算力、算法与数据，谁是AI近期发展的驱动力与瓶颈

Core Insights - The IEEE International Conference on Data Mining (ICDM 2025) highlighted the dynamic balance between computing power, algorithms, and data in shaping the future of AI [1][10] - The conference emphasized that while computing power is essential, the roles of algorithms and data are equally critical in driving AI advancements [1][10] Group 1: The Triangular Relationship - Computing power is recognized as the engine of current AI development, but it is part of a dynamic balance with algorithms and data [1] - Data is transitioning from a passive "fuel" to an active "bottleneck," with high-quality, domain-specific data becoming scarce and crucial for the next generation of AI models [1][2] Group 2: Algorithmic Innovations - Jure Leskovec from Stanford University proposed a "Relationship Foundation Model" (RFM) to bridge the gap between structured data and AI, enhancing efficiency in predicting outcomes without extensive coding [4][5] - The RFM model converts database tables into "temporal relationship graphs," significantly reducing reliance on domain expertise and streamlining data preparation [5] Group 3: Navigating Biological Complexity - John Quackenbush from Harvard University stressed the importance of network models in biological data analysis, arguing that high-quality annotated data is essential for accurate AI insights [6][7] - He highlighted that without appropriate algorithmic models, even powerful computing resources could lead to erroneous conclusions in complex biological contexts [7] Group 4: Practical Applications in Finance - Wesley Leeroy from the University of Pennsylvania demonstrated the use of AI models in financial data mining, achieving a 92% accuracy rate in identifying fraud through advanced computational architectures [8][9] - The research underscored the necessity of rigorous data preprocessing and feature engineering to ensure the quality of data, which is vital for effective AI applications in finance [9] Group 5: Future Directions - The conference concluded that the future of AI is not dominated by a single element; rather, it is a synergistic relationship between computing power, algorithms, and data [9][10] - Balancing these three elements is essential for overcoming current bottlenecks and advancing AI into new frontiers [9][10]