Core Viewpoint - The article emphasizes the importance of exploring and learning in the AI field as a means to contribute to society and the nation, highlighting the current opportunity for investors, practitioners, and researchers to deepen their understanding of technological trends and advancements in AI [1]. Group 1: AI Research Papers Overview - A collection of 30 influential AI papers recommended by Ilya Sutskever is presented, covering nearly 15 years of milestones in AI development, structured around the themes of "technical foundations, capability breakthroughs, and practical applications" [4]. - The selected papers span key transitions in AI from "perceptual intelligence" to "cognitive intelligence," including foundational works on CNNs, RNNs, Transformers, and cutting-edge research on RAG and multi-step reasoning [4][5]. Group 2: Learning and Application - The compilation breaks down complex technical terms like "residual mapping" and "dynamic pointer networks," aiding non-technical investors in understanding AI model capabilities, while providing practitioners with practical references for implementation [5]. - The article encourages readers to study the recommended papers during the holiday period to systematically understand the evolution of AI technology and to gain deeper insights into the opportunities and challenges in the current AI industry [5]. Group 3: Importance of the Recommended Papers - Ilya Sutskever stated that mastering the content of these 30 papers would provide a comprehensive understanding of 90% of the key knowledge in the current AI field [8]. - The papers cover a range of topics, including the effectiveness of recurrent neural networks, the structure and function of LSTM networks, and the introduction of pointer networks, all of which contribute to advancements in AI applications [8][9][10].

Core Viewpoint - The article emphasizes the importance of finding real-world applications for algorithms and codes in AI investment, highlighting the commitment of Jinqiu Fund to support innovative founders in the AI sector [1]. Group 1: Awards and Recognition - Jinqiu Fund has received several accolades, including being listed as one of the "2025 China's Investment Institutions in Artificial Intelligence" and "2025 China's Investors in Artificial Intelligence" by 36Kr [2][3]. - The fund is recognized for its contributions to the field of embodied intelligence, being included in the "2025 China's Investment Institutions in Embodied Intelligence" list [8]. Group 2: Industry Context - The article lists various prominent investment institutions in the AI sector, including Baidu Ventures, Sequoia China, and Hillhouse Capital, among others, indicating a competitive landscape for AI investments [6][9]. - The rankings mentioned are not in any particular order, suggesting a diverse range of players in the AI investment space [10][13]. Group 3: Future Commitment - Jinqiu Fund expresses a commitment to continue its innovative journey in AI investment, viewing the awards as a starting point rather than an endpoint [46][47].

为什么机器人还没走进你家？ 在过去十年里，我们见证了人工智能写诗作画、回答问题、甚至通过考试。但当这些"聪明大脑"让人 惊叹时，机器人却依然停留在实验室和展厅里：它们会在视频中完成惊艳的动作，却很难在你厨房里帮 忙洗碗，或者替你客厅里收拾好一地的玩具。 很多人直觉上以为，这是因为硬件不够强：机械手还不够灵巧，传感器还不够精密，马达还不够迅捷。 但事实是， 硬件的发展速度远快于软件，真正的瓶颈在于——机器人无法像人类一样理解和预测物理 世界。硬件不是问题，理解才是门槛 。 这背后涉及一个核心问题：当机器人伸手去触碰一个杯子、一块布料或一包薯片时，它能否在动作发生 之前，预判出会发生什么？会不会打滑？会不会被压碎？能否保持稳定？对人类来说，这些判断几乎是 下意识的，但 对机器人而言，却是需要复杂建模和计算的巨大挑战 。 近期发表在 Science Robotics 的一篇综述文章，正是聚焦于这一关键难题。由加州大学圣地亚哥分 校、MIT、斯坦福和谷歌等机构的顶尖学者联合撰写，文章系统梳理了"基于学习的动力学模型"——一 种让机器人能够从感知数据中直接学习"世界规则"的方法。 · 相比传统的解析物理模型，这种方法或 ...

9月24日晚，地瓜机器人与锦秋基金联手邀请来30 余位 「机器人头号玩家」 ，在杭州举办了一场机器人精酿Party。 来自 地瓜机器人 生态负责人胡春旭、云平台负责人秦玉森、算法负责人隋伟、锦秋基金合伙人臧天宇、锦秋基金投资副总裁Cindy、阿里云生态负责人 陈博 、 X-Man科沃斯蒲公英加速器总经理赵文景 空降现场，一起和科技大厂产品达人、技术专家、创业先锋们微醺开聊 "机器人的新一代故事" 。 现场机器人玩家们硬核开麦， 开发者们灵感捧杯 到我的客 杯精酿互诉机器 会 门对小对物 # # 地瓜机器人 醫 锦秋基金 ir ans and 12 12 statis 杯精酿互诉机器人信 杯里有精酿,哪里有 H El B 精蛋TE 地瓜机器人 鲨 锦桃基金 杯精酿互诉机器人信仰 I 力校准液制作中 # # 地瓜机器人 器 锦秋基金 同时，锦秋基金就现场大家对 VLA 不同观点的讨论，做了以下记录 挑战派 两条腿走路：上层大模型负责理解/任务分解，底层RL/规控负责约束满足与实时稳定；协同进化。 自主数据生成与仿真增强：用RL+物理仿真（动力学/碰撞/库伦摩擦）造数据、学策略，提高泛化；像"孩子学走路"靠自我试错 ...

「 Jinqiu Spotlight 」 追踪锦秋基金与被投企业的每一个光点与动态，为创业者传递一线行业风向。 锦秋基金于 2025 年完成对首形科技的投资。 锦秋基金（公众号：锦秋集， ID:jqcapital ）是一家双币早期投资机构，致力于推动通用人工智能的发展，积极寻找那些具有突破性技术和创新商业模 式的通用人工智能初创企业。 9 月 29 日， 首形科技（ AheadForm ） 宣布完成新一轮融资，这是首形科技本年度完成的第三轮融资， 本轮由蚂蚁集团与锦秋基金联合领投 ，厚雪 资本、弘晖基金、银杏谷资本共同投资，老股东顺为资本、招商局创投超额加注， Taihill 追投。 首形科技（ AheadForm ）是超高仿生情感交互机器人领军企业。这轮融资资金将主要用于情绪基座模型迭代和多场景应用落地。 首形科技 Origin 计划： 在互联网高度虚拟化的时代，我们曾爱上无数角色，却常常隔着屏幕、无法触碰。首形科技正在推动一场范式转变 ——让虚拟数字生命跨越冰冷屏 幕，具象为可感知、可交流、自主的实体。 首形科技具备机器人硬件与仿生运动算法的研发优势，能够在这个细分市场形成差异化的领先地位。首形研发的情绪 ...

Core Insights - Jinqiu Capital has led a 200 million yuan "angel++" round investment in Suzhou Lexiang Intelligent Technology Co., Ltd., focusing on consumer-grade embodied intelligent robots [2][6] - Lexiang Technology has completed its third round of financing within nine months since its establishment, with total angel round financing nearing 500 million yuan [3][7] - The company aims to accelerate the mass production of consumer-grade embodied intelligent products through this funding, targeting core component development and technology iteration [2][6] Company Overview - Lexiang Technology was founded by Guo Renjie, who has a strong background in robotics and management, previously serving as the executive president of a company that achieved 6 billion yuan in annual revenue [8] - The company has built a team of 90 members, with over 80% in R&D, attracting top talent from prestigious institutions to strengthen its technological capabilities [9] Product Development - Lexiang Technology is advancing its consumer-grade embodied intelligent products, with the W-bot robot gaining recognition at major tech events for its performance and design [10] - The W-bot has also made a breakthrough by becoming the first robot team leader in a sports event, showcasing its potential in various public scenarios [10] Market Position and Future Plans - The Chinese embodied intelligence market is experiencing rapid growth, particularly in the consumer segment, where Lexiang Technology aims to establish itself as a leader [16] - Following the recent financing, the company plans to increase R&D investment to transition embodied intelligence from a cutting-edge technology to a mainstream consumer product [16]

Core Viewpoint - Jinqiu Fund leads the A-round financing of Stardust Intelligence, focusing on long-term investments in groundbreaking AI startups, particularly in the field of general artificial intelligence [1][3]. Group 1: Company Overview - Stardust Intelligence is recognized as the pioneer of rope-driven AI robots, utilizing a unique design that mimics human tendon movement, allowing for high expressiveness and safety in complex operations [1][3]. - The company's Astribot S1 robot has been applied across various sectors, including research, commercial services, entertainment, and industrial applications, accelerating the commercialization of robotics [1][3]. Group 2: Technological Innovation - The ControlVLA framework, developed in collaboration with the Beijing General Artificial Intelligence Research Institute, addresses the challenges of adapting pre-trained VLA models to real-world tasks with limited data [2][3]. - ControlVLA's key innovations include a mechanism for object-centric representation, a ControlNet-style fine-tuning architecture, and a dual attention structure, significantly improving data efficiency and decision-making accuracy [2][3]. Group 3: Performance Metrics - ControlVLA achieves a success rate of 76.7% with only 10-20 demonstration samples across eight real-world tasks, outperforming traditional methods that require significantly more samples [2][12]. - The framework demonstrates robust performance in unseen objects and backgrounds, maintaining stable performance even in long-sequence decision-making tasks [2][12]. Group 4: Market Implications - The advancements presented by ControlVLA lower the deployment barriers for robotics in various real-world scenarios, making it a significant step towards practical applications of embodied intelligence [3][49]. - By reducing the need for extensive training data, ControlVLA enhances the feasibility of deploying robots in diverse environments, which is crucial for the future of automation and AI integration [3][49].

Core Insights - OpenAI's ChatGPT Pulse represents a significant advancement in AI technology, transitioning from a passive tool to an active daily assistant that personalizes user interactions by analyzing data such as chat history and calendars [1][2] - The next paradigm shift in AI is envisioned as creating an "automated researcher" capable of independently advancing scientific research over long time horizons, marking a move from reactive to proactive intelligence [2][4] Group 1: Automated Researcher Development - OpenAI's primary research goal for the next 1 to 5 years is to develop an "automated researcher" that can autonomously discover new knowledge and ideas, with a focus on automating machine learning research and other scientific fields [6][7] - The effectiveness of this automated researcher will be measured by its ability to perform reasoning over extended time spans, currently estimated at 1 to 5 hours for high school-level tasks [6][8] Group 2: New Evaluation Directions - Traditional evaluation benchmarks are becoming saturated, prompting OpenAI to shift focus from generic performance metrics to assessing the model's ability to make original scientific discoveries in economically valuable problems [8][9] - High-stakes competitions in mathematics and programming are seen as strong indicators of a model's potential for future research success, despite the saturation of these competitions [9][10] Group 3: Reasoning and Stability - The evolution of AI models towards "agents" capable of multi-step planning introduces a challenge in balancing long-term planning and memory retention, which are crucial for executing complex tasks [10][11] - OpenAI posits that the relationship between depth and stability is not a trade-off but rather a unified challenge, where enhancing reasoning capabilities can improve both long-term agency and execution quality [12][13] Group 4: Verifiability and Openness - The distinction between verifiable and open-ended problems is fluid, with the complexity and time scale of a problem influencing its nature as either verifiable or exploratory [15][16] - As the time frame for solving a problem extends, even clearly defined tasks can evolve into open-ended explorations requiring strategic and creative approaches [16][19] Group 5: Talent Development and Organizational Culture - OpenAI emphasizes the importance of resilience, experience, and a balance between long-term belief and truthfulness in its researchers, fostering an environment conducive to long-term exploration without short-term pressures [20][21] - The organization seeks diverse talent from various fields, prioritizing problem-solving skills and a willingness to tackle difficult challenges over social media prominence [21]

Core Insights - The article discusses the limitations of current intelligent robots in handling complex tasks and how Google DeepMind's Gemini Robotics 1.5 and ER 1.5 models address these challenges through innovative technology [1][3][50]. Group 1: Model Capabilities - Gemini Robotics 1.5 is a powerful VLA model that translates visual information and instructions into motion commands, demonstrating advanced reasoning capabilities before action [5][20]. - Gemini Robotics-ER 1.5 excels in embodied reasoning, capable of making detailed multi-step plans and utilizing external digital tools like Google Search for task execution [5][18]. - Both models enhance the ability of robots to perform diverse tasks such as household chores, warehouse picking (accuracy improved to 92%), and medical suturing (success rate of 89%) [2][3]. Group 2: Technical Innovations - The models create a "perception-reasoning-planning-execution" closed loop, allowing for seamless task execution in various environments [2][8]. - The "thinking budget" feature allows developers to control the trade-off between latency and accuracy, optimizing performance for different task complexities [23][47]. - Cross-entity learning capability enables skills learned on one robot to be transferred to another without additional training, significantly reducing adaptation costs [15][79]. Group 3: Safety and Security - The models incorporate advanced safety measures, including semantic safety filtering and physical constraint awareness, ensuring responsible deployment in human-centric environments [16][48]. - Gemini Robotics-ER 1.5 has undergone rigorous evaluation through the upgraded ASIMOV benchmark, demonstrating superior performance in understanding semantic safety and adhering to physical constraints [16][48]. Group 4: Development and Ecosystem - The ER 1.5 model has been made available to global developers through the Gemini API, fostering a collaborative ecosystem for rapid technological application [2][3]. - The models are designed to guide the evolution of physical agents, providing insights into technical pathways, safety standards, and developer empowerment [2][50].

锦秋基金于2023年年中投资了生数科技，是生数科技的早期机构投资人。 锦秋基金，作为12 年期的 AI Fund，始终以长期主义为核心投资理念，积极寻找那些具有突破性技术和创新商业模式的通用人工智能初创企业。 9月25日，锦秋基金被投公司生数科技正式发布新一代图生视频大模型Vidu Q2。新模型以" Vidu Q2 看AI演戏 "为主题，"细微表情生成"为核心提升场景，在极致表 情变化、推拉运镜、生成速度及语义理解方面取得突破性进展，实现从"生成视频"到"生成演技"，从"动态流畅"到"情感表达"的革命性跨越，标志着AI视频生成技 术正式从追求"形似"进入追求"神似"的新阶段，将为内容创作、影视产业、广告营销等领域带来全新升级。 以下为此次新闻的相关内容。 生数科技全球发布Vidu Q2，推动"视频生成"走向"演技生成"时代 9月25日，生数科技正式发布新一代图生视频大模型Vidu Q2。新模型以" Vidu Q2 看AI演戏 "为主题，"细微表情生成"为核心提升场景，在极致表情变化、推拉运 镜、生成速度及语义理解方面取得突破性进展，实现从"生成视频"到"生成演技"，从"动态流畅"到"情感表达"的革命性跨越，标 ...