I propose to consider the question, 'Can machines think?'
This should begin with definitions of the meaning of the terms 'machine' and 'think'.
The definitions might be framed so as to reflect so far as possible the normal use of words,
but this attitude is dangerous.
If the meaning of the words 'machine' and 'think' are to be found
by examining how they are commonly used
it is difficult to escape the conclusion that the meaning
and the answer to the question, 'Can machines think?'
is to be sought in a statistical survey such as a Gallup poll. But this is absurd. - A. M. Turing's (1950, p. 433) 'Computing Machinery and Intelligence'
According to the Physical Symbol System Hypothesis or PSSH (Newell & Simon, 1976):
A physical symbol system has the necessary and sufficient means for intelligent action
The 2 most important classes of physical symbol systems with which we are acquainted are human beings and computers
IMPLICATIONS of the PSSH:
The symbolic behaviour of human beings arises because we have the characteristics of a physical symbol system
General intelligent action calls for a physical symbol system
Appropriately programmed computers would be capable of intelligent action
Intelligent systems (natural or artificial) are effectively equivalent
According to the Heuristic Search Hypothesis or HSH (Newell & Simon, 1976):
Physical symbol systems solve problems using the processes of heuristic search
Solutions to a problem are represented as symbol structures
A physical symbol system exercises its intelligence in problem-solving by searching until the symbol structures of solutions are produced
According to the Logicist Manifesto (Bringsjord, 2008):
A person is the bearer of propositional attitudes:
The basic units are propositions or declarative statements (denoted by propositional variables p, q, etc) that convey propositional content
Propositions can carry such values as TRUE, FALSE, PROBABLE, UNKNOWN, etc
The basic processes over units of inference are the modes of reasoning (viz. deductive, inductive, abductive, analogical, etc)
Logic-based AI has three attributes:
EXAMPLE 1: Logic Theorist or LT (Newell, Shaw, & Simon, 1957)
LT is a logical theorem-proving program capable of providing proofs in propositional logic LT managed to prove 38 of the first 52 theorems in Whitehead & Russell's (1910, 1912, 1913) Principia Mathematica
LT was able to provide a more elegant proof for a logical theorem (THEOREM 2.85) than the one found in Whitehead & Russell (1910, 1912, 1913)
THEOREM 2.85: ((p ∨ q) → (p ∨ r)) → (p ∨ (q → r))
Simon claimed to have solved the mind-body problem with LT
However, the editors of the Journal of Symbolic Logic rejected a paper co-authored by Newell, Simon, and LT
EXAMPLE 2: Advice Taker or AT (McCarthy, 1959)
AT is program designed by John McCarthy and Marvin Minsky for solving problems by manipulating sentences in a formal language
AT will draw immediate conclusions from a premise set
The conclusions will be either declarative or imperative sentences