Core Insights - The article discusses the development of an innovative system called AI-Newton, which autonomously derives physical laws from raw experimental data without prior physical knowledge, marking a significant step towards AI-driven scientific discovery [2][4]. Group 1: Challenges in Current Scientific Discovery - Current scientific discovery faces two main challenges: long research cycles influenced by human biases and the limitations of existing AI methods, which are often opaque and struggle with complex systems [5]. Group 2: AI-Newton System Design - AI-Newton is inspired by human scientific reasoning but does not rely on human knowledge. It starts from basic observational data and autonomously defines physical concepts, successfully rediscovering core laws of Newtonian mechanics [6][10]. Group 3: Knowledge Discovery Engine - The core architecture of AI-Newton consists of a three-layer knowledge base (symbols, concepts, and laws), mimicking how human physicists organize knowledge, starting from simple concepts to complex physical laws [8]. Group 4: Workflow and Methodology - AI-Newton combines logical reasoning with symbolic regression, selecting experiments and concepts to analyze, and correcting laws through reasoning when they do not hold in new scenarios [12]. Group 5: Experimental Validation - The research team tested AI-Newton on 46 Newtonian mechanics experiments, successfully rediscovering key physical laws such as Newton's second law, conservation of energy, and the law of universal gravitation, averaging the discovery of about 90 physical concepts and 50 universal laws [14][17]. Group 6: Key Features of AI-Newton - AI-Newton exhibits two main features: progressive improvement, where it builds knowledge gradually, and discovery diversity, where the order and timing of discovered concepts and laws vary across different test cases [18][21]. Group 7: Future Prospects - The framework of AI-Newton shows strong potential for expansion, suggesting that with more advanced mathematical tools and natural language integration, it could tackle more complex physical concepts and contribute to the realization of Artificial General Intelligence (AGI) [21].