Core Viewpoint - The article discusses the emergence of the AI economy, highlighting its rapid development and the structural changes it brings to various industries and society as a whole [3][4]. Group 1: AI Economic Characteristics - The AI industry is characterized by non-linear and non-uniform growth, with economic activities related to AI advancing at an unprecedented scale while traditional industrial activities maintain their usual pace [3]. - Industry leaders, such as Elon Musk and Jensen Huang, predict significant economic transformations due to AI, including a potential fivefold increase in global GDP to $500 trillion [4]. Group 2: Scaling Law and AI Development - The Scaling Law is a foundational principle for the development of large AI models, with current research focusing on when and under what conditions it will converge [7]. - Key metrics indicate that the reasoning cost of large language models decreases by 90% every 12 months, and their capability doubles approximately every seven months [7]. Group 3: Digital Layer and Economic Impact - The "digital layer" is proposed as a crucial infrastructure for the AI economy, consisting of personal AI assistants and specialized AI agents that enhance understanding of consumers and producers [10][16]. - This digital layer is expected to significantly reduce transaction costs and improve efficiency in economic activities by automating information collection, decision-making, and actions [17][18]. Group 4: Employment and Workforce Changes - The emergence of AI employees is anticipated, with organizations likely to see changes in management, recruitment, and collaboration between human and AI workers [30]. - The shift towards a task-centered work system is expected to enhance economic efficiency by breaking down jobs into smaller, manageable tasks that AI can perform [26]. Group 5: Global Economic Dynamics - The article suggests that the global distribution of GDP will change as AI capabilities become more uniform across countries, potentially altering traditional international divisions of labor [35]. - Countries will need to assess their energy, computing power, data, and algorithm capabilities to effectively integrate AI into their economies [38].

本文的主要作者来自北京航空航天大学、北京大学、北京智源人工智能研究院和中科院自动化研究所。本 文的第一作者为北京航空航天大学博士生周恩申，主要研究方向为具身智能和多模态大模型。本文的共一 作者兼项目负责人为北京智源研究院研究员迟程。本文的通讯作者为北京航空航天大学教授盛律和北京大 学计算机学院研究员、助理教授仉尚航。 我们希望具身机器人真正走进真实世界，尤其走进每个人的家里，帮我们完成浇花、收纳、清洁等日常任 务。但家庭环境不像实验室那样干净、单一、可控：物体种类多、摆放杂、随时会变化，这让机器人在三 维物理世界中「看懂并做好」变得更难。 想象一下你下班回到家，对家用服务机器人说： 「按从左到右的顺序给每盆花浇水；喷壶要在每朵花上方 1–5 厘米处停住再浇，这样更均匀。」（如下图） 对人来说这很自然，但对机器人来说，难点不在「浇水」本身，而在指令里隐含了大量空间约束：既有 定 性 的（从左到右、在上方），也有 定量 的（1–5 厘米）。在杂乱的开放世界场景中，让机器人稳定遵循这 些约束，哪怕对目前最先进的视觉 - 语言 - 动作模型（VLA）也依然是挑战。 一个直接的突破口是：让视觉 - 语言模型（VLM）生 ...

在大语言模型（LLM）落地应用中，推理速度始终是制约效率的核心瓶颈。传统自回归（AR）解码虽能保证生成质量，却需逐 token 串行计算，速度极为缓慢； 扩散型 LLM（dLLMs）虽支持并行解码，却面临训练成本高昂、质量下降及 KV 缓存兼容问题；投机解码（Speculative Decoding）则需额外引入草稿模型，系统 复杂度大增。 Jacobi Forcing 核心优势： 破解并行解码的 "三元悖论" Jacobi Forcing 的创新之处在于打破了 "低代价、高速度、高质量" 的不可能三角，其核心优势体现在三大维度： 近期，来自 UCSD Hao AI Lab 和上海交大 Deng Lab 的团队提出了一种突破性解决方案 ——Jacobi Forcing，该方案无需重构模型架构，即可将标准 AR 模型转化为 原生因果并行解码器，在编码、数学等任务中实现最高 4 倍 wall-clock 提速和 4.5 倍 tokens-per-forward 提升，同时保持接近 AR 模型的生成质量，为 LLM 高效推 理开辟了新路径。 论文地址: https://arxiv.org/pdf/2512.1468 ...

Core Insights - 2025 is marked as a pivotal year in the AI industry, characterized by intense competition among AI giants, a talent war, and significant advancements in AI infrastructure and capabilities [6][10][13]. Group 1: AI Development and Learning - The rapid advancement in AI has created unprecedented opportunities for software development, with a notable shortage of skilled AI engineers [6][22]. - Structured learning is essential for aspiring AI developers to avoid redundant efforts and to understand existing solutions in the industry [7][8]. - Practical experience is crucial; hands-on project work enhances understanding and sparks new ideas in AI development [8][14]. Group 2: AI Infrastructure and Investment - The AI industry has seen capital expenditures surpassing $300 billion in 2025, primarily for building new data centers to handle AI tasks [26]. - Major companies are planning extensive infrastructure projects, with projected costs reaching up to $5.2 trillion by 2030 to meet anticipated demand for AI capabilities [26][31]. - Companies like OpenAI, Meta, Microsoft, and Amazon are investing heavily in data center capacities, with OpenAI planning to build 20 gigawatts of data center capacity globally [31]. Group 3: Talent Acquisition and Market Dynamics - A fierce competition for top AI talent has led to unprecedented salary offers, with some companies offering compensation packages comparable to professional sports stars [22][26]. - Meta's aggressive recruitment strategy has included significant financial incentives to attract talent from competitors, reflecting the high market value of AI professionals [22][27]. - Despite concerns about an AI bubble, investments in AI infrastructure are contributing to economic growth, particularly in the U.S. [29]. Group 4: Advancements in AI Models - The introduction of reasoning models has significantly improved the performance of large language models (LLMs), enhancing their capabilities in various tasks [20][21]. - AI agents are increasingly capable of automating complex coding tasks, with reports indicating that many companies are now relying on AI-generated code for senior-level tasks [33][39]. - The evolution of programming agents has led to a competitive landscape among AI companies, with advancements in code generation capabilities becoming a focal point [30][39].

Core Viewpoint - The article discusses the challenges in obtaining high-quality cardiovascular signals for wearable health monitoring and introduces a new unified multimodal generation framework called UniCardio, which aims to enhance signal denoising, interpolation, and modality translation for AI-assisted medical applications [2][7]. Group 1: Background and Challenges - Cardiovascular diseases are a leading cause of death, and signals like photoplethysmography (PPG), electrocardiography (ECG), and blood pressure (BP) provide different insights into the same physiological processes [3]. - There is a dilemma in monitoring: wearable signals are easy to obtain but prone to noise and interruptions, while high-quality signals require more invasive methods that are less practical for long-term use [3][4]. Group 2: Introduction of UniCardio - UniCardio is designed to perform two core functions: signal restoration (denoising and interpolation of low-quality signals) and modality translation (synthesizing hard-to-obtain signals based on available ones) [7]. - The framework utilizes a unified diffusion model to learn the multimodal conditional distribution relationships among different cardiovascular signals [11]. Group 3: Methodology - UniCardio employs a diffusion model that generates data from noise, using a unified noise mechanism for different modalities and gradually reconstructing target signals under conditional guidance [11]. - It incorporates modality-specific encoders and decoders to extract and restore physiologically meaningful waveform features, while task-specific attention masks are used to constrain information flow relevant to current tasks [13]. Group 4: Training Paradigm - The framework introduces a continual learning paradigm that incrementally incorporates different tasks to ensure sufficient training samples and balance task contributions, addressing the issue of catastrophic forgetting [13]. - This approach facilitates knowledge transfer across tasks and modalities, enhancing performance in more complex generation tasks [13]. Group 5: Experimental Results - UniCardio demonstrates consistent advantages in signal denoising, interpolation, and modality translation compared to task-specific baseline methods, highlighting the value of multimodal complementary information [15]. - In specific tasks, such as PPG and ECG interpolation, the introduction of multimodal conditions significantly reduces generation error and improves waveform recovery stability [16]. Group 6: Application and Validation - The generated signals from UniCardio have been validated in downstream cardiovascular applications, showing superior performance in abnormal state detection and vital sign estimation compared to using noisy or interrupted signals [18]. - The results indicate that UniCardio-generated signals not only resemble real signals numerically but also maintain functional usability for downstream analyses [19]. Group 7: Interpretability and Clinical Relevance - The framework provides a clinically friendly validation path, ensuring that generated signals retain recognizable diagnostic features for clinical experts [21]. - The observable intermediate states during the denoising process enhance the model's interpretability and credibility, making it suitable for integration into real medical workflows [23]. Group 8: Future Prospects - UniCardio advances cardiovascular signal generation from single-task, single-modality approaches to a more unified and scalable framework, with potential applications extending to fields like neuroscience and psychology that rely on multimodal physiological signals [25].

Core Insights - The article emphasizes the importance of data science and analytics in solving real-world problems and advancing the field through a combination of statistics, machine learning, and optimization techniques [3][11]. Part One: Introduction to Data Science and Analytics - The domain of data science and analytics aims to unify various techniques to advance the discipline and apply them to benefit society [3]. - The doctoral program in data science and analytics provides rigorous research training, enabling students to analyze data and make informed decisions [3]. Part Two: Interdisciplinary Research Directions - The program covers various interdisciplinary research directions, including statistical learning, industrial analytics, machine learning, business modeling, high-performance data analysis, and data visualization [6]. Part Three: Talent Development - The doctoral program is designed to equip students with skills to identify and solve practical research problems, proposing independent solutions related to data science and analytics [11]. - Students are expected to demonstrate mastery of the subject matter and contribute original, meaningful scientific insights [12]. Part Four: Training Model - The program offers full-time and part-time study options, with a duration of 3-4 years for full-time students and 6 years for part-time students [13]. - The program requires a total of 21 credits, including core and elective courses, and degrees are awarded by the Hong Kong University of Science and Technology [13]. Part Five: Learning Outcomes - Graduates will be able to identify relevance and insights in science and engineering, master various models and tools in data science, and demonstrate critical thinking and analytical skills [22]. - They will also be capable of conducting original research and effectively communicating their findings [23]. Part Six: Faculty - The article includes a section on faculty, indicating the expertise available to students in the program [24]. Part Seven: Application Guide - Applicants must hold a recognized bachelor's degree and meet specific English language requirements [27]. - The application timeline for the 2026-27 academic year is outlined, with deadlines for both domestic and international students [28][30].

机器之心编辑部 AI 大新闻，一桩接一桩。 早上刚传来 Manus 被 Meta 收购的消息，很快，围绕「全球大模型第一股」的竞速，也传来靴子落地的声响。 12 月 30 日，北京智谱华章科技股份有限公司（以下简称「智谱」）正式启动港股招股。招股期将持续至 2026 年 1 月 5 日，并计划于 2026 年 1 月 8 日以股票代码 "2513" 在香港联交所主板挂牌上市。 根据招股安排，智谱拟进行全球发售 3741.95 万股 H 股，其中香港公开发售 187.1 万股 H 股，国际发售 3554.85 万股 H 股。 IPO 的定价与募资规模也随之揭晓 —— 每股发行价定为 116.20 港元。在扣除相关发行费用后，预计本次募资规模约 43 亿港元，对应的 IPO 市值预计将超过 511 亿港元。 公开信息显示，智谱在私募市场的累计融资额已达 83.44 亿元，最新估值攀升至 243.77 亿元。这意味着，在迈向上市的关键一跃中，智谱的市值几乎实现翻倍， 如此幅度的「溢价上市」，也是一次难度不低的市场挑战。 基石投资者阵容同样颇为亮眼。公告显示，基石投资者合计拟认购 29.8 亿港元，占本次发行规模近七 ...

作者介绍：本文第一作者黄小虎同学，目前是香港大学的三年级在读博士生，导师是韩锴教授。黄小虎的研究方向是以视频为中心的领域，包括音视频生成、视 频理解以及视频识别。 视频 - 音频联合生成的研究近期在开源与闭源社区都备受关注，其中，如何生成音视频对齐的内容是研究的重点。 近日，来自香港大学和字节跳动的研究团队提出了一种简单有效的框架 ——JoVA，它支持视频和音频的 Token 在一个 Transformer 的注意力模块中直接进行跨模态 交互。为了解决人物说话时的 "口型 - 语音同步" 问题，JoVA 引入了一个基于面部关键点检测的嘴部区域特定损失 (Mouth-area specific loss)。 实验表明，JoVA 只采用了约 190 万条训练数据，便在口型同步准确率、语音质量和整体生成保真度上，达到了先进水平。 项目主页： https://visual-ai.github.io/jova/ 论文地址：https://arxiv.org/abs/2512.13677 一、研究背景与动机 目前的开源解决方案通常分为两大类别：一类是 "级联式"，即先生成视频再配音，或者先生成语音再驱动视频生成，这种方式 ...

机器之心编辑部 就在刚刚，Meta 完成了一项大收购，将智能体初创公司 Manus 收入麾下。 目前，双方交易的具体细节（包括具体收购金额等）尚未公布。 自今年 3 月推出全球首款通用 Agent 以来，Manus 迅速走红，成为人工智能领域的一大焦点。据公开资料显示，今年 4 月份，Manus 母公司宣布完成 7500 万美元 融资，估值接近 5 亿美元，投资方包括知名风投机构基准资本在内的多家投资主体。 此后，Manus 总部及核心研发团队搬到了新加坡。 如今，「靴子终于落地」，Manus 被 Meta 收购，迎来了新的发展机遇。 Meta 首席 AI 官 Alexandr Wang 表示，「很高兴 Manus 加入 Meta，帮助我们打造令人惊叹的 AI 产品！Manus 团队在探索当前模型的能力潜力方面处于全球领先地 位，致力于构建强大的智能体。」 Manus 创始人兼 CEO 肖弘发文称，「今天是一个我将终生难忘的时刻。当我们创办 Manus 时，很少有人相信通用 AI 智能体能够成功。我们被告知时机太早，目 标太宏大，挑战太艰难，但我们依然坚持建设。在怀疑、挫折和无数个夜晚的徘徊中，我们曾质疑自己 ...

在空间智能（Spatial Intelligence）飞速发展的今天，全景视角因其 360° 的环绕覆盖能力，成为了机器人导航、自动驾驶及虚拟现实的核心基石。然而，全景深度 估计长期面临 "数据荒" 与 "模型泛化差" 的瓶颈。 近日， 来自 Insta360 研究团队、加州大学圣地亚哥分校 (UCSD)、武汉大学以及加州大学默塞德分校的研究者 共同推出了 Depth Any Panora mas (DAP) 。这是首 个在大规模多样化数据集上训练的全景度量深度（Metric Depth）基础模型，不仅统一了室内外场景，更通过 200 万量级的数据引擎与创新的几何一致性设计，刷 新了多项 benchmark 纪录，在多种 open-world 场景下保持优异的效果。 模型对由 Gemini 或 DiT-360 等合成的全景图同样展现出了极佳的预测效果，生成的深度图边缘锐利、逻辑自洽，是空间 AIGC 链路中理想的几何基石。 除了静态 图像，DAP 在处理全景视频流时同样展现出了极佳的预测效果，具备优秀的帧间一致性与稳定性 。 论文标题：Depth Any Panoramas: A Foundation Mod ...