Group 1 - The core viewpoint of the article emphasizes that the future of AI lies in the development of Agents, which can autonomously interact with data and tools, driving innovation across various sectors [6][8]. - Dario Amodei's article "Machines of Loving Grace" highlights that humanity has underestimated both the benefits and risks of AI, necessitating a focus on risk management for a positive future [7]. - The discussion indicates that while traditional companies and AI firms must collaborate for effective market implementation, the adaptation of lagging economic sectors to these innovations is crucial [7][8]. Group 2 - Data is deemed irreplaceable, with Dario Amodei asserting that it embodies the knowledge and wisdom accumulated by enterprises, essential for fine-tuning AI models [10]. - Ali Ghodsi emphasizes that proprietary data is central to building competitive barriers, particularly industry-specific data that is critical for training AI models [10]. - The conversation also touches on the importance of data governance and the need for tools like Unity Catalog to manage data risks effectively [8][9]. Group 3 - The article discusses the rapid iteration of AI applications, suggesting that breakthroughs in product development hinge on overcoming key gaps in Agent product iteration [4]. - Both Amodei and Ghodsi express optimism regarding the "Scaling Law," indicating that practical applications require optimization beyond pre-training, while also addressing issues of data depletion and cost [9]. - The integration of MCP protocols is highlighted as a means to enhance the use of external data resources in AI tools [8].

【导读】历经八年 AI 浪潮，从感知到生成，再到智能体时代，人工智能正以惊人速度演进。CSDN 高级副总裁、Boolan 首席技术专家李建忠，在 2025 全 球机器学习技术大会上，绘制了一幅宏大的 AI 发展蓝图，并创造性地将其与生物智能演化史进行对比，揭示了"语言"在智能跃迁中的核心地位。跟随李建 忠的思考，洞见 AI 的过去、现在与激动人心的未来。 作者 | 李建忠 出品丨AI 科技大本营（ID：rgznai100） 大家好！回想起我在 2017 年创办全球机器学习技术大会（ ML-Summit ），在各位的支持下一起陪着 AI 一路走了八个年头，非常感慨。八年来，整个 人工智能领域也发生了波澜壮阔的变化。接下来我想和大家分享一下我对大模型最新发展的一些研究和思考。 我把 AI 的发展阶段和地球上从生物智能到人类智能的发展阶段做了一个对比，发现一些非常有意思的规律。大家首先来看 AI 发展的四个阶段。 第一阶段： 1940 年代开启人工智能的元年， 整个人工智能从 1940 年代图灵提出计算机理论模型和神经网络的初始构想，到 1956 年达特茅斯会议首 次提出人工智能，此后人工智能进入符号主义、行为主义 ...

Core Insights - The conversation emphasizes the importance of sustainable value in the next generation of AI, highlighting the confusion and uncertainty that often accompany major technological shifts [3][4] - Kevin Scott argues that the current era is the best time for entrepreneurs, advocating for active exploration and product development rather than passive observation [5] - The discussion also touches on the balance of value creation between startups and established companies like Microsoft, suggesting that both can benefit from new AI capabilities [6][7] Group 1: AI Value and Product Development - Kevin Scott believes that while models are valuable, their worth is realized only when connected to user needs through products [6] - The conversation stresses that product quality is paramount, and that successful exploration requires rapid iteration and responsiveness to data and feedback [5][6] - The scaling law in AI is not seen as having a limit currently, with Scott asserting that AI capabilities will continue to expand [8] Group 2: Data and Efficiency - The importance of high-quality data is highlighted, with synthetic data becoming increasingly significant in model training [9][10] - There is a noted gap in the ability to evaluate the impact of specific data on model performance, indicating a need for better assessment tools [9][10] Group 3: Future of AI Agents - The future of AI agents is discussed, with expectations for improved memory and task execution capabilities, allowing them to handle more complex tasks autonomously [21][22] - The interaction model between humans and agents is expected to evolve, moving towards more asynchronous operations [22] Group 4: Industry Dynamics and Trends - The conversation reflects on the dual existence of open-source and closed-source solutions in AI, suggesting that both will coexist and serve different needs [15] - The role of engineers and product managers is expected to change, with a greater emphasis on specialization and collaboration with AI agents [18][19] Group 5: AI's Impact on Technical Debt - Kevin Scott expresses optimism that AI can help mitigate technical debt, transforming it from a zero-sum problem to a non-zero-sum opportunity [31] - The potential for AI to accelerate product development and reduce the burdens of technical debt is seen as a significant advantage [30][31]

智能产业新媒体！智东西专注报道人工智能主导的前沿技术发展，和技术应用带来的千行百业产业升级。聚焦智能变革，服务产业升级。 在 GPT-4.5 发布 1 个多月后，Sam Altman 与 GPT-4.5 的 3 位核心技术人员进行了一场 45 分钟的高信息量对谈，首次披露了这款模型 研发耗时严重超 期 、 计算集群频繁故障 、 提升路径难以预测 等诸多不为人知的细节。 对于今后的模型训练范式，乃至如何重新理解 Scaling Law、以及数据效果，都有不少启发。 参与本次对谈的 3 位 OpenAI 员工分别为 Alex Paino（负责 GPT-4.5 的预训练机器学习算法）、Amin Tootoonchian（OpenAI 首席系统架构师）与 Daniel Selsam（研究数据效率与算法）。 以下文章来源于智东西 ，作者陈骏达 陈家阳 智东西 . TLDR Founder Park 正在搭建开发者社群，邀请积极尝试、测试新模型、新技术的开发者、创业者们加入，请扫码详细填写你的产品/项目信息，通过审核后 工作人员会拉你入群～ 进群之后，你有机会得到： 01 GPT-4.5两年前已启动， 项目耗时远超预期 ...

Core Viewpoint - The article discusses the advancements in AI technology, particularly focusing on the new AI Agent product "AutoGLM沉思" developed by 智谱, which aims to enhance the capabilities of AI in understanding and executing tasks based on natural language queries [3][4][17]. Group 1: Product Development and Features - "AutoGLM沉思" is an autonomous AI agent capable of exploring open-ended questions and executing operations based on the results, simulating human thought processes [4][5]. - The product can access various non-public APIs and has multi-modal understanding capabilities, allowing it to comprehend both text and images on web pages [5][6]. - A case study demonstrated that "沉思" could effectively manage a 小红书 account, gaining 5,000 followers in two weeks by summarizing popular topics from multiple sources [6][8]. Group 2: Comparison with Competitors - Compared to "Manus," which focuses on action and tool utilization, "沉思" emphasizes the thought process, showcasing its reasoning capabilities [9][10]. - "沉思" is currently a preview version that can perform tasks like research organization but is not yet fully operational for end-users [12][15]. - The new models released by 智谱, including GLM-Z1-Air, have significantly improved inference speed while reducing costs, indicating a competitive edge in the market [18]. Group 3: Strategic Insights and Future Directions - The CEO of 智谱 emphasized the importance of pre-training models, suggesting that future applications will revolve around model capabilities rather than just product interfaces [20]. - The company is exploring the concept of a "沉思大模型," which aims to enhance AI's real-time search, dynamic tool usage, and self-validation capabilities [17][20]. - The article highlights the need for AI agents to overcome current limitations in intelligence to avoid being blocked by third-party platforms, indicating ongoing challenges in the industry [25].

备受瞩目的 2025 全球机器学习技术大会（ML Summit 2025）将于 4 月 18-19 日在上海虹桥西郊庄园丽笙大酒店召开。本次盛会由 CSDN & Boolan 联合主办，汇聚了超 50 位来自学术界和工业界顶尖专家，共同探讨智能体、联邦学习、多模态大模型等热门 AI 技术实践。 作为全球机器学习技术大会的老朋友，新浪微博首席科学家及 AI 研发部负责人张俊林将带来《从 DeepSeek R1 的复现看深度思考模型的未来》的精 彩分享。 张俊林作为「大模型技术拆解得最通透的实战派」，在 2024 年的机器学习技术大会上，他对 Gemini 多模态架构、OpenAI o1 技术的硬核拆解，让 开发者直呼"终于有人讲透技术本质"。 系统梳理技术脉络： 回顾 DeepSeek R1 开源后的各类复现研究，涵盖 SFT 阶段的轻量适配（如 S1）与 RL 阶段的创新实践。 深度解析训练范式： 重点剖析其核心的两阶段训练模式——如何通过冷启动微调结合多领域数据优化进行 SFT，以及如何运用 GRPO 强化学习 与全场景对齐实现模型"深度思考"能力的跃迁。 探讨关键技术问题： 尝试解答一系列备受关注的核心问 ...

衡宇 李根 发自上海 量子位 | 公众号 QbitAI 可问题是这都已经2025年了……最早出发的具身智能创业者，在3年前的时间点已经下水。进展快速的具身智能公司，也已经开启场景验证和 落地。以及具身智能领域，也从不缺天才和大牛创业者。 还有什么样的创业团队，凭什么在此时此刻搅动如此风云？ 一位知情人士说，核心原因是团队豪华，堪称 梦之队 ，而且还是有过硬科技完整落地经验的工程派。也有人拿NBA篮球类比， "库里和约基 奇联手组了队，联盟大结局" ——库里是三分外线第一人，约基奇则被视为最全能的内线中锋，而这家公司背后的核心人物也是 两位自动驾 驶领域的第一人 。 据说这两人联手创业的进展传出后，获得了这样的评价： 陈亦伦带队，牛了；李震宇坐镇，稳了。 他们在上海，组建战队，取名 它石智航 TARS ，竞逐具身智能的GPT时刻。 他们创业的消息，实际流传已久，但现如今随着创纪录的1.2亿美元天使融资曝光，再也藏不住了。 中国具身智能最壕天使轮融资 它石智航（TARS） 官宣的新进展是这样的： 完成天使轮1.2亿美元融资，开启具身智能创业新征程。本轮融资由蓝驰创投、启明创投联合领投，线性资本、恒旭资本、洪泰基 ...

以下文章来源于CSDN ，作者万有引力 CSDN . 成就一亿技术人 作者 | 万有引力 出品 | CSDN（ID：CSDNnews） 在过去短短的几周里，大模型赛道的信息密度飙升至前所未有的高度。DeepSeek 连续 五天开源 ，直接引发了一场复现热潮；阿里巴巴通义实验室、 腾讯相继推出面向视觉文档的 RAG 系统 ViDoRAG、新一代混元快思考模型 Turbo S ，加速了大模型的演进步伐；马斯克用 20 万张 GPU 训练出的 Grok 3 ，超越了许多业界标杆，再次验证了"大力出奇迹"的定律； Claude 3.7 Sonnet 迎来编码能力大升级，AI 编程的技术平权时代正在加速到来； DeepSeek 论文与 Kimi"撞车"，越来越多公司开始布局稀疏注意力与线性注意力机制，这些技术正成为 Transformer 之后的关键探索方向；此外， Manus 模式的"虚拟机"概 念迅速走红，正在重塑大模型的运行方式... 在这场眼花缭乱的技术竞赛背后，真正值得我们关注的是什么？DeepSeek 的五连发 究竟意欲何为？在 545% 的成本利润率之下，其他大模型公司是 否也能找到盈利空间？面对行业变 ...

Investment Rating - Electronic Industry: Buy (Maintain) [6] - Communication Industry: Overweight (Maintain) [6] - Computer Industry: Buy (Maintain) [6] Core Insights - NVIDIA introduced the concept of Agentic AI, which represents a new reasoning paradigm that will continue to drive global data center construction. This evolution is categorized into three stages: Generative AI, Agentic AI, and Physical AI [12][13] - The global investment in data center construction is expected to reach $1 trillion by 2028, driven by the need for larger computational resources and data for training better models [2][17] - The Blackwell Ultra chip, designed for AI inference needs, will be supplied in the second half of 2025, with significant performance improvements over its predecessor [20][22] - NVIDIA's new AI inference service software, Dynamo, aims to maximize token yield in AI models and supports the development of AI agents [33][35] Summary by Sections 1. Agentic AI and Data Center Development - The introduction of Agentic AI is seen as a pivotal shift in AI technology, emphasizing autonomy and complex problem-solving capabilities [12][13] - The Scaling Law remains relevant, as it will expand to include inference and long-term reasoning, requiring substantial computational resources [14][17] 2. Blackwell Ultra Chip and Future Releases - The Blackwell Ultra chip will enhance AI performance significantly, with a 1.5 times improvement in AI capabilities compared to the previous generation [22] - The Vera Rubin series is expected to launch in 2026, featuring advanced architecture and enhanced memory capacity [22][23] 3. Quantum-x CPO Switch Launch - NVIDIA plans to release the 115.2T 800G Quantum-x CPO switch in the second half of 2025, which will offer substantial improvements in energy efficiency and network resilience [26][29] 4. Introduction of Dynamo and AI Frameworks - Dynamo will facilitate efficient AI inference by optimizing GPU resource utilization across different processing phases [33][35] - NVIDIA also introduced the AI-Q framework to enhance AI agents' reasoning capabilities and reduce development costs [37] 5. Investment Recommendations - The report suggests focusing on companies within the electronic communication and computer industries that are positioned to benefit from the advancements in AI and data center infrastructure [45][46] - Specific companies to watch include those involved in AI computing, robotics, and data platforms, highlighting a diverse range of investment opportunities [46][47]

"更好的模型可以导向更好的应用，但更好的应用和更多用户并不会导向更好的模型。" 文丨程曼祺 * 头图：Dota 2019 国际邀请赛决赛（TI9）中，OG 战队的 Ana 使用 IO（小精灵，图中球形发光体）的经典作战，OG 在 TI9 中夺冠。为什么用这个图？播客里有 答案。 ▲扫描上图中的二维码，可收听播客。《晚点聊 LateTalk》#99 期节目。欢迎在小宇宙、喜马拉雅、苹果 Podcast 等渠道关注、收听我们。 《晚点聊 LateTalk》是《晚点 LatePost》 推出的播客节目。"最一手的商业、科技访谈，最真实的从业者思考。" 上周四，我们发布图文访谈：《 晚点对话 MiniMax 闫俊杰：千万别套用移动互联网的逻辑来做 AI 》，这是这次访谈的音频版。 闫俊杰的一些 "非共识" 判断，引起不少讨论。 他认为，模型能力和用户规模并不是直接的飞轮关系："更好的模型可以导向更好的应用，但更好的应用和更多用户并不会导向更好 的模型。" 而今天（1 月 22 日）字节跳动发布 Doubao-1.5-pro 模型的技术报告里则提到："依托字节在推荐、搜索和广告领域的 AB Test 经 验，研发了基于 ...