下一代 AI 系统怎么改？让 AI 自己改？!

Group 1 - The core idea of the article revolves around the evolution of AI systems, particularly the concept of "self-evolution" where AI can improve itself without human intervention, marking a shift from traditional training methods [4][5][10] - The "Era of Experience" proposed by Richard Sutton and David Silver emphasizes that AI will learn primarily from its own experiences, moving beyond human knowledge limitations [4][6] - The Darwin Gödel Machine (DGM) is highlighted as a significant development in self-evolving AI, capable of modifying its own code to enhance performance, particularly in coding tasks [6][10] Group 2 - The article discusses the limitations of current AI models due to the depletion of human-generated data, prompting the need for new modeling paradigms that allow machines to interact with the world and generate their own experiences [4][5] - DGM's performance improvements are quantified, showing a rise from 20.0% to 50.0% on SWE-bench and from 14.2% to 30.7% on Polyglot after 80 iterations, demonstrating its self-learning capabilities [6] - The article contrasts self-evolution with traditional supervised learning (SL) and reinforcement learning (RL), noting that self-evolution relies on models generating their own training data, which introduces new challenges and opportunities [7][8]