人工智能行业大模型调研报告：向Al而行共筑新质生产力

Investment Rating - The report does not explicitly provide an investment rating for the industry Core Insights - The emergence of AI large models is driving a new wave of technological innovation and industrial transformation, providing new momentum for digital transformation and high-quality development across various sectors such as industry, finance, and broadcasting [3] - The necessity and development path of industry-specific large models are articulated through extensive research, emphasizing their value in cost reduction, efficiency enhancement, and business innovation [4][5] - The report highlights the importance of integrating large models into various industries, focusing on the need for specialized knowledge and the challenges of applying general large models to specific industry needs [9][10] Summary by Sections Development Background - Large models exhibit significant potential for general intelligence, with parameters scaling from millions to trillions, showcasing a strong ability to generalize across various tasks [26][28] - The "impossible triangle" problem indicates that large models struggle to balance specialization, generalization, and economic efficiency [34][35] - Industry-specific large models are seen as essential for bridging the gap between technology and specific industry needs, enabling effective application of AI [40] Application Progress - Different industries are at varying stages of adopting large model technology, with digital-native industries leading the way due to their high digitalization and data accumulation [72][73] - The report categorizes industries into three groups based on their adoption speed: digital-native industries, productive service industries, and heavy asset industries, with the latter being in a phase of localized exploration [74][75] - The application of large models in vertical scenarios shows a "smile curve" characteristic, where high-value activities in R&D/design and marketing/service are progressing faster than lower-value production processes [76][78] Implementation Methods - The report outlines various implementation methods for industry-specific large models, including prompt engineering, retrieval-augmented generation, fine-tuning, and pre-training [18] - Successful case studies in finance and research illustrate the practical applications of large models, demonstrating their effectiveness in enhancing operational efficiency and innovation [18][19] Safety and Governance - The report emphasizes the need for robust governance and safety measures in the development of industry-specific large models, highlighting the importance of aligning value and ensuring security throughout the model's lifecycle [19]