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  A Gödel machine is a selfimproving computer program that solves problems in an optimal way. It uses a recursive selfimprovement protocol in which it rewrites its own code when it can prove the new code provides a more optimal strategy. The machine was invented by Jürgen Schmidhuber (first proposed in 2003), but is named after Kurt Gödel who inspired the mathematical theories.

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  A Gödel machine is a selfimproving computer program that solves problems in an optimal way. It uses a recursive selfimprovement protocol in which it rewrites its own code when it can prove the new code provides a more optimal strategy. The machine was invented by Jürgen Schmidhuber (first proposed in 2003), but is named after Kurt Gödel who inspired the mathematical theories. The Gödel machine is often discussed when dealing with issues of metalearning, also known as "learning to learn." Applications include automating human design decisions and transfer of knowledge between multiple related tasks, and may lead to design of more robust and general learning architectures. Though theoretically possible, no full implementation has existed before. The Gödel machine is often compared with Marcus Hutter's AIXI, another formal specification for an artificial general intelligence. Schmidhuber points out that the Gödel machine could start out by implementing AIXI as its initial subprogram, and selfmodify after it finds proof that another algorithm for its search code will be more optimal.

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