他特别指出，没有数据智能化转型就无从谈起，但大部分企业90%的数据可能都无法真正使用。在这方 面，联想通过大量端侧设备帮助客户有效采集数据，同时提供知识库解决方案与知识图谱，助力企业完 成数据治理。 王立平还透露，智能制造是联想未来聚焦的一大重点行业，区别于咨询公司"卖药方"的模式，联想能够 依托自身智能制造经验，以内生外化模式为客户提供全生命周期服务。在"十五五"开局之际，联想期待 与更多客户并肩合作，把AI潜力真正转化为企业的竞争力与增长力。 （编辑 张明富） 联 1月7日，联想创新科技大会于CES2026期间在拉斯维加斯举行。联想集团副总裁、中国政企业务群总经 理王立平在接受媒体采访时表示，AI普惠时代，企业智能化转型已经从传统的"+AI"升级为"AI+"。联 想具备从业务咨询到交付实施的全面优势，既能为客户实现运营层面的降本增效，更能助力客户创新业 务模式、挖掘增长机会。 想集团副总裁、中国政企业务群总经理王立平 企业供图 他解释道："'+AI'仅是在现有工作环境中添加AI元素，判别式AI就能实现，'AI+'则依托AI原生组织， 是企业转型在思想与方法上的重大迭代，将带来业务模式的创新。"以伊利为例， ...

最后，王立平透露，智能制造是联想未来聚焦的一大重点行业，区别于咨询公司"卖药方"的模式，联想能够依托自身智能制造经验，以内生外化模式为客户 提供全生命周期服务。联想期待与更多客户并肩合作，把AI潜力真正转化为企业的竞争力与增长力。（青云） 他解释道："'+AI'仅是在现有工作环境中添加AI元素，判别式AI就能实现，'AI+'则依托AI原生组织，是企业转型在思想与方法上的重大迭代，将带来业务 模式的创新。"以伊利为例，联想帮助其使用AI重构从牧场到消费端的全价值链，实现原奶调配单吨运输成本显著下降，24小时到厂及时率稳达98%。 他特别指出，没有数据智能化转型就无从谈起，但大部分企业90%的数据可能都无法真正使用。在这方面，联想通过大量端侧设备帮助客户有效采集数据， 同时提供知识库解决方案与知识图谱，助力企业完成数据治理。 【环球网科技综合报道】1月8日消息，史上规模最大的联想创新科技大会于于日前在CES 2026期间举行。联想集团副总裁、中国政企业务群总经理王立平 表示，AI普惠时代，企业智能化转型已经从传统的"+AI"升级为"AI+"。联想具备从业务咨询到交付实施的全面优势，既能为客户实现运营层面的降本增 ...

Core Insights - The digital education market in China is projected to grow from RMB 12.7 billion in 2020 to RMB 21.3 billion by 2024, with a compound annual growth rate (CAGR) of 13.7% [1] - Shanghai Zhuoyue Ruixin Digital Technology Co., Ltd. (Zhuoyue Ruixin) has become a leading provider of digital teaching solutions in higher education, ranking second in revenue with a market share of 4% in 2024 [1][2] - The company's revenue has shown significant growth, with figures of RMB 400 million in 2022, RMB 653 million in 2023, and projected RMB 848 million in 2024 [2][4] Financial Performance - Zhuoyue Ruixin's gross profit margin has remained high, with rates of 44.1% in 2022, 60.7% in 2023, and 61.9% in 2024 [2][4] - The company reported a net loss of RMB 98.96 million in the first half of 2024, which is higher than the loss of RMB 88.86 million in the same period last year, primarily due to seasonal industry patterns [2][5] - The net profit is expected to reach RMB 105 million in 2024, marking a significant improvement from previous years [5] Business Segments - The digital teaching content services and products account for over 80% of Zhuoyue Ruixin's revenue, with a rising trend in recent years [3] - The revenue from knowledge graph services has surged, reaching 54.7% of total revenue in the first half of 2024, indicating its role as a key growth driver [3][4] - The digital teaching environment services, including cloud LMS and digital classroom services, have seen a decline in revenue share, contributing 5.4% and 3.4% respectively in the first half of 2024 [3][4] Market Trends - The digital education market in China is expected to expand to RMB 45.3 billion by 2029, with a CAGR of 16.3% [7] - The digital teaching content production market is projected to grow at a CAGR of 18.8%, reaching RMB 23.1 billion by 2029 [7] - The company plans to establish knowledge graph construction centers to enhance its growth strategy in response to the increasing demand for personalized education solutions [6] Customer Growth - Zhuoyue Ruixin's customer base has increased from 1,174 in 2022 to 1,738 in 2024, with the number of large clients rising from 245 to 449 [9] - The increase in both customer numbers and average revenue per customer indicates a strong growth cycle for the company [9]

Core Insights - The digital education market in China is projected to grow from RMB 12.7 billion in 2020 to RMB 21.3 billion by 2024, with a compound annual growth rate (CAGR) of 13.7% [1] - Shanghai Zhuoyue Ruixin Digital Technology Co., Ltd. (Zhuoyue Ruixin) has become a leading provider of digital teaching solutions in higher education, ranking second in revenue with a market share of 4% in 2024 [1][2] - Zhuoyue Ruixin's revenue has shown significant growth, with figures of RMB 400 million in 2022, RMB 653 million in 2023, and projected RMB 848 million in 2024 [2][4] Market Overview - The digital education market is expanding rapidly, driven by government support and the proactive response of higher education institutions to digital policies [1] - The digital teaching content service segment accounts for over 80% of Zhuoyue Ruixin's revenue, with a notable increase in the contribution from knowledge graph services [3][4] Financial Performance - Zhuoyue Ruixin's gross profit has increased alongside revenue, with gross profit figures of RMB 177 million in 2022, RMB 396 million in 2023, and RMB 525 million in 2024 [4] - The company reported a net profit of RMB 105 million in 2024, marking a significant improvement despite a loss of RMB 98.96 million in the first half of 2023 due to seasonal industry patterns [5] Product and Service Development - Zhuoyue Ruixin has introduced innovative products such as virtual simulation and knowledge graph services, enhancing the interactive and personalized learning experience for students [2][6] - The knowledge graph service has rapidly gained traction, contributing significantly to revenue growth, with its share rising to 54.7% in the first half of 2024 [3][6] Strategic Expansion - The company plans to establish knowledge graph construction centers to further enhance its growth trajectory and meet the increasing demand for personalized education solutions [6] - The digital education market is expected to continue expanding, with projections indicating a market size of RMB 45.3 billion by 2029, driven by trends in digital content production and digital teaching environments [7] Customer Growth - Zhuoyue Ruixin's customer base has grown from 1,174 in 2022 to 1,738 in 2024, indicating a strong demand for its services [9] - The increase in both the number of customers and average revenue per customer suggests that the company is entering a new growth cycle [9]

Summary of the Conference Call Company Overview - The conference call involved **Zero Point Data**, a company focused on data intelligence and decision-making software, integrating AI and knowledge graph technologies to enhance decision-making capabilities [1][2]. Key Points and Arguments 1. **Business Growth and Strategy**: - Zero Point Data reported resilient growth in its third-quarter results, with a significant increase in the gross margin attributed to the rising share of data decision intelligence software, which has reached nearly 40% of total revenue, up from 25% last year [1][2]. - The company has strategically decided to abandon low-margin consulting report services to focus on expanding its software business, aiming for the commercialization of AI applications [2][3]. 2. **Research and Development Focus**: - The peak period of R&D spending has passed, leading to a significant reduction in R&D expenses. The focus has shifted to developing low-hallucination AI based on knowledge graphs [3][4]. 3. **Financial Performance**: - Despite a slight decline in revenue, the company has improved its gross profit margin and net cash flow management, indicating a narrowing of losses and a positive outlook for the year [5][6]. 4. **Integration of Acquired Technologies**: - The integration of Haiyisi, a company specializing in knowledge graphs, has been successful, enhancing Zero Point's technical capabilities and forming a lightweight database and knowledge graph computing platform [7][8]. 5. **Market Position and Competition**: - The company is positioning itself against competitors like Palantir, emphasizing the importance of knowledge graphs in reducing costs and improving AI model performance [9][10]. 6. **Sector-Specific Applications**: - Zero Point is exploring applications of its technology in various sectors, including insurance and finance, with plans to launch products in these areas in the near future [11][12]. 7. **Client Engagement and Market Trends**: - The company has observed a shift in client needs, particularly in the B-end market, where demand is increasing but profit margins are under pressure due to intense competition [13][14]. 8. **AI Implementation Challenges**: - There are challenges in the practical implementation of AI solutions, with many clients struggling to effectively utilize existing AI tools, highlighting Zero Point's advantage in providing tailored solutions [19][20]. 9. **Future Business Model Innovations**: - Zero Point is exploring new business models, including a potential shift towards a results-based service model in the AI space, moving beyond traditional software sales to a more sustainable revenue model [26][27]. 10. **Strategic Partnerships**: - The company is open to collaborations with chip manufacturers and other tech firms to enhance its AI capabilities and edge computing solutions [21][22]. Other Important Insights - The company is actively seeking to integrate various data sources and technologies to enhance its service offerings, particularly in the insurance and financial sectors [23][24]. - There is a focus on developing a SaaS-like model for ongoing revenue generation, with an emphasis on delivering measurable results to clients [30][31]. This summary encapsulates the key discussions and insights from the conference call, highlighting Zero Point Data's strategic direction, financial performance, and market positioning.

Core Viewpoint - The article discusses the upcoming "China Large Language Model Conference" (CLM) scheduled for October 28-29, 2025, in Beijing, focusing on advancements in natural language processing and large models in AI, aiming to foster dialogue among top scholars and industry experts [2][3]. Group 1: Conference Overview - The first "China Large Language Model Conference" will take place in June 2024, gathering over a thousand participants and featuring discussions on the path of large models in China [2]. - The 2025 conference will continue the spirit of the first, emphasizing theoretical breakthroughs, technological advancements, and industry applications of large models [2][3]. Group 2: Keynote Speakers and Topics - Notable speakers include Academicians Guan Xiaohong and Fang Binhang, who will present on cutting-edge perspectives in AI and large model development [3]. - The conference will feature 13 high-level forums covering topics such as generative AI, knowledge graphs, embodied intelligence, emotional computing, and social media processing [3]. Group 3: Detailed Agenda - The agenda includes a series of invited reports and thematic discussions, with sessions on topics like the implications of reward functions in AI, ethical and safety-driven key technologies for large models, and the role of computational power in enhancing human intelligence [5][30][25]. - Specific sessions will address the collaboration between large models and AI-generated content, embodied intelligence, and the implications of large models in various sectors including healthcare and multilingual processing [8][10][12][16]. Group 4: Registration and Participation - The registration for the conference is now open, with further details available on the conference website [3][24]. - Participants are encouraged to join in exploring the boundaries of large models and advancing AI technology in China [3].

Group 1 - The conference on new productivity and artificial intelligence was held in Mianyang, Sichuan, attracting various stakeholders including business leaders, experts, and financial executives to share experiences and promote high-quality development [1] - Liu Jun, a prominent scientist, discussed the evolution of AI and its future direction, emphasizing that "intelligent agents" will be a key theme in the development of China's IT industry [3] - Various speakers presented practical examples and case studies on AI applications, including breakthroughs in technology and new operational scenarios [3][4] Group 2 - The core features of the next-generation enterprise automation products include low-threshold interactive design, intelligence and controllability, automatic response to anomalies, and team collaboration among digital employees [4] - Project presentations included topics such as knowledge management platforms driven by knowledge graphs and AI, and the integration of AI in financial services [4][5] - The discussions and exchanges at the conference are expected to contribute to Mianyang's high-quality development and further advancements in China's AI sector [5]

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]

香港聯合交易所有限公司與證券及期貨事務監察委員會對本申請版本的內容概不負責， 對其準確性或完整性亦不發表任何聲明，並明確表示概不就因本申請版本全部或任何部 分內容而產生或因倚賴該等內容而引致的任何損失承擔任何責任。 SHANGHAI ABLE DIGITAL SCIENCE&TECH CO., LTD. 上海卓越睿新數碼科技股份有限公司 （於中華人民共和國註冊成立的股份有限公司） 的申請版本 警 告 本申請版本乃根據香港聯合交易所有限公司（「聯交所」）及證券及期貨事務監察委員會（「證監會」）的要 求而刊發，僅用作提供資料予香港公眾人士。 申請版本為草擬本，其內所載資料並不完整，亦可能會作出重大變動。 閣下閱覽本文件，即代表 閣下 知悉、接納並向上海卓越睿新數碼科技股份有限公司（「本公司」，連同其附屬公司，統稱「本集團」）、其 獨家保薦人、整體協調人、顧問或包銷團成員表示同意： 本公司根據香港法例第32章《公司（清盤及雜項條文）條例》向香港公司註冊處處長登記本公司招股章程 後方會向香港公眾人士提出要約或邀請。倘於適當時候向香港公眾提出要約或邀請，務請有意投資者 僅依據向香港公司註冊處處長登記的本公司招股章程作出 ...

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]