Com1005 Machines and
Intelligence
Lecturers: Dr Amanda Sharkey,
Professor Phil Green
Lecture 1: What is Artificial
Intelligence?
• “Artificial Intelligence (AI) is the study of
intelligent behaviour (in humans, animals
and machines) and the attempt to find
ways in which such behaviour could be
engineered in any type of artifact”
(Whitby, 2003)
Varying defintions
• John McCarthy, “It is the science and engineering of
•
•
•
making intelligent machines, especially intelligent
computer programs.”
Herbert Simon: We call programs intelligent if they
exhibit behaviours that would be regarded intelligent if
they were exhibited by human beings.
Elaine Rich (1991) “AI is the study of how to make
computers do things at which, at the moment, people
are better.”
Astro Teller: AI is the attempt to make computers do
what they do in the movies.
Main themes of this semester
Artificial Intelligence – overview of progress
- Different approaches to creating intelligent
behaviour
- Computationalism .... Mind as computer
- Brain-like (Artificial Neural Nets)
- Brain, body and world (Embodied AI)
- Different goals
- Understanding and simulating intelligence
- Applied AI
- Creating the illusion of intelligence
• Lectures
• “Guru” lectures – AI research in the department
• Assessment over the year:
–
–
–
–
written assignment (this semester),
Group presentation (this semester),
practical assignment (next semester)
exam for whole year (next semester).
Origins of AI and early history of
digital computer
• 1941 Germany: Konrad Zuse, Z3 first general purpose
programmable computer
Colossus – 10 delivered to Bletchley Park.
Designed by British engineer Tommy Flowers to
break Nazi codes.
ENIAC (1945) electronic computer rewired by hand for
each task.
Manchester Mark 1 computer: 1948. General purpose
computer with stored programs.
Punched
paper
tape for
new job
AI: Where did it all begin?
• 1956 Dartmouth Summer Research Project
– month long ‘brain storming’ session
– Attendees: John McCarthy (Father of AI, inventor of
LISP). Invented term “artificial intelligence”
– Also Allen Newell, Herbert Simon, Marvin Minsky,
Oliver Selfridge, Claude Shannon and others
– Idea that “every aspect of learning, or any other
feature of intelligence can in principle be so precisely
described that a machine can be made to simulate it”
What is Artificial Intelligence?
• Attempt to understand intelligent entities
• Attempt to build intelligent entities
• Attempt to create the appearance of
intelligence
Understanding intelligent entities
• Can computers be intelligent?
• Or is intelligence unique to humans, or to
living beings?
• Can we use computers to help us
understand how we think?
John Searle:
• Strong AI: an appropriately programmed
computer really is a mind, can be said to
understand, and has other cognitive
states.
• Weak AI: a computer is a valuable tool
for study of mind – makes it possible to
formulate and test hypotheses rigorously
• Ray Kurzweil: “The singularity is near”
• Strong AI .... Artificial intelligence that
matches or exceeds human intelligence
• Artificial general intelligence
• Artificial narrow intelligence
Building intelligent machines
• Designing programs to perform tasks
intelligently
• Should computer programs think like
humans?
• Should they be programmed to operate
like human brains?
• Should they exploit different strengths?
Creating the appearance of
intelligence
What is intelligence?
Can psychology help?
• 1921 Journal of Educational Psychology asked 14 experts
for definitions
• 14 different definitions including
– The ability to carry on abstract thinking (Terman)
– The ability to adapt oneself adequately to relatively new
situations in life (Pintner)
– The capacity to acquire capacity (Woodrow)
– The capacity to learn or profit by experience (Dearborn)
• “few concepts in psychology have received more
devoted attention and few have resisted clarification so
thoroughly” (Reber, 1995)
Intelligence in humans
• What is intelligence?
• Intelligence is what is measured by
intelligence tests.
• IQ intelligence quotient
– Based on abstract reasoning ability
• 1915 Stanford-Binet test• Objective measure, aims – to identify those children who
need specialised, simplified education
• Uses concept of mental age versus chronological age
• Items in test age graded- mental age corresponds to
level achieved in test.
• Eg. A 4 year old should be able to complete the
following:
• Brother is a boy; Sister is a ……
Bright child – mental age above chronological age.
• Adult versions developed and
standardized.
• Weschler developed test for adults and
proposed Gaussian distribution of results
• 2/3 should be between 85 and 115, (100
mean) and 2.3% above 130 and below 70.
• Single factor, or multiple intelligences?
• Spearman, single factor g underlying
•
•
•
intelligence.
Gardner – multiple intelligences
Linguistic, musical, logical-mathematical, spatial,
bodily-kinesthetic, personal
But strong correlations between performance in
different areas – single underlying factor of
intelligence?
• Correlations of .4 and .6 between school
grades and Wechsler IQ test.
• Correlation with university results lower
• Correlation with job performance .51
(Hunter and Schmidt 1998)
• Problems:
– Difficult to find items which are independent of
culture and education
• E.g. pick odd word
• cello harp drum violin guitar
• Rich kids picked drum, poor kids cello
• Also IQ tests don’t measure
– Creativity
– Motivation
– Most geniuses also work very hard (e.g Mozart and
practice)
• Robert Sternberg (2003)
• Triarchic theory of intelligence
• 1. Analytical intelligence, the ability to complete academic,
problem-solving tasks, such as those used in traditional intelligence
tests. These types of tasks usually present well-defined problems
that have only a single correct answer
.
• 2. Creative or synthetic intelligence, the ability to successfully
deal with new and unusual situations by drawing on existing
knowledge and skills. Individuals high in creative intelligence may
give 'wrong' answers because they see things from a different
perspective.
• 3. Practical intelligence, the ability to adapt to everyday life by
drawing on existing knowledge and skills. Practical intelligence
enables an individual to understand what needs to be done in a
specific setting and then do it.
• Machines and IQ tests?
Intelligence and machines?
The Turing Test
Turing test
• Outside appearances used to judge intelligence
• Turing, A. (1950) Computing machinery and
•
•
intelligence, Mind, 59, 433-460.
I propose to consider the question ‘Can
machines think?’
When paper written, only 4 electronic computers
in existence, and it was before Dartmouth
conference and ‘birth’ of AI
Turing test
• Like Victorian parlour game. I interrogator, 1 woman, 1
•
•
•
•
•
man
1 interrogator, 1 person, 1 computer
Interrogator can ask any questions
Human must be truthful (trying to help the interrogator),
computer can try to force wrong identification
Experiment repeated with a range of people in 2 human
positions. Test passed if number of successful
identifications of computer is less than 30%
Machine can lie – e.g. if asked if it has ever written a
poem it could say it has, and it could pretend to get
calculations wrong.
Predictions
• 1950 Turing: “I believe that in about fifty years' time it will be
possible to programme computers, with a storage capacity of about
109, to make them play the imitation game so well that an average
interrogator will not have more than 70 percent chance of making
the right identification after five minutes of questioning. … I believe
that at the end of the century the use of words and general
educated opinion will have altered so much that one will be able to
speak of machines thinking without expecting to be contradicted.”
• 1965, Herbert Simon predicted that “machines will be capable,
within twenty years, of doing any work that a man can do”.
• 1967 Marvin Minsky “within a generation… few compartments of
intellect will remain outside the machine’s realm”
ELIZA
• Weizenbaum, J. (1965) ELIZA – a
computer program for the study of natural
language communication between man
and machine. Communications of the
Association for Computing Machinery, 9(1)
36-45.
•
•
•
•
•
•
•
•
•
•
•
•
•
Therapeutic interview with a teenager
Men are all 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 THAT YOU ARE DEPRESSED
It’s true that 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 Y0U IF YOU GOT SOME
HELP?
•
•
•
•
•
•
•
•
•
•
•
•
•
Perhaps I could learn to get along with my mother.
TELL ME MORE ABOUT YOUR FAMILY
My mother takes care of me
WHO ELSE IN YOUR FAMILY TAKES CARE OF YOU?
My father
YOUR FATHER
You are like my father in some ways
WHAT RESEMBLANCE DO YOU SEE?
You are not very aggressive, but I think you don’t
want me to notice that.
WHAT MAKES YOU THINK I AM NOT VERY
AGGRESSIVE?
You don’t argue with me
WHY DO YOU THINK I DON’T ARGUE WITH YOU?
You are afraid of me
• DOES IT PLEASE YOU TO BELIEVIE I
AM AFRAID OF YOU?
• My father is afraid of everybody
• WHAT ELSE COMES TO MIND WHEN
YOU THINK OF YOUR FATHER?
• Bullies
• DOES THAT HAVE ANYTHING TO DO
WITH THE FACT THAT YOUR
BOYFRIEND MADE YOU COME HERE?
Has the test been passed already?
•
•
•
•
•
•
•
•
•
•
•
•
•
Business executive and Eliza
Do you think that I can use the computer this morning?
WHY DO YOU ASK?
I might be able to make some additional Telcomp sales.
WHY AREN’T YOU SURE?
My prospective customers have never seen a demonstration of the system
DOES THIS MEAN VERY MUCH TO YOU?
Of course it does.
YOU SEEM VERY POSITIVE ABOUT THAT
This is ridiculous
WHAT DO YOU MEAN THIS IS RIDICULOUS?
Is there any reason why I can’t use the computer now?
TELL ME MORE ABOUT YOUR PROSPECTIVE CUSTOMERS
Parry the paranoid program.
Colby, K.M., Weber, S., Hildf, F.D. (1971) Artificial
Paranoia. Artificial Intelligence, 2, 1-25.
•
•
•
•
•
•
•
•
•
•
•
•
Why are you in the hospital?
I SHOULDN’T BE HERE
Who brought you here?
THE POLICE
What trouble did you have with the police?
COPS DON’T DO THEIR JOB
What do they do instead?
COPS ARREST THE WRONG PEOPLE
They arrest the right ones sometimes.
THE LAW SHOULD GET THOSE ITALIAN CROOKS
Are you Italian?
MY BACKGROUND IS BRITISH BUT I WAS BORN IN THIS COUNTRY
•
•
•
•
•
•
•
•
•
•
•
•
•
Do you know any Italian crooks?
I TRY TO AVOID THE UNDERWORLD
Are you afraid of the underworld?
THE MAFIA ARE THE WORST GANGSTERS
Are you connected with the Mafia?
YOU KNOW, THEY KNOW ME
How do they know you?
THEY SPY ON ME
Why do they?
THEY WANT TO GET FALSE EVIDENCE TO FRAME ME
What do they have against you?
THEY ARE OUT TO GET ME
What for?
•
•
•
•
•
•
•
•
•
•
I HAVE INCRIMINATING EVIDENCE AGAINST THE MAFIA
Are you sure of all this?
YOU DON’T BELIEVE ME?
I am doing my best…..
Silence
CAN’T YOU THINK OF A DECENT QUESTION TO ASK ME?
You seem hostile to me
SOME OF YOUR REMARKS ANNOY ME
I am sorry
FORGET THE EXCUSES, YOU GUYS ARE ALL ALIKE.
Loebner Test
• Competition started 1991 by Hugh Loebner
• Judges type questions on terminal, either human or
•
•
•
•
computer replies
Judges rate responses on how human-like they appear.
$2000 prize to the most human like. $100,000 prize if a
program does better than one of the humans
2010 competition: 23rd October in LA
http://www.loebner.net/Prizef/loebner-prize.html
1997 winner: Intelligent Research Ltd, partly
programmed by our NLP group
2009: “Do-Much-More” won....
2009 winner of Loebner Prize: “Do-Much-More”
Designed to respond with generalities, moving conversation along in
light hearted way.
No specialist knowledge.
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
Judge-1: Hi, how is it going?
Do-Much-More: Hello. How are you doing?
Judge-1: Good, who are you?
Do-Much-More: Well, I am studying engineering. That ought to give you an
idea about who I am.
Judge-1: No, not really?
Do-Much-More: I have a terrific yearning to know something more about
experiments?
Judge-1: Where are you from?
Do-Much-More: I don't know, but don't forget I can't be in three places at
once.
Judge-1: What lang~~~~city do you live in?
Do-Much-More: What city -- that's rather a tricky question. The trouble is,
one city seems to be much like another.
Judge-1: Not really. Where do you live?
Do-Much-More: In the old dairy next to the river, I believe.
Judge-1: Interesting, so you are a farmer then?
Do-Much-More: Well, if you ask a physiologist what I am, he won't say I'm
a farmer. He'll say I consist mostly of water -- enough to fill a large beer
tun -- plus some iron, phosphorus, and lots of other prosaic ingredients.
Judge-1: Wie~~eird answer to a simple question, wouldn't yous~ say?
Do-Much-More: I certainly would. Just watch me!
Judge-1: What will I see?
Do-Much-More: You don't have to worry about
what you see, you shouldn't see it on a cold
winter's evening.
Judge-1: What do you make of the Arctic
Monkeys?
Do-Much-More: Well, here's a clue: I make what a
keeper in a zoo would make. Try that for size!
Survey of 21 experts at AGI-09 conference (Baum
et al, 2010)
Summary
• Birth of AI, and first computers
• What is intelligence? – history of intelligence
•
•
•
testing
Turing test – a particular view of what
intelligence is. Takes simplifying view that if
behaviour of computer is indistinguishable from
human, it must be intelligent.
Early conversational programs
Loebner test