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Type inference
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Type inference
Type inference, sometimes called type reconstruction,, refers to the automatic detection of the type of an expression in a formal language. These include programming languages and mathematical type systems, but also natural languages in some branches of computer science and linguistics.
Typeability is sometimes used quasi-synonymously with type inference, however some authors make a distinction between typeability as a decision problem (that has yes/no answer) and type inference as the computation of an actual type for a term.
In a typed language, a term's type determines the ways it can and cannot be used in that language. For example, consider the English language and terms that could fill in the blank in the phrase "sing _." The term "a song" is of singable type, so it could be placed in the blank to form a meaningful phrase: "sing a song." On the other hand, the term "a friend" does not have the singable type, so "sing a friend" is nonsense. At best it might be metaphor; bending type rules is a feature of poetic language.
A term's type can also affect the interpretation of operations involving that term. For instance, "a song" is of composable type, so we interpret it as the thing created in the phrase "write a song". On the other hand, "a friend" is of recipient type, so we interpret it as the addressee in the phrase "write a friend". In normal language, we would be surprised if "write a song" meant addressing a letter to a song or "write a friend" meant drafting a friend on paper.
Terms with different types can even refer to materially the same thing. For example, we would interpret "to hang up the clothes line" as putting it into use, but "to hang up the leash" as putting it away, even though, in context, both "clothes line" and "leash" might refer the same rope, just at different times.
Typings are often used to prevent an object from being considered too generally. For instance, if the type system treats all numbers as the same, then a programmer who accidentally writes code where 4 is supposed to mean "4 seconds" but is interpreted as "4 meters" would have no warning of their mistake until it caused problems at runtime. By incorporating units into the type system, these mistakes can be detected much earlier. As another example, Russell's paradox arises when anything can be a set element and any predicate can define a set, but more careful typing gives several ways to resolve the paradox. In fact, Russell's paradox sparked early versions of type theory.
There are several ways that a term can get its type:
Especially in programming languages, there may not be much shared background knowledge available to the computer. In manifestly typed languages, this means that most types have to be declared explicitly. Type inference aims to alleviate this burden, freeing the author from declaring types that the computer should be able to deduce from context.
Hub AI
Type inference AI simulator
(@Type inference_simulator)
Type inference
Type inference, sometimes called type reconstruction,, refers to the automatic detection of the type of an expression in a formal language. These include programming languages and mathematical type systems, but also natural languages in some branches of computer science and linguistics.
Typeability is sometimes used quasi-synonymously with type inference, however some authors make a distinction between typeability as a decision problem (that has yes/no answer) and type inference as the computation of an actual type for a term.
In a typed language, a term's type determines the ways it can and cannot be used in that language. For example, consider the English language and terms that could fill in the blank in the phrase "sing _." The term "a song" is of singable type, so it could be placed in the blank to form a meaningful phrase: "sing a song." On the other hand, the term "a friend" does not have the singable type, so "sing a friend" is nonsense. At best it might be metaphor; bending type rules is a feature of poetic language.
A term's type can also affect the interpretation of operations involving that term. For instance, "a song" is of composable type, so we interpret it as the thing created in the phrase "write a song". On the other hand, "a friend" is of recipient type, so we interpret it as the addressee in the phrase "write a friend". In normal language, we would be surprised if "write a song" meant addressing a letter to a song or "write a friend" meant drafting a friend on paper.
Terms with different types can even refer to materially the same thing. For example, we would interpret "to hang up the clothes line" as putting it into use, but "to hang up the leash" as putting it away, even though, in context, both "clothes line" and "leash" might refer the same rope, just at different times.
Typings are often used to prevent an object from being considered too generally. For instance, if the type system treats all numbers as the same, then a programmer who accidentally writes code where 4 is supposed to mean "4 seconds" but is interpreted as "4 meters" would have no warning of their mistake until it caused problems at runtime. By incorporating units into the type system, these mistakes can be detected much earlier. As another example, Russell's paradox arises when anything can be a set element and any predicate can define a set, but more careful typing gives several ways to resolve the paradox. In fact, Russell's paradox sparked early versions of type theory.
There are several ways that a term can get its type:
Especially in programming languages, there may not be much shared background knowledge available to the computer. In manifestly typed languages, this means that most types have to be declared explicitly. Type inference aims to alleviate this burden, freeing the author from declaring types that the computer should be able to deduce from context.