Core Insights - The emergence of open-source large models, such as DeepSeek-R1, has sparked a revolution in the AI industry, challenging the debate over the merits of open-source versus closed-source models [2][3] - Open-source AI is still in its early stages, with significant potential for industry transformation, but challenges related to security and commercialization remain [2][11] Group 1: Open-Source AI Impact - Open-source models are reshaping global industry dynamics, enabling lower-cost access to advanced AI capabilities for small and medium enterprises [3][4] - The cost of using DeepSeek-R1 for inference is only 1/30 of OpenAI's model, allowing developers to create applications at a fraction of the cost, such as a legal document generation tool that saw a 90% cost reduction [4] - The rise of "Model as a Service" (MaaS) is changing the service model of traditional cloud providers, making it easier for startups to deploy AI applications without building their own infrastructure [4][5] Group 2: Security Challenges - Security has become a major concern with the rise of open-source AI, with 57% of IT decision-makers citing privacy and data security as top issues [6][9] - High-profile security incidents, such as unauthorized access to Hugging Face's platform, highlight the vulnerabilities associated with open-source models [6][7] - DeepSeek has faced significant security threats, including DDoS attacks and data breaches, indicating the challenges that open-source platforms must address [7][8] Group 3: Future Considerations - The demand for computational power remains high, even with reduced costs, necessitating better observability and security measures from cloud service providers [5][9] - The integration of edge computing with AI is creating new security challenges, requiring companies to develop more complex security frameworks [10] - As data becomes a critical asset, ensuring data privacy and security in AI deployments is essential for companies [10][11]

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 ...

【环球网科技综合报道】随着ChatGPT、Deepseek等生成式AI聊天机器人在全球范围内掀起热潮，其应 用场景正不断拓展，如今甚至"跨界"至投资领域。近期，社交媒体上涌现出大量散户分享使用生成式AI 进行选股投资的经验，他们认为，相比咨询专业投资顾问，利用AI制定投资策略不仅成本低廉，而且 似乎颇具成效。然而，这一新兴趋势背后，是否真的隐藏着财富密码？还是潜藏着不为人知的风险？ 然而，专家也提醒广大散户，在使用AI进行投资研究时，务必保持警惕，仔细核查AI模型所引用的资 料来源。由于AI训练数据可能存在时效性不足或质量参差不齐的问题，其生成的内容有时可能包含过 时信息甚至错误观点。更为严重的是，AI在处理复杂或小众内容时，还可能出现"幻觉"现象，即输出与 事实不符或逻辑混乱的信息。 "AI幻觉本质上源于模型对知识点的掌握不足或概率计算错误。"一位不愿具名的AI领域专家解释 道，"对于不具备相应专业知识的散户而言，很难在短时间内准确识别这些错误信息，从而可能误导其 投资决策。" 面对AI在投资领域的应用热潮，专家呼吁广大散户保持理性态度，既要看到AI在信息处理方面的潜 力，也要清醒认识到其局限性和潜在风险。 ...

Core Viewpoint - The collaboration between SenseTime and Dongfeng Motor in the field of intelligent driving represents a deep integration of technology and industry, providing a practical model for the sector [1] Technological Breakthroughs - SenseTime showcased the R-UniAD solution based on the VLAR architecture, achieving near real-time interactive simulation and reducing the need for real data by two orders of magnitude [3] - The system demonstrated a perception accuracy of 98% compared to real data and improved simulation efficiency by five times over industry benchmarks [3] - The joint team has made significant progress in developing an end-to-end autonomous driving system, which features a "dual insurance" architecture combining a learning-based decision system with a rule-driven safety redundancy [3] Industry Collaboration - SenseTime has partnered with four car manufacturers, covering seven vehicle models, and plans to mass-produce an end-to-end solution based on the NVIDIA Thor platform by Q4 2025 [3] - The company has maintained a leading market share in intelligent cockpit software for five consecutive years, with cumulative deliveries exceeding 3.6 million units [3] Ecological Evolution - SenseTime introduced the first in-vehicle AI operating system, "Jueying Qianji," with a response latency of less than 300ms, supporting over 100 intelligent agents [6] - The upgraded "New Member" system showcases multimodal recognition and deep thinking capabilities, providing personalized services during real-time demonstrations [6] - The "Travel Medical" system integrates medical-grade health monitoring and connects with third-party medical services [8] Industry Observations - The recent regulatory adjustments are viewed as a turning point towards maturity rather than a pause in the intelligent transformation process [8] - The penetration rate of L2-level assisted driving reached 62% in 2024, with a 37% year-on-year increase in user complaints regarding functional safety [8] - The collaboration model between Dongfeng and SenseTime reflects a dual-track strategy of "independent control + open collaboration" in the intelligent transformation of traditional car manufacturers [8] Common Industry Challenges - The introduction of AI-native technologies like world models and reinforcement learning is breaking through the limitations of rule-driven systems [11] - There is an urgent need to establish compliant data collection systems and unified safety assessment standards within the industry [11] - The deep binding of AI companies and vehicle manufacturers may be a key pathway to address these common challenges [11]

生成式AI 一、 OpenAI 称刚刚对GPT 4o模型进行了升级，个性化更强 1. 优化了记忆存储机制，使AI能更智能地记忆和回忆对话信息； 2. STEM领域推理能力显著提升，可更好解决数学、科学、工程等复杂问题； 3. 对话风格更加主动自然，擅长引导对话方向，同时回复更贴近真实交谈。 https://mp.weixin.qq.com/s/oZVIP1hLb2ZZu5E9VNr5Zw 二、 实测免费DeepResearch！轻量版，速度更快重视脉络梳理 1. OpenAI发布基于o4-mini的轻量版DeepResearch，免费用户可使用，付费用户获额外使 用额度； 2. 轻量版与满血版相比，用时更短、内容更精简，但保持相近的智能水平； 3. 实测显示轻量版更注重梳理重点脉络，适合需要快速了解概况的场景。 https://mp.weixin.qq.com/s/0vZvNaAhEQQOqUfg3YiIdQ 2. 系统通过层级化分解和提交历史分析来理解代码全局结构，已索引3万个仓库，处理超40 亿行代码； 3. 使用方式简单，只需将github.com替换为deepwiki.com即可访问对应仓库的AI文档 ...

Core Viewpoint - Digital China Group Co., Ltd. reported a revenue of 31.78 billion yuan for Q1 2025, marking an 8.6% year-on-year increase, with a net profit of 224 million yuan, up 10.4% [1] Group 1: Financial Performance - Overall revenue reached 31.78 billion yuan, reflecting an 8.6% year-on-year growth [1] - Non-GAAP net profit was 224 million yuan, showing a 10.4% increase compared to the previous year [1] - Operating cash flow recorded a net inflow of 2.73 billion yuan [1] Group 2: AI Strategy and Implementation - The company focused on AI technology transformation, enhancing its digital technology stack and launching multiple AI projects [1] - New direct customer opportunities increased by 78% year-on-year, with significant growth in flagship AI projects [1] - The company successfully implemented AI solutions in various industries, including retail, automotive, and manufacturing [1] Group 3: R&D and Product Development - R&D expenses grew by 9.3% year-on-year, emphasizing the commitment to generative AI and AI accessibility [2] - The launch of the DeepSeek version of the KunTai integrated machine and the KunTaiCube toolbox supports private deployment and offers cost-effective smart upgrade solutions [2] - The company secured an 800 million yuan order for its self-branded products from China Telecom for the 2024-2025 procurement project [2] Group 4: Automotive Industry Focus - Digital China introduced the "AI for Process Maturity Model" to outline the AI evolution blueprint for the automotive industry [2] - The company launched an "end-to-end one-stop AI workspace" and the "Digital China Intelligent Vehicle Solution" to enhance productivity and reduce costs for automotive enterprises [2] - Multiple "ten million-level" orders were won for digital marketing and data compliance projects from leading automotive companies [2]

到2030年，全球半导体公司计划在新晶圆厂（fabs）建设上投资约1万亿美元，行业年收入也有望 突破1万亿美元。这一数字尚未包括生成式AI（gen AI）在中等乐观情境下可能带来的额外增长潜 力。除了满足日益增长的市场需求，这些投资也将增强各地区在半导体价值链上的供应弹性。 然而，尽管这场全球范围的大规模投资有望显著扩展半导体的产能版图，实现其预期效益的道路却 并不平坦。除了已公布的建设项目本身执行难度大之外，至少还有五大结构性障碍，特别在北美和 欧洲市场可能长期制约新增投资带来的实质进展：包括资本与运营成本结构、材料需求增长、关键 原材料及封装环节的离岸集中、物流与处理瓶颈，以及人才短缺等问题。若行业希望实现投资的长 期价值，就必须正视并逐一应对这些挑战。 底层半导体资本和运营成本动态 近年来，全球多国通过战略政策推动半导体制造和供应链的本土化。例如，美国推出《两党基础设 施法案》、《芯片与科学法案》、《通胀削减法案》及各州激励措施，试图吸引企业在本土建厂。 类似的激励措施也在欧洲、印度、日本、中国大陆、东南亚、韩国与中国台湾陆续推出，从而带动 全球晶圆厂建设热潮。 不过，从实际成本结构来看，美国本土建设先 ...

社交媒体已经变成了「媒体」，而不是「社交」。 我们不再是一个传统意义上的社交网络了，所以我们也不是在垄断社交网络。 扎克伯格在出席 Meta 反垄断案庭审作证时， 试图通过这种说法来削弱 Meta 在社交网络领域的垄断指控。这是一种策略性地重新定义， 目的是让 Meta 看起来不像是控制了一个特定市场，而是参与了一个更广泛、竞争更激烈的 媒体平台市场。 上周一，美国联邦贸易委员会（FTC）指控 Meta 收购 Instagram 和 WhatsApp 以此非法垄断社交媒体市场的案件在华盛顿开庭。该反垄断案在 2021 年 曾因对于 「个人社交网络服务」 的市场定义过于宽泛而被驳回。 据纽约客报道，Meta 创始人马克· 扎克伯格 在反垄断诉讼庭审期间表示，如今的社交媒体平台已今非昔比，Meta 近年来关注的重点是 「娱乐、了解世 界及发现新鲜事物的整体概念」。社交媒体已经逐渐从「连接人与人」演变成更类似传统媒体的形态，充斥着名人制作的推广视频、评论员对新闻事件的 评论内容、流行文化的聚合片段等。换句话说， 社交媒体已经变成了「媒体」，而不是「社交」。 以下为纽约客的评论文章《Mark Zuckerberg S ...

其中，该行指出，公司预计截至2月国内生活类场景下付费渗透率达到5.2%，突破此前5%的目标，主要 系生成式AI对传统功能的重构，比如用AI去双下巴功能取代传统的手动推图，实现对体验和效果的更 好满足，从而促进订阅转化率的提升。该行认为，公司基于长期沉淀的人像修图场景数据，通过低频功 能迭代+高频体验升级的模式，有利于用户留存与回流维持在健康水平。公司预计25年生活类付费会员 增长有望提供订阅收入弹性，维持生活类产品10%的长期订阅渗透率目标。 该行续指，根据SensorTower，1Q以来，定位欧美日韩市场的Airbrush、BeautyPlus MAU较为稳定；AI 换装等功能在东南亚出圈，带动海外版Wink、美颜相机MAU环比大幅增长。受益于较高付费意愿和高 级产品定位，公司预计截至24年Airbrush付费率远高于国内，而其他出海产品还在积极扩张新市场阶 段，短期尚未激进变现，对比成熟市场，远期付费率还有较大提升空间。 智通财经APP获悉，美图公司(01357)涨近3%，截至发稿，涨2.74%，报4.88港元，成交额9258.87万港 元。 中金发布研报称，近期，该行邀请美图管理层在2H24业绩后与投资 ...

曾几何时，商汤科技、旷视科技、云从科技、依图科技并称为"AI 四小龙"，承载着AI行业的无限期 待。时光流转，"AI 六小龙""杭州七小龙"等名号层出不穷，"AI 四小龙"在时代的洪流中逐渐失色。 从财报数据来看，"AI 四小龙"曾经的辉煌已如过眼云烟。 商汤科技2024 年度财报显示，全年总营收 37.72 亿元，却伴随着 43.06 亿元的净亏损，自 2018 年至 2024 年累计亏损超 546 亿元；云从科技2024 年总营收约 3.98 亿元，归母净亏损达6.63亿元。 持续亏损"老大难" 被誉为"AI 四小龙"之一的云从科技，往昔头顶着明星企业的光环，承载着行业与资本的诸多期待。然 而，在高研发投入下，亏损却不断增加，面临着商业化的难题。 数据显示，2024年，云从科技实现营业总收入3.98亿元，同比减少36.60%；营业利润-6.49亿元，上年同 期为-6.55亿元；归属于母公司所有者的净利润-6.37亿元，上年同期为-6.43亿元；归属于母公司所有者 的扣除非经常性损益的净利润-6.63亿元，上年同期为-6.89亿元。 从更长时间跨度来看，自2017 年起至 2024 年，云从科技仿佛陷入了一 ...