Physical symbol system

Two lines of evidence suggested to Allen Newell and Herbert A. Simon that "symbol manipulation" was the essence of both human and machine intelligence: the development of artificial intelligence programs and psychological experiments on human beings.

First, in the early decades of AI research there were a number of very successful programs that used high level symbol processing, such as Newell and Herbert A. Simon's General Problem Solver or Terry Winograd's SHRDLU.[5] John Haugeland named this kind of AI research "Good Old Fashioned AI" or GOFAI.[6] Expert systems and logic programming are descendants of this tradition. The success of these programs suggested that symbol processing systems could simulate any intelligent action.

And second, psychological experiments carried out at the same time found that, for difficult problems in logic, planning or any kind of "puzzle solving", people used this kind of symbol processing as well. AI researchers were able to simulate the step by step problem solving skills of people with computer programs. This collaboration and the issues it raised eventually would lead to the creation of the field of cognitive science.[7] (This type of research was called "cognitive simulation".) This line of research suggested that human problem solving consisted primarily of the manipulation of high level symbols.

In Newell and Simon's arguments, the "symbols" that the hypothesis is referring to are physical objects that represent things in the world, symbols such as <dog> that have a recognizable meaning or denotation and can be composed with other symbols to create more complex symbols.

However, it is also possible to interpret the hypothesis as referring to the simple abstract 0s and 1s in the memory of a digital computer or the stream of 0s and 1s passing through the perceptual apparatus of a robot. These are, in some sense, symbols as well, although it is not always possible to determine exactly what the symbols are standing for. In this version of the hypothesis, no distinction is being made between "symbols" and "signals", as David Touretzky and Dean Pomerleau explain.[8]

Under this interpretation, the physical symbol system hypothesis asserts merely that intelligence can be digitized. This is a weaker claim. Indeed, Touretzky and Pomerleau write that if symbols and signals are the same thing, then "[s]ufficiency is a given, unless one is a dualist or some other sort of mystic, because physical symbol systems are Turing-universal."[8] 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. Since any digital computer is Turing-universal, any digital computer can, in theory, simulate anything that can be digitized to a sufficient level of precision, including the behavior of intelligent organisms. The necessary condition of the physical symbol systems hypothesis can likewise be finessed, since we are willing to accept almost any signal as a form of "symbol" and all intelligent biological systems have signal pathways.

Hubert Dreyfus attacked the necessary condition of the physical symbol system hypothesis, calling it "the psychological assumption" and defining it thus:

• The mind can be viewed as a device operating on bits of information according to formal rules.[9]

Dreyfus refuted this by showing that human intelligence and expertise depended primarily on unconscious instincts rather than conscious symbolic manipulation. Experts solve problems quickly by using their intuitions, rather than step-by-step trial and error searches. Dreyfus argued that these unconscious skills would never be captured in formal rules.[10] However, advances in sentient [11] and common sense reasoning[12] has set forth empirical data that scholars are seriously considering in juxtaposition to "the psychological assumption".

John Searle's Chinese room argument, presented in 1980, attempted to show that a program (or any physical symbol system) could not be said to "understand" the symbols that it uses; that the symbols themselves have no meaning or semantic content, and so the machine can never be truly intelligent from symbol manipulation alone.[13]

In the sixties and seventies, several laboratories attempted to build robots that used symbols to represent the world and plan actions (such as the Stanford Cart). These projects had limited success. In the middle eighties, 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. Brooks (and others, such as Hans Moravec) discovered that our most basic skills of motion, survival, perception, balance and so on did not seem to require high level symbols at all, that in fact, the use of high level symbols was more complicated and less successful.

In a 1990 paper Elephants Don't Play Chess, robotics researcher Rodney Brooks took direct aim at the physical symbol system hypothesis, arguing that symbols are not always necessary since "the world is its own best model. It is always exactly up to date. It always has every detail there is to be known. The trick is to sense it appropriately and often enough."[14]

George Lakoff, Mark Turner and others have argued that our abstract skills in areas such as mathematics, ethics and philosophy depend on unconscious skills that derive from the body, and that conscious symbol manipulation is only a small part of our intelligence.