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专访北京移动刘南:“5G+工业互联网”还需关注个性化需求
Core Viewpoint - The integration of 5G and industrial internet is crucial for expanding the large-scale application of 5G, facing challenges such as high costs and fragmented demand across various industries [3][4]. Group 1: 5G and Industrial Internet Integration - The deep integration of 5G and industrial internet is essential for expanding 5G applications, currently facing challenges in cost and demand matching [3][4]. - High costs associated with 5G network construction and equipment upgrades pose a significant burden, especially for small and medium-sized enterprises [3]. - The current "5G + industrial internet" applications primarily address common needs, lacking sufficient alignment with the unique demands of different industries [3]. Group 2: 5G-A Development and Its Implications - 5G-A is a key transitional phase towards 6G, enhancing network bandwidth and providing valuable insights for future 6G development [5]. - The development of 5G-A has led to the emergence of new application scenarios, indicating that 6G should focus on deeper integration with vertical industries [5]. - The emphasis on industry collaboration in 5G-A development highlights the need for a robust ecosystem to support 6G advancements [5]. Group 3: AI for Industry Trends - AI for Industry is expected to experience rapid growth, integrating with 5G-A and industrial internet to create a comprehensive intelligent system [9][10]. - The expansion of AI applications is moving from a few leading sectors to broader industries, enhancing production efficiency and reducing costs [9][10]. - Key issues for the information and communication industry include data quality and security, computational power support, standardization, and talent cultivation [10][11]. Group 4: Challenges in AI Model Development - The transition from general AI models to specialized industry applications faces challenges such as data barriers, algorithm precision, and scene adaptation [10][11]. - Data governance is prioritized to address the scarcity of high-quality datasets, utilizing proprietary tools for data cleaning and transformation [10][11]. - Collaboration with industry partners is essential for developing benchmark applications and optimizing models through practical scenarios [12].
大模型也有“不可能三角”,中国想保持优势还需解决几个难题
Guan Cha Zhe Wang· 2025-05-04 00:36
Core Insights - The rise of AI large models, particularly with the advent of ChatGPT, has sparked discussions about the potential of general artificial intelligence leading to a fourth industrial revolution, especially in the financial sector [1][2] - The narrative suggesting that the Western system, led by the US, will create a technological gap over China through its "algorithm + data + computing power" advantages is being challenged as more people recognize the potential and limitations of AI [1][2] Group 1: Historical Context and Development - The concept of artificial intelligence dates back to 1950 with Alan Turing's "Turing Test," establishing a theoretical foundation for AI [2] - The widespread public engagement with AI is marked by the release of ChatGPT in November 2022, indicating a significant shift in AI's development trajectory [2] Group 2: Current State of AI in Industry - The arrival of large models signifies a new phase in AI development, where traditional machine learning and deep learning techniques can work in tandem to empower manufacturing [4] - AI applications in the industrial sector are transitioning from isolated breakthroughs to system integration, aiming for deeper integration with various industrial systems [5] Group 3: AI's Impact on Manufacturing - AI can enhance productivity, efficiency, and resource allocation in the industrial sector, serving as a crucial engine for economic development [5] - The current landscape in China features a coexistence of large and small models, with small models primarily handling structured data and precise predictions, while large models excel in processing complex unstructured data [5][6] Group 4: Challenges in AI Implementation - AI's application in manufacturing is still in its early stages, with significant reliance on smaller models for specific tasks, while large models are yet to be fully integrated into production processes [8][9] - The industrial sector faces challenges such as high fragmentation of data, lack of standardized solutions, and the need for highly customized AI applications, which complicates the deployment of AI technologies [10][11] Group 5: Future Directions and Strategies - The goal is to achieve a collaborative system of large and small models, avoiding a singular focus on either, to explore the boundaries of AI capabilities and steadily advance application deployment [20][21] - A phased approach is recommended for AI integration in industry, starting with traditional small models in high-precision environments and gradually introducing large models in less critical applications [19][24] - The development of a robust evaluation system tailored to industrial applications is essential for assessing the performance of AI models in real-world settings [19][26]