Core Viewpoint - The article discusses the emergence of world models in AI, highlighting their significance in overcoming the limitations of previous VLA models and their potential applications in robotics and autonomous systems [4][9][22]. Group 1: World Model Development - The world model is a concept that addresses the inherent limitations of VLA models, which struggle to understand physical laws and require vast amounts of data for training [9][28]. - The introduction of the "Awakening" world model 3.0 allows robots to learn physical interactions and adapt to new environments, significantly reducing the dependency on specific scene data [8][10]. - The world model enables robots to transition from rote learning to understanding general principles, enhancing their ability to perform tasks across various scenarios [10][28]. Group 2: Practical Applications - The "Daxiao Robot" is utilizing the world model to deploy robotic dogs for urban management tasks, such as monitoring illegal parking and drone activity [6][7][12]. - The company plans to validate the world model's capabilities through real-world applications, starting with robotic dogs and expanding to more complex robotic forms in the future [16][56]. - The integration of the world model into robotic systems aims to create a closed-loop feedback mechanism, allowing for continuous improvement based on real-world performance [14][15][16]. Group 3: Commercial Strategy - The company intends to focus on B2B applications initially, targeting sectors like smart cities and urban management where autonomous capabilities are in high demand [58]. - Future plans include expanding into logistics and home environments, leveraging existing resources and partnerships to reduce entry costs [17][56]. - The strategy emphasizes collaboration with existing platform providers while also developing proprietary solutions to enhance product reliability and performance [50][52].

Core Insights - The article discusses various advanced directions in autonomous driving research, emphasizing the importance of deep learning and traditional methods for different academic backgrounds [2][3]. Group 1: Research Directions - Key areas of focus include VLA, end-to-end learning, reinforcement learning, 3DGS, and world models, which are recommended for students in computer science and automation [2]. - For mechanical and vehicle engineering students, traditional methods like PnC and 3DGS are suggested due to their lower computational requirements and ease of entry [2]. Group 2: Paper Guidance Services - The article announces the launch of a paper guidance service that covers various topics such as end-to-end learning, multi-sensor fusion, and trajectory prediction [3][6]. - The service includes support for topic selection, full process guidance, and experimental assistance [6]. Group 3: Publication Success - The guidance service has a high acceptance rate for papers submitted to top conferences and journals, including CVPR, AAAI, and ICLR [7]. - The article highlights the range of publication venues, including CCF-A, CCF-B, and various SCI categories [10].

Economic Indicators - The latest November core CPI increased by 2.6% year-on-year, marking a new low since 2021 and falling below expectations, indicating a trend of slowing inflation [1] - Kevin Hassett, a leading candidate for the Federal Reserve chair, praised the CPI report as "shockingly good," noting that wage growth is outpacing price increases and predicting substantial tax refunds for American taxpayers next year [3][5] Federal Reserve Outlook - Hassett suggested that the Federal Reserve has significant room to lower interest rates due to improving supply-side conditions that are expected to suppress inflation in the long term [5] - Following the CPI data release, market expectations for aggressive easing by the Fed in 2026 increased, particularly regarding the likelihood of rate cuts in March next year [5] Market Dynamics - The S&P 500 faced technical resistance while attempting to breach the 6800-point level, leading to significant volatility and a rapid sell-off triggered by quantitative funds and high-leverage long positions [7][9] - The current market is in a "negative Gamma" zone, where small retreats can lead to systemic sell-offs, emphasizing the need for the index to maintain above 6800 points for stability [9][16] Options Market - A record $7.1 trillion in nominal value of options contracts is set to expire on the upcoming "quadruple witching" day, which typically amplifies market volume and volatility [14] - The dominance of zero-day-to-expiration (0DTE) options is expected to disrupt true asset pricing, shifting the focus from inflation data to liquidity pressures [16] Meta's AI Strategy - Meta is developing two ambitious AI models, "Mango" for video and "Avocado" for language, with plans for a 2026 release, reflecting a strategic pivot amid a $70 billion capital expenditure [17][19] - The company has shifted its approach from open-source to potentially proprietary models, aiming to establish a competitive edge in AI technology [20][22] - Meta's dual development of Mango and Avocado is seen as a defensive strategy to enhance user engagement and maintain its competitive position against rivals like Google and OpenAI [21][22]

这家名为 Advanced Machine Intelligence Labs （AMI Labs）的新公司，计划于明年一月正式亮相，目标估值 30亿欧元 （约247亿人 民币）。 克雷西 发自 凹非寺 量子位 | 公众号 QbitAI LeCun在Meta的Last Day还没来，新公司又被曝出更多细节。 前脚LeCun本人在播客当中宣布了新公司名称，现在融资和估值目标就被《金融时报》曝光了。 AMI Labs的研究方向，就是LeCun一直主推的"世界模型"，而且将走开源路线，老东家Meta也将与其保持合作。 另外，曝料也透露了AMI Labs的 CEO人选并非LeCun本人 ，而是他的一位老部下。 LeCun不当CEO 新公司AMI Labs定于2026年1月在巴黎正式启动，在Meta逐渐转向封闭生态的背景下，LeCun选择了他在学术界一贯坚持的开源路线。 而且在技术层面，AMI Labs选择了比主流的LLM更具挑战性的道路—— 死磕"世界模型" 。 因为在LeCun看来，基于自回归机制的LLM存在根本性的逻辑缺陷，它们只是在统计概率上预测下一个字符，并不真正理解物理世界的运行规 律。 为此，新公司将通过 ...

具身纪元 . 以下文章来源于具身纪元 ，作者具身纪元 见证具身浪潮，书写智能新纪元 编辑丨 具身纪元 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! 姚顺雨的在人工智能下半场的文章《The Second Half》，他说：在AI的下半场，技术方案已经很成熟，瓶颈变成了评估。 在具身智能的下半场，模型评估更加重要，也更加复杂。 完整评估单一策略，本身就不容易。 传统的评估方法需要在真机上去测试 ，困难也接踵而至： 第一点，成本高 ：在真实硬件上进行大规模测试既费时又费力 尤其是当需要对比多个不同的策略版本时。 如果要提升测试效率，多个硬件的部署在所难免，这又是额外的成本。 控制测评变量的沉默成本也不小，比如要减轻光照的影响，要挑同样光线的情况去做测评 第二点，覆盖面有限： 测评需要设置不同的情况来测试模型是否能够依旧表现出色，但在真实场景中很难穷尽所有现实的情况，比如干扰物、杂乱的桌面和光线等 第三点，安全性风险： 测试机器人的安全性，往往意味着要给机器人去尝 ...

Core Insights - Artificial Intelligence (AI) is becoming the core engine driving the development of new productive forces, but traditional scaling methods are no longer sufficient for maintaining rapid iteration in AI technology [1] - The key paths for advancing AI technology and industrial upgrading in China are collaboration and integration [2] Industry Developments - China is promoting "AI+" at the national strategic level, aiming for a comprehensive layout in response to rapid technological advancements [2] - The AI+ model, driven by large models, has permeated nearly all industries within a few years, but faces challenges such as high-end computing power shortages and high application costs [2][3] - The HAIC2025 conference emphasized "open computing" to combine the advantages of various AI industry chain enterprises, moving from isolated technological breakthroughs to collaborative industrial ecosystems [2][3] Technological Innovations - The scaleX supercluster, designed for trillion-parameter models and complex tasks, was showcased at HAIC2025, achieving a 20-fold increase in computing density per cabinet and significantly lowering overall ownership costs [4] - The supercluster supports multiple brands of AI acceleration cards and is compatible with mainstream computing ecosystems [4] Future Directions - The future of AI development is characterized by "two supers," "one openness," and "two integrations," focusing on ultra-node and ultra-density computing, open ecosystems, and the integration of various computing resources [6][7] - AI superclusters are seen as a promising direction, overcoming traditional communication bottlenecks and enhancing computational efficiency [7] Practical Applications - The HAIC2025 conference highlighted numerous successful AI+ applications, including the world's first multimodal language model focused on geographic science, which addresses global change and sustainable development issues [8] - Examples of AI+ applications include the rapid iteration of domestic electric vehicles, supported by AI computing in design and testing, and the "5G+ smart highway" project in Gansu province, which utilizes AI for traffic management [8]

商汤科技联合创始人、执行董事、大晓机器人董事长王晓刚表示，当前，具身智能产业进入加速落地的 关键阶段。大晓机器人以生态协同为核心，与具身厂商、硬件、芯片、云服务、数据厂商等多领域伙伴 达成战略合作，共同搭建全链路自主可控的具身智能生态，将推动具身智能实现规模化商业落地与产业 蓬勃发展。 具身智能时代，行业面临数据量级的断崖式缺口。大晓机器人提出"以人为中心"的ACE具身研发范式， 将人类与物理世界的互动规律作为核心研究起点，以环境式数据采集为引擎，构建了一套从"环境式数 据采集—开悟世界模型3.0—具身交互"的全链路技术体系。在该范式中，环境式数据采集可实现一年千 万小时的数据收集，而开悟世界模型3.0则不断放大真实数据价值，使其达到上亿小时数据规模的效 果。 12月18日，商汤"1+X"架构新成员——大晓机器人正式发布行业首创的ACE(Ambient Capture Engine)具 身研发范式、首个开源且商业应用的开悟世界模型3.0(Kairos3.0)以及让具身本体拥有自主空间智能的具 身超级大脑模组A1，与行业伙伴共同构建全链自主可控、开放共赢的产业级生态体系。 复旦大学大数据学院教授、上海创智学院全 ...

Core Viewpoint - Tesla's AI leader Ashok Elluswamy revealed the technical methodology behind Tesla's Full Self-Driving (FSD) in a recent article, emphasizing the choice of an end-to-end neural network model and addressing the challenges faced in practice [4][6]. Group 1: End-to-End Neural Network Model - Tesla's decision to adopt an end-to-end neural network model is driven by the need to address complex driving scenarios that cannot be pre-defined by rules, such as the "trolley problem" and second-order effects [6][10]. - The end-to-end model is described as a complete overhaul of previous architectures, fundamentally changing design, coding, and validation processes, leading to a more human-like driving experience [11][19]. - The model outputs driving instructions alongside interpretable "intermediate results," utilizing technologies like generative Gaussian splatting to create dynamic 3D models of the environment in real-time [8][17]. Group 2: VLA and World Model Concepts - VLA (Vision-Language-Action) is an extension of the end-to-end model that incorporates language information, allowing for a more visual representation of driving behavior [12][14]. - The world model aims to establish a high-bandwidth cognitive system based on video/image data, addressing the limitations of language models in understanding complex, dynamic environments [15][19]. - The relationship between end-to-end, VLA, and world models is clarified, with end-to-end serving as the foundation, VLA as an upgrade, and the world model as the ultimate form of understanding spatial dynamics [12][19]. Group 3: Industry Perspectives and Trends - The industry is divided into three main technical routes: end-to-end, VLA, and world model, with companies like Horizon Robotics and Bosch primarily adopting end-to-end due to lower costs and higher stability [13][19]. - VLA has faced criticism from industry leaders who argue that its reliance on language models may not be essential for effective autonomous driving, emphasizing the need for spatial understanding instead [16][19]. - Tesla's recent publication has reignited discussions in the industry, positioning the company at the forefront of current technological directions and providing a systematic analysis of practical applications [20].

12月18日，在首届光合组织人工智能创新大会（HAIC2025）主论坛上，商汤科技、大晓机器人与中科 曙光正式达成战略合作。三方将围绕国产化人工智能基础设施与具身智能关键技术方向，依托各自技术 与产业优势，协同推动软硬一体的"算力基础设施+世界模型+具身智能"生态建设，进一步加速AI能力 向物理世界延展。 ...

Core Insights - The report from Andreessen Horowitz (a16z) signals a significant shift in the internet landscape, indicating that the foundational elements built over the past fifteen years are collapsing as AI agents replace human users [1][2] - The transition to AI-driven interactions will lead to a "recursive storm" in infrastructure, fundamentally altering the way businesses operate and compete [1][3] Infrastructure Crisis - The internet infrastructure was originally designed for human users, characterized by low concurrency and predictable behavior, but this assumption will be overturned by 2026 [2] - The emergence of Agentic Architecture will replace existing backend logic, as AI agents will execute thousands of tasks in milliseconds, resembling a DDoS attack rather than typical user traffic [3] Business Logic Transformation - The traditional attention economy, which focused on screen time and user engagement, is becoming obsolete as AI can complete tasks without human interaction [6] - Future monetization models will shift from "per user" to "per outcome," with a focus on ROI rather than user engagement metrics [6][7] - SEO will be replaced by GEO (Generative Engine Optimization), emphasizing machine readability over human-centric design [7] SaaS Evolution - A future "multi-agent collaboration network" in the B2B sector will enable various AI agents to negotiate and process information autonomously, creating a new competitive landscape for SaaS companies [9] - The core competency for SaaS firms will shift from feature accumulation to ecosystem connectivity [9] Experience and Service Enhancement - The concept of "world models" will transform media from passive consumption to interactive environments, leading to highly customized service models across various sectors [9] Educational and Healthcare Innovations - The emergence of "AI-native universities" will allow for real-time updates to curricula based on the latest research and student feedback, enabling personalized education [12] - Healthcare will transition from low-frequency, high-cost treatments to high-frequency, subscription-based preventive care, creating a new demographic of "health MAUs" [12]