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In computability theory, a system of data-manipulation rules (such as a model of computation, a computer's instruction set, a programming language, or a cellular automaton) is said to be Turing-complete or computationally universal if it can be used to simulate any Turing machine [1] [2] (devised by English mathematician and computer scientist Alan Turing).
Among the 88 possible unique elementary cellular automata, Rule 110 is the only one for which Turing completeness has been directly proven, although proofs for several similar rules follow as simple corollaries (e.g. Rule 124, which is the horizontal reflection of Rule 110). Rule 110 is arguably the simplest known Turing complete system. [2] [5]
The Turing test, originally called the imitation game by Alan Turing in 1949, [2] is a test of a machine's ability to exhibit intelligent behaviour equivalent to that of a human. In the test, a human evaluator judges a text transcript of a natural-language conversation between a human and a machine. The evaluator tries to identify the machine ...
Although Horn clause logic programs are Turing complete, [1] [2] for most practical applications, Horn clause programs need to be extended to "normal" logic programs with negative conditions. For example, the definition of sibling uses a negative condition, where the predicate = is defined by the clause X = X :
Computer science pioneer Alan Turing created the test in 1950 asking the question, "Can. Skip to main content. Sign in. Mail. 24/7 Help. For premium support please call: 800-290 ...
Turing complete set, a related notion from recursion theory; Completeness (knowledge bases), found in knowledge base theory; Complete search algorithm, a search algorithm that is guaranteed to find a solution if there is one; Incomplete database, a compact representation of a set of possible worlds
For more than 70 years, the Turing Test has been a popular benchmark for analyzing the intelligence of computers. But experts say it's far beyond obsolete. ... 24/7 Help. For premium support ...
The Summer Camp Test hints at what we need more of in AI: Systems built to solve real problems, from the mundane (like summer camp logistics) to the game-changing (like novel pharmaceutical research).