Introduction To AI History

1
Introduction To AI History

• G51IAI Introduction to AI
• Andrew Parkes
• http//www.cs.nott.ac.uk/ajp/

2
Donald Knuth on AI

• Well-respected CS Professor at Stanford, in an
  interview
• The hardest applications and most challenging
  problems, throughout many years of computer
  history, have been in artificial intelligence
• AI has been the most fruitful source of
  techniques in computer science.
• It led to many important advances, like data
  structures and list processing artificial
  intelligence has been a great stimulation
• (c) 1993 Computer Literacy Bookshops
• http//www.literateprogramming.com/clb93.pdf
• well worth reading for a (controversial?) view of
  CS as a whole

3
Predictions

• Within 10 years a computer will be a chess
  champion
• Herbert Simon, 1958
• Conversion from Russian to English, when
  presented with
• The spirit is willing but the flesh is weak
  produced
• The vodka is good but the meat is rotten
• National Research Council, 1957

4
Alan Turing

• Founder of computer science, mathematician,
  philosopher and code breaker

5
Alan Turing

• 1912 (23 June) Birth, Paddington, London
• 1931-34 Undergraduate at King's College,
  Cambridge University
• 1935 Elected fellow of King's College, Cambridge
• 1936 The Turing machine On Computable
  Numbers... Submitted
• 1936-38 At Princeton University. Ph.D. Papers in
  logic, algebra, number theory
• 1938-39 Return to Cambridge. Introduced to
  German Enigma cipher problem

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Alan Turing

• 1939-40 Devises the Bombe, machine for Enigma
  decryption
• 1947-48 Papers on programming, neural nets, and
  prospects for artificial intelligence
• 1948 Manchester University
• 1950 Philosophical paper on machine
  intelligence the Turing Test
• 1954 (7 June) Death by cyanide poisoning,
  Wilmslow, Cheshire

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The Turing Test

• Proposed by Alan Turing in 1950
• Suggested as a way of saying when we could
  consider computers to be intelligent
• You need to read and understand The Turing Test
  (also see next lecture)

8
The Chinese Room

• If the Turing Test was passed Turing would
  conclude that the machine was intelligent
• In 1980 John Searle devised a thought experiment
  which he called the Chinese Room (Searle, 1980)
• Searle, J.R. 1980. Minds, brains and programs.
  Behavioural and Brain Sciences, 3 417-457, 1980

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The Chinese Room

• You need to read and understand the Chinese Room
  (also see next lecture)
• You need to be able to have an opinion about The
  Turing Test and Chinese Room

10
Landmarks in AI

• Physical Symbol System Hypothesis
• ELIZA (A Therapist)
• MYCIN (First Expert System)
• XCON

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Physical Symbol System Hypothesis

• Newell and Simon 1976
• A physical system consists of a set of entities,
  called symbols,
• At any instant of time the system will contain a
  collection of these symbol structures.
• Besides these structures, the system also
  contains a collection of processes that operate
  on expressions to produce other expressions
  processes of creation, modification, reproduction
  and destruction.
• A physical symbol system is a machine that
  produces through time an evolving collection of
  symbol structures. Such a system exists in a
  world of objects wider than just these symbolic
  expressions themselves.

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Physical Symbol System Hypothesis

• Newell and Simon 1976
• The Physical Symbol System Hypothesis (PSSH)
• A physical system has the necessary and
  sufficient means for general intelligent action.

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ELIZA

• Eliza (Weizenbaum, 1966, one of the first AI
  programs
• allowed dialogue with a user (via text like a
  chat line)
• at the time it was impressive
• Based on a set of rules that it used to match the
  left hand side of the users input.
• If two or more rules then used one at random
• This added to the illusion that ELIZA has some
  understanding.
• Some people really became hooked on talking to it
• It clearly has no understanding at all, but is
  just a keyword match
• This is depressing about our own abilities to
  judge intelligence?

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MYCIN

• MYCIN (Shortliffe, 1976) is an expert system
  that
• diagnoses infectious diseases
• is considered as one of the first expert systems
  as it was attempting to replicate the doctors
  knowledge in a computer program.
• uses backwards reasoning to work backwards from
  the patients illness to the possible cause.

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MYCIN

• Uses rules such as
• "If the organism has a certain characteristic as
  determined by the lab results, then it is likely
  that the organism is x."
• Uses certainty factors
• numeric indications as to how much confidence we
  have in the conclusion of the rule given the
  evidence that was presented.
• there are methods for combining certainty factors
  in order to give an overall certainty factor

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XCON/R1

• One of the most cited expert systems
• Developed by DEC (Digital Equipment Corporation)
• Ensured the customer was supplied with all the
  components and software that was needed to make
  up the specified computer system that they had
  ordered.
• For example, if a customer orders a disc drive
  then DEC has to ensure that the customer also
  receives the disc controller and the relevant
  cables.
• As a single system can be built from thousands of
  separate components, it was not easy for a human
  to ensure that the customer had everything he/she
  needed to build the system.

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XCON/R1

• Ensured the customer was supplied with all the
  components and software that was needed to make
  up the specified computer system that they had
  ordered.
• XCON/R1 was an expert system that did this job
  automatically.
• It saved DEC millions of dollars a year and
  raised the profile of expert system to a level
  that had not been seen before.

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Expert Systems

• Forward Chaining reasoning from the facts
  towards conclusions
• Backward Chaining reasoning from goal backwards
  looking for supporting facts
• (see the notes)
• General Problems of Expert Systems
• Obtaining the rules in the first place,
  knowledge elicitation, from the expert is very
  hard and tedious
• rule systems are fragile or brittle a few
  missing or incorrect rules can lead to ridiculous
  results
• Urban Myth? A system asked male patient if they
  are pregnant, because did not have world
  knowledge to know that the answer must be no

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ASIDE UKs AI Winter

• Comments by Jim Howe (head of Edinburgh AI)
• http//www.dai.ed.ac.uk/AI_at_Edinburgh_perspecti
  ve.html
• By this time, the high level of discord had
  become known to the School's main sponsors, the
  Science Research Council.
• Its reaction was to invite Sir James Lighthill to
  review the field. Although his report which was
  published early in 1973 supported AI research
  related to automation and to computer simulation
  of neurophysiological and psychological
  processes, it was highly critical of basic
  research in foundational areas such as robotics
  and language processing.
• Lighthill's report provoked a loss of confidence
  in AI by the academic community in the UK which
  persisted for a decade - the so-called "AI
  Winter"."

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Summary

• Turing
• Landmarks
• Physical Symbol Hypothesis
• Eliza
• Mycin
• XCON
• ( many others read AIMA!)
• Next Philosophy of AI
• Turing Test, Chinese Room, Mathematical Limits
• As usual, you should also read the web materials
  on these topics!

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Questions?