Difference between revisions of "Nonperson predicate"

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A '''Nonperson Predicate''' is a theorized test used to distinguish between a person and anything that isn't a person. The need for such a test arises from the possibility that when an [[Artificial General Intelligence]] predicts a person's actions, it may develop a model of them so complete that the model itself qualifies as a person. As the AGI investigates possibilities, all the negative situations the model experiences would generate a large amount of negative [[utility]]. Simulating a sufficiently complex model of a person is a [[computational hazard]]. Such a situation may be avoidable by limiting the complexity of any model of a person that an AGI creates, as discussed in Computational Hazards.  
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{{arbitallink|https://arbital.com/p/nonperson_predicate/|Nonperson predicate}}A '''Nonperson Predicate''' is a theorized test which can definitely distinguish computational structures which are ''not'' people; i.e., a predicate which returns 1 for all people, and returns 0 or 1 for nonpeople; thus if it returns 1, the structure may or may not be a person, but if it returns 0, the structure is definitely not a person. In other words, any time at least one trusted nonperson predicate returns 0, we know we can run that program without creating a person. (The impossibility of perfectly distinguishing people and nonpeople is a trivial consequence of the halting problem.)
  
Any practical implementation would likely consist of a large number of nonperson predicates of increasing complexity. For most nonpersons, an predicate will quickly return that it is not a person and conclude the test. Although any number of the predicates may be used before the test claims that something is not a person, it is crucial that any predicate in the test never claims that a person isn't. If unavoidable, it is preferable that the AGI considers nonpersons persons than considering a person a nonperson.  
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The need for such a test arises from the possibility that when an [[Artificial General Intelligence]] predicts a person's actions, it may develop a model of them so complete that the model itself qualifies as a person (though not necessarily the ''same'' person). As the AGI investigates possibilities, these simulated people might be subjected to a large number of unpleasant situations. With a trusted nonperson predicate, either the AGI's designers or the AGI itself could ensure that no actual people are created.
  
=== See Also ===
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Any practical implementation would likely consist of a large number of nonperson predicates of increasing complexity. For most nonpersons, a predicate will quickly return that it is not a person and conclude the test. Although any number of the predicates may be used before the test claims that something is not a person, it is crucial that any predicate in the test never claims that a person isn't a person. Unclassifiable cases being in-principle unavoidable, it is preferable that the AGI errs on the side of considering possible-persons as persons.
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== See Also ==
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* [[Computational hazard]]
 
* [[Philosophical zombie]]
 
* [[Philosophical zombie]]
  
=== Blog Posts ===
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== Blog Posts ==
* [http://lesswrong.com/lw/x4/nonperson_predicates/ Nonperson Predicates] by Eliezer Yudkowsky
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* [http://lesswrong.com/lw/x4/nonperson_predicates/ Nonperson Predicates] by Eliezer Yudkowsky
 
* [http://lesswrong.com/lw/d2f/computation_hazards/ Computational Hazards] by Alex Altair
 
* [http://lesswrong.com/lw/d2f/computation_hazards/ Computational Hazards] by Alex Altair

Latest revision as of 03:24, 14 October 2016

Arbital has an article about

A Nonperson Predicate is a theorized test which can definitely distinguish computational structures which are not people; i.e., a predicate which returns 1 for all people, and returns 0 or 1 for nonpeople; thus if it returns 1, the structure may or may not be a person, but if it returns 0, the structure is definitely not a person. In other words, any time at least one trusted nonperson predicate returns 0, we know we can run that program without creating a person. (The impossibility of perfectly distinguishing people and nonpeople is a trivial consequence of the halting problem.)

The need for such a test arises from the possibility that when an Artificial General Intelligence predicts a person's actions, it may develop a model of them so complete that the model itself qualifies as a person (though not necessarily the same person). As the AGI investigates possibilities, these simulated people might be subjected to a large number of unpleasant situations. With a trusted nonperson predicate, either the AGI's designers or the AGI itself could ensure that no actual people are created.

Any practical implementation would likely consist of a large number of nonperson predicates of increasing complexity. For most nonpersons, a predicate will quickly return that it is not a person and conclude the test. Although any number of the predicates may be used before the test claims that something is not a person, it is crucial that any predicate in the test never claims that a person isn't a person. Unclassifiable cases being in-principle unavoidable, it is preferable that the AGI errs on the side of considering possible-persons as persons.

See Also

Blog Posts