世界500强CIO齐聚第八届南方信息大会丨汉数创始人陈开冉受邀发表演讲

Core Insights - The eighth Southern Information Conference, hosted by the Guangdong Chief Information Officer Association, gathered top scholars and CIOs from leading companies to discuss the challenges and opportunities for CIOs in the AI era [1][2]. Group 1: Importance of High-Quality Data - High-quality datasets are likened to "high-octane gasoline," crucial for the performance and application of AI models [3]. - The shift from a "model-centric" to a "data-centric" approach emphasizes that high-quality labeled data is key to unlocking AI's value, directly impacting the effectiveness of large models [4]. Group 2: Addressing Challenges in AI Implementation - High-quality datasets can mitigate the "hallucination" problem, which refers to the generation of incorrect or unsubstantiated information by AI models, especially in the absence of specialized data [5]. - By incorporating industry-specific knowledge and data, large models can transition from being generalists to specialists, enhancing their ability to provide valuable insights in specialized fields [6]. Group 3: The "Konghu" Data Cloud Platform - The "Konghu" high-quality data platform is designed to meet the high data demands of large models and address challenges in data integration during digital transformation [7]. - The platform integrates over 3.8 billion enterprises, 250,000 buildings, and 3 billion products across 18 vertical sectors, providing a comprehensive data ecosystem for AI applications [8]. - A "three-stage" data integration model simplifies the data acquisition process, making it more accessible for agile AI development [10]. Group 4: Bridging the Gap for AI Models - The "MCP service market" aims to connect internal and external data and tools, facilitating real-time access to high-quality data for large models [11]. - The "Konghu" data cloud has established partnerships with major model providers like ByteDance, Alibaba, and Baidu, enhancing the accessibility of data for enterprises [12]. Group 5: Future Directions - The company aims to leverage high-quality datasets to help industry-specific large models overcome challenges and provide satisfactory answers in professional applications [14]. - The focus is on expanding the breadth and depth of data coverage and building an open and win-win data ecosystem to drive productivity in various industries [14].