Self-Improving, Self-Referential Problem Solvers: Achieving Optimal Efficiency

Title: Self-Improving, Self-Referential Problem Solvers: Achieving Optimal Efficiency

Research Question: Can we create a self-improving, self-referential problem solver that can modify itself in arbitrary computable ways and do so in a most efficient manner?

Methodology: The researchers introduced a novel class of problem solvers called G¨odel machines. These machines are universal problem solvers that interact with some (partially observable) environment and can modify themselves without essential limits apart from the limits of computability. They use a proof technique searcher to test computable proof techniques (programs whose outputs are proofs) until they find a provably useful self-rewrite.

Results: The researchers showed that such a self-rewrite is globally optimal - no local maxima! This is because the code first had to prove that it is not useful to continue the proof search for alternative self-rewrites. They also presented an optimal order of complexity method, called Bias-Optimal Proof Search (BIOPS), which allows a surviving proof searcher to use the optimal order of problem solver to solve remaining proof search tasks.

Implications: The G¨odel machines represent a significant advancement in the field of artificial intelligence. They can modify themselves in arbitrary computable ways and do so in a most efficient manner, achieving a level of self-improvement and self-referentiality that was previously thought to be unattainable. This could have profound implications for the future of artificial intelligence and machine learning, potentially leading to the creation of highly efficient, self-improving systems that can solve complex problems with minimal human intervention.

Link to Article: https://arxiv.org/abs/0309048v5 Authors: arXiv ID: 0309048v5