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A deep dive into self-improving AI and the Darwin-Gödel Machine
A deep dive into self-improving AI and the Darwin-Gödel Machine.
To illustrate this, consider a simple analogy: just as you cannot guarantee that a new software update will improve your computer’s performance without actually running it, an AI system faces an even greater challenge in predicting the long-term consequences of modifying its own complex codebase. FeatureAlphaEvolveDGMFocusEvolving functions and codebasesEvolving the agent itselfLevel of InnovationAlgorithmic levelAgent-level (toolset, methodology)Role of LLMLLM acts as “genetic operators” to modify algorithmsLLM serves as the “brain” to evolve itself with better tools and strategiesEvaluationWell-defined problems with automated evaluatorsOpen-ended environmentsTo better understand the differences between the two approaches, let us take a look at the following analogy: By iteratively rewriting its own code based on empirical feedback, DGM demonstrates how AI systems could move beyond human-designed architectures to autonomously explore new designs, self-improve, and potentially give rise to entirely new species of digital intelligence.
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