Core Insights - DeepSeek has emerged as a significant player in the AI large model landscape, driving widespread adoption among individuals, enterprises, and governments due to its low cost, high performance, and open ecosystem [1] - The large-scale application of AI models is crucial for rapid iteration and development in China, but it faces challenges such as low stability of underlying frameworks, barriers to cross-industry integration, and limited ecological support [1] - The current strategic opportunity period for AI development in China necessitates efforts in technological breakthroughs, industry adaptation, and risk warning to create a conducive environment for AI model applications [1] Group 1: Challenges in AI Model Application - The complexity and lack of interpretability in AI models, particularly deep neural networks, pose significant challenges for industry applications, leading to unreliable outputs and "hallucinations" [2] - Specific industries, such as manufacturing, face adaptation difficulties due to the complex and multimodal nature of their data, which existing models struggle to accurately interpret [3] - The fragmented approach to integrating AI models across industry chains increases long-term collaboration costs, as many companies overlook the importance of coordinated applications [4] Group 2: Economic Impact and Efficiency - The high operational costs associated with AI models, such as DeepSeek-R1, can lead to significant financial losses for companies, highlighting the need for cost-effective solutions [4] - Data integration across the supply chain can dramatically enhance operational efficiency, with reported improvements in order response speed and anomaly handling when fully integrated [5] - The rapid penetration of AI models into industries may lead to exponential increases in the costs for latecomers, limiting their ability to catch up with established players [6] Group 3: Regulatory and Ethical Considerations - The current ecosystem for AI model application is underdeveloped, with weak foundations in data, standards, and ethics, which could hinder the promotion of AI models [6] - The scarcity of high-quality training data, particularly in sensitive areas like healthcare, poses a significant barrier to effective AI model training and deployment [6] - The lack of a robust standard system for addressing ethical, legal, and social implications of AI models is a critical issue, as highlighted by the EU's AI regulatory draft [6][7]
三大难题掣肘AI大模型落地
Zhong Guo Chan Ye Jing Ji Xin Xi Wang·2025-07-24 22:18