建设高质量数据集，让人工智能更聪明（新视点）

Core Insights - High-quality datasets are essential for the effective training of large models in artificial intelligence, akin to how refined oil is necessary for cars [1][2] - The Chinese government is actively promoting the construction of high-quality datasets through initiatives like the "Three-Year Action Plan" and the release of industry-specific datasets [1][3] Group 1: Importance of High-Quality Datasets - High-quality datasets significantly impact the intelligence of AI models, with recent training of deep learning models highlighting their importance [1] - The value of data elements is increasingly recognized as a core area of competition in artificial intelligence, necessitating the aggregation and sharing of high-quality datasets [2] Group 2: Challenges in Dataset Construction - There are challenges in building high-quality datasets, including diverse data needs across different industries, complicating data processing and management [2] - The lack of unified standards for measuring the quality of data from various sources can lead to inconsistencies, affecting model training and prediction accuracy [2] Group 3: Guidelines for Dataset Construction - The "Guidelines for High-Quality Dataset Construction" categorize datasets into three types: general datasets, industry general datasets, and industry-specific datasets, each serving different purposes [3] - The National Data Bureau aims to enhance the quality of datasets as a catalyst for AI's role in the real economy, promoting a collaborative ecosystem for data annotation [3]