Group 1 - The event "Dancing with Social Robots" was held at the National Exhibition and Convention Center in Tianjin, focusing on the cultural phenomenon of robots entering various domains such as classrooms and public spaces [1] - Experts discussed the coexistence with social intelligent robots and the underlying reasons for their integration into society [1] Group 2 - Professor Vanessa Evers from Twente University emphasized the need to build a "world model" for achieving social intelligence in robots, using the example of fishing to illustrate the complexity of sensory inputs required for decision-making [3] - Current limitations include the need for digitalizing the entire world, as existing trials are confined to limited environments like classrooms and hospitals, making implementation challenging despite the availability of various sensors [3] - Evers highlighted that robots can learn human expressions and etiquette by analyzing YouTube videos, but their operational methods do not need to mimic humans exactly, suggesting the use of optimized mechanical arms instead of human-like ones [3] - The ultimate goal of developing social robots raises questions about their integration into human life versus providing a space for self-expression, with concerns about misuse prompting a call for public and governmental discussions on technology's development and application boundaries [3] - Evers pointed out that energy issues pose significant challenges in the laboratory, particularly for soft robots that require efficient energy transmission similar to human blood, while battery technology is progressing slowly [3]

Group 1: Company Insights - Shenzhen Lingfeng Asset recently conducted research on the listed company Siwei Tuxin, highlighting the trend of intelligent driving equality becoming a key industry focus [1] - The company noted that mid-to-high-level assisted driving functions are gradually being integrated into lower-end models, establishing intelligent driving as a leading business segment [1] - Siwei Tuxin's data compliance business shows a clear growth trend, with AI-enhanced data loops aiding automakers in rapid algorithm iteration and optimization [1] Group 2: Product Development and Market Trends - The world model is being utilized for behavior prediction and trajectory generation, with productization aimed at OEMs and Tier 1 suppliers [1] - The company emphasized the need for intelligent driving orders to achieve certain sales volumes to realize economies of scale, alongside internal cost control and operational efficiency improvements positively impacting profitability [1] - The implementation of new national standards for two-wheeled vehicles is expected to create new market demands for Jiefa Technology's SoC cockpit products, aligning with leading automakers' overseas expansion needs [1] Group 3: Financial Projections and Growth - Jiefa Technology anticipates a revenue growth of over 12% in 2024, with an additional 3 million sets of basic driving point products and 600,000 sets of cockpit products expected to be secured by Q1 2025 [1] - The company is confident in achieving significant loss reduction by 2025, supported by the successful launch of its fifth-generation SoC product, the AC8025AE [1] - Jiefa Technology's automotive-grade MCU chip AC7870 has been successfully launched, meeting ISO 26262 ASIL-D functional safety standards, applicable across various scenarios [1]

Core Viewpoint - The article emphasizes the rapid evolution and commercialization of autonomous driving technologies, highlighting the importance of community engagement and knowledge sharing in this field [9][14][19]. Group 1: Job Opportunities and Community Engagement - Huawei is actively recruiting for various positions in its autonomous driving division, including roles focused on end-to-end model algorithms, perception models, and efficiency optimization [1][2]. - The "Autonomous Driving Heart Knowledge Planet" serves as a platform for technical exchange, targeting students and professionals in the autonomous driving and AI sectors, and has established connections with numerous industry companies for job referrals [7][14][15]. Group 2: Technological Trends and Future Directions - The article outlines that by 2025, the focus will be on advanced technologies such as visual large language models (VLM), end-to-end trajectory prediction, and 3D generative simulations, indicating a shift towards more integrated and intelligent systems in autonomous driving [9][22]. - The community has developed over 30 learning pathways covering various subfields of autonomous driving, including perception, mapping, and AI model deployment, which are crucial for industry professionals [19][21]. Group 3: Educational Resources and Content - The knowledge platform offers exclusive rights to members, including access to academic advancements, professional Q&A sessions, and discounts on courses, fostering a comprehensive learning environment [17][19]. - Regular webinars featuring experts from top conferences and companies are organized to discuss practical applications and research in autonomous driving, enhancing the learning experience for participants [21][22].

Core Viewpoint - Stand Robot (Wuxi) Co., Ltd. has submitted an application for listing on the Hong Kong Stock Exchange, with CITIC Securities and Guotai Junan International as joint sponsors [1] Company Overview - Stand Robot is a global leader in industrial intelligent mobile robot solutions, focusing on empowering smart factories across various industrial scenarios [4] - The company is recognized as the fifth largest provider of industrial intelligent mobile robot solutions and the fourth largest provider of industrial embodied intelligent robot solutions globally, according to Zhaoshang Consulting [4] - Stand Robot has a diverse customer base, with over 400 clients, many of whom are leaders in their respective fields, particularly in high-tech industries such as 3C, automotive, and semiconductors [4][6] Technological Advancements - The company is one of the few in the industry to achieve independent research and development of full-stack technology and has pioneered proprietary operating systems for industrial intelligent robots in China [5] - Stand Robot has made significant breakthroughs in positioning, navigation, control, and perception technologies, enabling robots to operate with intelligence, efficiency, stability, and safety [5] - The company is capable of dispatching over 2,000 robots in a single simulated scenario, a feat that is uncommon in real industrial settings [5] Financial Performance - Stand Robot's revenue for the years 2022, 2023, and 2024 was approximately RMB 96.3 million, RMB 162.2 million, and RMB 251.5 million, respectively [7] - The company reported losses of approximately RMB 128 million, RMB 100.3 million, and RMB 45.1 million for the same years [7] - The gross profit for the years 2022, 2023, and 2024 was RMB 12.4 million, RMB 51.2 million, and RMB 97.2 million, respectively [8]

Core Viewpoint - The article discusses the challenges and opportunities faced by SenseTime's autonomous driving division, particularly focusing on the competitive landscape and the importance of technological advancements in the industry. Group 1: Company Developments - The head of the world model development for SenseTime's autonomous driving division has left the company, which raises concerns about the future of their cloud technology system and the R-UniAD generative driving solution [2][3]. - SenseTime's autonomous driving division has successfully delivered a mid-tier solution based on the J6M model to GAC Trumpchi, but the mid-tier market is expected to undergo significant upgrades this year [4]. Group 2: Market Dynamics - The mid-tier market will see a shift from highway-based NOA (Navigation on Autopilot) to full urban NOA, which represents a major change in the competitive landscape [4]. - Leading companies are introducing lightweight urban NOA solutions based on high-tier algorithms, targeting chips with around 100 TOPS computing power, which are already being demonstrated to OEM clients [4]. Group 3: High-Tier Strategy - The key focus for SenseTime this year is the one-stage end-to-end solution, which has shown impressive performance and is a requirement for high-tier project tenders from OEMs [5]. - Collaborations with Dongfeng Motor aim for mass production and delivery of the UniAD one-stage end-to-end solution by Q4 2025, marking a critical opportunity for SenseTime to establish a foothold in the high-tier market [5][6]. Group 4: Competitive Landscape - SenseTime's ability to deliver a benchmark project in the high-tier segment is crucial for gaining credibility with OEMs and securing additional projects [6][7]. - The current window of opportunity for SenseTime in the high-tier market is limited, as many models capable of supporting high-tier software and hardware costs are being released this year [6][8].

当AI大模型以星火燎原之势渗透至千行百业，作为其重要落地载体的具身智能，正以"现实版钢铁侠"的姿态，成为科技展会中"最靓的仔"。 从通信技术中来，往通信世界里去 人形机器人向来是科技展会中最吸睛的存在。 一大早，智元机器人展台早已挤满前来参观的观众。远征A2手持毛笔，一笔一画写着"福"字；灵犀X2不仅用"内心戏"模式与观众互动，还向观众表演了 一段太极拳。这些能力的背后，既有智元对模型架构的创新构建，也少不了通信技术的支持。 智元打造了"本体—小脑—大脑"的软硬件技术架构，让人形机器人实现了运动智能、交互智能和作业智能。"我们将一些基本能力，比如手脚运动，做在 本体和小脑中，使机器人在断网的情况下，也能实现基本操作。"智元机器人首席运营官邱恒告诉《IT时报》记者，"大脑"作为人形机器人智慧的关键， 由云平台+具身算法构建而成，通信技术被运用其中。"有了通信技术的加持，就像给人形机器人配备了一台可以实时获取信息的手机，联网后能获得更 多智慧，一些复杂问题也将交由云端处理，交互就会更加'聪明'。" 具备这些能力后，人形机器人将走进通信场景。智元旗下的远征A2、精灵G1、灵犀X2等多款机器人将进入展厅、营业厅、机房 ...

" 互联网视频数据是唯一可以 scale up 的道路 。 " 作者丨 郭海惟 编辑丨 陈彩娴 作为一名具身大脑的创业者，卢宗青有着金光闪闪的履历： 他是紧随 DeepMind之后，中国新生代的强化学习研究者。北京大学计算机学院长聘副教授，担任过智源 研究院多模态交互研究中心负责人，负责过首个国家自然科学基金委原创探索计划通用智能体项目，还同 时在NeurIPS、ICLR、ICML等机器学习的国际顶级会议担任领域主席。 早在 2023年，他旗下团队便有利用多模态模型研究通用 Agent 的研究尝试，让 Agent 玩《荒野大镖客 2》和办公，使其成为第一个从零开始在AAA级游戏中完成具体任务的 LLM 智能体。相关论文几经波折， 今年终于被 ICML 2025 录用。不过他自述对那份研究其实不够满意，因为"泛化性不足"。 当完成那些研究以后，卢宗青意识到 "当前的多模态模型缺乏与世界交互的能力"。因为模型缺少学习物 理交互的数据，所以 我们看到的那些泛化的能力本质都是 "抽象"的，它终究无法理解动作和世界的关 系，自然也无法预测世界 。 这如今成为他想在具身智能创业的起点：开发一个通用的具身人工智能模型。 卢 ...

以下文章来源于APPSO ，作者发现明日产品的 APPSO . AI 第一新媒体，「超级个体」的灵感指南。 #AIGC #智能设备 #独特应用 #Generative AI 本文来自微信公众号： APPSO （ID：appsolution） ，作者：appso，原文标题：《这个AI生图神器首次发布视频模型：不卷分辨率，但网友直呼画面 惊艳超预期|附提示词》，题图来自：AI生成 面对迪士尼和环球影业的版权诉讼，老牌文生图"独角兽"Midjourney没有放慢节奏，反而于今天凌晨顶着压力推出了首个视频模型V1。 调色精准、构图考究、情绪饱满，风格依旧在线。 不卷分辨率、不卷长镜头、Midjourney卷的，是一股独有的氛围感和审美辨识度。Midjourney是有野心的，目标剑指"世界模型"，但目前略显"粗糙"的 功能设计，能否让其走得更远，恐怕还是一个未知数。 你卷你的分辨率，我走我的超现实。 Midjourney一直以奇幻、超现实的视觉风格见长，而从目前用户实测的效果来看，其视频模型也延续了这一美学方向，风格稳定，辨识度高。 省流版如下： 上传或生成图像后点击"Animate"即可，单次任务默认输出4段5秒视频 ...

欢迎大家 点击【预约】 按钮 文字版速览 预约 我 下一场直播 本文重点 观点来自： 6 月 16 日本周一直播 【 强 烈建议直接看】 本段视频精华，逻辑更完整 自动驾驶系统要像老司机一样理解复杂的交通场景，不仅是识别路况，更要对潜在风险做出预判——例 如，看到前车旁边有人过马路被遮挡，系统要能预测行人可能出现的位置，从而保证行车安全和平稳。 没有对物理世界和事件的深刻理解，自动驾驶无法实现真正的安全与智能。 更广泛来看，具备成熟世界模型的机器人将极大提升生产力，推动经济飞速发展，带动运输、物流、公 共和私人交通等行业变革。我认为，拥有这一技术优势的企业将成为未来市场的最大受益者，提前布局 相关机会尤为重要。 此外，量子计算技术也在加速发展。黄仁勋最近在欧洲演讲中提到，量子计算的拐点即将到来，这将进 一步促进科学研究和AI进步，加速人类科技革命的步伐。我认为，这场科技革命的节奏将越来越快， 未来几年内我们可能迎来多次类似蒸汽机或电力革命级别的突破，全球经济和社会结构都将因此发生深 刻变革。 以上内容仅为案例展示，不构成投资建议，投资有风险，交易需谨慎。 注：基金投顾服务由盈米--小帮投顾服务团队提供!投资有 ...

Core Insights - The article emphasizes the growing importance of large models (VLM) in the field of intelligent driving, highlighting their potential for practical applications and production [2][4]. Group 1: VLM and VLA - VLM (Vision-Language Model) focuses on foundational capabilities such as detection, question answering, spatial understanding, and reasoning [4]. - VLA (Vision-Language Action) is more action-oriented, aimed at trajectory prediction in autonomous driving, requiring a deep understanding of human-like reasoning and perception [4]. - It is recommended to learn VLM first before expanding to VLA, as VLM can predict trajectories through diffusion models, enhancing action capabilities in uncertain environments [4]. Group 2: Community and Resources - The article invites readers to join a knowledge-sharing community that offers comprehensive resources, including video courses, hardware, and coding materials related to autonomous driving [4]. - The community aims to build a network of professionals in intelligent driving and embodied intelligence, with a target of gathering 10,000 members in three years [4]. Group 3: Technical Directions - The article outlines four cutting-edge technical directions in the industry: Visual Language Models, World Models, Diffusion Models, and End-to-End Autonomous Driving [5]. - It provides links to various resources and papers that cover advancements in these areas, indicating a robust framework for ongoing research and development [6][31]. Group 4: Datasets and Applications - A variety of datasets are mentioned that are crucial for training and evaluating models in autonomous driving, including pedestrian detection, object tracking, and scene understanding [19][20]. - The article discusses the application of language-enhanced systems in autonomous driving, showcasing how natural language processing can improve vehicle navigation and interaction [20][21]. Group 5: Future Trends - The article highlights the potential for large models to significantly impact the future of autonomous driving, particularly in enhancing decision-making and control systems [24][25]. - It suggests that the integration of language models with driving systems could lead to more intuitive and human-like vehicle behavior [24][25].