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Minds and Machines - Eli Alshanetsky

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Minds and Machines - Eli Alshanetsky Powered By Docstoc
					Summer 2011
Monday, 07/18
              Blade Runner
• Was Deckard a replicant?
               Blade Runner

• Is there a characteristic feature that makes us
  human, as opposed to a machine or a
  replicant?
                  Blade Runner
• What do you think of the "Voight-Kampff" test? Is it a
  better measure of humanity (or of the human kind of
  cognition) than the Turing test? Is emotion a better
  mark of being human than reason? Are emotion and
  reason even separable?
              Blade Runner
• Could machines that don’t initially have
  emotions develop emotions on their own,
  without being explicitly programmed to
  have/develop them?
         Physical Symbol Systems
• A physical device that contains a set of interpretable and
  combinable items (symbols) and a set of processes that can
  operate on the items.
• A kind of automatic formal system.
• Symbol = stable entities that are capable of semantic
  interpretation, that can participate in processes of internal
  manipulation, e.g. copying, erasing, conjoining, and that
  can be organized so as to preserve semantic sense.
• Must be located in a wider web of real-world items and
  events.
• A symbolic expression designates an object if “given the
  expression, the system can either affect the object itself or
  behave in ways depending on the object.”
        Physical Symbol Systems
• The Physical Symbol System Hypothesis: A
  physical symbol system has the necessary and
  sufficient means for general intelligent action.
• Sufficient, since any such system “of sufficient
  size” can always be programmed so as to support
  intelligent behavior.
• Necessary, since nothing can be intelligent unless
  it is an instance of a physical-symbol system
  (PSS).
• This is an empirical hypothesis. All cases of
  intelligent action will, as a matter of scientific
  fact, turn out to be produced by a PSS.
    Symbol Systems and the Brain
• The working of the individual neurons in the brain
  may not be important in understanding the mind
  as a physical symbol system.
• What matters is the operation of the “virtual
  machine”, that can be realized in all sorts of ways.
• The role of symbols may be occupied by higher-
  level brain processes/structures. It may even be
  that different types of brain processes/structures
  play the role of the same symbol at different times.
• You should not expect to find things like EI, EII,
  EIILIIL (etc) in the brain! Just some physical thing
  that plays the role of these symbols.
 Semantically Transparent Systems
  Systems whose computational operations are
  defined over “familiar symbolic elements”
  (Clark). For example:
• A chess-playing program that applies
  procedures to symbols for rook, king,
  checkmate.
• A sentence parser that uses symbols for noun,
  verb, subject.
• A program for reasoning about liquids that has
  symbols for liquid, flow, edge.
      Why Treat Thought as Symbol
            Manipulation?
• Thinkers are physical devices whose behavior patterns
  are reason respecting.
• A pedestrian witnesses a car crash, runs to a
  telephone, and punches out 911.
• Common sense Psychology makes sense of all this at a
  stroke by depicting the agent as seeing a crash and
  wanting to get help.
• The simplest scientific explanation is that the agent’s
  brain contains symbols that represent the event as a
  car crash and that the computational state-transitions
  occurring inside the system then lead to new sets of
  states (more symbols) whose proper interpretation is,
  e.g. “seek help”, “find a telephone”, and so on.
     Why Treat Thought as Symbol
           Manipulation?
• The thought “it is raining” often leads to the
  thought “let’s go indoors”.
• Many of our thoughts are related to our other
  thoughts in virtue of their meaning in this way.
• One explanation of such rational thought-
  transitions appeals to general syntactic rules
  that manipulate semantic representations in a
  way that preserves semantic sense. (think of
  the PQ— system again…)
     Why Treat Thought as Symbol
           Manipulation?

• We have the capacity to understand an infinite
  range of sentences and to produce an infinite
  range of thoughts.
• “Billy left his tricycle on the moon”.
• What could explain our capacity to
  understand infinite new sentences like this
  and to entertain infinite thoughts of this sort?
      Why Treat Thought as Symbol
            Manipulation?
• The ability to entertain certain thoughts is
  intrinsically connected to the ability to entertain
  certain other thoughts.
• We don't find speakers who know how to express
  in their native language the fact that John loves
  the girl but not the fact that the girl loves John.
  This is appears true for expressions of any n-place
  relation, e.g. Eli (x) gave his paper (y) to Jon (z).
• One explanation appeals to general rules for
  conjoining (interpreted) symbols in the language.
• We’ll talk a lot more about this next week…
   Examples: Story Understanding
• A computer program that deploys scripts.
• The scripts uses a symbolic event description
  language to encode background information
  about certain kinds of situations, e.g. restaurant
  visits.
• Takes as input a short story: “Jack goes into the
  restaurant, orders a hamburger, sits down. Later,
  he leaves after tipping the waiters.” You can then
  ask: “Did Jack eat the hamburger?” and the
  computer answers “yes” by applying the script.
             Examples: SOAR
• ongoing project to implement general
  intelligence by computational means.
• Uses symbol processing architecture.
• All long-term knowledge is stored in a format
  called production memory.
• Knowledge is encoded in the form of
  condition-action structures: “If such-and-such
  is the case, then do so and so”.
              Examples: SOAR
• When it encounters a problem, it transfers all
  potentially relevant knowledge into a working-
  memory buffer.
• A decision procedure then selects an action to
  perform on the basis of relative desirability.
• SOAR is able to work towards a distant goal by
  creating and attempting to resolve sub-goals that
  reduce distance between current state and
  overall solution.
• Learns by “chunking”.
      Research Program (GOFAI)
• Design a program that can solve problems and
  interact with the environment in human ways.
• If such a program is found, a good case can be
  made that it’s actually implemented by
  human brains.
• We can thus study minds directly (by studying
  the software) without worrying about the
  messy details of the brain.

				
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posted:3/25/2013
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