美国时间 12 月 14 日，扫地机器人鼻祖 iRobot 正式申请破产保护，并同意将其 100% 股权出售给主要代工伙伴及最大债权人——深圳杉川机器人有限公 司（PICEA）。 这个结局早有预兆。上月初，公司就已经在 SEC 文件中直言：如果无法立即获得新的资金支持，或无法与杉川达成解决逾期账款的协议，将不得不大幅 缩减业务甚至停止运营，并极有可能寻求破产保护。如今，这一预言成为现实。 一个时代的开创者 自 1990 年成立以来，很少有公司能像 iRobot 一样深刻地改变机器人行业。 iRobot 由麻省理工学院人工智能实验室成员创立，创始团队包括"现代机器人之父"、具身智能领域奠基人之一 Rodney Brooks。 图 | Rodney Brooks（来源：Wikipedia） 1986 年，Brooks 提出智能是"具身化"和"情境化"的，1991 年发表著名论文《没有表征的智能》，强调智能行为可通过物理交互直接涌现，无需复杂内部 模型。这一理念深刻影响了 iRobot 的早期技术路径。 创立初期，公司重点开发政府与国防用途的机器人。iRobot 的 PackBot 机器人在军事领域发挥了关键作用： ...

Core Insights - The article highlights the pivotal moment in the development of artificial intelligence (AI) marked by the creation of the ImageNet database, which consists of over 14 million meticulously labeled images across 22,000 categories, significantly enhancing the effectiveness and accuracy of AI algorithms in object recognition [1][3]. Group 1: Impact of ImageNet - ImageNet, created by Fei-Fei Li, played a crucial role in validating the effectiveness of AI neural network algorithms, leading to the deep learning revolution in the AI field [2][3]. - Fei-Fei Li, recognized as the "Mother of AI," has made significant contributions to AI, including her role as a professor at Stanford University and her leadership in the Stanford AI Lab [3]. Group 2: Fei-Fei Li's Contributions - In 2017, Fei-Fei Li joined Google as Vice President and Chief Scientist of AI and Machine Learning, where she established the Google AI China Center and initiated the AI4ALL nonprofit organization to promote AI education among women and minority groups [4]. - Li founded her startup, World Labs, focusing on solving complex problems in AI, particularly in spatial intelligence, achieving a valuation of over $1 billion within four months of its establishment [4]. Group 3: Innovations in Spatial Intelligence - World Labs released a groundbreaking AI model capable of generating interactive, editable, and expandable virtual 3D scenes from a single image or text input, marking a significant step towards spatial intelligence [5]. - Fei-Fei Li emphasizes that spatial intelligence will enable machines to perceive, reason, and act within 3D spaces, representing the next frontier in AI development [6].

而83岁的谢诺夫斯基，依然在实验室里追问那个问题。 也许没有人比他更适合回答今天AI缺失的那些碎片。他见证了神经网络从"异端"到"改变世界"的全过 程；他既懂物理学的简洁优雅，也懂生物学的复杂混沌；他和辛顿一起打开了AI的大门，又眼看着这 扇门后的世界变得越来越陌生。 1984年的一天，物理学家特伦斯·谢诺夫斯基和心理学家杰弗里·辛顿坐在实验室里，盯着黑板上的方程 发呆。那是AI的第二个寒冬，神经网络陷入僵局。人们都知道多层网络更强大，但没人知道怎么训练 它。 "如果我们把神经网络想象成一团气体呢？"谢诺夫斯基突然说。 这个疯狂的想法最终变成了玻尔兹曼机，这是一个用统计物理学重新定义"学习"的数学模型。它证明了 只要找到合适的能量函数，神经网络就能像气体从高温降到低温一样，自发地调整到最优状态。 这成为现代深度学习的理论基石之一。 但两人后续的志趣却互相有所偏离。辛顿发现了更实用的反向传播算法，带领深度学习走出寒冬，最终 迎来ChatGPT主导的AI时代。而谢诺夫斯基选择了回到神经科学实验室，用几十年时间解剖大脑的每一 个回路，试图回答那个最初的问题：大脑究竟是如何工作的？ 40年后，辛顿因玻尔兹曼机获得20 ...

高频选股因子周报（20251208- 20251212） 高频因子走势分化，多粒度因子显著回撤。AI 增强组合均 大幅度回撤。 本报告导读： 上周（特指 20251208-20251212，下同）高频因子走势分化，多粒度因子显著回撤。 AI 增强组合均大幅度回撤。 投资要点： | | | | [Table_Authors] | 郑雅斌(分析师) | | --- | --- | | | 021-23219395 | | | zhengyabin@gtht.com | | 登记编号 | S0880525040105 | | | 余浩淼(分析师) | | | 021-23185650 | | | yuhaomiao@gtht.com | | 登记编号 | S0880525040013 | [Table_Report] 相关报告 低频选股因子周报（2025.12.05-2025.12.12） 2025.12.13 绝对收益产品及策略周报（251201-251205） 2025.12.10 上周估值因子表现较好，本年中证 2000 指数增强 策略超额收益为 28.22% 2025.12.10 红利风格择时周报（1201 ...

Core Insights - The article highlights the advancements in 3D Gaussian Splatting (3DGS) technology, particularly its application in autonomous driving, and emphasizes the need for structured learning pathways in this rapidly evolving field [2][4]. Group 1: 3DGS Technology and Developments - Tesla's introduction of 3D Gaussian Splatting at ICCV has garnered significant attention, indicating a shift towards feed-forward GS algorithms in the industry [2]. - The rapid iteration of 3DGS technology includes static reconstruction (3DGS), dynamic reconstruction (4DGS), and surface reconstruction (2DGS), showcasing the need for effective learning resources [4]. Group 2: Course Offering - A comprehensive course titled "3DGS Theory and Algorithm Practical Tutorial" has been developed to provide a structured learning roadmap for newcomers, covering essential theories and practical applications [4]. - The course is designed to help participants understand point cloud processing, deep learning, real-time rendering, and coding practices, with a focus on hands-on experience [4]. Group 3: Course Structure - The course consists of six chapters, starting with foundational knowledge in computer graphics and progressing to advanced topics such as feed-forward 3DGS and its applications in autonomous driving [8][9][10][11][12]. - Each chapter includes practical assignments and discussions to enhance understanding and application of the concepts learned [8][9][10][11][12]. Group 4: Target Audience and Prerequisites - The course is aimed at individuals with a background in computer graphics, visual reconstruction, and programming, particularly those interested in pursuing careers in the 3DGS field [17]. - Participants are expected to have a foundational understanding of probability, linear algebra, and programming languages such as Python and PyTorch [17].

Core Insights - A research team from MIT, the University of Waterloo, and Samsung Electronics has developed a new method to increase transistor density on chips by stacking additional layers of transistors on existing circuits, which could significantly enhance chip performance and energy efficiency [2][4][5]. Group 1: New Manufacturing Method - The new method involves adding a layer of micro-switches on top of completed chips, similar to traditional chip stacking techniques, to increase the number of transistors integrated into a single chip [2]. - The research team utilized a 2-nanometer thick layer of amorphous indium oxide to construct additional transistors without damaging the sensitive front-end components during the manufacturing process [3][6]. Group 2: Energy Efficiency and Performance - This innovative approach allows for the integration of logic devices and memory components into a compact structure, reducing energy waste and improving computational speed [4][5]. - The new transistors exhibit a switching speed of just 10 nanoseconds, with significantly lower voltage requirements compared to existing devices, leading to reduced power consumption [6]. Group 3: Future Implications - The research indicates that if future processors can utilize both this new technology and traditional chip stacking methods, the limits of transistor density could be greatly surpassed, countering the notion that Moore's Law is reaching its end [3][4]. - The team aims to further integrate these backend transistors into single circuits and enhance their performance, exploring the physical properties of ferroelectric hafnium zirconium oxide for potential new applications [7].

Core Insights - The article discusses the transformative potential of Artificial Intelligence (AI) as the fourth industrial revolution, emphasizing its role in enhancing productivity and reducing costs across various sectors [1][5]. Group 1: AI Development Drivers - AI is driven by the need to improve efficiency and reduce costs, addressing pain points in consumer-related scenarios such as entertainment, travel, and health [3]. - The application of AI in consumer sectors includes labor replacement and productivity enhancement through technologies like voice recognition and intelligent customer service [3]. - In the business sector, AI is widely adopted in finance, public safety, and healthcare, reflecting a strong demand for efficiency improvements [3]. Group 2: Historical Context and Evolution - AI is positioned as the fourth productivity revolution, following the steam, electrical, and information technology revolutions, with significant historical milestones marking its development [5]. - The evolution of AI has seen three major waves of growth, each driven by breakthroughs in underlying algorithms, with the current wave characterized by deep learning advancements [8][12]. - The first wave of AI in the 1950s was limited by computational performance, while the second wave in the 1980s faced challenges due to the high costs of expert systems [9][11]. Group 3: AI Industry Structure - The AI industry can be segmented into three layers: foundational support (hardware and data), technology (algorithm development), and application (commercial solutions) [6][7]. - Major players in the foundational layer include international tech giants like Nvidia and Intel, while the technology layer features companies like Google and IBM focusing on specific AI applications [6][7]. - The application layer is where AI technologies are commercialized, with a relatively low entry barrier due to the global open-source community [6]. Group 4: Current AI Landscape - The current state of AI is classified as "weak AI," focusing on specific tasks such as speech and image recognition, with significant performance exceeding human capabilities in certain areas [30][33]. - AI's impact on global GDP is projected to be substantial, with estimates suggesting a 14% increase, translating to approximately $15.7 trillion in growth [37]. - The rapid advancement of deep learning algorithms and the availability of vast datasets are expected to drive widespread AI application across various industries [38][39]. Group 5: Future Opportunities - The article highlights the potential for AI to revolutionize content generation and distribution, particularly through platforms like TikTok and Douyin, which utilize AI-driven recommendation systems [46][52]. - The emergence of generative AI (AIGC) is seen as a significant opportunity, with advancements enabling the creation of diverse content types, including text, images, and videos [54][61]. - The integration of AI into various sectors is anticipated to accelerate, driven by technological advancements and supportive policies from governments [44][45].

公开资料显示，苏箐曾担任华为车 BU 智能驾驶产品部部长，负责华为自动驾驶系统方案。2022 年 1 月，苏箐正式从华为离职。同年 10 月，苏箐加入地平 线。 苏菁称，关于特斯拉 FSD V12 到底是不是最强的问题，业内争议很大，但这个问题不重要，重要的是 FSD V12 证明了一段式端到端技术的可行性，推动智 驾技术范式从规则驱动转向数据驱动。他认为，一段式端到端在智驾行业的普及将带来两大趋势的行业演进。 一是智驾系统会在未来几年内越来越"类人"，这将使 L2 级辅助驾驶迎来巨大的发展红利期，城区辅助驾驶将逐步普及到 10 万元级别车型。 二是 L2 和 L4 级别的智驾方法论统一，同样的开发范式，不仅能提升 L2 辅助驾驶体验，同时也能以更低的部署成本和几乎无限制部署区域扩张，落 地一个 L4 系统（Robotaxi）。 2024年,以FSD V12成熟为标准, 智能驾驶迎来内在底层技术 范式与外部用户感知体验的一次 重构。其意义,堪比核能从理论 迈入工程。 苏 等 地平线副总裁兼首席架构师 2025 地平线技术生态大会 HORIZON 75 TOGETHER Z IT之家 12 月 12 日消息，据 ...

Core Insights - The article discusses the evolution of AI research and the transition from scaling to a renewed focus on innovative research methods, emphasizing the importance of "taste" in research and the potential for breakthroughs in AI learning mechanisms [10][12][16]. Group 1: Transition of AI Research - The AI development is shifting from a scaling era (2020-2025) back to a research-focused era, as the scaling laws of pre-training are becoming ineffective due to limited data [17]. - The future of AI is expected to involve new algorithms rather than just increasing computational power [17]. Group 2: SSI's Strategy - Safe Superintelligence Inc. (SSI) aims to develop superintelligence without intermediate products, focusing solely on research rather than market competition [12]. - Ilya Sutskever, co-founder of SSI, believes that the company’s funding of $3 billion is entirely directed towards research, unlike larger companies that allocate funds to user services and sales teams [13]. Group 3: Research Methodology - Ilya emphasizes the importance of a "Value Function" in AI learning, suggesting that current reinforcement learning (RL) methods are inefficient and may hinder the model's capabilities [16][20]. - He proposes that future breakthroughs in AI will come from enabling models to make intuitive judgments during the learning process [19]. Group 4: Emotional Intelligence in AI - Ilya argues that emotions serve as a crucial decision-making tool for humans, and AI currently lacks this capability, which may be essential for achieving AGI [22]. - He suggests that empathy could be a fundamental aspect of AI development, allowing AI to understand and care for sentient life [24]. Group 5: Market Dynamics - The future AI market is expected to be competitive and specialized, with companies focusing on niche areas rather than a single entity dominating superintelligence [28]. - This specialization will create high barriers to entry for new competitors, similar to ecological balances in nature [28].

【导读】目标疯狂，一路偶然！奥特曼回顾OpenAI十年，坦承吃到了时代的红利。 今天一睁眼，大家都被OpenAI十周年的生日祝福刷屏了。 转眼间，这个改变了全世界的AI初创，如今已经成为巨头。 一位OpenAI的老员工，晒出自己在2019年在OpenAI第一天上班的照片 凌晨，和GPT-5.2一起来临的，还有OpenAI的十周年。 OpenAI发布了一支短片，配文只有两个词：「10年」。 这支短片，其实讲的不是产品，而是一种信念。 画面从OpenAI注册那天开始： 一群技术宅，挤在厨房里，讨论一个听起来像科幻小说中的目标：AGI。 但这支十周年视频，也留下了一个明显的空白。 镜头里，没有Ilya和Mira的身影。 而他们，恰恰是为 OpenAI 打下前五年地基的人。 有些人，奠基了历史，却没出现在纪念片里 如今，OpenAI估值800亿美元、超过1000名员工，打造了全球用户最多的大语言模型。 但要回顾OpenAI的10年，奥特曼绝对是主角。 奥特曼亲自发文，庆祝十周年 十年前，AI连猫和狗都分不清。 但OpenAI的创始人相信，深度学习能走得更远，相信它可能成为人类的一项重大胜利。 然而，过去的11位联合创 ...