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    \textcolor{red}{DARTMOUTH AND BEYOND} \\
    John McCarthy, Stanford University
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  $\bullet$ The symbolic role of the Dartmouth Summer Workshop on
  Artificial Intelligence in establishing AI as a field of research
  was more important than the specific results obtained at the
  meeting.  I didn't expect this.

  $\bullet$ The most important results presented at the meeting were
  those of Newell, Simon, and Shaw.  Their work, which had been done
  previously, included the list processing system IPL and its
  use to program their Logic Theory Machine which corresponded to
  protocols of subjects given the task of proving logical theorems as
  pure symbolic manipulation, i.e. with no explanation of logic.
  
  $\bullet$ Alex Bernstein of IBM reported on his design for a full
  game chess playing program. My discovery of the alpha-beta
  heuristic for chess.
  
  $\bullet$ The results obtained at the meeting included Minsky's
  ideas for a geometry theorem prover that only tried to prove
  sentences true in a diagram, Solomonoff's work on algorithmic
  complexity.  My work on logical AI only started two years later.
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    \textcolor{red}{PREHISTORY OF THE DARTMOUTH WORKSHOP} \\
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  $\bullet$ The four organizers of the 1956 Dartmouth Workshop on
  artificial intelligence were John McCarthy, Marvin Minsky, Nathaniel
  Rochester, and Claude Shannon.
  
  $\bullet$ My own interest in AI was triggered by attending the
  September 1948 Hixon Symposium held at Caltech.  At this symposium,
  the computer and the brain were compared, the comparison being
  rather theoretical, since there wern't any stored programmed
  computers yet.  The idea of intelligent computer programs isn't in
  the proceedings, though maybe it was discussed.  Turing already had
  the idea in 1947.  I developed some ideas about intelligent finite
  automata but found them unsatisfactory and didn't publish.  My
  Princeton PhD thesis was about differential equations.
  
  $\bullet$ Marvin Minsky was independently interested in AI and in
  1950, while a senior at Harvard built, along with Dean Edmunds,
  built a simple neural net learning machine.  At Princeton, Minsky
  pursued his interest in AI, and his 1954 PhD thesis established the
  criterion for a neuron to be a universal computing element.
  
  $\bullet$ Claude Shannon proposed a chess program in 1950, built
  many small relay machines exhibiting some features of intelligence,
  one being a mouse searching a maze to find a goal.
  
  $\bullet$ In the summer of 1952 Shannon supported Minsky and me at
  Bell Telephone Laboratories.  The result of my efforts was a paper
  on the inversion of functions defined by Turing machines.  I was
  unsatisfied with this approach to AI also, because it didn't
  permit the direct expression of facts about the world.
  
  $\bullet$ Also in 1952 Shannon and I invited a number of researchers
  to contribute to a volume entitled Automata Studies that finally
  came out in 1956.
  
  $\bullet$ In the summer of 1952 Shannon supported Minsky and me at
  Bell Telephone Laboratories.  The result of my efforts was a paper
  on the inversion of functions defined by Turing machines.  I was
  unsatisfied with this approach to AI also.
  
  $\bullet$ Also in 1952 Shannon and I invited a number of researchers
  to contribute to a volume entitled Automata Studies that finally
  came out in 1956.
  
  $\bullet$ I came to Dartmouth College in 1954 and was invited by
  Nathaniel Rochester of IBM to spend the summer of 1955 in his
  Information Research Department in Poughkeepsie, NY.  Rochester had
  been the designer of the IBM 701 computer.  Rochester became
  interested in AI and his department sponsored important work until
  IBM had a fit of stupidity in 1959.
  
  $\bullet$ That summer Minsky, Rochester, Shannon, and I proposed the
  Dartmouth workshop.  The proposal to the Rockefeller Foundation was
  written in August 1955, and is the source of the term
  \emph{artificial intelligence}.  The term was chosen to nail the
  flag to the mast, because I (at least) was disappointed at how few
  of the papers in \emph{Automata Studies} dealt with making machines
  behave intelligently.  We wanted to focus the attention of the
  participants.
  
  $\bullet$ The original idea of the proposal was that the
  participants would spend two months at Dartmouth working
  collectively on AI, and we hoped would make substantial advances.
  
  $\bullet$ It didn't work that way for three reasons.  First the
  Rockefeller Foundation only gave us half the money we asked for.
  Second, the participants all had their own research agendas and
  weren't much deflected from them.  Therefore, the participants came
  to Dartmouth at varied times and for varying lengths of time.  The
  main reason is that AI presents many more difficulties than were
  known in 1956.

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    \textcolor{red}{WHAT HAPPENED AT DARTMOUTH?} \\
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  $\bullet$ Newell and Simon were the stars---with list processing and
  the logic theory machine.
  
  $\bullet$ Minsky's diagram based geometry theorem proving idea.
  Rochester got Herbert Gelernter to do it, but IBM had a fit of
  stupidity in 1959 and lost its advantage in AI.

$\bullet$ Solomonoff's start on algorithmic complexity.

$\bullet$ Alex Bernstein's chess program.  My alpha-beta heuristic for
chess-like games.

$\bullet$ My own ideas on logical AI came two years later.

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    \textcolor{red}{A SAMPLE OF WHAT AI HAS ACCOMPLISHED?} \\
  \end{center}
  
  $\bullet$ As in any scientific field, we need to distinguish basic
  research from applications.  At present too large a fraction of the
  work is going into applications.
  
  $\bullet$ Basic AI research accomplishments include success in chess
  as a \emph{drosophila} for AI (and by way of contrast, non-success
  in \emph{go}, formalisms for action including situation calculus and
  event calculus, non-monotonic programming systems like Microplanner
  and Prolog, theory of non-monotonic reasoning, including
  circumscription and logic of defaults.  The surprising success of
  propositional satisfiability programs and their widespread
  applications.  The Causal Calculator.

  $\bullet$ Applied results include many classification applications,
  robotics, computer vision, and driving a vehicle.
  
  $\bullet$ There quite a few accomplishments I would include if
  reminded.  I almost forgot propositional satisfiability.  Apologies
  to the overlooked.

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\begin{slide}
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    \textcolor{red}{WHEN WILL WE HAVE HUMAN-LEVEL AI?---Kurzweil's blunder.} \\
  \end{center}
  
  $\bullet$ \textcolor{blue}{This is the wrong question.}  We can't
  yet extrapolate from present AI to human-level in 2029 or any other
  fixed date.
  
  $\bullet$ \textcolor{blue}{The right question.}  We will reach
  human-level AI when someone solves some basic problems.  Maybe five
  years---maybe 500 years.  The genetic code came 100 years after
  Mendel.

  $\bullet$ \textcolor{blue}{What problems?}
  
  $\bullet$ How to do nonmonotonic reasoning in general.  Formalizing
  entities that don't have if-and-only-if definitions.  Sufficiently
  self-aware systems.  \textcolor{blue}{I know more, but most likely
    there are problems no-one knows about yet.}
%bob melisso weekend america npr
  
  $\bullet$ Three classical problems of AI are the
  \textcolor{blue}{frame problem} of how to avoid specifying what
  doesn't change when an event occurs, the
  \textcolor{blue}{qualification problem} of avoiding specifying every
  niggling qualification for an action to be successful, and the
  \textcolor{blue}{ramification problem} of avoiding specifying all
  the side effects of an event.  All three have been solved in
  important contexts and for important applications, but I think none
  have been solved at the human leve of intelligence.
  
  $\bullet$ These ideas require extensions to logic, but much
  efficiency has been achieved by restrictions.  We need logical
  systems that can reason about their own methods.

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\begin{slide}
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    \textcolor{red}{THREE OF THE PROBLEMS BETWEEN US AND HUMAN-LEVEL AI?} \\
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  $\bullet$ \textcolor{blue}{Non-monotonic reasoning.}  If I hire you
  to build me a bird cage, you must presume the bird can fly.  But if
  you then learn my bird is a penguin, you can no longer infer that.
  Non-monotonic logic has formalized these, e.g. with
  \emph{circumscription}, and appled them to cases like birds, but a
  general way of doing non-mon is yet to come.
  
  $\bullet$ \textcolor{blue}{Logical treatment of partly defined
    objects.}  Examples: The snow and rock that make up Mount Everest,
  the wants of the U.S.  Idea: \textcolor{blue}{An object ill-defined
    in general, may have an if-and-only-if definition in a particular
    context.  Start from the easy contexts.}  Example: mother to a
  small child.
  
  $\bullet$ \textcolor{blue}{Self awareness.}  Example: How do you
  know you can't infer whether George Bush is sitting or standing at
  this moment.  How should a robot know?
  
  $\bullet$ I know several more problems AI has to solve, e.g.
  formalized contexts.  \textcolor{blue}{There are probably several
    important problems no-one knows about yet.}  How long will it be
  before they are identified and solved---or turn out to be automatic
  consequences of a general method?

  $\bullet$ These ideas are elaborated in articles reprinted on my web
  site http://www-formal.stanford.edu/jmc/.

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\begin{slide}
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    \textcolor{red}{HOW LONG TO HUMAN LEVEL?} \\
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  $\bullet$ Maybe five years.  Maybe 500 years.  If your interest is
  in dates, begin by speculating about when general non-monotonic
  reasoning will be understood.
  
  $\bullet$ Human-level AI is more likely to result from individual
  basic research, theoretical and experimental, than from research
  programmes recommended by commmitees.  None of my work was
  on a topic suggested by a committee.

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    \textcolor{red}{A MODEST PROPOSAL} \\
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  $\bullet$ This proposal is based on the proposition that new
  concepts are probably necessary for human-level AI, and these
  concepts are more likely to come from individuals rather than
  programmes proposed by committees.

  $\bullet$ The proposal is for a programme of individual fellowships.

  $\bullet$ Each fellowship supports the individual for five years.

  $\bullet$ The programme is aimed at graduate students or new
  postdocs.

  $\bullet$ The recipient can move among institutions.

  $\bullet$ Except in unusual circumstances no intermediate progress
  reports are required.
  
  $\bullet$ The programme could be run by NSF or DARPA or started with
  the \$7 million that AAAI has left.

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    \textcolor{red}{} \\
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  $\bullet$

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