Artificial
Intelligence
CS 404
Berrin Yanikoglu
1
To Know
• Basic history of AI
– Know some of the important events or at least what
happend in different eras and current state
• Difficulty of defining intelligence and some of
the attempts
– Fleeting nature of the definition
– Difference of humanly/rational thinking/acting
• Turing test
• Rational agents
• …
2
Course Info
• Webpage:
– http://people.sabanciuniv.edu/~berrin/cs404/
• Linked from SuCourse
– Assignments, discussions and announcements will
be in Sucourse, the above page is just a permanent
plavce for CS404 info.
3
On May 12th, 1997, the
best chess player in the
world, Gary Kasparov,
lost a six-game chess
match to a computer
named “Deep Blue 2”
What was so significant
about this event?
Being able to program a computer to defeat a Grand Master level
chess player had been a long-standing goal of the science of
artificial intelligence - and now it has been achieved
What is Artificial Intelligence?
Intelligence is difficult to define and understand,
even for philosophers and psychologists who spend
their lives studying it. But this elusive quality is, to
many people, the characteristic that sets humans
apart from other species
“What is intelligence, anyway? It is only a word that people use
to name those unknown processes with which our brains solve
problems we call hard. But whenever you learn a skill yourself,
you are less impressed or mystified when other people do the
same.
This is why the meaning of “intelligence” seems so elusive: It
describes not some definite thing but only the momentary
horizon of our ignorance about how minds might work.”
- Marvin Minsky, AI researcher
What is Artificial Intelligence?
Smart programs?
– Not really. Studying what is possible and underlying theories
are very important.
– How does a slow, tiny brain (biological or electrical) perceives,
understands, and manipulates a complex world?
6
Studying AI
• Started out in 1950s
– The Dartmouth meeting in 1956
• Turned out much more difficult than anyone had
imagined
• Currently encompasses a large variety of subfields,
– from general areas such as perception and logical reasoning
to
– specific tasks such as playing chess, writing poetry…
– bringing together philosophy, logic, computer science,
cognitive science and cognitive neuroscience
7
What is AI?
So it’s not surprising that defining artificial intelligence (AI) is hard. In general,
artificial intelligence is the field of science devoted to making computers
perceive, reason, and act in ways that have, until now, been reserved for human
beings.
4 types of historical definitions:
acting or thinking humanly
acting or thinking rationally
8
Thinking Humanly: Cognitive Science
• How do humans think?
– Requires scientific theories of internal activities of the brain
• What level of abstraction? ``Knowledge'' or ``circuits''?
• How to validate?
– Bring together computational models from AI and
experimental techniques from psycho-physics to model the
human mind
• 1) Predicting and testing behavior of human subjects (Cognitive
Science; top-down)
• 2) Direct identification from neurological data (Cognitive
Neuroscience; bottom-up)
– Both approaches are now distinct from AI
– Most of the machinery of the human mind is not available to
our conscious experience
9
Thinking Logically
• Let’s give up imitating the human thinking process, but try to
achieve logical thinking
• Laws of formal logic to formalize the thinking process
– Socrates is a man; All men are mortal => Socrates is mortal
• Difficulties:
– How to take informal knowledge and state in formal terms especially
when knowledge is less than 100% certain
– Even problems with just a few dozen facts can be prohibitive
10
Acting Humanly - Turing Test
11
Acting Humanly - Turing Test
•
UCSD Turing Page:
•
Captchas
http://cogsci.ucsd.edu/~asaygin/tt/ttest.html
– Tests to identify humans from bots on the Internet, to deny services to webcrawlers or
spammers
12
Acting Humanly - Turing Test
•
Chatterbots:
– One of the most complex (and entertaining!)
chatter bots are at: http://www.simonlaven.com/
• choose complex chatterbots, choose Barry, go to
downloads and then choose CGI talk (this year they
dont have the online CGI talk, and the downloaded
program didnt work for me)
– Or this one works with no downloads: The ALICE
Foundation
•
•
•
•
...
Human: You sound pretty smart
ALICE: In what way do I sound like that?
Human: never mind, hey, how is the weather?
ALICE: Oh well thanks for trying to explain it to me anyway.
You've got my full attention. Sunny.
Human: where are you?
ALICE: I am in Dr. Richard S. Wallace's computer in Oakland,
California. Where are you?
Human: i am in istanbul, do you know where it is?
ALICE: Tell me a little about your life in istanbul do me know
where it is.
...
13
Acting Rationally
• Rationality: ideal concept of intelligence
• Rational behavior: doing the right thing
– that which is expected to maximize goal achievement, given the
available information
– Reflexes do not necessarily involve thinking---e.g., blinking reflex,
but they are rational
– Thinking should be in the service of rational action
• Caveat: computational limitations make perfect rationality
unachievable
design best program for given machine resources
• We will emphasize rational agents in this course.
14
Why do we want artificial intelligence?
• To relieve our mental labour, just as machines
relieved our physical labour last century
• It should make the machines themselves easier
to use
• It might give some insight into the workings of
our own minds
15
History of AI
•
•
1943
1950
•
1950s Early AI programs, including Samuel's checkers program, Newell & Simon's
Logic Theorist (proving theorems), Gelernter's
Geometry Engine, Shannon
and Turing writing chess programs
McCulloch and Pitts: Artificial Neuron Model
Turing's ``Computing Machinery and Intelligence'‘
– Shortage of computer times => Development of time sharing (=> DEC)
– Creation of LISP (McCarthy)
•
•
1956
1965
•
1960sEarly development of knowledge-based systems; Minsky’s microworlds (blocks
as home to various projects: vision, planning, nat. Lang. Understanding, ...)
Dartmouth meeting: ``Artificial Intelligence'' coined
Robinson's complete algorithm for logical reasoning resolution method
– ANALOGY program (what is this figure most similar to?)
– Algebra STUDENT program (one egg costs ... How much does twenty eggs cost?)
16
History of AI
• 1966--74 Dose of Reality
• Very little domain knowledge:
– Swithing from one domain to another, the programs failed miserably
• AI discovers computational complexity
– Early programs worked by representing the basic facts and trying out a
series of steps to solve the problem which was only tractable within
micro worlds; NP-completeness showed that scaling up to larger
problems was not always viable
• Neural network research almost disappears
17
History of AI
• 1980--88 Expert systems industry booms
– After all, they work, even if in limited domains
– An expert system is a software designed to replicate the decision-making
process of a human expert, within a narrow topic. At the heart of every
expert system is a knowledge base representing ideas from the specific
field of expertise
– A knowledge-based system derives knowledge from experts as well as
other sources like government regulations, statistical databases, company
guidelines, etc.
– In practice, the terms expert system and knowledge-based system are
often used interchangeably
• While a database contains only facts, a knowledge base also contains a
system of if-then rules for determining and changing the relationships
between those facts
18
Digression: Expert Systems
•Expert systems are widely used in many different areas:

American Express uses one to automate checking for fraud and misuses of its
no-limit credit card. This has to be done in 90 secs while the customer waits,
and the cost of an error can be high

DENDRAL, an expert system that examines the spectroscopic analysis of an
unknown chemical compound and predicts its molecular structure

DEC’s XCON configures complex computer systems. It
work of > 300 human experts, with fewer mistakes

PIERS, an expert system used to diagnose blood samples in St Vincent Hospital,
Sydney

...
reportedly does the
•Current success is in reasonably narrow topics, eg mineral prospecting, medical
diagnoses, air traffic control, etc. But the real goal is to build something that has
a broad understanding of the world - which requires common sense
History of AI
• 1988--93 Expert systems industry start losing its power
• Successful only in very narrow domains
• Building a successful expert system is much more than simply
buying a reasoning system and filling it with rules
• 1985--95 Neural networks return to popularity
• 1988-- With strengthened foundations, AI becomes hot
again - resurgence of probabilistic and decision-theoretic
methods, genetic algorithms, belief networks,...
20
Current State
• Which of the following can be done at present!?
–
–
–
–
–
–
–
–
Play a decent game of table tennis
Drive along a curving mountain road
Drive in the center of Istanbul
Play a decent game of bridge
Discover and prove a new mathematical theorem
Write an intentionally funny story
Give competent legal advice in a specialized area of law
Translate spoken English into spoken Swedish in real time
21
Current State
• Which of the following can be done at present!?
–
–
–
–
–
–
–
–
Play a decent game of table tennis
Drive along a curving mountain road
Drive in the center of Istanbul
Play a decent game of bridge
Discover and prove a new mathematical theorem
Write an intentionally funny story
Give competent legal advice in a specialized area of law
Translate spoken English into spoken Swedish in real time
22
Current State
• Limited domain speech/natural language understanding
programs
• Chess playing programs (machines)
• Medical expert systems challenging doctors
• ...
23
Artificial Intelligence and the Humans
•What does the advent of the intelligent machine mean for human
beings?
•Are artificial intelligences just extensions of human intelligence?
•When AARON creates a drawing, who is the artist, Cohen or AARON?
•When expert systems make decisions, who is responsible? the user, the
programmer, the software company, or somebody else?
•Should we think of intelligent machines as some new sort of life, one
with which we must now share the world?
•Could AIs be our evolutionary successors?
•How will AI affect our own sense of self?
•AI is beginning to force us to confront these hard philosphical
questions…
Syllabus
•
http://people.sabanciuniv.edu/~berrin/cs404/syllabus.htm
• In short (AIMA 3rd ed.)
– Introduction:
• Chapters 1-2
– Problem Solving
• Chapters 3-6
– Knowledge and Reasoning
• Chapters 7-9
– Planning:
• Skipped, with just a brief overview
– Uncertain Knowledge and Reasoning
• Chapters 13,14,16; skip 15 and 17
– Learning
• overview + one classification method (decision trees)
– Communicating, Perceiving, Acting
• overview + one problem in computer vision
– Conclusions
25
Seeing, Hearing and Understanding
An intelligent computer must be able to recognize its surrounding
environment and adapt to changes in it. To do this it must be able
to “see” and “hear” what’s going on
Computer vision is the capability of a computer to mimic the ways
that human brains process and interpret light waves to produce a
model of reality. Though it’s very easy for people to do that, it’s
very difficult for computers to do build and update their models
Hearing, Seeing and Understanding
The ability of a computer to recognize the speech of a user and
take action based on the words spoken is called speech recognition
or voice recognition. The computer matches spoken words against
stored speech patterns to determine what was said
Natural language processing is the ability of a computer to build
knowledge representations corresponding to the meaning in
sentences made up of recognized words. This is very difficult,
because human language is full of ambiguities, vagueness and
depends on a lot of commonsense knowledge of the world
Machine Learning
•We’ve seen how difficult collecting and maintaining knowledge is. If there
was a lot, it could be impossible to do by hand
•It would help if the machine could build up its own knowledge from
experiences in the world, like a child learning how to walk. The ability of the
machine to discover knowledge from observations of the world is called
machine learning
For example, some of the best game-playing programs learn from past
experiences. If a move pays off, a learning program is more likely to use that
(or similar moves) in future games. If a move results in a loss, the program will
remember to avoid similar moves
Robots - AI Embodied
• Japanese companies such as Honda,
Fujitsu and Sony are racing to
develop humanoids
• The Honda ASIMO (right) is a good
example
• Improved walking stability over
earlier models
• Smaller size is about marketing and Robocup eligibility
• Intelligence quite limited - some
commands sent by remote control
• Simple voice recognition functions
trigger pre-programmed actions
• Will cost about the same a luxury
car