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A GRADUATE COURSE WWW.RIBLER.COM Artificial Intelligence
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A graduate Course ribler

Jan 06, 2016

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Artificial Intelligence. A graduate Course www.ribler.com. Randy Ribler PhD from Virginia Tech Postdoc at University of Illinois (UIUC) I was here before in 2006 I’m absolutely delighted to be back in 2013. A Visiting Professor. Hobbs Hall. Lynchburg College. Central Virginia. - PowerPoint PPT Presentation
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Page 1: A graduate Course ribler

A GRADUATE COURSEWWW.RIBLER.COM

Artificial Intelligence

Page 2: A graduate Course ribler

A VISITING PROFESSOR

Randy Ribler PhD from Virginia Tech Postdoc at University of Illinois (UIUC)

I was here before in 2006 I’m absolutely delighted to be back in

2013

Page 3: A graduate Course ribler

LYNCHBURG COLLEGE

Hobbs Hall

Page 4: A graduate Course ribler

CENTRAL VIRGINIA

Page 5: A graduate Course ribler

LYNCHBURG COLLEGE

Small “liberal arts” college 2000+ students 50-60 Computer Science Majors 3 fulltime professors

Page 6: A graduate Course ribler

MY PRIMARY INTERESTS IN AI

Genetic Algorithms Rule Induction Algorithms for playing games

Chess Chinese Chess Dots and Boxes

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WHAT DO YOU WANT OUT OF THIS CLASS? I would like the class to be project-

centered, but first I need to know: Who are you? What do you want to get out of this class? What are your career goals? What do you already know about AI? What subfields of AI are you interested in?

Page 8: A graduate Course ribler

WHAT IS ARTIFICIAL INTELLIGENCE

Do you know a definition? Do you have your own criteria? Have you heard of the Turing Test? What do you think it is?

Page 9: A graduate Course ribler

ALAN M. TURING

Computing Machinery and Intelligence (1950)- Can machines think?What are the meanings of the words“machine” and“think?”

Today we still have problems defining Artificial IntelligenceWhat are the meanings of the words“artificial” and “intelligence?”

Page 10: A graduate Course ribler

COMPUTING MACHINERY AND INTELLIGENCE (1950)

Imitation Game New “form of the problem,” “Can machines

think?” Participants

Man Woman Interrogator (man or woman)

The Game Each participant is in a different room Interrogator does not know who is in each room Interrogator sends written questions to other participants Man tries to convince interrogator that he is the woman Woman tries to convince interrogator that she is the woman

Page 11: A graduate Course ribler

TURING TEST

Imitation Game Substitute the man with a machine

“Will the interrogator decide wrongly as often when the game is played like this as he does when the game is played between a man and a woman?”

This will replace the question “Can machines think?”

Page 12: A graduate Course ribler

SAMPLE QUESTIONS TYPES FROM TURING Write me a sonnet

Machine might say its not good at it Perform some mathematics

Machine might have to appear slow Solve a chess problems

Page 13: A graduate Course ribler

MACHINE DEFINITION

Digital Computer Not biological clone of a man Not necessarily every computer Can we imagine a computer that can do

well in the game?

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TURING’S PREDICTION

In “fifty years’ time” (year 2000) After 5 minutes of interrogation the

interrogator will make the correct identification about 70% of the time.

Page 15: A graduate Course ribler

TURING’S ANTICIPATED OBJECTIONS

Theological “God has given an immortal soul to every man and

woman, but not to any other animal or to machines. Hence no animal or machine can think.”

Turing rejects this with an interesting argument “ In attempting to construct such machines we should not be

irreverently usurping His power of creating souls, any more than we are in the procreation of children: rather we are, in either case, instruments of His will providing .mansions for the souls that He creates.”

Such arguments have often been found unsatisfactory in the past. In the time of Galileo it was argued that the texts, "And the sun stood still .

Page 16: A graduate Course ribler

TURING’S ANTICIPATED OBJECTIONS

The "Heads in the Sand" Objection “The consequences of machines thinking

would be too dreadful. Let us hope and believe that they cannot do so." 

“I do not think that this argument is sufficiently substantial to require refutation. ”

Page 17: A graduate Course ribler

TURING’S ANTICIPATED OBJECTIONS

The Mathematical Objection We know that some functions are not

computable, so there must be some questions that the computer will be unable to answer.

Yes, but we don’t have any proof that a person can answer these questions either

Failing in some areas is not necessarily lack of the ability to think

Page 18: A graduate Course ribler

TURING’S ANTICIPATED OBJECTIONS

The Argument from Consciousness  "Not until a machine can write a sonnet or compose a

concerto because of thoughts and emotions felt, and not by the chance fall of symbols, could we agree that machine equals brain-that is, not only write it but know that it had written it. No mechanism could feel (and not merely artificially signal, an easy contrivance) pleasure at its successes, grief when its valves fuse, be warmed by flattery, be made miserable by its mistakes, be charmed by sex, be angry or depressed when it cannot get what it wants.“ - Jefferson

Page 19: A graduate Course ribler

TURING’S ANTICIPATED OBJECTIONS

We can’t even know these things about a person? (solipsist point of view)

We can ask questions as part of the interrogation that probe understanding “In the first line of your sonnet which reads

"Shall I compare thee to a summer's day," would not "a spring day" do as well or better?”

Page 20: A graduate Course ribler

TURING’S ANTICIPATED OBJECTIONS

Arguments from Various Disabilities  "I grant you that you can make machines

do all the things you have mentioned but you will never be able to make one to do X." 

X = Enjoy strawberries and cream X = Make mistakes

Turing claims most of these objections are disguised forms of the argument from consciousness

Page 21: A graduate Course ribler

TURING’S ANTICIPATED OBJECTIONS

Lady Lovelace's Objection - "The Analytical Engine has no pretensions to originate anything. It can do whatever we know how to order it to perform“ Machines can change their instructions Machines can surprise us – (debugging)

Page 22: A graduate Course ribler

TURING’S ANTICIPATED OBJECTIONS

Argument from Continuity in the Nervous System - “The nervous system is certainly not a discrete-state machine. A small error in the information about the size of a nervous impulse impinging on a neuron, may make a large difference to the size of the outgoing impulse. It may be argued that, this being so, one cannot expect to be able to mimic the behaviour of the nervous system with a discrete-state system.”

Page 23: A graduate Course ribler

TURING’S ANTICIPATED OBJECTIONS

The machine can approximate the continuous sufficiently to fool the interrogator in

Page 24: A graduate Course ribler

TURING’S ANTICIPATED OBJECTIONS

The Argument from Informality of Behaviour  It is not possible to produce a set of rules purporting

to describe what a man should do in every conceivable set of circumstances. One might for instance have a rule that one is to stop when one sees a red traffic light, and to go if one sees a green one, but what if by some fault both appear together? One may perhaps decide that it is safest to stop. But some further difficulty may well arise from this decision later. To attempt to provide rules of conduct to cover every eventuality, even those arising from traffic lights, appears to be impossible. With all this I agree.

Page 25: A graduate Course ribler

TURING’S ANTICIPATED OBJECTIONS

The Argument from Extrasensory Perception  Turing thought that “statistical

evidence, at least for telepathy, is overwhelming”

Of course, this has been shown to be incorrect, but it is interesting to think about.