Core Viewpoint - The article emphasizes the importance of integrating artificial intelligence (AI) into various industries, particularly in the financial sector, to enhance service quality and operational efficiency while ensuring inclusivity and security in AI applications [3]. Group 1: AI Models - The choice between open-source and closed-source models is not just a technical issue but has profound implications for application. Open-source models promote equality and cost savings but may have slower iteration rates and higher error rates, while closed-source models offer stability and reliability but limit customization and transparency [4]. - The financial industry should focus on "AI+" rather than solely on building large models, combining the advantages of both open-source and closed-source models to enhance service quality and internal management efficiency [4]. Group 2: Decision-making AI vs. Generative AI - Decision-making AI excels in scenarios requiring high interpretability and accuracy, dominating over 80% of current applications in finance, particularly in risk assessment and fraud detection. In contrast, generative AI is more suited for creative tasks and is primarily used in non-core areas like customer service [5]. - The trend indicates that as the capabilities of large models improve, generative AI may see exponential growth and work in tandem with decision-making AI, blurring the lines between the two [5]. Group 3: AI Inclusivity and Computing Power - The demand for GPU computing power is expected to remain in a "tight balance" as AI becomes more widespread, necessitating efforts to optimize existing resources and expand capacity [8]. - Companies should adopt engineering methods to reduce operational costs and enhance resource efficiency while building high-performance computing centers to support AI applications [8]. Group 4: Safety and Security in AI Applications - As AI inclusivity increases, the stability and security of AI applications must be prioritized to protect public interests. This includes establishing safety measures and enhancing data quality to build trust in AI systems [9]. - There is a need to prevent model resonance to mitigate systemic risks, as the concentration of mainstream models may lead to vulnerabilities across institutions. Developing a reliable knowledge base and differentiated model training is essential for enhancing the resilience of the financial system [9].

在人工智能飞速发展的今天，理论知识已不足以应对复杂多变的现实挑战，只有掌握AI的实战能力， 才能打造面向未来的核心竞争力。 12月6日-7日，由百度文心大模型与新京报贝壳财经联合主办的"文心导师培训营——'金融+AI数据'专 场"在北京举办。 三十余位来自银行、证券、保险、基金及学术机构的资深从业者与研究学者，以"AI数据驱动金融智 能"为主线，开展了一场系统性、强实战性的学习研讨，为金融行业与AI融合的实战提供了清晰的方法 论和实践路线。 决策式AI与生成式AI协作 助力金融业智能化转型 同时，学员们还了解了提示词万能公式，通过吃透"大模型理解提示词核心机制"这一根本原理，让学员 们在工作中能更好地将大模型从"随机游走"引导至"精准生成"模式。 实战是本次培训营的亮点环节，四大工作坊分别对应复杂金融业务体系的拆解与重构能力、提示词优化 迭代与场景适配能力、领域知识数据构建与方法论总结能力、模型效果的系统评估与调优能力等关键能 力的提升。 来自不同机构的学员围绕风控、投研、合规等真实场景展开方案共创，产出了多组具备业务价值的高质 量数据与评估框架。理论与实践的交织，让学员们真正解锁AI这一"生产力"的密钥。 ...

12月6日-7日，百度文心大模型联合新京报贝壳财经举办"文心导师培训营——'金融+AI数据'专场"活 动。 北大国发院副院长、BiMBA商学院院长黄卓在开营仪式上表示，基于金融行业重视合规性、可解释性 及数据安全、隐私保护等特点，该行业可以通过决策式AI与生成式AI协作的方式进行智能化转型。"决 策式AI的特点是比较精准，生成式AI则擅长逻辑推理和创造。不同的AI可以完成金融行业中不同的任 务。" 在开营仪式上，黄卓表示，新一代人工智能技术有四大特点。首先，区别于上一代决策式人工智能，新 一代人工智能可以创造性地生成内容；其次，新一代人工智能改变了人机交互的方式，从以计算机为核 心变成以人为核心；第三，新一代人工智能具备多模态能力，可以生成文字、视频、图像等多模态内 容；第四，新一代人工智能具有逻辑推理能力，可以完成一些复杂任务，作为大脑赋能工作。 作为一项颠覆性的技术，新一代人工智能将带来多重机遇。黄卓认为，人工智能作为新技术趋势，将带 动核心产业链的发展，包括算力、算法、数据等。 此外，"AI+商业应用"的机遇也值得关注。"这是中美在人工智能战略上的较大差异，人工智能是美国科 技产业的制高点，美国更强调人 ...

Summary of Baifeng Cloud's Conference Call Company Overview - Baifeng Cloud is the only financial AI company listed in Morgan Stanley's China AI 60 list, showcasing its prominence in the fintech sector [2][3] - The company maintains a gross margin above 70% and a strong net profit margin, with cash and cash equivalents reaching 3.729 billion RMB by mid-2025, indicating robust self-sustainability [2][3] - Baifeng Cloud serves over 8,000 institutional clients with a core customer retention rate of 98% [2][3] Financial Performance - The adjusted net profit for 2024 was 376 million RMB, with 254 million RMB reported for the first half of 2025 [13] - The company has a low debt-to-asset ratio and has repurchased over 200 million RMB worth of shares in the previous year [13][25] Service Models - Baifeng Cloud offers two primary service models: Results as a Service (RaaS) and Business as a Service (BaaS), contributing 31% and 69% to revenue respectively in the first half of 2025 [2][5] - RaaS helps clients achieve operational or marketing KPIs using AI models, while BaaS enhances efficiency and reduces costs through collaboration between AI agents and human staff [5] Technological Advancements - The company utilizes decision-making AI technology, backed by 11 years of industry experience and extensive data labeling, achieving a system stability of 99.999% [6][23] - Daily data requests exceed 300 million, with AI product penetration reaching 80% [2][3] Market Expansion and Future Directions - Baifeng Cloud plans to expand its industry footprint into sectors such as internet, telecommunications, retail, and healthcare, while enhancing AI talent recruitment [8][22] - The company aims to optimize its RaaS and BaaS models to provide customized services across various industries [8] Competitive Advantages - Baifeng Cloud focuses on vertical scenarios, leveraging rich data accumulation and unique algorithmic experience to provide tailored solutions [15][19] - The company’s MASS business, which started in 2014, has expanded from risk control in banking to marketing operations, with significant contributions from large clients [20][21] Customer Experience Innovations - Innovations include voice robots and customizable digital avatars, which have significantly improved customer engagement and operational efficiency [9][10] ESG Initiatives - The company emphasizes humanistic care through green office practices, employee training, and public welfare activities, enhancing employee satisfaction and corporate social responsibility [17] Research and Development - R&D expenses are projected to increase, with 302 million RMB spent in the first half of 2025, up from 226 million RMB in the same period last year [26] - The company is focused on developing its large model technology, with significant advancements in voice recognition and natural language processing capabilities [24] Conclusion - Baifeng Cloud is positioned for continued growth in the fintech sector, leveraging its technological advancements, strong financial performance, and strategic market expansion plans to solidify its leadership in the industry [8][22]

Core Viewpoint - The training conducted by Zhang Ziming emphasized the integration of AI technologies into the fund industry, highlighting the importance of AI in enhancing operational efficiency and decision-making processes. Group 1: AI Development and Application - Zhang Ziming outlined the evolution of AI technology from its early stages to its current applications, focusing on the distinction between generative AI, which emphasizes content creation, and decision-making AI, which focuses on optimizing decisions [3]. - The training included a detailed explanation of structured prompt engineering frameworks such as CO-STAR, TCREI, and CRISPE, demonstrating how to generate high-quality marketing content for funds using the RBTR method [3]. Group 2: Fund Marketing Techniques - Practical techniques for generating marketing content, images, and videos using AI were showcased, with participants experiencing the entire process from market analysis to complete marketing copy generation [3]. - Zhang Ziming demonstrated the use of DeepSeek to analyze fund product selling points and quickly generate attractive marketing content related to trending themes like carbon neutrality [3]. Group 3: Investment Research Empowerment - The training introduced professional AI tools like Reportify and Alpha派, showcasing their applications in data collection, information organization, and visual analysis, significantly enhancing the efficiency of investment research personnel [4]. - Zhang Ziming emphasized that AI is not meant to replace investment researchers but to free them from tedious information processing, allowing them to focus on value judgment and decision-making [4]. Group 4: Future Directions of AI in Business - The Unforeseen AI Research Institute aims to assist more enterprises in achieving "AI+" strategic transformation, maintaining competitive advantages in the era of intelligence through dual-driven strategies of "AI strategy + technology empowerment" [6].

Summary of Baifengyun Conference Call Company Overview - Baifengyun reported a net profit exceeding 200 million yuan in the first half of 2025, with a net profit margin of 12% and an adjusted margin of 16, indicating strong profitability [2][3] - The company employs approximately 1,400 staff, with 57% in R&D, and an average annual income exceeding 2 million yuan, reflecting a strong emphasis on R&D and high-value talent [2][3] - Baifengyun has served over 8,000 institutions, including banks, internet finance companies, and major internet firms like Alibaba and Baidu, showcasing a broad client base [2][3] Business Structure and Core Advantages - The business is divided into two main segments: Model as a Service (MaaS) and Business as a Service (BaaS) [3] - **MaaS** contributes about one-third of total revenue, providing decision-making support for financial institutions through AI technology, with over 300 million daily calls [2][3][4] - **BaaS** accounts for approximately two-thirds of revenue, utilizing intelligent voice robots for sales and customer operations, relying on generative AI technology [2][3][5] - The launch of the Cyber Star platform aims to enhance internal efficiency across various sectors, significantly reducing contract review times [2][9] Financial Performance - Baifengyun has maintained stable revenue growth of over 20% annually in recent years, with the first half of 2025 exceeding expectations [12] - The BUS financial cloud business saw a 45% year-on-year revenue growth in the first half of 2025, driven by the expansion into broader financial scenarios [14] Market Dynamics and Challenges - The company faces policy uncertainties impacting performance, particularly in the insurance sector, which has shown negative growth since 2024 due to regulatory pressures [13][14] - Despite these challenges, Baifengyun remains optimistic about long-term prospects, having demonstrated resilience in past downturns [13][22] Technological Innovations - The intelligent voice robot technology has proven effective in enhancing customer communication efficiency, with cost reductions to one-fifth of human labor costs and achieving 99% accuracy in voice recognition [7][8] - Baifengyun differentiates itself by utilizing a strong mathematical foundation for user segmentation, focusing on vertical small models rather than general large models [8] Future Outlook - The company plans to continue investing in R&D, with a 30% increase in spending in the first half of 2025, focusing on AIGC and computing clusters [19] - Baifengyun aims to expand its AI business into non-financial sectors, anticipating gradual increases in revenue contribution from these areas [19] Conclusion - Baifengyun is positioned as a resilient player in the AI technology sector, with a commitment to long-term growth despite short-term challenges, maintaining confidence in its strategic direction and market adaptability [22]

Core Viewpoint - The event aimed to enhance the organizational and innovative capabilities of grassroots science and technology workers in the context of AI, with over 220 participants attending the specialized training [1]. Group 1: AI Trends and Tools - Zhang Ziming, the Vice President of the Unknown AI Research Institute, delivered a presentation titled "AI Trend Insights and Practical Applications," discussing the development trajectory of AI and emphasizing that generative AI has become the core engine driving innovation in science popularization content [3]. - A detailed comparative analysis of mainstream domestic AI tools such as DeepSeek, Wenxin Yiyan, and Tongyi Qianwen was provided, highlighting differences in product ecosystems and functional designs [5]. - Practical logic and application techniques for AI tools were shared, stressing the importance of selecting tools based on specific scenarios to enhance the quality and innovation of science popularization content [5]. Group 2: Future Directions - The successful hosting of the event marks a significant step in integrating AI technology with science popularization practices, with plans for ongoing collaboration to conduct more targeted and high-quality science communication activities [8].

近日，未可知人工智能研究院成功举办了民营企业家AI战略工作坊，吸引了众多民营企业家的积极参与。本次工作坊聚焦于生成式AI与提示词 工程的基本原理、AI提示词实用技巧与工作应用，以及AI办公工具协同提升组织效能，旨在帮助民营企业家更好地理解和应用AI技术，提升企 业竞争力。 一、深度解析生成式AI与提示词工程 工作坊 第一天上午，未可知人工智能研究院副院长张孜铭先生为企业家们带来了精彩的分享。张副院长首先深入解析了生成式AI与决策式AI的 区别，指出生成式AI的核心在于生成新的内容，如AI绘画、生成对话等，而决策式AI则更关注如何做出满意决策，如人脸识别、风险管理等。 他还回顾了生成式AI的发展脉络，从早期的萌芽阶段到如今的快速发展，强调了技术进步对AI应用的推动作用。 在AI提示词技巧方面，张副院长详细介绍了提示词工程的重要性，并分享了多种实用技巧。他指出，提示词（Prompt）是为AI模型提供的输 入，通过设计和调整输入，可以有效改善模型性能、控制生成结果。他介绍了经典提示词框架如CO-STAR、TCREI、CRISPE等，并通过实际 案例展示了如何运用这些框架来优化AI生成内容。他还分享了如何通过符号划重 ...

近日， 未可知人工智能研究院副院长张孜铭老师受邀前往南方基金总部，开展了一场主题 为"DeepSeek+：企业的AI战略落地"的企业培训。 此次培训聚焦于 AI技术在企业战略中的应用与落地 ，旨在帮助南方基金的员工们深入理解AI 如何赋能企业运营、提升工作效率并推动业务创新，为企业的数字化转型提供有力支持。 张孜铭老师是未可知人工智能研究院副院长 ，北京大学与新加坡国立大学双硕士，著有 《DeepSeek使用指南》等多部作品，并参与起草了《生成式人工智能数据应用合规指南》等 行业标准。他在人工智能领域拥有深厚的学术背景和丰富的实践经验，为本次培训提供了坚实 的专业支撑。 培训中，张老师还分享了 未可知人工智能研究院在 AI培训、解决方案开发、咨询研究等方面 的丰富经验和成果。 他介绍了研究院的 专家团队 ，他们在人工智能领域拥有深厚的学术背景 和丰富的实践经验，为研究院的发展提供了坚实的人才支撑。 针对企业如何落地 AI战略，张老师提出了诸多建议。 他强调，企业应积极培养员工的AI技 能，掌握AI工具的使用方法；同时，要善于发现工作中的重复性任务，思考如何用AI替代，将 AI融入业务流程的标准化操作中。他还 ...