Author:
George D. Montañez
Affiliation:
Carnegie Mellon University, United States
Keyword(s):
Turing Test, Design Detection, Intelligent Agents.
Related
Ontology
Subjects/Areas/Topics:
Agents
;
Artificial Intelligence
;
Cognitive Systems
;
Computational Intelligence
;
Conversational Agents
;
Evolutionary Computing
;
Soft Computing
;
Symbolic Systems
Abstract:
“Can machines think?” When faced with this “meaningless” question, Alan Turing suggested we ask a different, more precise question: can a machine reliably fool a human interviewer into believing the machine is human? To answer this question, Turing outlined what came to be known as the Turing Test for artificial intelligence, namely, an imitation game where machines and humans interacted from remote locations and human judges had to distinguish between the human and machine participants. According to the test, machines that consistently fool human judges are to be viewed as intelligent. While popular culture champions the Turing Test as a scientific procedure for detecting artificial intelligence, doing so raises significant issues. First, a simple argument establishes the equivalence of the Turing Test to intelligent design methodology in several fundamental respects. Constructed with similar goals, shared assumptions and identical observational models, both projects attempt to dete
ct intelligent agents through the examination of generated artifacts of uncertain origin. Second, if the Turing Test rests on scientifically defensible assumptions then design inferences become possible and cannot, in general, be wholly unscientific. Third, if passing the Turing Test reliably indicates intelligence, this implies the likely existence of a designing intelligence in nature.
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