Artificial Intelligence:

Will robots inherit the earth?

Prabhas Chongstitvatana
Department of Computer Engineering
Chulalongkorn University

This is the first draft of the talk
AI lecture   Sat 17 Nov 2001

Abstract

It is impossible to elaborate any field of knowledge as broad as Artificial Intelligence in a few hours.  What I try to do is to give a general view of AI accompanied with some demos of the research results in the field.  My selection of topics to discuss is definitely bias, depends on my schooling. I just don't have deep enough knowledge to qualify to talk on all topics in AI.  I shall try to address some broad claim such as "Can machines really think the way humans do?".  I hope to show practical aspect of AI in our society.

Outline

References

What is "intelligence"

The triumph of DeepBlue over Kasparov is the public demonstration of "Machine Intelligence" equals to human.

Does the machine "think"?

Turing Test:
A. M Turing, born 23 June 1912, London; Died 7 June 1954, Manchester England; Pioneer in developing computer logic as we know it today. One of the first to approach the topic of artificial intelligence. He creates the concept of "The Turing Machine" and "Turing's Test."   As a mathematician he applied the concept of the algorithm to digital computers. His research into the relationships between machines and nature created the field of artificial intelligence.

<chat with Eliza>

Definition of intelligence by John McCarthy:

Intelligence is the computational part of the ability to achieve goals in the world. Varying kinds and degrees of intelligence occur in people, many animals and some machines.

What is AI?

David B. Leake: Indiana University. [To appear, Van Nostrand Scientific Encyclopedia, Ninth Edition, Wiley, New York, 2002.] "Artificial intelligence (AI) is a branch of computer science that studies the computational requirements for tasks such as perception, reasoning, and learning, and develops systems to perform those tasks. AI is a diverse field whose researchers address a wide range of problems, use a variety of methods, and pursue a spectrum of scientific goals."

John McCarthy (Stanford)
It is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable.

What AI can do for the society

Why AI?

. . .To achieve their full impact, computer systems must have more than processing power--they must have intelligence. They need to be able to assimilate and use large bodies of information and collaborate with and help people find new ways of working together effectively. The technology must become more responsive to human needs and styles of work, and must employ more natural means of communication.
--Barbara Grosz and Randall Davis

. . . Exactly what the computer provides is the ability not to be rigid and unthinking but, rather, to behave conditionally. That is what it means to apply knowledge to action: It means to let the action taken reflect knowledge of the situation, to be sometimes this way, sometimes that, as appropriate. . . .
In sum, technology can be controlled especially if it is saturated with intelligence to watch over how it goes, to keep accounts, to prevent errors, and to provide wisdom to each decision.
-Allen Newell, from Fairy Tales

AI and creativity

Harold Cohen: U. of California - San Diego, Computer programs that create art.  <toronto/aaron.html>

Excerpt from Robert MATTHEWS is science correspondent of The Sunday Telegraph. New Scientist Volume 144. Issue 1955.

What is the creative act?

Francis Crick described '. . the dramatic feeling of the sudden enlightenment that floods the mind when the right idea finally clinches into place'

Margaret Boden, professor of psychology and philosophy at the University of Sussex, rejects the idea that creativity is just the novel combination of old ideas. If this were true, a program which simply substitutes one formula for another logically equivalent one in a theorem could be considered 'creative'. Boden argues that creativity involves the expansion of a field of endeavour using ideas which could not emerge simply by following the usual rules.

Douglas Lenat:  AM (Artificial Mathematician),  Eurisko (Program that learn and invent new rules)
In the mid-1970s, a young computer scientist at Stanford University named Douglas Lenat created a stir by unveiling AM, a program said to discover fundamental results in mathematics. Based on the Lisp programming language, AM had been provided with a collection of very basic concepts drawn from mathematical set theory, such as 'intersection' and 'union', combined with about 200 'heuristics' - or rules - for such tasks as proposing new things to do, checking truths and spotting regularities. Once set running, AM used these heuristics to look for new concepts that emerged, and decide what constituted an 'interesting' discovery. AM discovered - or, more precisely, rediscovered - the existence of addition, multiplication, de Morgan's rules of Boolean algebra, the existence of prime numbers and the concept of unique factorisation. Perhaps most astonishing of all, AM suggested that every even number greater than 4 can be written as the sum of two prime numbers.  This is Goldbach's conjecture, a famous unproven idea in number theory first put forward by a Prussian mathematician in the 18th century.

Lenat himself went on to develop Eurisko, a program which, unlike AM, could evolve new rules for discovery as it went along. Eurisko proved particularly effective when overseen by a human able to weed out duff ideas. Helped by Lenat, Eurisko even won a war game, using a strategy based on small, fast-moving attack vessels. Human competitors thought it ludicrous - until Eurisko beat them all.

Humour

Kim Binsted: at Edinburgh University, AI researcher Kim Binsted has developed Jape-1, a program for telling jokes. The program builds up the jokes according to simple 'templates', such as 'What do you get if you cross an X with a Y ?', and chooses words for X, Y and the pay-off word Z according to properties of the words, such as their sound and associations.

Can you spot the Jape-1 jape, and the two from The Crack-a-joke Book by human joke-merchant Kaye Webb?

A What do you give a hurt lemon? Lemonade.
B What kind of tree can you wear? A fir coat.
C What runs around a forest making other animals yawn? A wild boar.

William Chamberlain: In 1984, the New-York based writer and computer programmer William Chamberlain published 'The Policeman's Beard is Half-constructed', the collected works of Racter, a program that uses the basic rules of syntax to bolt together random words and phrases. Some of Racter's output are eerily reminiscent of the musings of French philosophers. Can you decide which of these quotes is from Racter, and which are pearls of wisdom from the influential French social philosopher Simone Weil?

A 'Distance is the soul of reality'
B 'Reflections are images of tarnished aspirations'
C 'Love is not consolation, it is light'

(Answers below:)

1 A is by Jape-1
2 B is by Racter

Branches of AI

According to Prof. John McCarthy:   These are branches of AI The study of AI has expanded into several subfields.  These are some of the well-known:

Ariticial Neuron Networks

<from intro/approaches.html>
The human brain is made up of a web of billions of cells called neurons, and understanding its complexities is seen as one of the last frontiers in scientific research. It is the aim of AI researchers who prefer this bottom-up approach to construct electronic circuits that act as neurons do in the human brain. Although much of the working of the brain remains unknown, the complex network of neurons is what gives humans intelligent characteristics. By itself, a neuron is not intelligent, but when grouped together, neurons are able to pass electrical signals through networks.

A century earlier the true / false nature of binary numbers was theorized in 1854 by George Boole in his postulates concerning the Laws of Thought. Boole's principles make up what is known as Boolean algebra, the collection of logic concerning AND, OR, NOT operands. For example according to the Laws of thought the statement: (for this example consider all apples red)

Apples are red-- is True
Apples are red AND oranges are purple-- is False
Apples are red OR oranges are purple-- is True
Apples are red AND oranges are NOT purple-- is also True

Boole also assumed that the human mind works according to these laws, it performs logical operations that could be reasoned. Ninety years later, Claude Shannon applied Boole's principles in circuits, the blueprint for electronic computers. Boole's contribution to the future of computing and Artificial Intelligence was immeasurable, and his logic is the basis of neural networks.

McCulloch and Pitts, using Boole's principles, wrote a paper on neural network theory. The thesis dealt with how the networks of connected neurons could perform logical operations. It also stated that, one the level of a single neuron, the release or failure to release an impulse was the basis by which the brain makes true / false decisions. Using the idea of feedback theory, they described the loop which existed between the senses ---> brain ---> muscles, and likewise concluded that Memory could be defined as the signals in a closed loop of neurons.

How a NN is trained

NetTalk  is a program that used NN to learn to pronounce a word by mapping text to phoenemes
<Rivest ML lecture  "lecture16-nettalk.ps">

Artificial Life

Artificial Life relates to Biology in much the same fashion that Artificial Intelligence relates to Psychology. The goal of artificial life, or ALife, is to provide a different focus for researchers in biology. Rather than emphasize an analytic approach - attempting to understand the complex phenomena of life by breaking them down into simpler units - ALife offers a synthetic perspective - it begins with simple rules and concepts, and combines them to see what complex phenomena are produced. What is surprising, and what has helped legitimize ALife as a research pursuit, is how accurately computer models developed through this methodology have reflected our observations of biological life. An example may help clarify what it is we mean when we talk about combining simple rules in computer simulation to acheive life-like results.

Game of Life
<answer the question of biological "society" such as whether the universe "open" or "close">
<run demo game of life>
Cellular Automata, of which Life is an example, were suggested by Stanislaw Ulam in the 1940s, and first formalized by von Neumann. Conway's "Game of Life" was popularized in Martin Gardner's mathematical games column in the October 1970 and February 1971 issues of Scientific American.  (Shorter notes on life are alse given in the column in each month from October 1970 to April 1971, and well as November 1971, January 1972, and December 1972.)

The rules for the game of life are quite simple. The game board is a rectangular cell array, with each cell either empty or filled. At each tick of the clock, we generate the next generation by the following rules:

    if a cell is empty, fill it if 3 of its neighbors are filled   (otherwise leave it empty)
    if a cell is filled,
         it dies of loneliness if it has 1 or fewer neighbors
         continues to live if it has 2 or 3 neighbors
         dies of overcrowding if it has more than 3 neighbors

Conway has demonstrated that it is possible to construct the basic building blocks of a computer from Life using modified glider guns. See the last chapter of Elwyn R. Berlekamp, John H. Conway, and Richard K. Guy, "Winning Ways", Academic Press, New York, 1982, ISBN 0-120911-507.

Philosophical issue

<from comp.ai.faq>
It useful to divide AI into two classes: strong AI and weak AI. Strong AI makes the bold claim that computers can be made to think on a level (at least) equal to humans.  Weak AI simply states that some "thinking-like" features can be added to computers to make them more useful tools... and this has already started to happen (witness expert systems, drive-by-wire cars and speech recognition software).  What does 'think' and 'thinking-like' mean?  That's a matter of much debate.

John Searl "Chinese room" argument

Machines will "think" differently from human.

. . . because computers lack bodies and life experiences comparable to humans', intelligent systems will probably be inherently different from humans.
- David L. Waltz

Future AI

Interview with Michel Negroponte (the director of MIT Media Lab) - 13th November, 2001
"I doubt I will ever meet (in my life time) an independent, self sufficient, thinking robot. But I suspect my grandchildren will. In the more immediate future I think the trend will be towards smart, interactive human helpers and appendages - for instance smart eye glasses, smart shoes, smart tooth brushes and even smart mini-doctors that will be tiny electronic devices that people plant on/in their bodies to give personalized health evaluations. Imagine a toothbrush that could "see" cavities and urge you to see your dentist ... I'd not only buy one, I need one!"

About the speaker

Prabhas Chongstitvatana is an associate professor in the department of computer engineering, Chulalongkorn university.  He graduated with an electrical engineering bachelor degree from Kasetsart university in 1981 and earned his doctoral degree from the department of artificial intelligence (now division of informatics) from Edinburgh university in 1992.  His research involves robotics and vision and currently evolutionary computation, especially its application in robot programming.

homepage: www.cp.eng.chula.ac.th/faculty/pjw
email:  prabhas@chula.ac.th

Handouts  (for the class)

1  Basic questions, John McCarthy, <"node1-what.html">
2  Turing paper 1950
Alan M. Turing (1950). Mind 59 (Oct 1950): 433-60. {"Originally published by Oxford University Press on behalf of MIND (the Journal of the Mind Association), vol. LIX, no. 236, pp. 433-60, 1950.
<"turing.html">
3  Will robots inherit the earth (Minsky), <sciam.inherit.htm>
4  Chinese room argument
<whatisai/JOHN SEARLE'S CHINESE ROOM ARGUMENT>

References

Web:
AAAI  www.aaai.org
McCarthy page <node5-what.html is references>
www.generation5.org <news, robotics and AI>

Textbooks:
This list is a small selection of all fine books in AI.  These are my favourites.

  1. Artificial Intelligence: A Modern Approach. A textbook by Stuart Russell and Peter Norvig. 1995. Upper Saddle River, NJ: Prentice Hall.
  2. Norvig, Peter, Paradigms of Artificial Intelligence Programming: Case Studies in Common Lisp, Morgan Kaufmann Publishers Inc., 1992.
  3. Sterling, Leon, and Shapiro, Ehud, The Art of Prolog -- Advanced Programming Techniques, 2nd edition, The MIT Press, 1994.  This one is the text with full of AI programming and to balance the view the following is the view of AI from the opposite continent written by my class mate.
  4. Dean, Thomas and Allen, James and Aloimonos, Yiannis, Artificial Intelligence: Theory and Practice, Addison-Wesley Publishing Company, 1995.
  5. Luger, G.F. and Stubblefield, W.A., Artificial Intelligence: Structures and Strategies for Complex Problem Solving, 3rd ed. , Addison-Wesley Publishing Company, 1997. This is a more uptodate text.
  6. Nilsson, Nils J., Principles of Artificial Intelligence, Morgan Kaufmann Publishers Inc., 1980.
  7. Nilsson, Nils, Artificial Intelligence: A New Synthesis, Morgan Kaufmann Publishers Inc., March 1998. The first one is the one I read when I started learning about AI, the second one is the modern uptodate version.
  8. Elaine Rich & Kevin Knight, "Artificial Intelligence", 2nd edition, McGraw-Hill, New York, 1991. ISBN 0-07-052263-4. One of the more popular introductory texts to AI, giving a very good general overview of most AI topics. In some places the book sacrifices depth for breadth, and a few more recent topics are neglected.
  9. Patrick Henry Winston, "Artificial Intelligence", Third Edition, Addison Wesley, Reading, MA, 1992, ISBN 0-201-53377-4. A classic early AI text. This text is very much hands-on, with actual toy examples.
  10. Boden, Margaret A. 1977. Artificial Intelligence and Natural Man. New York:Basic Books. Although the examples are somewhat dated, this book remains a clear discussion of the goals and controversial issues of AI. This book used to be the standard textbook for many universities in the 80s.
  11. Simon, Herbert. 1996. Sciences of the Artificial. 3rd edition. Cambridge, MA: MIT Press. A classic book, originally published in 1969, that examines several presuppositions of AI. Updates throughout the book take into account advances in cognitive science and the science of design. (H. Simon died last year 2000, he is a pioneer in AI and a Noble laureate in Economics)