近日， 未可知人工智能研究院副院长张孜铭老师受邀前往西南空管局 ，开展了一场以" 聚焦数字化转型如何用 AI提升工作质效打造智慧型团 队 "为主题的分享会。 此次分享会旨在帮助 西南空管局 工作人员深入了解人工智能技术在办公领域的应用，提升工作效率，推动区域数字化转型。 张孜铭老师作为 人工智能领域的资深专家 ，拥有丰富的学术背景和实践经验。 他不仅是北京大学管理学硕士、新加坡国立大学金融工程硕士，还担任《AIGC：智能创作时代》一书的作者，参与了多项国家级人工智能标准 的起草工作。 在分享会上，张老师深入浅出地介绍了 人工智能的发展历程、技术原理以及在不同行业的应用案例 ，让在场的政府工作人员对AI有了更全 面、更深刻的认识。 在分享过程中，张老师着重讲解了 AI在办公场景中的应用 。他指出，随着人工智能技术的不断进步，AI工具已经能够帮助人们高效完成文本 生成、数据处理、图像设计等任务。 例如，通过AI生成PPT，可以在短时间内快速生成高质量的演示文稿，大大节省了制作时间；利用AI处理Excel数据，能够快速进行数据分析和 图表绘制，提高工作效率。 张老师还现场展示了如何使用AI工具进行文本创作、图像生成 ...

近日，苏州迎来了一场备受瞩目的AI 战略盛宴。 未可知人工智能研究院院长杜雨博士受邀为明道企业家书院的一代企业家和二代企业家们举办 了一场主题为 "传统企业靠 AI 翻身？" 的 AI 战略工作坊 ，旨在助力传统企业借助 AI 技术实 现转型升级，开拓新的发展路径。 专业引领，深度剖析 AI 战略实施要点 工作坊伊始，杜雨博士便凭借其深厚的专业素养，深入浅出地为在场企业家们解读了 AI 战略 的实施要点。他强调， AI 战略的落地关键在于将组织的宏观战略转化为全体员工都能理解并 切实执行的行动。 为此，他详细阐述了描述 AI 战略、衡量 AI 战略以及制定 AI 行动地图这 三大核心环节。 在描述 AI 战略时，杜雨博士指出，企业需将宏大目标拆解为一个个小目标，并进一步转化为 具体可操作 的目标描述。 例如，对于传统制造企业而言，可设定通过 AI 技术降低生产成本、提升生产效率、创新产品 壁垒等细分目标，并详细阐述运用 AI 智能客服降低人力成本、打造行业专属知识大脑吸引代 理商合作、运用 AI 调度系统提高供应链效率等具体实现路径，让企业家们清晰地看到 AI 技术 如何融入企业日常运营的各个环节，为企业带来 ...

他提到，研究院致力于培养AI创新人才，通过 与各地企业、政府、院校 的深度合作，推动人工智能技术在各行业的应用落地。此外，他还介 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。 针对地产从业者如何利用 AI赋能自身发展 ，张老师提出了诸多建议。他强调，地产从业者应积极学习AI技术，掌握其基本原理和应用场景， 利用AI工具提升工作效率，如通过DeepSeek进行精准信息搜索、生成营销文案、制作思维导图等。同时，他还鼓励员工在工作中 大胆尝试AI 技术，挖掘AI在不同领域的应用潜力，探索新的商业模式和解决方案。 近日， 未可知人工智能研究院副院长张孜铭老师受邀前往华润置地， 为华润置地的员工们带来了一场主题为 "AI赋能地产业：地产办公与营 销的AI提效" 的企业培训。 本次培训旨在帮助地产从业者深入了解 人工智能技术在地产领域 的应用，提升工作效率，推动行业创新。 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，华中师范大学信息管理与信息系统、华中科技大学计算机科 学与技术双学士，同时也是《DeepSeek使用指南》 ...

Core Viewpoint - The rapid development of artificial intelligence (AI) technology is reshaping the ecosystem of the fund industry, with significant implications for digital transformation and intelligent upgrades in asset management [1][5]. Group 1: Historical Development of the Fund Industry - The evolution of the fund industry has progressed from financial IT in the 1970s, to financial internet in the 1990s, and now to financial AI, marking each technological shift as a qualitative leap for the industry [3]. Group 2: Current State and Challenges of AI in the Fund Industry - Despite some achievements in AI applications within the financial sector, generative AI has not yet become mainstream, with traditional machine learning and rule engines still dominating [6]. - Industry pioneers are exploring generative AI applications, accumulating valuable experiences that are crucial for the future development of the fund industry [8]. Group 3: Application Scenarios and Strategies - Research indicates that within the next 18 months, scenarios such as intelligent investment research, smart office, and intelligent marketing have lower complexity and faster application progress, while intelligent investment research, advisory, and risk control are more complex and slower to implement [10][11]. - The fund industry should develop differentiated strategies based on various business scenarios, prioritizing breakthroughs in easier-to-implement areas before expanding into more complex scenarios [11]. Group 4: Case Studies and Best Practices - International examples such as Bloomberg's BloombergGPT, S&P Global's Kensho tools, and domestic practices like Eastmoney's intelligent research assistant demonstrate the strong empowering potential of AI in finance [15][17]. - Notable domestic practices include E Fund's EFundGPT platform, which enhances efficiency and decision-making accuracy across multiple business scenarios, and Huaxia Fund's comprehensive AI investment system that has achieved significant results in various areas [27][30]. Group 5: Strategic Insights from Leading Asset Management Firms - BlackRock's Aladdin system integrates various AI technologies to achieve intelligent investment decision-making, real-time risk management, and personalized customer service [20][22]. - Vanguard has diversified its AI applications across quantitative investment, fund advisory, personalized advertising, and cross-platform trading systems, showcasing a robust AI strategy [23]. Group 6: Recommendations for AI Strategy Development - A toolkit for enhancing AI strategy development includes customized AI strategy workshops, establishing AI knowledge update mechanisms, demand-oriented vendor selection, promoting deep integration of industry-academia-research, forward-looking investment layouts, and synergy through acquisitions [32][33]. - These initiatives aim to deepen internal AI capabilities, optimize external resource integration, and strengthen strategic positioning and competitive advantages for sustainable development in the AI era [33].

在数字浪潮汹涌澎湃的今天，人工智能正以前所未有的速度重塑着世界的模样。近日， 成都高 新区迎来了一场意义非凡的政务培训，未可知人工智能研究院副院长张孜铭先生亲临现场， 为 高新区的政务工作者们带来了一场关于人工智能（AI）的深度分享，一同探索智能时代的发展 密码。 一、培训现场：思想碰撞，智慧交锋 成都市IC产业园会议中心内，气氛热烈而庄重。张孜铭副院长的讲解深入浅出，从AI的起源讲 起，沿着时间的脉络， 详细梳理了人工智能的发展历程 。他提到，从早期的萌芽阶段，到如 今的快速腾飞，AI正逐步渗透到我们生活的每一个角落，成为推动社会进步的关键力量。 在讲解过程中， 张副院长结合实际案例，生动展示了 AI在不同领域的应用成果。 从智能家居 的便捷生活，到医疗健康领域的精准诊断，再到电商行业的智能推荐，AI的多面性让在场的每 一位听众都惊叹不已。他强调，AI不仅是技术的革新，更是思维方式的转变，它让我们能够以 全新的视角看待问题，寻找解决方案。 二 、 AIGC：内容生产力的大变革 培训的核心内容之一是 AIGC（生成式人工智能内容）。张副院长指出，AIGC正引发一场内容 生产力的革命。 与传统的PGC（专业生产 ...

Core Insights - The article emphasizes that the AI market is projected to be ten times larger than the cloud computing market, with significant growth expected over the next 10 to 20 years [4][6]. - It highlights the importance of application layers in creating value within the AI sector, suggesting that successful companies will focus on specific verticals and customer needs [10][11]. - The emergence of the "agent economy" is discussed, where AI agents will play a crucial role in business operations and interactions, transforming how work is conducted [36][38]. Market Opportunities - Pat Grady poses essential questions regarding the significance of AI and the timing for investment, framing the discussion around the potential of AI as a trillion-dollar opportunity [2]. - The comparison between cloud computing and AI transformation indicates that AI's starting market size is expected to be at least an order of magnitude larger than that of early cloud computing [4]. - AI is not only disrupting the service market but also the software market, with companies evolving from simple tools to more intelligent, automated solutions [6]. Application Layer Value - Historical analysis shows that major technological revolutions have led to significant revenue generation at the application layer, a trend expected to continue with AI [10]. - Companies should focus on specific functionalities and customer needs to create value, especially as AI models become more capable [11]. - Key factors for building successful AI companies include avoiding "vibe revenue," ensuring trust, and establishing a clear path to healthy profit margins [16][17]. User Engagement and Breakthroughs - There has been a notable increase in user engagement with AI applications, with daily active users of tools like ChatGPT rising significantly [19][20]. - Two critical areas of focus for 2024 are advancements in voice generation technology and programming capabilities, which are expected to enhance accessibility and efficiency in software development [22][24]. Vertical Agents and Intelligent Economy - The development of vertical agents, which are specialized AI systems trained for specific tasks, is seen as a promising opportunity for entrepreneurs [31][32]. - The concept of the "agent economy" is introduced, where AI agents will facilitate transactions and interactions, creating a new economic framework [36][38]. - Key challenges in realizing this vision include establishing persistent identities for agents, developing seamless communication protocols, and ensuring security and trust [39][40]. Transformative Changes in Work and Management - The shift towards an agent economy will fundamentally alter management practices and decision-making processes, requiring a new understanding of AI capabilities [41][43]. - The anticipated integration of AI agents into organizational structures is expected to lead to unprecedented levels of operational efficiency and economic transformation [44].

Core Viewpoint - The article emphasizes the transformative impact of AIGC (Artificial Intelligence Generated Content) on the banking industry, highlighting its potential to enhance content production efficiency and revolutionize various banking processes [2][5][29]. Group 1: Evolution of Content Production - The training led by Dr. Du Yu reviewed the evolution from WEB1.0 to WEB3.0 and the metaverse, analyzing the shift from PGC (Professionally Generated Content) to UGC (User Generated Content) and now to AIGC [2]. - AIGC is recognized as a key driver of content production efficiency, with its capabilities in generating multi-modal content such as text, audio, images, and videos [2]. Group 2: Distinction Between AI Types - Dr. Du Yu differentiated between generative AI, which focuses on creating new content, and decision-making AI, which is aimed at making satisfactory decisions [3]. Group 3: Applications of AIGC in Banking - AIGC shows significant potential in various banking applications, including product marketing, risk management, and process optimization [5][11]. - In product marketing, AIGC can analyze vast historical data to predict customer needs and enhance retail credit loan scales [5]. - AIGC's multi-modal data processing capabilities have improved mortgage loan operations by efficiently extracting important information from various document types [8]. Group 4: Risk Management Enhancements - AIGC can enhance risk management by enabling dynamic risk detection and covering various fraud and default scenarios, thus improving overall risk management capabilities [11]. Group 5: Practical Case Studies - Successful case studies include: - Guangfa Bank utilizing AIGC for customer service efficiency, significantly improving response times and customer satisfaction [14]. - Industrial and Commercial Bank of China innovating in financial report generation through AIGC, enhancing efficiency and quality [18]. - Beijing Bank exploring AIGC applications through its self-developed models and partnerships [22][25]. Group 6: Challenges and Solutions - The article identifies challenges such as inadequate innovation mechanisms, talent shortages, and data quality issues that hinder AIGC implementation in banking [29][30][33]. - Recommendations include enhancing internal collaboration, investing in talent development, and improving data governance to support AIGC applications [30][33][34].

近日， 未可知人工智能研究院携手全景网，在大湾区成功举办了以"AI赋能财务，智启CFO新 未来" 为主题的行业沙龙。 本次沙龙吸引了众多大湾区企业的首席财务官（CFO）及财务领域专业人士参与，共同探讨人 工智能技术在财务工作中的应用前景与实践路径。 未可知人工智能研究院院长杜雨博士作为本次沙龙的主讲嘉宾，为在场的 CFO们带来了一场 关于AI如何助力企业降本增效的精彩分享。 杜雨博士指出，随着人工智能技术的飞速发展， 生成式AI正逐渐成为产业革新和经济增长的新 引擎。 在财务领域，AI的应用不仅能提高工作效率，还能为企业带来更精准的决策支持。 全景网自 1999年成立以来，一直致力于为中国证券市场提供创新服务，其打造的网上路演、 投资者互动平台等服务已成为 行业标杆 。 凭借多年积累的品牌影响力、强大的资本圈资源以及雄厚的采编和专业服务力量， 全景网如今 已发展成为覆盖媒体、路演互动、金融服务等业务的一站式资本服务平台。 此次与未可知人工智能研究院的联合举办，旨在借助双方优势，为大湾区的财务精英们搭建一 个前沿技术交流与思想碰撞的平台。 | | 学收 (亿 | 同比增长 | 归母净利 | 同比增长 | 毛利率 ...

近日， 未可知人工智能研究院副院长张孜铭老师受邀前往武汉市蔡甸区政府 ，开展了一场以" AI赋能办 公，开启智慧新未来 "为主题的分享会。 此次分享会旨在帮助蔡甸区政府工作人员深入了解人工智能技术在办公领域的应用，提升工作 效率，推动区域数字化转型。 张孜铭老师作为 人工智能领域的资深专家 ，拥有丰富的学术背景和实践经验。 他不仅是北京大学管理学硕士、新加坡国立大学金融工程硕士，还担任《AIGC：智能创作时 代》一书的作者，参与了多项国家级人工智能标准的起草工作。 在分享会上，张老师深入浅出地介绍了 人工智能的发展历程、技术原理以及在不同行业的应用 案例 ，让在场的政府工作人员对AI有了更全面、更深刻的认识。 在分享过程中，张老师着重讲解了 AI在办公场景中的应用 。他指出，随着人工智能技术的不 断进步，AI工具已经能够帮助人们高效完成文本生成、数据处理、图像设计等任务。 例如，通过AI生成PPT，可以在短时间内快速生成高质量的演示文稿，大大节省了制作时间； 利用AI处理Excel数据，能够快速进行数据分析和图表绘制，提高工作效率。 张老师还现场展示了如何使用AI工具进行文本创作、图像生成等操作，让在场人员直 ...

在人工智能技术飞速发展的今天，掌握AI工具已成为提升工作效率和生活品质的必备技能。 近日， 未可知人工智能研究院院长杜雨博士与认证AI培训师章星灿 携手为余杭区打造了四场为期5天的AI夜校课程，旨在帮助学员从零开始掌握AI工具， 轻松应对工作与生活中的各种挑战！ 课程走进 杭州余杭区阿里巴巴青橙国际创新创业中心 。现场60%学员为阿里系员工。 课程亮点： 1. 全面覆盖AI应用场景 课程涵盖提示词优化、AI机器人制作、PPT生成、Excel数据分析、照片处理、海报设计、自媒体运营、短视频剪辑、时间管理、健康与财富管理等十 大主题，满足学员多样化的需求。 2. 注重实操，即学即用 每节课均配备实战案例，学员可在课堂上直接完成作品，真正做到"学以致用"。例如： 学员收获： 学员故事 "作为阿里国际站运营，今天终于看懂算法同事的工作逻辑了！"——学员Lydia AI时代，学习永不停止！快来加入余杭区AI夜校，与杜雨博士和章星灿讲师一起，开启你的智能生活新篇章！ 这场AI赋能之旅并非终点，而是 青年科技工作者更好拥抱数智时代的起点。 未可知人工智能研究院将持续聚焦科技前沿热点，密切贴合青年科技工作者需求， 通过开展多 ...