国庆中秋双节同至。 我们深信，用探索的精神为祖国献礼，用学习的态度为社会贡献，是迎接新时代的最佳方式。对于 关注AI领域发展的投资者、从业者与研究者而言，这更是沉淀专业认知、把握技术趋势的黄金窗 口。面对快速迭代的AI浪潮，一份兼具权威性与系统性的技术资料，能让您的假期学习事半功倍， 高效完成专业能力的跃升。 今 天 ， 我 们 为 大 家 选 了 来 自 Ilya Sutskever 推 荐 的 30 篇 前 沿 论 文 合 集 （ Ilya Sutskever's Top 30）。 Primers • Ilya Sutskever's Top 30 覆盖近15年AI领域里程碑成果的合集，以 "技术底层-能力 突破-场景落地"为逻辑主线 ，串联起AI从"感知智能"到"认知智能"的关键跃迁：从奠定深度学习 基础的 CNN 、 RNN ，到重构自然语言处理领域的 Transformer 与 自注意力机制 ，再到推动 RAG 、 多步推理 等前沿方向的核心研究，每一篇论文都是对应技术领域的"奠基之作"，直接关联 当前AI产业落地的核心能力底座。 这份清单既清晰拆解了"残差映射""动态指针网络"等专业术语的技术逻辑 ...

作者 | 华卫 人类与 AI 间的对决，自 2016 年的 AlphaGo 打赢世界围棋冠军李世石起，就开始不断出现在大众视线中，出圈的例子更是不少。 曾担任《最强大脑》节目首席科学家的刘嘉，也亲眼见证过这样一场比赛。当时，还是百度大脑首席科学家的吴恩达带着搭载百度大脑的智能机器人小 度上了舞台，与人类组选手比拼起"看照片认脸"。面对多轮挑战，最终人类最顶尖的面孔识别选手不敌 AI。 这个结果，好似当头一棒重重敲向了此时正往北京师范大学副校长一职奔赴的刘嘉。他火速向学校递交辞呈，重新钻进实验室，将全部心思转投到 了脑科学与 AI 的交叉研究中。 回到 2025 年的今天，我们更是已置身于一个几乎被 AI 包围的时代。去年，诺贝尔物理学奖和图灵奖双双花落 AI 领域。今年年初爆火的 DeepSeek 让"无所不知"的大模型遍布朋友圈，随后 Manus 的横空出现又将 AI 完全自主的蓝图放到大众眼前。AI 真的将超越人类吗？身处于现在的时代，这个话 题已被推至现实议程，越来越多的人能够感觉到一种深切的危机感。 在今年 6 月出版的新书《通用人工智能：认知、教育与生存方式的重构》中，刘嘉用"近乎疯狂"几个字来形容 ...

"卓睦鸟医疗大模型"不仅是一个技术产品，更是一个贯穿健康服务全链路的智能引擎。中康科技在2025 年3月发布的"医疗健康全场景智能体"，正是依托天宫一号和卓睦鸟医疗中台两大引擎，结合行业高质 量数据、专业知识和领先的大模型蒸馏技术，打造出围绕商用、医疗、药店、健康管理和研发多场景智 能化体解决方案，这些智能体能够实现"智能决策、敏捷行动、结果可控"的业务闭环，帮助医疗健康行 业提升效率与服务质量。 "卓睦鸟医疗大模型"依托中康科技在健康产业近二十年的数据积累与行业洞察，构建了覆盖百万级医学 文献、指南、教材、药品说明书等公开语料，以及千万级脱敏临床数据、病种记录与非结构化病历的高 质量训练资源。模型参数量达700亿，通过大规模预训练与多阶段指令微调，结合数据清洗、去重、增 强等先进工艺，不断提升其在真实医疗场景中的泛化能力与响应精度。 近日，国内权威医疗大模型评测平台MedBench发布2025年度最新测评结果。在新一轮测评中，中康科 技"卓睦鸟医疗大模型"再次脱颖而出，勇夺榜单综合排名第2、医学语言理解单项排名第1的亮眼成绩， 彰显其在自然语言处理与医疗专业知识深度融合方面的显著优势。 ...

陈丹琦新作来了。 他们提出了一 个结合RLHF和RLVR优点的 方法， RLMT（Reinforcement Learning with Model-rewarded Thinking，基于模型奖励 思维的强化学习） 。 它要求模型在回答之前生成CoT，然后使用人类偏好训练的奖励模型来评价输出。 支持在基础模型上直接使用， 甚至不需要SFT，可以大幅节省后训练成本 。 | Model Avg. | WB AE2 AH2 CWv3 | | | --- | --- | --- | | Our model | | | | L3.1-8B-I-RLMT 54.1 50.4 58.7 22.9 | | 84.3 | | Other models | | | | L3.1-70B-Instruct 32.1 16.3 42.0 10.6 | | 59.4 | | Q2.5-72B-Instruct 45.2 44.4 50.2 19.9 | | 66.3 | | GPT-40 53.2 46.2 56.5 32.1 | | 77.8 | | Claude3.7-Sonnet 58.9 47.8 58.1 39.3 | | ...

Group 1 - The article discusses the ongoing low interest rate environment, which allows for a dynamic dilution of debt costs relative to economic growth, providing self-financing space for fiscal expansion [1] - Generative artificial intelligence is highlighted for its ability to instantly convert unstructured text into computable factors, significantly reducing information friction and the barriers to strategy development [1] - Global capital reallocation is driving a reassessment of risk premiums and governance premiums, with asset boundaries shifting due to geographical restructuring of industrial chains [1] Group 2 - The book "Investment Opportunities from a Global Perspective" by Shi Hanbing systematically analyzes the rotation patterns of global assets such as gold, silver, and new energy, proposing that "capital flows equal wealth flows" [3] - The book "The Financial Large Language Model" focuses on the underlying principles and technical pathways of large models, demonstrating their application in various financial scenarios [9][10] - "Fiscal Policy in a Low-Interest Rate Era" by Olivier Blanchard argues that when actual interest rates remain below potential growth rates, government debt costs are naturally diluted by economic growth, allowing for self-financing fiscal expansion [14][15]

Core Insights - The article emphasizes the importance of timely macroeconomic intelligence and micro trading data in driving sell-side research and investment decisions. LSEG and XTech have developed a predictive model that utilizes leading indicators to provide actionable market signals for research institutions and investors [1] - LSEG's solutions combine macroeconomic forecasting with microstructure analysis, enabling sell-side researchers and investment professionals to identify signals amidst vast information, thereby enhancing research efficiency and investment returns [1] Group 1: Event Details - The event titled "From Micro Trading to Macro Economy: LSEG Quantitative Insights Shanghai Exchange" is organized by LSEG, inviting professionals from funds, quantitative research, and consulting firms to discuss data-driven investment futures [1] - The agenda includes a keynote presentation by Dr. Arman Sahovic, LSEG's Director of Front Office Solutions for the Asia-Pacific region, followed by a panel discussion featuring industry experts [2][5] - The event is scheduled for November 6, 2025, from 16:30 to 19:00 in Lujiazui, Shanghai, with a registration and approval process for attendees [2][6] Group 2: Analytical Solutions - LSEG's text analysis solutions convert unstructured data into actionable insights, identifying new alpha opportunities through advanced natural language processing and machine learning techniques [8] - The global macro forecasting service, developed in collaboration with Exponential Technology, provides institutional investors with practical insights into global economic trends, analyzing key indicators such as the U.S. Consumer Price Index (CPI) and retail sales data [10] - LSEG's news analysis service quantifies corporate sentiment and provides valuable metadata to enhance quantitative investment strategies, covering over 40,000 companies since 2003 [12]

Core Insights - Market sentiment monitoring has become a crucial tool for corporate decision-making in the era of information explosion [1] - The selection of a professional and reliable service provider is a focal point for many companies, with key considerations including technical strength, data coverage, and service flexibility [1] Group 1: Data Monitoring Capabilities - A company's technical reserves often determine the depth of its services, exemplified by Beijing Blue Pacific Technology Co., Ltd., which has established a unique technical barrier in the big data field [3] - Blue Pacific has built a nationwide monitoring network that enables efficient collection and analysis of internet information, allowing companies to obtain market dynamics in real-time [3] - The timeliness and accuracy of data are core values of sentiment monitoring, with Blue Pacific leveraging its self-built IDC data center and numerous data detection nodes to ensure broad coverage and high precision [3] Group 2: Innovative Service Models - Blue Pacific integrates big data technology with mobile internet applications, offering customized solutions that transform complex technology into practical tools for non-technical managers [4] - The company's continuous optimization of data models enhances the analytical capabilities of vast information, helping businesses identify potential risks and uncover hidden market opportunities [4] - Blue Pacific's successful data support solutions in government evaluation demonstrate the broad applicability of its technology across various industries [4] Group 3: Sustainable Solutions - Companies should focus on whether service providers can offer sustainable solutions, with Blue Pacific maintaining sensitivity to cutting-edge technologies [4] - The company's rapid technological iteration and deep industry engagement highlight its ability to provide reliable technical support in a fast-changing market environment [4]

Core Viewpoint - Google achieved a significant victory in a 5-year antitrust case, avoiding forced breakup, with generative AI companies like OpenAI playing a crucial role in this outcome [2] Group 1: Antitrust Case and Market Impact - The U.S. government has intensified antitrust scrutiny on Silicon Valley giants, with Google being a key target, facing lawsuits since 2020 for its dominance in the search engine market [2] - A recent ruling by Judge Amit Mehta determined that Google does not need to divest its Chrome browser or Android operating system but must open more search result data to competitors and establish an antitrust technology committee [2] - Following the ruling, Google's stock surged over 8%, reflecting increased market confidence [2] Group 2: Role of Generative AI - The ruling highlighted the impact of generative AI, noting that more users are turning to AI chatbots like ChatGPT for information instead of traditional search engines, which reduces the necessity for a complete breakup of Google [2] - New AI browsers, such as Perplexity's Comet and OpenAI's upcoming browser, are redefining information retrieval through deep learning and natural language processing [3] - Despite the emergence of AI search engines, traditional search giants maintain a strong competitive advantage due to their established ecosystems and user data integration [3] Group 3: Future of Search Engines - Traditional search engines hold critical resources for the development of generative AI, including significant computing power and vast amounts of data [4] - The transition to AI-driven search is at a crossroads, with questions about whether new AI search engines can overcome cost and technical barriers, and whether traditional giants can successfully adapt to AI [4] - The ruling is considered one of the most impactful court decisions in the tech industry this century, providing a reference for other companies facing antitrust scrutiny, such as Meta, Amazon, and Apple [4]

Core Insights - The customer service team at Pinduoduo plays a crucial role in maintaining user experience and resolving transaction disputes, but they face significant pressure, especially during peak promotional periods [1][3][5] Group 1: Sources of Pressure on Customer Service - The volume of inquiries surges geometrically during promotions and new product launches, overwhelming the customer service team [3] - A large proportion of customer inquiries consist of repetitive, standardized questions, leading to inefficiencies and potential burnout among staff [4] - Customer service representatives often bear the brunt of negative emotions from dissatisfied users, requiring strong emotional management skills [5] - The rapid changes in platform rules and product information necessitate continuous learning, adding to the workload and stress of customer service personnel [6] Group 2: Role of Intelligent Customer Service Agents - Intelligent Customer Service Agents (AI) are emerging as a key solution to alleviate the pressures faced by human customer service representatives [6] - These AI agents can operate 24/7, effectively handling a large volume of simple inquiries, especially during peak times, allowing human agents to focus on more complex issues [7] - AI agents serve as intelligent assistants, providing standardized responses to frequently asked questions, thus freeing human agents from repetitive tasks [9] - Advanced AI agents possess emotional analysis capabilities, enabling them to identify and manage user emotions, which helps mitigate the emotional burden on human agents [9] Group 3: Human-Machine Collaboration - The goal of intelligent customer service agents is not to replace human agents but to work collaboratively, enhancing overall service quality and efficiency [8] - By filtering out low-value inquiries and providing real-time support, AI agents enable human representatives to handle more sensitive and complex issues with greater confidence [9] - The integration of AI in customer service represents a future direction for e-commerce platforms, improving user experience and operational efficiency [8][9]

Group 1 - Elon Musk believes that approximately 80% of Tesla's future value will come from the Optimus robot [2] - The mission of the Optimus robot is to liberate human labor by taking over tedious or dangerous jobs, with plans for commercialization by 2026 [2][3] - Market sentiment is mixed, with a prediction that the likelihood of Optimus being launched before 2027 is only 40% according to Kalshi [3] Group 2 - The humanoid robot industry integrates advanced technologies from mechanical engineering, electronics, computer science, and artificial intelligence [3] - The Chinese humanoid robot market is projected to reach approximately 2.76 billion yuan in 2024, with significant growth expected by 2027 [4] - Global humanoid robot shipments are expected to reach 38,000 units by 2030 according to Qianzhan Industry Research Institute [5] Group 3 - Major tech companies and startups are actively pursuing mass production of humanoid robots, despite challenges such as high R&D costs and market acceptance [7] - The development of humanoid robots is expected to bring new productivity and lifestyle changes to society as technology advances and market demand grows [7]