The broad information-processing skills of a digital stored program computer are referred to as symbol manipulation.
From the 1960s through the 1980s, seeing the computer as
fundamentally a symbol manipulator became the norm, leading to the scientific
study of symbolic artificial intelligence, now known as Good Old-Fashioned AI
(GOFAI).
In the 1960s, the emergence of stored-program computers sparked
a renewed interest in a computer's programming flexibility.
Symbol manipulation became a comprehensive theory of
intelligent behavior as well as a research guideline for AI.
The Logic Theorist, created by Herbert Simon, Allen Newell,
and Cliff Shaw in 1956, was one of the first computer programs to mimic
intelligent symbol manipulation.
The Logic Theorist was able to prove theorems from Bertrand
Russell's Principia Mathematica (1910–1913) and Alfred North Whitehead's
Principia Mathematica (1910–1913).
It was presented at Dartmouth's Artificial Intelligence Summer Research Project in 1956. (the Dartmouth Conference).
John McCarthy, a Dartmouth mathematics professor who invented the phrase "artificial intelligence," convened this symposium.
The Dartmouth Conference might be dubbed the genesis of AI
since it was there that the Logic Theorist first appeared, and many of the
participants went on to become pioneering AI researchers.
The features of symbol manipulation, as a generic process that underpins all types of intelligent problem-solving behavior, were thoroughly explicated and provided a foundation for most of the early work in AI only in the early 1960s, when Simon and Newell had built their General Problem Solver (GPS).
In 1961, Simon and Newell took their knowledge of AI and
their work on GPS to a wider audience.
"A computer is not a number-manipulating device; it is
a symbol-manipulating device," they wrote in Science, "and the
symbols it manipulates may represent numbers, letters, phrases, or even
nonnumerical, nonverbal patterns" (Newell and Simon 1961, 2012).
Reading "symbols or patterns presented by appropriate
input devices, storing symbols in memory, copying symbols from one memory
location to another, erasing symbols, comparing symbols for identity, detecting
specific differences between their patterns, and behaving in a manner
conditional on the results of its processes," Simon and Newell continued
(Newell and Simon 1961, 2012).
The growth of symbol manipulation in the 1960s was also influenced by breakthroughs in cognitive psychology and symbolic logic prior to WWII.
Starting in the 1930s, experimental psychologists like Edwin
Boring at Harvard University began to advance their profession away from
philosophical and behavioralist methods.
Boring challenged his colleagues to break the mind open and
create testable explanations for diverse cognitive mental operations (an approach that was adopted by Kenneth Colby in his work on PARRY in the 1960s).
Simon and Newell also emphasized their debt to pre-World War
II developments in formal logic and abstract mathematics in their historical
addendum to Human Problem Solving—not because all thought is logical or follows
the rules of deductive logic, but because formal logic treated symbols as
tangible objects.
"The formalization of logic proved that symbols can be
copied, compared, rearranged, and concatenated with just as much definiteness
of procedure as [wooden] boards can be sawed, planed, measured, and glued [in a
carpenter shop]," Simon and Newell noted (Newell and Simon 1973, 877).
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You may also want to read more about Artificial Intelligence here.
See also:
Expert Systems; Newell, Allen; PARRY; Simon, Herbert A.
References & Further Reading:
Boring, Edwin G. 1946. “Mind and Mechanism.” American Journal of Psychology 59, no. 2 (April): 173–92.
Feigenbaum, Edward A., and Julian Feldman. 1963. Computers and Thought. New York: McGraw-Hill.
McCorduck, Pamela. 1979. Machines Who Think: A Personal Inquiry into the History and Prospects of Artificial Intelligence. San Francisco: W. H. Freeman and Company
Newell, Allen, and Herbert A. Simon. 1961. “Computer Simulation of Human Thinking.” Science 134, no. 3495 (December 22): 2011–17.
Newell, Allen, and Herbert A. Simon. 1972. Human Problem Solving. Englewood Cliffs, NJ: Prentice Hall.
Schank, Roger, and Kenneth Colby, eds. 1973. Computer Models of Thought and Language. San Francisco: W. H. Freeman and Company.
