人工智能新风吹进千行百业，落地攻坚仍面临三大难题

Core Insights - The article discusses the challenges faced by the AI industry as it attempts to transition from experimental applications to widespread implementation in various sectors, highlighting three main difficulties: technology adaptability, data quality and availability, and high costs [9][10]. Group 1: AI Application Challenges - AI is entering a "deep water" phase where large-scale deployment faces significant challenges, including technology that is not specialized enough for specific industries [10]. - The current state of foundational models is still developing, with issues such as poor interpretability and high hallucination rates, making it difficult to find suitable application scenarios [10][11]. - The industrial sector faces a "three highs" dilemma: high entry barriers, high operational costs, and high safety risks, necessitating a deep understanding of complex processes and implicit knowledge [10][11]. Group 2: Data Quality Issues - High-quality data is essential for training industry-specific models, but there is a notable lack of quality data across different sectors, leading to "data islands" and inconsistent data quality [10][11]. - Legal restrictions, such as data security laws and personal information protection laws, hinder the large-scale application of existing data, particularly in the healthcare sector [11][12]. - The transition from non-digital to digital data is also constrained by intellectual property laws, further exacerbating the shortage of high-quality industry-specific data [11][12]. Group 3: Cost Barriers - The high costs associated with customized AI services, including computing power, model development, and data management, pose a significant burden for small and medium-sized enterprises (SMEs) [10][12]. - There is a need for differentiated support policies for various types of enterprises, including state-owned enterprises, industry leaders, and SMEs, to facilitate the implementation of AI initiatives [12].