视网膜静脉阻塞（ RVO）作为 全球第二大致盲性视网膜血管疾病 ， 给全球数千万患者带来困扰 。想象一 下，要在 平均 直径仅 151微米 的血管上做手术 ——这比头发丝还细，而医生的手部震颤幅度却 可 达 182 微米。 传统治疗方法如 抗血管内皮生长因子治疗 只能缓解症状，无法根除病因，患者需要反复治疗，费用高昂且有 感染风险。而 视网膜静脉插管 （ RVC） 手术 虽然能直接清除血栓，但因为操作难度太大，很少有医生能够 掌握。 近日 ，约翰斯 ·霍普金斯大学的研究团队在 国际顶刊 《 Science Robotics》 上发表了一项突破性成果。 他们开发出 基于深度学习的自主视网膜静脉插管系统 ，在离体猪眼实验中实现了 90%的成功率 。即便模拟 呼吸导致的眼部运动，成功率仍高达 83% 。 ▍ 失明患者 新希望，在比发丝更细的血管上 操作手术 这套系统的核心亮点在于 机器人即可自主完成导航、接触、穿刺等一系列超越人类生理极限的微米级操作 ， 为 RVO治疗开辟了精准、高效的新路径。 在固定猪眼实验中，系统将 针体导航时间从 57.45秒缩短至30.56秒 ， 穿刺及回撤时间从 43.55秒压缩至 9. ...

Core Insights - The article emphasizes that AI is entering a critical phase of competition between China and the US, with both countries focusing on their unique strengths in computing power and supply chain capabilities to define their own "Industrial 5.0" [5][6] - It highlights 2025 as the "Year of the Intelligent Agent," where AI evolves from a mere tool to a digital counterpart capable of task execution, leading to a significant reduction in entrepreneurial barriers and the emergence of a new wave of startups [6][30] - The article discusses the importance of AI in transforming various industries, with a focus on the integration of AI into everyday business practices and the potential for significant economic growth [28][64] Group 1: AI Competition Landscape - The competition in AI is characterized by a bipolar structure between China and the US, with the US investing over $350 billion in AI infrastructure by 2025, while China is projected to invest 630 billion RMB [46][49] - The article notes that the US holds 74.5% of global computing power, while China accounts for 14%, indicating a significant disparity in resources [49] - The future of AI is seen as a race between the two nations, with both focusing on different paths: the US on closed-source models and China on open-source models [60][61] Group 2: AI Applications and Innovations - The article outlines the emergence of various AI applications across industries, such as AI-driven banking solutions that cater to elderly customers, showcasing how AI can enhance user experience [81][98] - It highlights the rapid growth of AI in content creation, with AI-generated media becoming a significant part of the cultural landscape, particularly in sectors like AI comics, which saw a 600% increase in production [73][78] - The integration of AI into supply chain management is exemplified by companies like Xiamen Guomao, which is developing AI-driven decision-making tools for commodity trading [85][88] Group 3: Intelligent Agents and Future Trends - The concept of "Intelligent Agents" is introduced as a transformative force in personal and professional settings, with AI tools enhancing productivity and efficiency [99][100] - The article discusses the potential for AI to redefine personal capabilities, suggesting that skills may need to be re-evaluated in the context of AI advancements [78] - It predicts that the next decade will see the rise of four trillion-dollar markets in China, including the robotics sector, which is expected to play a crucial role in the future of manufacturing [124][126]

文 /巴九灵（微信公众号：吴晓波频道） "人工智能AI革命是一场事关国运的世纪大竞赛。" 大家好，我是吴晓波。 AI闪耀中国科技人文秀，我们今天会度过一个难忘的夜晚，AI之夜。 刚才我在后台听有人在弹奏《花开在眼前》。我知道一个是机器人，一个是八岁的北京小姑娘，她叫李 睿宸，来自于北京东城区黑芝麻小学。还有一家是来自北京的机器人公司，叫做灵心巧手。 你们分得清吗？哪个是人弹的，哪个是机器人弹的？ 我完全分不清。一个是硅基人类，一个是碳基人类，他们用自己的双手在弹奏一首《花开在眼前》。我 想这样的场景指向一个正在发生的未来。 所以今天我们用一个晚上的时间来讨论AI，讨论它发展到一个怎样的阶段，它跟国家、产业、每一个 人又有什么关系。 我从哪里谈起呢？我想穿越到1950年，去一趟英国的伦敦，见一个38岁的英国数学家，他的名字叫艾伦 ·图灵。 （播放AI生成的视频）你们好，我是艾伦·图灵，我在1950年的伦敦向你们问好，五年前人 类发明电脑，它的体积大得像一间房子，单张卡片可存储960个byte，运算速度是每秒5000 次加法运算。不过现在的我确实在想一个问题，也许它真的很荒唐，不过我还是想提出来， 机器会思考吗？ ...

Core Viewpoint - The article emphasizes that the AI revolution is a significant competition that will impact national fortunes, highlighting the rapid advancements and implications of AI technology in various sectors [2][22]. Group 1: AI Development History - The concept of artificial intelligence was first introduced in 1956 at the Dartmouth Conference, marking the beginning of a long journey in AI research [11]. - Key milestones include the introduction of deep learning by Geoffrey Hinton in 2006 and the launch of GPT-3.5 in 2022, which significantly advanced AI capabilities [17][18]. - The article notes that AI has now entered everyday life and industries, with significant developments in China and the U.S. [18][19]. Group 2: AI Investment Landscape - By 2025, the U.S. is expected to invest over $350 billion in AI infrastructure, while China’s investment is projected to reach 630 billion RMB [41]. - The article highlights that the U.S. currently dominates AI computing power, holding 74.5% of global capacity, compared to China's 14% [43]. - The investment in AI infrastructure in China is compared to the historical investment in high-speed rail, indicating a significant commitment to AI development [41]. Group 3: AI Applications and Innovations - The article discusses the emergence of AI in various industries, including banking, where Shanghai Bank has become the first AI-native mobile bank [75]. - It highlights the rapid growth of AI-driven content production, such as AI-generated comics, which have seen a 600% increase in production [67]. - The use of AI in sectors like healthcare, logistics, and manufacturing is emphasized, showcasing its transformative potential [78][81]. Group 4: Competitive Landscape - The article outlines the competitive dynamics between the U.S. and China in AI, with both countries pursuing different strategies: the U.S. focusing on closed-source models and China on open-source models [54][55]. - It mentions that by 2025, over 80% of the world's large models will be developed in the U.S. and China, with significant advancements in image generation and text capabilities [46][49]. - The competition extends to autonomous driving, with both countries making strides in developing self-driving technologies [57]. Group 5: Future Trends and Predictions - The article predicts that the next decade will see the emergence of four trillion-dollar markets in China, including the robotics sector, which is expected to play a crucial role in manufacturing upgrades [118][120]. - It discusses the potential for AI to redefine personal capabilities and the importance of adapting to new technologies in various industries [72][98]. - The article concludes with a call for recognition of the ongoing AI revolution and its implications for the future [58].

对于从事自动化和计算机的同学，建议搞深度学习，VLA、端到端、世界模型都是很好的方向，从入门、到 工作甚至读博都有很大空间。对于机械和车辆的同学，可以先学习传统PnC、3DGS这些方向算力低、入手简 单。 剩下的就是一些方法论的提升了，多看论文多交流，慢慢形成自己的思考和idea。 对很多新人研究者，一个 好的idea需要踩很多次坑。如果你还是新人，不知道怎么入门，可以看看我们推出的论文辅导。 论文辅导上线了！ 端到端、VLA、世界模型、强化学习、3D目标检测、多传感器融合、3DGS、BEV感知、Occupancy Network、多任务学习、语义分割、轨迹预测、运动规划、扩散模型、Flow matching、点云感知、毫米波雷 达、单目感知、车道线/在线高精地图等方向。 如果您有任意论文发表需求，支持带课题/研究方向咨询，欢迎联系我们， 微信：paperguidance 提供的服务 论文选题； 论文全流程指导； 实验指导； 点击下方 卡片 ，关注" 自动驾驶之心 "公众号 戳我-> 领取 自动驾驶近30个 方向 学习 路线 最近收到不少同学的咨询，很多都是计算机、车辆、自动化和机械方向的同学。 先看自驾一些 ...

点击下方 卡片 ，关注" 自动驾驶之心 "公众号 戳我-> 领取 自动驾驶近30个 方向 学习 路线 这两天有小伙伴在群里抛出这个问题，非常有建设性，分享给大家？ 探讨feed-forward GS在自驾场景落地的难点目前在哪里？ 目前来看Feed-forward的相关方法在点云精度还是差一点的，尤其是ff的方法在私有数据的域上精度不稳定。前馈方法的前景是广阔的，毕竟克服了per scene优化 的弊端，值得持续尝试预研和落地。 关于3DGS相关的技术栈，很多同学想入门却苦于没有有效的学习路线图：既要吃透点云处理、深度学习等理论，又要掌握实时渲染、代码实战。 为此自动驾驶之 心联合 工业界算法专家 开展了这门 《3DGS理论与算法实战教程》！ 我们花了两个月的时间设计了 一套3DGS的学习路线图，从原理到实战细致展开。全面吃透 3DGS技术栈。 第二章则正式进入到3DGS的原理和算法部分。 整体上第二章的设计思路是带大家先打好基础，先详细梳理3DGS的原理部分及核心伪代码，接着讲解动态重建、 表面重建、鱼眼重建和光线追踪的经典文章和最新的算法，由点及面层层深入。实战我们选取了英伟达开源的3DGRUT框架，适合 ...

Core Viewpoint - The article discusses the rapid development and deployment of Google's Tensor Processing Unit (TPU), highlighting its significance in deep learning and machine learning applications, and how it has evolved to become a critical infrastructure for Google's AI projects [4][5][10]. Group 1: TPU Development and Impact - Google developed the TPU in just 15 months, showcasing the company's ability to transform research into practical applications quickly [4][42]. - The TPU has become essential for various Google services, including search, translation, and advanced AI projects like AlphaGo [5][49]. - The TPU's architecture is based on the concept of systolic arrays, which allows for efficient matrix operations, crucial for deep learning tasks [50][31]. Group 2: Historical Context and Evolution - Google's interest in machine learning began in the early 2000s, leading to significant investments in deep learning technologies [10][11]. - The Google Brain project, initiated in 2011, aimed to leverage distributed computing for deep neural networks, marking a shift towards specialized hardware like the TPU [13][15]. - The reliance on general-purpose CPUs for deep learning tasks led to performance bottlenecks, prompting the need for dedicated accelerators [18][24]. Group 3: TPU Architecture and Performance - TPU v1 was designed for inference tasks, achieving significant performance improvements over traditional CPUs and GPUs, with a 15x to 30x speedup in inference tasks [79]. - The TPU v1 architecture includes a simple instruction set and is optimized for energy efficiency, providing a relative performance per watt that is 25 to 29 times better than GPUs [79][75]. - Subsequent TPU versions, such as TPU v2 and v3, introduced enhancements for both training and inference, including increased memory bandwidth and support for distributed training [95][96].

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].

Report Summary 1. Report Industry Investment Rating No industry investment rating is provided in the report. 2. Core Viewpoints of the Report - In the US, multiple quantitative enhancement products were issued last week, with an increasing issuance speed at the end of the year. Various asset classes in US ETFs maintained inflows, and alternative strategies such as long - short equity performed well. US domestic stock - type mutual funds still faced significant redemption pressure, while bond funds had a slight inflow [2]. 3. Summary by Directory 3.1 US ETF Innovation Products: Multiple Quantitative Enhancement Products Issued - Last week, 43 new products were issued in the US, including 6 individual stock leveraged products and 3 digital currency - related products. One product combined crude oil and Bitcoin with 2x leverage, and Simplify's US stocks + futures strategy also had a 1:1 investment ratio. Motley Fool issued 3 single - factor ETFs, each holding about 150 stocks [5][6]. - BlackRock's quantitative team issued an alternative product, and NEOS issued a long - short equity product. Hedgeye's 130/30 product also adopted a long - short strategy. Global X issued a gold miners ETF, Franklin Templeton issued a small - cap enhanced ETF, and Sterling Capital's stock option product used a quantitative stock - selection strategy [7]. - Columbia issued 6 ETFs, 3 bonds and 3 stocks. The stock products mainly used a quantitative enhancement strategy with semi - annual rebalancing [8]. 3.2 US ETF Dynamics 3.2.1 US ETF Fund Flows: All Asset Classes Maintained Inflows - In the past week, US ETF inflows remained above $40 billion, and domestic stock products had inflows of over $30 billion. There was a significant difference in fund flows between BlackRock's S&P 500 ETF (outflow) and Vanguard's products (inflow). Russell 2000 and high - yield bond ETFs had inflows, indicating a relatively high risk appetite [2][9]. - S&P 500 ETFs had significant recent fund fluctuations, Russell 2000 ETFs had continuous inflows, and gold also returned to an inflow state [13]. 3.2.2 US ETF Performance: Alternative Strategies such as Long - Short Equity Performed Well - Many long - short equity products were issued last week. In the past two years, products replicating futures and combining multiple hedge fund strategies have been increasing. Among the top ten alternative strategy products in the US, State Street's multi - strategy product and Convergence's long - short equity product performed best [14]. 3.3 Recent Fund Flows of US Ordinary Public Offering Funds - In October 2025, the total amount of non - money public offering funds in the US was $23.7 trillion, an increase of $0.22 trillion from September. The S&P 500 rose 2.27% in October, and the scale of domestic stock - type products increased by 0.9%, but the redemption pressure was still high. - From November 25th to December 3rd, domestic stock funds in the US had outflows of over $15 billion. Hybrid products had continuous outflows, while bond funds had a slight inflow [15].

Report Industry Investment Rating No information about the report industry investment rating is provided in the content. Core Viewpoints of the Report - The issuance speed of US ETFs at the end of the year has increased again, with multiple quantitative enhancement products being issued [2][7]. - The capital inflow of US ETFs has remained above $40 billion, and the risk appetite of capital has remained at a high level [2][13]. - Stock long - short and other alternative strategies of US ETFs have performed well [2][19]. - The redemption pressure of US non - money mutual funds in October 2025 was still high, and domestic stock funds and hybrid products have continued to experience outflows recently, while bond funds have seen a slight inflow [2][20]. Summary by Relevant Catalogs 1. US ETF Innovation Products: Multiple Quantitative Enhancement Products Issued - Last week, 43 new products were issued in the US, including 6 individual stock leverage products and 3 digital currency - related products [2][7]. - Motley Fool issued 3 single - factor ETFs, each holding about 150 stocks [9]. - BlackRock's quantitative team, NEOS, Hedgeye, Global X, Franklin Templeton, Sterling Capital, and Columbia all issued different types of ETFs last week, with many using quantitative strategies [10][11]. 2. US ETF Dynamics 2.1 US ETF Capital: All Types of Assets Maintain Inflows - In the past week, the inflow of US ETFs has remained above $40 billion, and the inflow of domestic stock products has exceeded $30 billion [2][13]. - The S&P 500 ETF of BlackRock continued to have the largest outflow, while the products of Vanguard had a large - scale inflow of over $40 billion, with a capital flow difference of over $80 billion between the two. The Russell 2000 and high - yield bond ETFs had inflows [2][15]. 2.2 US ETF Performance: Stock Long - Short and Other Alternative Strategies Perform Well - Many stock long - short products were issued last week, and products combining futures replication and multiple hedge fund strategies have been increasing in the past two years. Among the top ten alternative strategy products in the US, the multi - strategy product of State Street and the stock long - short product of Convergence performed the best [2][19]. 3. Recent Capital Flows of US Ordinary Mutual Funds - In October 2025, the total amount of US non - money mutual funds was $23.7 trillion, an increase of $0.22 trillion compared to September. The scale of domestic stock products increased by 0.9%, but the redemption pressure was still high [2][20]. - From November 25th to December 3rd, the outflow of US domestic stock funds remained above $15 billion. Hybrid products have continued to experience outflows recently, while bond funds have seen a slight inflow [2][20].