Core Points - The article emphasizes the importance of continuous learning within the Chinese Communist Party (CCP) as a key to its success and adaptability in changing times [4][5][6] - It highlights the historical context of learning in the CCP, tracing back to Mao Zedong's call for a "learning competition" and the establishment of a daily learning system for cadres [5][6] - The article discusses the current emphasis on enhancing the "learning ability" of party members as a response to complex global challenges and the need for knowledge in various fields [7][8] Group 1 - The Central Political Bureau has conducted over 100 collective learning sessions since the 18th National Congress, covering a wide range of topics from economic reforms to advanced technologies [4] - The learning system established by the CCP serves as a strong foundation for enhancing its governance capabilities [4][6] - Xi Jinping's call for a "learning revolution" reflects the necessity for party members to adapt to modern demands and continuously improve their knowledge and skills [5][7] Group 2 - The 19th National Congress report identifies "learning ability" as the foremost skill to be enhanced within the party [7] - The article stresses that the tasks of learning have become more challenging in the current era, necessitating a proactive approach to knowledge acquisition [7][8] - The commitment to learning is framed as essential for the CCP's future success and its ability to navigate complex political and social landscapes [8]

Core Insights - The article discusses the integration of AI, particularly the DeepSeek model, into the automotive finance sector, highlighting the importance of data accumulation, scenario understanding, and algorithm innovation as key to building competitive barriers in the industry [1][6]. Industry Trends - The fusion of AI with automotive finance is advancing, with a focus on leveraging data and innovative algorithms to address compliance and transparency challenges [1][4]. - Hong Kong is positioned to become a critical node in cross-border data governance and standard-setting due to its status as an international financial center [1][6]. Data Security and Compliance - The company adheres to strict compliance requirements in data usage, employing federated machine learning to allow collaborative AI model training without sharing raw data, and implementing data anonymization for internal use [4][5]. - The challenges in applying AI in vertical fields include ensuring high-quality outcomes while maintaining transparency in decision-making and adhering to stringent data security regulations [5]. Opportunities and Challenges - In the next 3 to 5 years, opportunities in automotive finance will arise from AI empowering the industry and serving companies with international expansion strategies, with Hong Kong playing a significant role in unified data governance [6][7]. AI Model Development - The DeepSeek model is noted for its lower costs and strong algorithm capabilities, achieved through innovations in algorithm engineering and structure [7]. - The company has developed the YiXin-Distill-Qwen-72B inference model, the first open-source large-scale inference model in the automotive finance sector, which performs comparably to DeepSeek-r1 [7][8]. AI Innovation and Application - The company aims to automate complex decision-making processes in automotive finance, significantly enhancing industry efficiency through AI-driven solutions [8]. - The company possesses three core advantages: a vast repository of automotive data assets, extensive experience in AI training and inference, and a comprehensive talent pool, computational power, and high-quality data [8].

Core Viewpoint - The competition aims to advance research in spatial intelligence and embodied intelligence, focusing on visual perception as a key supporting technology for applications in autonomous driving, smart cities, and robotics [2][4]. Group 1: Competition Purpose and Significance - Visual perception is crucial for achieving spatial and embodied intelligence, with significant applications in various fields [2]. - The competition seeks to promote high-efficiency and high-quality research in spatial and embodied intelligence technologies [4]. - It aims to explore innovations in cutting-edge methods such as reinforcement learning, computer vision, and graphics [4]. Group 2: Competition Organization - The competition is organized by a team of experts from institutions like Beijing University of Science and Technology, Tsinghua University, and the Chinese Academy of Sciences [5]. - The competition is sponsored by Beijing Jiuzhang Yunjing Technology Co., Ltd., which also provides technical support [5]. Group 3: Competition Data and Resources - Participants will have access to real and simulated datasets, including multi-view drone aerial images and specific simulation environments for tasks [11]. - The sponsor will provide free computing resources, including H800 GPU power, for validating and testing submitted algorithms [12][13]. Group 4: Task Settings - The competition consists of two tracks: Spatial Intelligence and Embodied Intelligence, each with specific tasks and evaluation methods [17]. - Spatial Intelligence requires building a 3D reconstruction model based on multi-view aerial images, while Embodied Intelligence involves completing tasks in dynamic occlusion scenarios [17]. Group 5: Evaluation Methods - Evaluation for Spatial Intelligence includes rendering quality and geometric accuracy, with scores based on PSNR and F1-Score metrics [19][20]. - For Embodied Intelligence, evaluation focuses on task completion and execution efficiency, with metrics such as success rate and average pose error [23][21]. Group 6: Submission and Awards - Results must be submitted in a specified format, and top-ranking teams will have their results reproduced for evaluation [24]. - Awards for each track include cash prizes and computing vouchers, with a total of 12 awards distributed among the top teams [25].

Core Insights - Bosch announced an investment of over €2.5 billion in artificial intelligence by 2027, predicting that sales of software, sensor technology, high-performance computing units, and vehicle communication components will double by 2035, potentially exceeding €10 billion in sales [1][3] - The company aims to leverage AI in advanced driver assistance and autonomous driving, combining AI with deep industrial knowledge to enhance vehicle safety and reduce product development cycles [1][3] Investment and Sales Projections - Bosch's investment in AI is part of a broader strategy to capitalize on the growing market for autonomous driving technologies [1] - The company forecasts that by 2035, the sales of relevant components will surpass €10 billion, driven by advancements in AI and sensor technologies [1][3] Technological Advancements - Bosch has deployed AI in cameras and radar systems to enhance object recognition and environmental perception, allowing vehicles to make informed driving decisions [1] - The integration of generative AI models enables Bosch to simulate various driving conditions, enhancing the training of AI systems with over 200 petabytes of global traffic scene data [2] Collaborative Efforts and Global Strategy - Bosch is collaborating with innovative players in AI technology to apply new advancements directly to products, particularly in the context of autonomous driving [3] - The company has established a successful partnership with Chery in China, creating an AI computing cluster and utilizing local data for model training through federated learning [3] Market Trends and Consumer Influence - The trend towards advanced driver assistance systems is driven by consumer demand, with Bosch believing that these technologies will be crucial for attracting buyers in the Chinese market [3] - Bosch anticipates that the expansion of autonomous driving technology will lead to long-term commercial success, with significant growth expected in various global markets [3]

作者丨 视觉语言导航 编辑丨 视觉语言导航 点击下方 卡片 ，关注" 具身智能之心 "公众号 >> 点击进入→ 具身 智能之心 技术交流群 更多干货，欢迎加入国内首个具身智能全栈学习社区 ： 具身智能之心知识星球 (戳我) ， 这里包含所有 你想要的。 主要贡献 自主导航无人机的基础 | UAV Type | Perception | Control Archi- | Decision System | Autonomy | Task | | Communication | | --- | --- | --- | --- | --- | --- | --- | --- | | | Modality | tecture | | Level | Adapt- | | Interface | | | | | | | ability | | | | Traditional | Monocular or | Rule-based | Deterministic. | Level 1-2 | Static | | Line-of-sight, | | UAVs | stereo RGB | flight con- | s ...

Group 1 - The article emphasizes the importance of choosing a reliable programming training institution, particularly in the context of limited local options in Tongling [1][3] - It suggests that courses should be practical and cover mainstream technologies like Python, Java, and front-end development, with a focus on real project experience [3][4] - The quality of instructors is highlighted, recommending prospective students to attend trial classes to assess teaching effectiveness [3][4] Group 2 - The learning environment is crucial; institutions with mechanisms for accountability, such as project reviews and attendance tracking, are preferred to prevent students from losing motivation [3][4] - Online learning is presented as a viable option, especially for students in non-first-tier cities, with a success story of a student transitioning to a tech job after completing an online Java course [4][5] - The article mentions a specific online education provider, Wangshidai Education, which has a strong reputation, high employment rates, and offers comprehensive Java courses with practical projects [4][5]

暑期消费热力十足，"后高考经济""毕业经济"热度居高不下，电子产品、生活电器消费迅速升温，折射 出年轻人消费新趋势。消费需求集中释放带动线下门店客流攀升，苏宁易购门店凭借品质化产品、场景 化体验、定制化服务承接暑期家电3C消费热潮。 苏宁易购相关负责人介绍，高考后至放榜期间，以电子产品为代表的"奖励式消费"迎来高峰。6月10日 至6月25日，手机、电脑、智能手表手环销售同比增长43%、69%、234%。不少家长为孩子备上"数码全 家桶"，手机、电脑、平板等数码套购订单增长近一倍。在北京中塔Suning Max店，胡同学在父母的陪 同下购入包括手机、电脑、平板、耳机在内的"数码四大件"，"查完分就来了，和爸妈一起把开学装备 选一下，店里品牌型号比较全，整套买也有折扣，很方便。" 一批莘莘学子即将迈入大学，另一批年轻人离开校园走入社会，开启新生活，苏宁易购门店为不同阶段 的年轻人提供生活方式新提案。在南京徐庄广场Suning Fun，彭同学即将和好友开启合租生活，"虽然 是租房，但生活是自己的，我们买了投影仪，不仅提升幸福感，未来搬家也方便"，在门店的露营装备 区，彭同学准备一并购入移动电源，"周末出去玩用得上" ...

温馨提示 ： 点击下方图片，查看运营团队2025年6月最新原创报告（共235页） 说明： 欢迎 约稿、刊例和商务合作、行业人士交流 ， 行业交流记得先加入 "机器人头条"知识星球 ，后添加（ 微信号： lietou100w ）微信； 若有侵权、改稿请联系编辑运营（微信：li_sir_2020）； 正文： 随着人工智能和大模型技术发展，具身智能赛道成为如今最火赛道之一；具身智能技术领域具体会涉及到大语 言模型（LLM）、视觉多模态模型（VLM）、强化学习（Reinforcement Learning）、深度强化学习（Deep Reinforcement Learning）、模仿学习（Imitation Learning）等诸多前沿技术。 人形机器人发展多年，从最初基于 模型的控制算法（LIPM+ZMP），到动态模型控制和最优控制算法 （MPC+WBC），到如今的模拟+强化学习（IL+RL），当然现阶段也有不少人形机器人公司采用MPC方式，各类 算法没有绝对的替代关系，各有优劣；IL+RL是目前人形机器人公司最常提起的概念，基本都是高校和头部科技 大厂内研发机构在研究，也是为什么目前人形机器人初创公司以"学院派" ...

近期北京邮电大学方斌教授团队联合清华大学、 意大利比萨圣安娜大学、英国伦敦国王学院和德国汉堡大学 发布了基于掌状视触觉传感器的柔性接触仿真与操作学习，为基于视触觉传感器的柔性操作提供了新的思路。 相关工作发表在机器人、自动化领域 JCR Q1 期刊 IEEE Transactions on Automation Science and Engineering 。 研究背景： 可变形物体操控是机器人领域一个经典且极具挑战性的任务。相较于刚性物体，可变性物体具复杂的变形特性 （包括弹性变形、塑性变形和弹塑性变形），大量的自由度 (DOF) 需要复杂的建模方法，这使该问题更加复 杂。同时，可变形物体广泛存在于医院、工业和家庭环境中。因此，可变形物体操控在机器人技术发展中发挥 着至关重要的作用。 为此，本文开发了一款可变形物体与基于视觉的触觉传感器之间的软接触模拟器，该模拟器能够模拟视触觉传 感器与弹性、塑性以及弹塑性物体之间的接触变形。在此模拟器的基础上，本文提出了基于视触觉传感器的可 变形物体操控基准，包括可迁移的观测值、任务和专家演示系统。最后，本文搭建了相应的实验平台，完成了 相关任务的 Sim-to-rea ...

过去，灵巧手更多是实验室中的符号——高自由度、仿生结构、极高成本；而如今，伴随软硬协同能力的提升、控制算法的不断演进、触觉与多模态感知 的加速融合，灵巧手正逐步从科研走向应用的临界点。一方面，它是工业自动化对"异形抓取""多任务执行"能力的新需求延伸；另一方面，它也是服务机 器人在家庭、医疗、养老等场景中迈向"真实可用"的关键一环。 当AI从云端走向实体，具身智能正逐渐成为通往下一代通用人工智能的关键路径。在这一演进过程中，灵巧手作为"通用机器人"实现复杂操作与自然交互 的核心执行器，正迎来前所未有的技术突破与商业想象空间。 值得注意的是，这一领域正快速演变为全球技术博弈与资本布局的热点。从Shadow Robot与DeepMind合作攻克多任务抓取，到中国本土初创企业灵心巧 手凭借超高自由度结构在仿生手赛道突围，一批聚焦结构创新、感知控制一体化的新兴力量，正在不断刷新我们对"灵巧"这一词的理解。 我们希望通过本报告，为关注具身智能、机器人末端执行器、智能制造升级的产业人士与投资机构，提供一份具备前瞻视角与产业落点的深度参考。本篇 报告将围绕以下三大维度系统展开： 产业定义与技术演进 应用场景与商业趋势 竞争 ...