PowerPoint Presentation Lecture

1
artificial intelligence fdm 20c introduction
to digital media lecture 26.01.2005
warren sack / film digital media department /
university of california, santa cruz
2
last time

• social networks as science
• social networks as technology
• social networks as popular culture
• social networks as art
• mini-project 2 add yourself to friendster

3
outline for today (1 of 2)

• artificial intelligence the founding document
• who was turing? what is he famous for?
• a reading of turings article computing
  machinery and intelligence in which the
  following is highlighted
• gender the role of the woman in the imitation
  game
• the aesthetics of the game the aesthetics of the
  uncanny
• the prescient insights of turing on gender and
  the body, that would turn out -- now -- to be
  most useful for trying to understanding online
  role-playing games and also some of the central
  weaknesses of decades of ai research (especially
  oversights made about the role of the body in
  models of thinking)

4
outline (2 of 2)

• a short history of artificial intelligence in
  software
• planning as a technical problem
• GPS as a solution The General Problem Solver
  by Herbert Simon, Allen Newell, and Clifford
• demo of GPS
• story generation as a planning problem
• TALESPIN as a solution
• demo of micro-talespin
• story understanding as a plan recognition problem
• FRUMP as a solution
• question answering as a problem
• ELIZA as a solution
• demo of ELIZA

5
alan turing

• Founder of computer science, artificial
  intelligence, mathematician, philosopher,
  codebreaker, and a gay man
• see http//www.turing.org.uk/turing/

6
alan turing (1912-1936)

• 1912 (23 June) Birth, Paddington, London
• 1926-31 Sherborne School
• 1930 Death of friend Christopher Morcom
• 1931-34 Undergraduate at King's College,
  Cambridge University
• 1932-35 Quantum mechanics, probability, logic
• 1935 Elected fellow of King's College, Cambridge
• 1936 The Turing machine, computability,
  universal machine

7
alan turing (1936-1946)

• 1936-38 Princeton University. Ph.D. Logic,
  algebra, number theory
• 1938-39 Return to Cambridge. Introduced to
  German Enigma cipher machine
• 1939-40 The Bombe, machine for Enigma decryption
• 1939-42 Breaking of U-boat Enigma, saving battle
  of the Atlantic
• 1943-45 Chief Anglo-American crypto consultant.
  Electronic work.
• 1945 National Physical Laboratory, London
• 1946 Computer and software design leading the
  world.

8
alan turing (1947-1954)

• 1947-48 Programming, neural nets, and artificial
  intelligence
• 1948 Manchester University
• 1949 First serious mathematical use of a
  computer
• 1950 The Turing Test for machine intelligence
• 1951 Elected FRS. Non-linear theory of
  biological growth
• 1952 Arrested as a homosexual, loss of security
  clearance
• 1953-54 Unfinished work in biology and physics
• 1954 (7 June) Death (suicide) by cyanide
  poisoning, Wilmslow, Cheshire.

9
turings imitation game (1 of 3)

• The new form of the problem can be described in
  terms of a game which we call the imitation
  game. It is played with three people, a man, a
  woman, and an interrogator who may be of either
  sex. The interrogator stays in a room apart from
  the other two. The object of the game for the
  interrogator is to determine which of the other
  two is the man and which is the woman.

10
turings imitation game (2 of 3)

• It is the man's object in the game to try and
  cause the interrogator to make the wrong
  identification.
• The object of the game for the woman is to
  help the interrogator.

11
turings imitation game (3 of 3)

• We now ask the question, What will happen when
  a machine takes the part of the man in this
  game? 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? These questions replace our original
  question, Can machines think? (Turing, 1950,
  pp. 433-434)

12
walker/sack/walker online caroline

• walker My hair is still wet from the shower
  when I connect my computer to the network,
  sipping my morning coffee. I check my email and
  find it there in between other messages an email
  from Caroline.
• sack (citing turing) The interrogator asks
  Will you please tell me the length of your
  hair?
• walker The first lines in my essay on Online
  Caroline really are striking in their insistence
  on a feminine imagery, ...

13
walker/sack/walker online caroline

• walker The first lines in my essay on Online
  Caroline really are striking in their insistence
  on a feminine imagery, ... and especially since
  the images I used (of wet hair and a shower) are
  so typical of the male objectifying gaze Sack
  refers to imagine shampoo ads with half-naked
  women or the shower scene in Psycho. Why on earth
  did I choose such a way to ground my reading of
  Online Caroline?

14
walker/sack/walker online caroline

• what is this virtual body evoked by turing and
  walker and online caroline?
• do you have a gender when you are online?

15
artificial intelligence a definition

• ... artificial intelligence AI is the science
  of making machines do things that would require
  intelligence if done by humans
• Marvin Minsky, 1963

16
artificial intelligence research areas

• Knowledge Representation
• Programming Languages
• Natural Language (e.g., Story) Understanding
• Speech Understanding
• Vision
• Robotics
• Machine Learning
• Expert Systems
• Qualitative Simulation
• Planning

17
planning as a technical problem

• GPS is what is known in AI as a planner.
• Newell, Alan, Shaw, J. C., and Simon, Herbert A.
  GPS, A Program That Simulates Human Thought. In
  Computers and Thought, ed. Edward A. Feigenbaum
  and Julian Feldman. pp. 279-293. New York, 1963
• To work, GPS required that a full and accurate
  model of the state of the world (i.e., insofar
  as one can even talk of a world of logic or
  cryptoarthimetic, two of the domains in which GPS
  solved problems) be encoded and then updated
  after any action was taken (e.g., after a step
  was added to the proof of a theorem).
• demo implementation from Peter Norvigs
  Paradigms of Artificial Intelligence Programming
  (see www.norvig.com)

18
a problem with ai planning

• the frame problem This assumption that
  perception was always accurate and that all of
  the significant details of the world could be
  modeled and followed was incorporated into most
  AI programs for decades and resulted in what
  became known to the AI community as the frame
  problem i.e., the problem of deciding what
  parts of the internal model to update when a
  change is made to the model or the external
  world.
• Cf., Martins, J. Belief Revision. In
  Encyclopedia of Artificial Intelligence, Second
  Edition. Stuart C. Shapiro (editor-in-chief), pp.
  110-116. New York, 1992

19
story generation as planning

• James Meehan, "The Metanovel Writing Stories by
  Computer", Ph.D. diss., Yale University, 1976.
• demo micro-talespin
• see http//web.media.mit.edu/wsack/micro-talespin
  .txt

20
problems with story generation missing common
sense

• Examples of Talespins missing common sense(from
  Meehan, 1976)
• Answers to questions can take more than one form.
• Dont always take answers literally.
• You can notice things without being told about
  them.
• Gravity is not a living creature.
• Stories arent really stories if they dont have
  a central problem.
• Sometimes enough is enough.
• Schizophrenia can be disfunctional.

21
story understanding as a plan recognition problem

• G. DeJong (1979) FRUMP Fast Reading
  Understanding and Memory Program
• demonstration script
• The demonstrators arrive at the demonstration
  location.
• The demonstrators march.
• Police arrive on the scene.
• The demonstrators communicate with the target of
  the demonstration.
• The demonstrators attack the target of the
  demonstration.
• The demonstrators attack the police.
• (From DeJong, 1979 pp. 19-20)

22
story understanding as plan recognition

• demo micro-sam
• Richard Cullingford, Script application
  computer understanding of newspaper stories,
  Ph.D. diss., Yale University, 1977.

23
question answering as a problem

• ELIZA as a solution
• J. Weizenbaum, ELIZA -- A Computer Program for
  the Study of Natural Language Communication
  between Man and Machine, Communications of the
  Association for Computing Machinery, vol. 9, no.
  1 (January 1965), pp. 36-45.
• demo see www.norvig.com for source code

24
next time

• human-computer interaction