来源：环球网 9月26日，由中国生产力促进中心协会主办的新质生产力人工智能大会暨对接交流会在四川绵阳举行。 作为本届科博会的子活动之一，此次会议吸引了来自全国多个省市的企业负责人、高校专家以及金融投 资机构高管等嘉宾到场，通过经验共享、智慧碰撞和项目路演，为新质生产力的高质量发展注入新活 力。 "九科下一代企业自动化产品的核心特性体现在低门槛交互式辅助设计、智能性与可控性兼备、自动应 对异常与变动以及数字员工团队组织与协作等方面。"燕伟桐一句句掷地有声地介绍，让人们直观感受 到了民营经济在前沿领域的"硬实力"。 项目路演环节，深圳市鼎驰科技发展有限公司AI科技中心总经理、中国生产力促进中心协会AI联合创 新实验室副主任魏士博就《知识图谱、大模型与智能体融合驱动的企业级海量专业文档知识管理平台》 项目、中国工商银行绵阳分行科技金融中心负责人曾成就《拥抱智能浪潮 赋能金融未来》项目、成蹊 （香港）智能科技有限公司董事长闫婧以《以AI智能体服务科学家创业融资及企业成长》项目、江苏 榕树智能科技有限公司创始人付寅峰就《AI赋能情绪心理健康》项目、格陆博科技有限公司产品经理 王梦迪就《智能线控制动及底盘域控技术发展》项 ...

Core Insights - The 2025 Global Digital Economy Conference was held in Beijing, where the China Academy of Information and Communications Technology released the latest assessment results for trustworthy security in 2025, highlighting the achievements of Ping An Property & Casualty Insurance in AI technology innovation and application [1][2] Group 1: AI Product Evaluation - Ping An Property & Casualty Insurance successfully passed the evaluation of nine AI products, which focus on sales, underwriting, claims, and risk control, showcasing their strong application effects and business adaptability [2][3] - The evaluation assessed six dimensions including integration capability, application capability, model performance, security capability, compatibility, and operational management [3] Group 2: AI Capability Construction - The company is actively building an "insurance + technology + service" model, enhancing its AI capabilities in areas such as intelligent search, image processing, knowledge graph, and simulation prediction [4][5] - AskBob, the intelligent search and dialogue engine, utilizes pre-trained large model technology to improve employee efficiency, achieving over 90% effective response rate in underwriting inquiries [4] Group 3: Business Empowerment and Value Restructuring - In 2025, the company completed the localized deployment of the DeepSeek large model, creating AI assistants for various business scenarios, which enhances operational efficiency and customer experience [6][7] - The AI assistant for sales, "Chuang Xiao Bao," enables precise marketing outreach to millions of customers and addresses challenges in non-auto sales [6] - The underwriting process has been transformed from manual to AI-driven, increasing self-underwriting rates by 17 percentage points and reducing initial quote response time to under 2 hours [7] Group 4: Risk Control System - The company has established a comprehensive digital risk control system that includes preemptive measures, real-time warnings, and post-event reviews, significantly enhancing disaster prevention and risk identification capabilities [7] - AI auditing technology is employed for full-chain risk reviews, resulting in annual loss reductions exceeding 5 billion yuan [7]

Core Insights - Knowledge Graphs (KG) are structured networks of entities and their relationships, providing a powerful tool for semantic understanding and data integration in artificial intelligence [1][2][3] - The concept of Knowledge Graphs was popularized by Google in 2012, building on decades of research in semantic networks and ontologies [1][8] - Future innovations will focus on automating the construction of Knowledge Graphs, enhancing reasoning capabilities, and integrating them closely with AI models [1][9] Definition and Structure - Knowledge Graphs represent knowledge as a network of entities (nodes) and their relationships (edges), allowing for flexible data modeling [2] - Each node corresponds to a real-world concept identified by a unique ID or URI, while edges represent specific relationships between entities [2] Role in Artificial Intelligence - Knowledge Graphs play a crucial role in machine reasoning and semantic understanding by providing structured background knowledge for AI systems [3][4] - They facilitate knowledge integration by linking information from multiple sources, creating a unified view [3][5] - Knowledge Graphs enhance semantic richness, improving the performance of AI technologies like machine learning and natural language processing [3][5] Significance and Benefits - Knowledge Graphs embed knowledge into AI systems, reducing the need for extensive training data by providing prior knowledge [5][6] - They improve transfer learning by allowing AI systems to apply knowledge across different tasks without retraining [6] - Knowledge Graphs contribute to explainable AI by providing transparent representations of facts and their connections, enhancing trust in AI decisions [6][7] Data Integration and Interoperability - Knowledge Graphs use shared vocabularies and identifiers to achieve interoperability between systems, acting as a common language for data integration [7] - They are essential for building large-scale AI systems, as demonstrated by Google's use of Knowledge Graphs to enhance search results [7] Historical Evolution - The term "Knowledge Graph" gained popularity in 2012, but its underlying concepts date back to the 1960s with semantic networks [8] - The development of standards like RDF and OWL has facilitated the interconnection of data on the web, laying the groundwork for modern Knowledge Graphs [8] Recent Developments - From 2023 to 2025, significant progress is expected in integrating Knowledge Graphs with large language models (LLMs) to enhance reasoning capabilities [9][10] - Research is focused on using LLMs as external knowledge sources for Knowledge Graphs, improving fact accuracy and handling complex queries [10][11] Emerging Trends - The collaboration between Knowledge Graphs and LLMs is a key research area, aiming to combine symbolic reasoning with neural language understanding [16] - There is a growing emphasis on domain-specific Knowledge Graphs, particularly in fields like biomedicine and law, which require customized ontologies and algorithms [16] - Advances in Knowledge Graph embedding techniques are expected to address challenges related to dynamic knowledge and multimodal data integration [16][12]