physical symbol system hypothesis
In plain wordsThe 1976 claim by two AI pioneers that a machine which stores and rearranges symbols according to rules has everything needed for general intelligence, and that anything generally intelligent must be such a machine.
The thesis of Allen NewellAllen Newell, prop. n.: An American computer scientist who, with Herbert Simon, wrote early AI programs that solved problems the way people do, and who sought a single theory of how the mind works. and Herbert A. SimonHerbert A. Simon, prop. n.: An American scientist of decision-making who, with Allen Newell, wrote some of the first AI programs and argued that minds and computers both think by manipulating symbols. that 'a physical symbol system has the necessary and sufficient means for general intelligent action.' A physical symbol system is a machine that holds symbol structures — expressions built from tokens that designate objects or processes — and runs processes that create, copy, modify and destroy them; a digital computer running a program is the paradigm case. Sufficiency means a suitably organized symbol system can be made generally intelligent; necessity means any generally intelligent system, including the human mind, is one. Newell and Simon presented it as an empirical hypothesis, to be judged by evidence rather than proof, and it served as the theoretical charter of symbolic AIsymbolic AI, n.: An approach to AI that writes knowledge down as human-readable symbols and rules, such as 'every bird has wings', and reasons by manipulating them step by step instead of learning patterns from data. and of cognitive architecturescognitive architecture, n.: A blueprint for a complete thinking system — how memory, attention, learning and decision-making fit together — used both to build general AI agents and to model how the human mind works. such as Soar.
Origin
Stated by Allen Newell and Herbert A. Simon in their 1975 ACM Turing Award lecture, published as 'Computer Science as Empirical Inquiry: Symbols and Search' (Communications of the ACM, March 1976), where it was paired with a companion heuristic search hypothesis.
Usage note
Critics include Hubert Dreyfus, who argued that intelligence rests on embodied know-how (Dreyfus's critique of AIDreyfus's critique of AI, n.: The philosopher Hubert Dreyfus's argument, from the 1960s on, that rule-based AI could never match human intelligence, because much human skill rests on bodily know-how and intuition rather than explicit rules.), John Searle's Chinese roomChinese room, n.: A thought experiment: a person who knows no Chinese follows a rulebook to answer Chinese messages so well that outsiders think they understand. If the person doesn't understand, does a computer running a program? argument, connectionists, and those pressing the symbol grounding problemsymbol grounding problem, n.: The puzzle of how words or symbols inside a machine can mean anything by themselves, rather than only pointing to more symbols — like a dictionary that defines every word using other words.. Some defenders argue that neural networks running on digital computers are themselves symbol systems at the implementation level; critics reply that this empties the hypothesis of content.
Related
- symbolic AIsymbolic AI, n.: An approach to AI that writes knowledge down as human-readable symbols and rules, such as 'every bird has wings', and reasons by manipulating them step by step instead of learning patterns from data.
- good old-fashioned AIgood old-fashioned AI, n.: A nickname for the classic style of AI, dominant from the 1950s to the 1980s, that tried to make computers intelligent by writing knowledge and rules down by hand.
- Allen NewellAllen Newell, prop. n.: An American computer scientist who, with Herbert Simon, wrote early AI programs that solved problems the way people do, and who sought a single theory of how the mind works.
- Herbert A. SimonHerbert A. Simon, prop. n.: An American scientist of decision-making who, with Allen Newell, wrote some of the first AI programs and argued that minds and computers both think by manipulating symbols.
- symbol grounding problemsymbol grounding problem, n.: The puzzle of how words or symbols inside a machine can mean anything by themselves, rather than only pointing to more symbols — like a dictionary that defines every word using other words.
- Chinese roomChinese room, n.: A thought experiment: a person who knows no Chinese follows a rulebook to answer Chinese messages so well that outsiders think they understand. If the person doesn't understand, does a computer running a program?
- heuristicheuristic, n.: A rule of thumb: a quick, usually good-enough way to make a decision or to estimate how close you are to a goal, without any guarantee of finding the best answer.
- connectionismconnectionism, n.: The view that minds and intelligent machines are best built from huge networks of simple, neuron-like units whose connection strengths are learned, rather than from explicit rules and symbols.
Sources
- Newell, A. & Simon, H. A. (1976). Computer Science as Empirical Inquiry: Symbols and Search. Communications of the ACM 19(3), 113–126.
Mentioned in
symbolic AIsymbolic AI, n.: An approach to AI that writes knowledge down as human-readable symbols and rules, such as 'every bird has wings', and reasons by manipulating them step by step instead of learning patterns from data., Allen NewellAllen Newell, prop. n.: An American computer scientist who, with Herbert Simon, wrote early AI programs that solved problems the way people do, and who sought a single theory of how the mind works., computational theory of mindcomputational theory of mind, n.: The idea that thinking is a kind of computation: the mind processes information by following rules, much as a computer runs a program, and the brain is the machine doing it., General Problem SolverGeneral Problem Solver, prop. n.: A 1950s program meant to solve any well-defined puzzle by repeatedly comparing where it was with where it wanted to be and choosing steps that closed the gap., good old-fashioned AIgood old-fashioned AI, n.: A nickname for the classic style of AI, dominant from the 1950s to the 1980s, that tried to make computers intelligent by writing knowledge and rules down by hand., Herbert A. SimonHerbert A. Simon, prop. n.: An American scientist of decision-making who, with Allen Newell, wrote some of the first AI programs and argued that minds and computers both think by manipulating symbols., Logic TheoristLogic Theorist, prop. n.: A 1956 computer program that proved theorems from a classic logic textbook by searching cleverly rather than trying every possibility. Many historians count it as the first AI program., cognitive architecturecognitive architecture, n.: A blueprint for a complete thinking system — how memory, attention, learning and decision-making fit together — used both to build general AI agents and to model how the human mind works., language of thought hypothesislanguage of thought hypothesis, n.: The theory that we think in an inner, wordless mental language with its own symbols and grammar, which the brain manipulates when we reason, much as a computer manipulates code.