Turing-complete

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Turing-complete is a term used in computability theory to describe a system of data-manipulation rules (such as a programming language, an instruction set architecture, or a cellular automaton) that has the computational power to simulate any Turing machine. A Turing machine, conceived by Alan Turing, is a theoretical model of computation that can, given enough time and memory, solve any computable problem. Therefore, if a system is Turing-complete, it means it can theoretically perform any computation that any other programmable computer can perform.

Most modern programming languages, like Python, Java, C++, and JavaScript, are Turing-complete. In the blockchain world, the scripting language of Ethereum (Solidity, used for smart contracts) is also Turing-complete. This allows developers to write highly complex and versatile smart contracts capable of executing arbitrary logic. However, Turing-completeness also introduces potential complexities and risks, such as the possibility of infinite loops or unintended resource consumption (e.g., the Halting Problem, which states its impossible to determine for all possible inputs whether a program will finish running or continue forever). Consequently, systems like Ethereum implement mechanisms like “gas” to limit computational steps and prevent abuse.

 

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