The physical symbol system hypothesis (PSSH) is a position in the philosophy of artificial intelligence formulated by Allen Newell and Herbert A. Simon.
[3] The latest version is called the computational theory of mind, associated with philosophers Hilary Putnam and Jerry Fodor.
This was "symbol manipulation" -- the people were iteratively exploring a formal system looking for a matching pattern that solved the puzzle.
This line of research suggested that human problem solving consisted primarily of the manipulation of high-level symbols.
These programs were very successful, demonstrating skills that many people at the time had assumed were impossible for machines, such as solving algebra word problems (STUDENT), proving theorems in logic (Logic Theorist), learning to play competitive checkers (Arthur Samuel's checkers), and communicating in natural language (ELIZA, SHRDLU).
"[12] The widely accepted Church–Turing thesis holds that any Turing-universal system can simulate any conceivable process that can be digitized, given enough time and memory.
However, AI research has so far not been able to produce a system with artificial general intelligence -- the ability to solve a variety of novel problems, as human do.
Thus, the PSSH is not relevant to positions which refer to "mind" or "consciousness", such as John Searle's Strong AI hypothesis:
[14][15]Nils Nilsson has identified four main "themes" or grounds in which the physical symbol system hypothesis has been attacked.
If sub-symbolic AI programs, such as deep learning, can intelligently solve problems, then this is evidence that the necessary side of the PSSH is false.
Rodney Brooks of MIT was able to build robots that had superior ability to move and survive without the use of symbolic reasoning at all.
"[19] In 2012 AlexNet, a deep learning network, outperformed all other programs in classifying images on ImageNet by a substantial margin.