以生成式AI为代表的新技术浪潮日新月异，正带来一场深刻的技术、商业与社会变革，推动人类社会 从信息社会向智能社会转变。全世界热切期待AI到来的同时，也非常关心人工智能将带来哪些新机 遇、新挑战。 为此，我们发起了一项《AI & Society 百人百问》研讨，广泛邀请AI技术大咖、AI独角兽创始人、AI投 资人，以及社会学家、心理学家、国际关系专家、科幻作家等，用多元视角，深入研讨人工智能技术引 发的广泛影响，发掘AI时代的共识和非共识，共同推动人工智能始终朝着"助人发展，与人为善"的方向 可持续发展。 4.我们业界一直有一个基本的观点就是模型算法或架构是模型的下限，而数据决定模型的上 限。从行业来看，央国企等大甲方的主要职责是要能够把自己行业数据整理好和清洗好，这 是发展行业AI的关键。 5.具身智能ToC端的应用的核心是感性能力，如果将来机器人真的要走进千家万户，它必须 要能够跟我们共情，能够理解我的情感诉求，才有可能真正在 ToC 应用当中发挥作用。 6.某种程度上，我们今天采集的数据离具身智能涌现出泛化性所需达到的临界点，还相差非 常大的数量级，相较于语言大模型可能不止两三个数量级的差别。促进具身智能 ...

徐一平 腾讯研究院 高级研究员 王强 腾讯研究院 资深专家 以生成式AI为代表的新技术浪潮日新月异，正带来一场深刻的技术、商业与社会变革，推动人类社会从 信息社会向智能社会转变。全世界热切期待AI到来的同时，也非常关心人工智能将带来哪些新机遇、新 挑战。 为此，我们发起了一项 《AI & Society 百人百问》 研讨，广泛邀请AI技术大咖、AI独角兽创始人、AI 投资人，以及社会学家、心理学家、国际关系专家、科幻作家等，用多元视角，深入研讨人工智能技术 引发的广泛影响，发掘AI时代的共识和非共识，共同推动人工智能始终朝着"助人发展，与人为善"的方 向可持续发展。 本期，我们非常荣幸地邀请到肖仰华老师，为我们开启一次AI的思想远航。 复旦大学计算与智能创新学院教授、博导，上海科学智能研究院AI科学家，上海市数据科学重点实验室主任。长期从事大数据、 认知智能研究。 曾荣获ICDE2024十年影响力论文奖、ACL2023杰出论文奖。发表 CCF-A 、B 类等论文 300 余篇。出版学术专 著与教材三部。曾获华为、阿里、美团等机构的科研奖项。担任 Applied Intelligence 等多个国际期刊副主编或 ...

" 未来 5 到 10 年内，我们有 50% 的概率实现通用人工智能（AGI）。 " Google DeepMind CEO、 诺贝尔化学奖得主 Demis Hassabis 在本月初的一个采访中给出了惊人的预测。 AI 浪潮正以前所未有的速度加速进行中，在这条被追捧、被加速、甚至被神化 的 AGI 之路上，我们是否也踩过无数"看似正确却最终失败"的岔路？ 早在 2019 年，"强化学习之父"Richard Sutton 发表了著名的《The Bitter Lesson》（苦涩的教训）一文，他指出：过去 70 年，AI 研究最大的弯路 就是我们太依赖人类已有的知识与直觉，而历史一再表明，真正能带来突破的，反而是那些"摒弃人类经验、依靠计算规模"的方法。 在 Sutton 看来，算力与数据才是推动 AI 向前的根本引擎，而不是人类引以为傲的"智慧"本身。 这句话听起来也许刺耳，却道出了一个时代的真相——在这场智能革命中，许多我们曾深信不疑的"路径"与"经验"，正在变成新纪元的障碍。 这也正是我们本期《万有引力》直播对话的主题——「 通往 AGI 之路的苦涩教训」 。 6 月 27 日 12:00-14:00，C ...

Core Viewpoint - The report from CITIC Securities indicates that the demand for AI large model training and inference is continuously growing, with system-level computing expected to become the next generation of AI computing infrastructure [1] Group 1: System-Level Computing - System-level computing is anticipated to become the next generation of AI computing infrastructure, driven by the need for generality in foundational infrastructure to address future model developments [1] - The scaling law is rapidly evolving in post-training and online inference stages, with innovations in model architecture enhancing training capabilities [1] - The focus on hardware deployment for achieving higher throughput and lower latency in inference is becoming critical, with a shift towards cluster-based inference models [1] Group 2: Technical Aspects - The development of single-chip computing capabilities is outpacing advancements in communication technology, making communication efficiency a key factor for cluster performance [3] - Two primary methods for building large clusters are identified: Scale up (increasing resources per node) and Scale out (increasing the number of nodes), with Scale up being a significant future direction [3] - Notable examples include NVIDIA's NVL72 system and Huawei's CloudMatrix384 super node, which provide insights into industry development [3] Group 3: Industry Dynamics - The semiconductor industry typically utilizes mergers and acquisitions for technology integration and market expansion, with leading companies often pursuing these strategies to enhance their market position [4] - NVIDIA's acquisition of Mellanox exemplifies this strategy, expanding its NVLink technology to include RDMA networks for large-scale computing [4] - AMD's acquisition of ZT Systems has strengthened its system architecture design capabilities and data center solution delivery experience, contributing to the core of AI solutions [4][5]

Core Insights - Kimi, once a prominent player in the AI space, has seen a decline in attention as newer models from companies like Quark, Tencent, and Alibaba gain traction [1][2] - The initial hype around Kimi was driven by its technological scarcity, particularly its long-text processing capabilities, which were unmatched at the time [2][3] - Kimi's early valuation of $3 billion was supported by its unique technology, the founder's impressive background, and the capital's anxiety to find a domestic alternative to leading AI models [4][5] Technology and Market Position - Kimi's long-text processing ability, which expanded from 200,000 to 2 million words, was a significant technological breakthrough that positioned it as a leader in the AI field [2][3] - The founder, Yang Zhilin, had a strong academic and entrepreneurial background, which enhanced investor confidence in Kimi's potential [3][4] - The competitive landscape was characterized by a rush to find alternatives to ChatGPT, leading to Kimi's rapid user acquisition through aggressive marketing strategies [4][5] Financial Strategy and User Acquisition - Kimi faced challenges in managing its newfound capital, leading to excessive spending on user acquisition, with monthly advertising costs peaking at 220 million RMB [6][7] - Despite a significant increase in daily active users (DAU) from 508,300 to 5,897,000, this growth was primarily driven by financial investment rather than product quality [8][9] - The pressure from investors to demonstrate commercial viability led Kimi to prioritize user numbers over technological development, resulting in a loss of strategic direction [8][9] Challenges and Strategic Missteps - Kimi's marketing strategy shifted focus from its core user base in academia and professional fields to entertainment sectors, diluting its brand identity [11][12] - The company struggled with maintaining its technological edge as competitors began to catch up, particularly with the emergence of open-source models [12][13] - Kimi's reliance on user growth without a solid feedback loop or data quality management led to a false sense of security regarding its market position [13] Future Opportunities - Kimi has potential avenues for recovery, including enhancing the value density of its products and focusing on deep search capabilities for specific industries [15][17] - The company could benefit from developing comprehensive tools for developers, improving its API offerings to facilitate easier integration for enterprise clients [18][19] - Emphasizing quality over quantity in user engagement and product offerings could help Kimi regain trust and market relevance [20][21] Strategic Recommendations - Kimi needs to establish a clear commercial strategy from the outset, ensuring that its products meet genuine market demands and have viable monetization paths [29][30] - The focus should shift towards building a sustainable revenue model based on user payments rather than relying on external funding for growth [31] - A strategic approach that prioritizes understanding and fulfilling real user needs will be crucial for Kimi's long-term success in the competitive AI landscape [31][32]

Core Viewpoint - The article discusses the rapid rise of large model technology globally, emphasizing Alibaba's Tongyi Qwen model's international success and its strategic focus on multilingual capabilities to cater to a global audience [2][3]. Group 1: Multilingual Strategy - Tongyi Qwen supports 119 languages, with a core strategy prioritizing multilingual data optimization from the outset to ensure equitable access to AI technology for global users [2][3]. - The team has developed a complex cultural annotation system to address the challenges of multilingual safety and cultural alignment, covering thousands of detailed categories to ensure compliance and effectiveness across different regions [3][12]. - The current industry faces a "multilingual reasoning challenge," where models often mix languages during processing, leading to inconsistencies. The team has adopted a compromise strategy to use native languages for strong languages and English for low-resource languages to maintain output stability [3][16]. Group 2: Scaling Law and Knowledge Density - The article highlights the importance of scaling model size and data volume while also focusing on increasing "knowledge density," which refers to the concentration of useful knowledge within the training data [19][20]. - Recent trends show that smaller models with higher knowledge density can outperform larger models, indicating a shift in focus from merely increasing data volume to refining data quality [20][21]. - The team is exploring data synthesis methods to enhance training data quality, which includes generating new knowledge and filtering redundant data to improve knowledge density [22][23]. Group 3: AI Integration and Future Prospects - The integration of AI models into various devices, such as smart glasses and earphones, is a growing trend, with the company planning to release smaller model versions optimized for these applications [28][30]. - The article discusses the potential for AI to enhance user experiences in everyday tasks, such as real-time translation and contextual assistance, although challenges remain in achieving seamless integration [30][32]. - The company acknowledges the importance of balancing the use of synthetic data with human-generated content to maintain diversity and avoid narrowing the model's knowledge base [25][26].

创业者怎么更有可能成功？ Sam Altman和YC创始人的那场对话里，给了一些关键信息，他说：剧烈变化，总是给新公司带来最多 机会。 这句话听起来简单，背后藏着一个很深刻的逻辑： 当整个行业、整个社会都处在剧变期时，原来的规则会被打破，大玩家可能变得迟钝；反应不过来，小 团队、新创公司，更有机会快速切入，做出别人没做过的事。 变化体现在哪儿呢？首先，AI技术正在发生质的飞跃。 过去几年，AI发展不是线性的进步，而是指数级的突破。像AGI（通用人工智能）和HI（高智能水平的 AI）这些概念，十年前听起来是科幻小说里的东西，但现在，它们正一步步走向现实。 OpenAI是典型的例子。 2015年它刚成立时，很多人觉得这个目标太疯狂了，谁会相信一台机器能像人一样思考和创造？可到了 今天，GPT系列模型已经成了全球第五大网站，每天服务上亿用户。 技术拐点已经到来。就像互联网刚出现的时候，谁能想到后来会诞生亚马逊、谷歌、Facebook这些巨 头？现在的AI，就是当年的互联网。 Sam说，在稳定时期，市场格局是固定的。大公司有资源、有渠道、有品牌，新创公司很难突围。但在 技术剧变期就不一样了。 举个例子： 一 以前做语 ...

Core Viewpoint - The article discusses the rise and challenges faced by Kimi, an AI company, highlighting the impact of FOMO (Fear of Missing Out) on its growth and subsequent issues, including a shift in investor sentiment and operational strategies [10][22]. Group 1: Company Overview - Kimi has transitioned from a promising AI startup to facing significant challenges, including a decline in its competitive edge and user growth [7][22]. - The company was once valued at $30 billion, largely due to FOMO-driven investments, particularly from Alibaba, which invested nearly $800 million [14][15]. Group 2: Business Strategy and Challenges - Kimi's aggressive user acquisition strategy involved significant spending on marketing, reminiscent of past failed models like ofo bike-sharing [16][17]. - The reliance on the "Scaling Law" and "data flywheel" theories has been criticized, with experts suggesting that merely increasing data and computational power does not guarantee improved model performance [18][20]. Group 3: Market Dynamics and Future Outlook - The AI landscape is shifting, with new models challenging existing paradigms, indicating a need for Kimi to adapt its technological approach [21]. - Kimi's recent controversies, including arbitration cases and ethical concerns, have severely impacted its ability to secure further funding, particularly from state-owned enterprises [22][23].

Core Insights - The pursuit of "superintelligence" represents a significant ambition among leading AI companies like Meta, OpenAI, and Google DeepMind, with substantial investments being made in this direction [1][3][4] - Sam Altman of OpenAI suggests that building superintelligence is primarily an engineering challenge, indicating a belief in a feasible path to achieve it [3][4] - Meta AI researcher Jack Morris argues that the current approach of using large language models (LLMs) and reinforcement learning (RL) may not be sufficient to construct superintelligence [1][2] Group 1: Current Approaches and Challenges - Morris outlines three potential methods for building superintelligence: purely supervised learning (SL), RL from human validators, and RL from automated validators [2] - The integration of non-text data into models is believed not to enhance overall performance, as human-written text carries intrinsic value that sensory inputs do not [2][6] - The concept of a "data wall" or "token crisis" is emerging, where the availability of text data for training LLMs is becoming a concern, leading to extensive efforts to scrape and transcribe data from various sources [8][19] Group 2: Learning Algorithms and Their Implications - The two primary learning methods identified for potential superintelligence are SL and RL, with SL being more stable and efficient for initial training [10][22] - The hypothesis that superintelligence could emerge from SL alone is challenged by the limitations of current models, which may not exhibit human-level general intelligence despite excelling in specific tasks [15][16] - The combination of SL and RL is proposed as a more viable path, leveraging human feedback or automated systems to refine model outputs [20][22][28] Group 3: Future Directions and Speculations - The potential for RL to effectively transfer learning across various tasks remains uncertain, raising questions about the scalability of this approach to achieve superintelligence [34] - The competitive landscape among AI companies is likely to intensify as they seek to develop the most effective training environments for LLMs, potentially leading to breakthroughs in superintelligence [34]

机器之心报道 编辑：Panda 400 多年前，宋应星著成《天工开物》。这是一部写给匠人、也写给未来的书。它让人相信：技术不是死物，而是人与世界持续互动的方式。 如今，代码系统早已成为现代文明的骨架。它们运行在日常软件、银行服务、交通调度等各式系统中，也支撑着我们所依赖的 AI 算法本身。但和古代器物一样， 再精妙的程序也难免出现 bug—— 有些是逻辑失误，有些是环境变迁，有些甚至源于协作失控。比如，就在前几天，AWS、谷歌云、Azure 和 Cloudflare 都发生了 中断，连带着 ChatGPT 和 Cursor 等热门 AI 应用也一并短暂失联；而这一事故的原因可能是一次错误的自动配额更新导致谷歌的 API 管理系统出现了故障。 同时，bug 修复也是软件工程中最基础，却也是最复杂、最消耗人力的任务之一。特别是在真实的 GitHub 项目中，修一个 bug 并不是「找到一行错字那么简 单」，它常常需要： 那么，我们能否使用 AI 智能体来完成这些任务呢？ 当然可以！但我们需要的绝不是传统的用于解决单独编程任务的 AI 编程模型，而是需要像人类开发者一样能够理解历史上下文、进行多轮推理、在模糊与不确 ...