Artificial Intelligence

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2001: A Space Odyssey

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What is Intelligence?

 No good definition  We think of it as being “human”  More than the ability to do one task well  More than manipulating symbols Plymouth State College

Computers

 Calculate quickly and accurately  Relieve us of tedious tasks  Help us to do some tasks better  Can entertain us (Games)  Can provide much information (Internet) Plymouth State College

Artificial Intelligence

  What is AI?

Group of related technologies used for development machines to emulate human-like qualities

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Artificial Intelligence

 Types of AI        Virtual reality Robotics Natural language processing Fuzzy logic Expert systems Neural networks Genetic algorithms Plymouth State College 12.6

Artificial Intelligence

 Some early experiments failed  A.I. scientists ridiculed Plymouth State College

Game Playing

 Early days of AI - Researchers thought that teaching computers to play games such as chess would enable them to understand something about human intelligence.

  Found it easy to have computers play games.

Found it difficult to go beyond game playing and into the realm of human intelligence.

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Easy Computer Problems

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Difficult Computer Problems

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 Emotion  Motivation  Deception

“Human” qualities

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Computer “Intelligence”

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Computer Control

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What is Intelligence—Artificial or Not?

 The search for intelligence:    Plato (400 BC) - This Greek philosopher believed that ethereal spirits were rained down from heaven and entered the body.

Aristotle (Plato’s student) - The heart must contain the soul and the brain’s function was to cool the blood.

Galen - Treated fallen gladiators with spinal cord injuries. Noted that feeling lost in certain limbs sometimes came back.

  Galvani - Used Benjamin Franklin’s findings about static electricity to show that static electricity stimulated the nerves causing a frog to jump.

Subsequently - Human nervous system found to be a complex network of billions of neurons.

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What is Intelligence—Artificial or Not?

 Maillardet’s Automaton (1805):      Object having human form.

Disguised as a young boy.

Machine containing levers, ratchets, cams and other mechanical devices.

Could draw several complex images.

Because it had human form and could draw complex images, a certain feeling of intelligence was ascribed to the machine.

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Artificial Life

    What is artificial life?

A field of study that deals with computer instructions that try to simulate human responses What English mathematician and computer pioneer created a test in 1950 to determine computer intelligence?

Alan Turing Plymouth State College 12.16

What is Intelligence—Artificial or Not?

 Alan Turing (1912 - 1954)  Proposed a test - Turing’s Imitation Game  Tests the intelligence of the computer.

 Attempts to see of a person (Interrogator) can tell the difference between a human and a computer in answers to questions.

 If the interrogator can’t tell the difference, the computer is considered to have intelligence.

?

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What is Intelligence—Artificial or Not?

 Claude Shannon’s comparison of the human brain and the computer:      Difference in size: The brain has a million more parts.

Difference in structural organization: The seemingly random local structure of nerve networks differ vastly from the precise wiring of a computer.

Differences in reliability: The brain can operate reliably for decades.

Differences in logical organization: The brain is largely self organizing. Digital computers do only a few narrowly defined tasks well.

Differences in input-output equipment: Brain is designed with input organs and output muscles and glands. Computers operate in an abstract environment of numbers and operations on numbers.

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Fundamental Concepts in Artificial Intelligence

 Rule-based or Expert systems - Consists of rules of the form IF (condition) THEN (action).

  IF (it is raining AND you must go outside) THEN (put on your raincoat) Plymouth State College 12.19

Expert Systems Components

 Knowledge Base  “Inference Engine”  User Interface Plymouth State College

Expert Systems

 Knowledge of experts  Understand question (Input)  Lookup facts and rules (Storage)  Make decision (Processing)  Display decision (Output) Plymouth State College

Expert Systems

 Expert systems are commercially the most successful domain in Artificial Intelligence.

 IF (some condition) THEN (some action)  These programs mimic the experts in whatever field.

Auto mechanic Cardiologist Organic compounds Mineral prospecting Infectious diseases Diagnostic internal medicine VAX computer configuration Engineering structural analysis Audiologist Plymouth State College Telephone networking Delivery routing Professional auditor Manufacturing Pulmonary function Weather forecasting Battlefield tactician Space-station life support Civil law 12.22

Expert Systems

 Harold Cohen created an expert system called AAORN to create art.

Early drawings by AARON Plymouth State College 12.23

Expert Systems

 Intelligent Agents:  Computerized agents that might...

      respond to verbal commands as if it were human.

be a personal assistant that would access electronic communications.

take phone calls.

make appointments.

locate individuals by phone.

find research material.

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Fundamental Concepts in Artificial Intelligence

 For any of these models of the human knowledge system to work, it must be able to make use of this knowledge in three different ways:    Knowledge acquisition - Must be some way of putting information or knowledge into the system.

Knowledge retrieval - Must be able to find knowledge when it is wanted or needed.

Reasoning with knowledge - Must be able to use that knowledge through “thinking” or reasoning.

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Fundamental Concepts in Artificial Intelligence

 Knowledge retrieval (by searching):  Brute-force search - Searching all possible moves, and then selecting the best.

 Looking for a museum in a small town example:  Drive around, down every street, until you find one!

 Heuristic search - Uses rules of thumb, intuition. (The solution is not always guaranteed.)  Looking for a museum in a small town example:  Look for the museum down the town’s main street (museums are usually on the “main street” in small towns!) Plymouth State College 12.26

Fundamental Concepts in Artificial Intelligence

 Machine learning: Writing intelligent computer programs that are capable of learning.

 Example: Teaching a computer to play a game. The more the computer plays, the more strategies it will learn.

 Common sense:     The computer must be able to make inferences from the knowledge base.

Answers to problems might not be listed. The computer will need to come up with its own answers! This has been a very difficult area in Artificial Intelligence.

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Pattern Recognition

 Humans have the ability to understand or recognize the

relationship

among various parts of patterns in: visual object, sound patterns, smells, and taste.

 Pattern recognition using computers has been applied in many areas including:  Robot vision      Speech recognition Fingerprint identification Handwriting identification Optical character recognition (OCR) Weather data analysis and satellite data analysis Plymouth State College 12.28

Pattern Recognition

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Speech-pattern recognition:

 Problems - Accents, continuous speech, words that sound the same but are spelled differently.

  He walked to the store.

He walked to this door.

 Some commercial products - Some commercial products do recognize continuous speech. (not yet perfect) Plymouth State College 12.29

Game Playing

 Major problem in game playing: How to make the moves?

 Involves:   Processing or searching many moves (see game tree) By adding heuristics (rules of thumb / intuition), decisions can be made by the computer as to what is the best move.

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Game Playing

 Chess    MacHack (1974) by Richard Greenblatt of MIT - First program to compete in a chess tournament. Deep Blue (1996) by IBM - Garry Kasparov, world-champion chess player, won over Deep Blue 4 points to 2.

Deep Blue (1997) by IBM - Garry Kasparov conceded victory to Deep Blue, 3.5 points to 2.5.   The fastest chess-playing program processes 200,000 positions per second.

 (Thousands of moves more than a human is capable of.) Still, the best chess players beat most computer programs.

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Natural Language Communication

 The semantics (meaning of the words) is the goal in communication with the computer.

 Eliza (1965) by Joseph Weizenbaum of MIT   Simulation of a therapist mirroring a patient’s conversation in a positive manner.

The program fooled people into believing it was a therapist.

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Natural Language Communication

              All men are alike.

IN WHAT WAY They’re always bugging us about something or other.

CAN YOU THINK OF A SPECIFIC EXAMPLE Well, my boyfriend made me come here.

YOUR BOYFRIEND MADE YOU COME HERE He says I’m depressed much of the time.

I AM SORRY TO HEAR YOU ARE DEPRESSED It’s true, I am unhappy.

DO YOU THINK COMING HERE WILL HELP YOU NOT TO BE UNHAPPY I need some help, that much seems certain.

WHAT WOULD IT MEAN TO YOU IF YOU GOT SOME HELP Perhaps I could learn to get along with my mother.

TELL ME MORE ABOUT YOUR FAMILY 

Eliza conversation

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Natural Language Communication

 Early failures in natural language translation  Translation of English to Russian:  Typed in English (sentence to be translated...):  The spirit is willing, but the flesh is weak.

 Translated by the program into Russian:  The vodka is strong, but the meat is rotten.

 Translation programs have come a long way since then!

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Neural Networks

 Neuron: Basic building-block of the brain.

  There are several specialized types, but all have the same basic structure: The basic structure of an animal neuron. Plymouth State College 12.35

Neural Networks

 Artificial models of the brain are of two distinct types:  Electronic: Has electronic circuits that act like neurons.

 Software: This version runs a program on the computer that simulates the action of the neurons.

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Neural Networks

 Neural Network:    A collection of neurons which are interconnected.

The output of one connects to several others with different strength connections.

Initially, neural networks have no knowledge. (All information is learned from experience using the network.) Neuron 1 Input 1 Input 2 Input 3 Output from Neuron 1 Output from Neuron 2 Neuron 2 Plymouth State College 12.37

Fuzzy Logic

 Probability that a statement is true  Combined with other AI technologies  Washing Machine  Variable speed limits Plymouth State College

Finding Information

 Intelligent agent  Software that performs work tasks  Example: monster.com

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Next Week

 Your PowerPoint presentation is due  You will be able to present it in class for extra credit Plymouth State College 12.40

Exam in Two Weeks

Chapters 7 & 8 from the Textbook Lectures since Exam 2

Final Exam Week

 Final exam is scheduled Tue. & Thu. 5:00 – 6:15  You may take it on either day Plymouth State College 12.42