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| public:t-713-mers:mers-24:concepts_terms [2024/11/04 11:54] – [Models] thorisson | public:t-713-mers:mers-24:concepts_terms [2024/11/04 11:57] (current) – [Models] thorisson |
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| | What it is | A model is a "cartoon" of a phenomenon -- an information structure that captures the most important (preferably all the important) aspects of a phenomenon in question. | | | What it is | A model is a "cartoon" of a phenomenon -- an information structure that captures the most important (preferably all the important) aspects of a phenomenon in question. | |
| | All scientific theories present a model | No matter how explicit or implicit, all scientific theories are models of the world. //Best known example: E=mc^2// | | | All scientific theories present a model | No matter how explicit or implicit, all scientific theories are models of the world. //Best known example: E=mc^2// | |
| | Science vs. Mathematics | Mathematics is **axiomatic**: Some a-priori premises are (and must be) assumed. \\ Science is non-axiomatic: We do not know the full set of rules that govern the universe, and we will never know. | | | Science vs. Mathematics | Mathematics is **axiomatic**: Some a-priori premises are (and must be) assumed. \\ Science is non-axiomatic: We do not know the full set of rules that govern the universe, and we will never know (for sure). | |
| | Science vs. Engineering | In science we look for the model; \\ in engineering we mold the world to behave like our model. \\ \\ // Example for science: // \\ [phenomenon] // We see something interesting and call it "**intelligence**". // \\ [model] // We come up with theories for how it works. // \\ [empirical research] // We test the models through systematic creation and evaluation of hypotheses. // \\ [create new models] // We revise the theory. // \\ \\ // Example for engineering:// \\ [model] // We have a theory of intelligence. // \\ [engineering] // We want to build an intelligent system. // \\ [implementation] // We use the theory as a blueprint. // \\ [requirements and theory] // We make sure it behaves to its specifications. // | | | Science vs. Engineering | In science we look for the model; \\ in engineering we mold the world to behave like our model. \\ \\ // Example for science: // \\ [phenomenon] // We see something interesting and call it "**intelligence**". // \\ [model] // We come up with theories for how it works. // \\ [empirical research] // We test the models through systematic creation and evaluation of hypotheses. // \\ [create new models] // We revise the theory. // \\ \\ // Example for engineering:// \\ [model] // We have a theory of intelligence. // \\ [engineering] // We want to build an intelligent system. // \\ [implementation] // We use the theory as a blueprint. // \\ [requirements and theory] // We make sure it behaves to its specifications. // | |
| | Why AI is special | Most research fields rely primarily on either the scientific research approach or the engineering approach. \\ //AI is special in that it is a commitment to doing both.// \\ (Although it does not always operate accordingly.) | | | Why AI is special | Most research fields rely primarily on either the scientific research approach or the engineering approach. \\ //**AI is special in that it is committed to doing both.**// \\ (Although it does not always operate accordingly.) \\ AI is also the only field of science explicitly committed to the phenomenon of intelligence. | |
| | \\ Science + Math | We strive to make scientific theories (models of the world) mathematical because of the //compactness, precision,// and //specificity// this can give us. However, it is not guaranteed solely through the use of math because //a model must detail how it maps to the thing it is a model of//. If this is not done properly the math provides //no benefits//. \\ Mapping a model to its reference: A good scientist does it properly; a bad scientist does it sloppily; the wannabe ignores it happily. \\ Bottom line: Being mathematical is //no guarantee// for good science - it is neither necessary nor sufficient. | | | \\ Science + Math | We strive to make scientific theories (models of the world) mathematical because of the //compactness, precision,// and //specificity// this can give us. However, it is not guaranteed solely through the use of math because //a model must detail how it maps to the thing it is a model of//. If this is not done properly the math provides //no benefits//. \\ Mapping a model to its reference: A good scientist does it properly; a bad scientist does it sloppily; the wannabe ignores it happily. \\ Bottom line: Being mathematical is //no guarantee// for good science - it is neither necessary nor sufficient. | |
| | Science + Engineering + Math: The Holy Trinity | The three fields so defined support each other: Building better scientific models helps us engineer better; engineering better helps us build new tools for doing science better. Both are bootstrapped by philosophy and clarified through math. | | | Science + Engineering + Math: The Holy Trinity | The three fields so defined support each other: Building better scientific models helps us engineer better; engineering better helps us build new tools for doing science better. Both are bootstrapped by philosophy and clarified through math. | |
