branch of computer science that is

concerned with the automation of intelligent behavior.

• The major problem in defining the AI are :

proper definition of intelligence itself.

Is intelligence a single faculty, or is it a name for a collection of distinct and unrelated

abilities?

Exactly what happens when learning occurs?

What is creativity? What is intuition?

Can intelligence be inferred from observable behavior, or does it require evidence of

a particular internal mechanism?


Artificial Intelligence

Is it necessary to pattern an intelligent computer program after what is known about

human intelligence, or is a strict “engineering” approach to the problem sufficient?

Is it even possible to achieve intelligence on a computer, or does an intelligent entity

require the richness of sensation that might be found only in a biological existence?

• These are all unanswered questions and all of them have helped to

shape the problems and solution methodologies that constitute the

core of modern AI.

• Artificial intelligence is still a young discipline, and its structure,

concerns and methods are not clearly defined than those of a more

mature science such as physics.


History of AI

• The field of AI research was founded at a conference on the

campus of Dartmouth College in1956. The attendees became

the leaders of AI research for many decades. They and their

students wrote programs that were, to most people, simply

astonishing: Computers were solving word problems in

algebra, proving logical theorems and speaking English.

• By the middle of the 1960s, research in the U.S. was heavily

funded by the Department of Defense and laboratories had

been established around the world. AI's founders were

profoundly optimistic about the future of the new field: Herbert

Simon predicted that "machines will be capable, within twenty

years, of doing any work a man can do“.


History of AI

• They had failed to recognize the difficulty of some of the problems they faced. In 1974, in response to the criticism of Sir James and ongoing pressure from the US Congress to fund more productive projects, both the U.S. and British governments cut off all undirected exploratory research in AI. The next few years, when funding for projects was hard to find, would later be called the "AI winter".

• In the early 1980s, AI research was revived by the commercial success of expert systems , a form of AI program that simulated the knowledge and analytical skills of one or more human experts. By 1985 the market for AI had reached over a billion dollars. At the same time, Japan's fifth generation computer project inspired the U.S and British governments to restore funding for academic research in the field. However, beginning with the collapse of the Lisp Machine market in 1987, AI once again fell into disrepute, and a second, longer lasting AI winter began.


History of AI

• In the 1990s and early 21st century, AI achieved its

greatest successes. Artificial intelligence is used for

logistics, data mining, medical diagnosis and many other

areas throughout the technology industry. The success

was due to several factors:

• the increasing computational power of computers,

• a greater emphasis on solving specific sub problems ,

• the creation of new ties between AI and other fields

working on similar problems


Strong AI and Weak AI

• Strong AI means that machines act intelligently and they

have real conscious minds.

• Weak AI says that machines can be made to act as if they

are intelligent.


Success Stories • Deep Blue defeated the reigning world chess champion Garry

Kasparov in 1997

• During the 1991 Gulf War, US forces deployed an AI logistics planning and scheduling program that involved up to 50,000 vehicles, cargo, and people

• NASA's on-board autonomous planning program controlled the scheduling of operations for a spacecraft

• Proverb solves crossword puzzles better than most humans

• Robot driving: DARPA grand challenge 2003-2007

• 2006: face recognition software available in consumer cameras


Example: DARPA CHALLENGE

• Grand Challenge • Cash prizes ($1 to $2 million) offered to first robots to complete a long

course completely unassisted • Stimulates research in vision, robotics, planning, machine learning,

reasoning, etc.

• 2004 Grand Challenge: • 150 mile route in Nevada desert • Furthest any robot went was about 7 miles • … but hardest terrain was at the beginning of the course

• 2005 Grand Challenge: • 132 mile race • Narrow tunnels, winding mountain passes, etc • Stanford 1st, CMU 2nd, both finished in about 6 hours

• 2007 Urban Grand Challenge • In Victorville, California


Stanley Robot Stanford Racing Team www.stanfordracing.org

2004: Barstow, CA, to Primm, NV

150 mile off-road robot race across

the Mojave desert

Natural and manmade hazards

No driver, no remote control

Fastest vehicle wins the race (and 2

million dollar prize)

HAL: from the movie 2001

• HAL

– part of the story centers around an intelligent computer called HAL

– HAL is the “brains” of an intelligent spaceship

– in the movie, HAL can

• speak easily with the crew

• see and understand the emotions of the crew

• navigate the ship automatically

• diagnose on-board problems

• make life-and-death decisions

• display emotions

• In 1969 this was science fiction: is it still science fiction?


Can we build hardware as complex as the

brain?


Can we build hardware as complex as the

brain? • How complicated is our brain?

• a neuron, or nerve cell, is the basic information processing unit

• estimated to be on the order of 10 12 neurons in a human brain

• many more synapses (10 14) connecting these neurons

• cycle time: 10 -3 seconds (1 millisecond)

• How complex can we make computers? • 108 or more transistors per CPU

• supercomputer: hundreds of CPUs, 1012 bits of RAM

• cycle times: order of 10 - 9 seconds

• Conclusion • YES: in the near future we can have computers with as many basic processing

elements as our brain, but with • far fewer interconnections (wires or synapses) than the brain

• much faster updates than the brain

• but building hardware is very different from making a computer behave like a brain!


Can Computers beat Humans at Chess?

• Chess Playing is a classic AI problem • well-defined problem

• very complex: difficult for humans to play well

• Conclusion: • YES: today’s computers can beat even the best human


1200

1400

1600

1800

2000

2200

2400

2600

2800

3000

1966 1971 1976 1981 1986 1991 1997

Ratings

Po

ints

Ra

tin

gs

Human World Champion

Deep Blue

Deep Thought

Can Computers Talk?

• This is known as “speech synthesis” • translate text to phonetic form

• e.g., “fictitious” -> fik-tish-es

• use pronunciation rules to map phonemes to actual sound • e.g., “tish” -> sequence of basic audio sounds

• Difficulties • sounds made by this “lookup” approach sound unnatural

• sounds are not independent • e.g., “act” and “action”

• modern systems (e.g., at AT&T) can handle this pretty well

• a harder problem is emphasis, emotion, etc. • humans understand what they are saying

• machines don’t: so they sound unnatural

• Conclusion: • NO, for complete sentences

• YES, for individual words


Can Computers “see”?

• Recognition v. Understanding (like Speech) • Recognition and Understanding of Objects in a scene

• look around this room

• you can effortlessly recognize objects

• human brain can map 2d visual image to 3d “map”

• Why is visual recognition a hard problem?

• Conclusion: • mostly NO: computers can only “see” certain types of objects under limited

circumstances

• YES for certain constrained problems (e.g., face recognition)


Intelligent Systems in Everyday Life


Intelligent Systems in Everyday Life

• Customer Service – automatic voice recognition

• The Web – Identifying your age, gender, location, from your Web surfing

– Automated fraud detection

• Digital Cameras – Automated face detection and focusing

• Computer Games – Intelligent characters/agents


Different Types of Artificial Intelligence

1.Modeling exactly how humans actually think

2.Modeling exactly how humans actually act

3.Modeling how ideal agents “should think”

4.Modeling how ideal agents “should act”

• Modern AI focuses on the last definition

• we will also focus on this “engineering” approach

• success is judged by how well the agent performs


Thinking humanly

• Cognitive Science approach

• Try to get “inside” our minds

• E.g., conduct experiments with people to try to “reverse-engineer”

how we reason, learning, remember, predict

• Problems

• Humans don’t behave rationally

• The reverse engineering is very hard to do

• The brain’s hardware is very different to a computer program


Acting humanly: Turing test

• Turing (1950) "Computing machinery and intelligence“

• "Can machines think?" "Can machines behave intelligently?“

• Operational test for intelligent behavior: the Imitation Game

• Suggests major components required for AI:

- knowledge representation

- reasoning,

- language/image understanding,

- learning


Summary of Today’s Lecture

• Artificial Intelligence involves the study of: • automated recognition and understanding of signals

• reasoning, planning, and decision-making

• learning and adaptation

• AI has made substantial progress in • recognition and learning

• some planning and reasoning problems

• …but many open research problems

• AI Applications • improvements in hardware and algorithms => AI applications in

industry, finance, medicine, and science.


References

• Artificial Intelligence: Structures and Strategies for

Complex Problem Solving

• Chapter 1


artificial intelligence · comprehend ideas, to use language, and to learn. ... greatest successes....

Documents