专题：2025《中国企业家》影响力企业家年会 12月5日-7日，由《中国企业家》杂志社主办的"2025（第二十三届）《中国企业家》影响力企业家年 会"（原中国企业领袖年会）在北京举行，主题为"涌现·无限——共创智能商业新形态"。峰瑞资本创始 合伙人李丰出席并演讲。 作为企业家，要不要今天开始和大家一样赶紧用智能体？李丰回答称，需要先评价一下企业内部和行业 链条中数字化水平是不是比较高了。"如果不高，也许还要再等一等。如果企业自身数字化水平很高 了，也许内部可以用一些智能体。" 他提到，这一轮AI是从大语言模型开始的，数据来自于过去超过40年互联网公开文本数据的积累，才 喂出了今天的大语言模型。 除了企业外，李丰表示，大语言模型最适用的垂直智能体场景，就是在任何商务过程和价值实现过程 中，作为自然语言作为交互方式，进行多轮对话，且实现了价值兑现，这样的行业最容易使用和利用到 AI智能体的垂直场景。 他举例到，比如金融行业，全链条数字化，主要靠专业技术和专业技能进行对话，告诉客户投资原因， 如何选金融产品，风险和潜在收益是什么。还有医疗行业，医生会用数字化设备检测身体，告诉患者如 何预防疾病等。这些行业都是最容易 ...

Core Insights - The focus of academia and industry is shifting towards VLA (Vision-Language-Action) for enhancing autonomous driving capabilities, providing human-like reasoning in vehicle decision-making processes [1][4] - Traditional methods in perception and lane detection are becoming mature, leading to a decline in interest, while VLA is seen as a critical area for development by major players in the autonomous driving sector [4][6] Summary by Sections Introduction to VLA - VLA is categorized into modular VLA, integrated VLA, and reasoning-enhanced VLA, which are essential for improving the reliability and safety of autonomous driving [1][4] Course Overview - A comprehensive course on autonomous driving VLA has been designed, covering foundational algorithms and practical applications, aimed at deepening understanding of the perception systems in autonomous driving [6][21] Course Structure - The course consists of six chapters, starting with an introduction to VLA algorithms, followed by foundational knowledge in Vision, Language, and Action, and culminating in practical assignments [11][19] Chapter Highlights - Chapter 1 provides an overview of VLA algorithms and their development history, along with benchmarks and evaluation metrics [12] - Chapter 2 focuses on the foundational algorithms related to Vision, Language, and Action, including deployment of large models [13] - Chapter 3 discusses VLM (Vision-Language Model) as an interpreter in autonomous driving, covering classic and recent algorithms [14] - Chapter 4 delves into modular and integrated VLA, emphasizing the evolution of language models in planning and control [15] - Chapter 5 explores reasoning-enhanced VLA, introducing new modules for decision-making and action generation [16][18] Practical Applications - The course includes hands-on coding exercises, allowing participants to engage with real-world applications of VLA technologies, such as ReCogDrive and Impromptu VLA [15][18] Learning Outcomes - Participants are expected to gain a thorough understanding of current advancements in VLA, master core algorithms, and apply their knowledge to projects in the autonomous driving field [23][21]

当地时间12月8日，美国总统特朗普在社媒平台上称，在确保美国国家安全的前提下，将"允许英伟达向 中国及其他国家的合格客户交付其H200芯片产品"，但会对每颗芯片收取一定费用。 特朗普还表示，美国商务部正在敲定具体细节，同样的方案也将适用于超微半导体、英特尔以及其他美 国公司，美方将从相关芯片出口中收取25%的分成。在特朗普宣布这一消息后，英伟达股价在盘后交易 中上涨了1.2%。 对此，英伟达方面向《中国经营报》记者确认了该消息，并表示："向商业客户供应H200是一种值得肯 定的举措。" 据了解，英伟达H200芯片发布于2023年11月，2024年第二季度开始供货，核心升级在于全球首创的 141GB HBM3e内存系统，使处理超大模型的能力实现质的飞跃。 相比H100，英伟达H200的内存容量提升76%，带宽增加43%，AI推理性能提升最高90%，特别适合大语 言模型和科学计算。由于市场波动和销售渠道不同，英伟达H200的当前市场报价主要集中在20万至25 万元区间（约合2.8万至3.5万美元），而包含多块H200 GPU、服务器、网络和冷却基础设施在内的整体 H200整机系统（如DGX H200）售价可能超过 ...

然而，据克里希纳测算，仅建设一座1吉瓦的数据中心，按最新美元价值计算，就需要投资约800亿美 元。如果一家企业承诺建设20到30吉瓦的数据中心，其资本支出将高达1.5万亿美元——几乎相当于特 斯拉（Tesla）当前的市值。 他估计，若所有超大规模云厂商合计扩建至约100吉瓦的容量，也需要约8万亿美元的投资，而要覆盖这 笔投入所需的利润规模更是惊人。 克里希纳表示："在我看来，这类投资绝无可能获得回报。因为8万亿美元的资本支出意味着仅支付利息 就需要约8,000亿美元利润支撑。" 此外，由于技术快速迭代，数据中心所依赖的芯片会很快过时。 他强调称："你必须在五年内充分利用所有设备，因为五年之后，你就得把整套设备淘汰，并重新采 购。" 克里希纳补充说，这股投资热潮的部分动机，源于科技巨头竞相成为首个实现通用人工智能（即能够匹 敌或超越人类智能的AI）的企业。 但在他看来，尽管大语言模型的性能持续提升，以现有技术实现通用人工智能的概率"最多只有1%"。 尽管谷歌（Google）、亚马逊（Amazon）等科技巨头高调宣扬其在AI基础设施领域投资数百亿美元， 但IBM首席执行官阿文德·克里希纳却质疑这些投资难以获得预期 ...

Core Insights - OpenAI CEO Sam Altman has declared a "red alert" status due to increasing competition from Google and other AI rivals, particularly following the release of Google's Gemini 3 model [2][6] - The competitive landscape has shifted, with Google now posing a significant threat to OpenAI's ChatGPT, which previously led the market [5][6] Group 1: Competitive Landscape - Google has launched its Gemini 3 model, which has been integrated into its vast ecosystem, achieving 650 million monthly active users in October [4] - Altman acknowledged the need for OpenAI to improve ChatGPT significantly, indicating that the company is under pressure to respond to Google's advancements [6] - The AI race has intensified, with OpenAI needing to secure additional funding of $100 billion while also increasing subscription revenue to meet investor expectations [5] Group 2: Historical Context - Google was once considered the leader in AI research, having developed foundational technologies like the Transformer architecture and the BERT model [4] - The emergence of ChatGPT marked a pivotal moment, shifting the focus of AI development and forcing Google to defend its position in the market [5] - Altman's internal memo suggests that OpenAI employees may need to cancel winter plans to focus on improving ChatGPT, reflecting the urgency of the situation [6]

但他指出，大语言模型缺乏身体这一核心短板，导致其无法提供人类沟通所需的性别、年龄、地域等基 础变量，难以建立稳定信任关系。为此，虚拟数字人、实体机器人相继出现，这一变革对人类沟通模式 产生了深远挑战。 专题：复旦大学"对话"系列讲座：AI与大学之未来 复旦大学"对话"系列讲座"AI与大学之未来"于12月8日举行。复旦大学新闻学院教授邓建国在演讲中表 示，未来人机共生是必然趋势，大学应跳出传统显性知识传授的框架，重点培育元知识、默会知识与实 践型知识，同时强化人类独特的共情与聚生能力，以应对沟通形态变革带来的挑战。 邓建国首先解析了人工智能的发展根基：芯片、数据、算法三大元素，在摩尔定律的驱动下，借助移动 传感器产生的海量数据与强大芯片的分析能力，催生了如今的大语言模型。 专题：复旦大学"对话"系列讲座：AI与大学之未来 "即便有了具备身体形态的 AI，人类仍渴望线下真实的互动与联结。" 邓建国以 "异地恋奔现" 为例，强 调人类学习与沟通的本质是多元、多信道、社会性的聚生过程，单纯的人工语音或线上交互难以满足深 层需求。 邓建国强调，AI 或许能替代制作类知识与部分思考类知识，但人类基于碳基生命的共情能力、聚 ...

Core Insights - DeepSeek has released two new models, DeepSeek-V3.2 and DeepSeek-V3.2-Speciale, which have garnered attention for their performance in comparison to leading models like OpenAI's GPT-5 and Google's Gemini3 Pro [1][2] Model Features - DeepSeek-V3.2 is designed as a high-efficiency assistant with strong reasoning and agent capabilities, aimed at automating complex tasks such as report generation and coding [2] - DeepSeek-V3.2-Speciale focuses on solving high-difficulty mathematical problems and academic research, pushing the limits of open-source model reasoning [2] Technological Innovations - The new models incorporate two significant breakthroughs: Domain-Specific Architecture (DSA) and Thinking Tool Invocation technology [2] - DSA enhances efficiency by allowing the model to retrieve only the most relevant information, reducing resource consumption [2] - Thinking Tool Invocation enables multi-round reasoning and tool usage, allowing the model to think, execute, and iterate on tasks autonomously [2] Market Positioning - The release of these models aims to bridge the performance gap between open-source and closed-source models, providing a competitive edge for open-source development [3][4] - DeepSeek's focus on practicality and generalization is intended to create pressure on closed-source vendors, transforming aspirations into competitive realities [4] Community Engagement - DeepSeek has updated its official web platform, app, and API to the new version, while the Speciale version is currently available only as a temporary API for community evaluation [4]

Core Insights - UBS highlights the recent advancements in AI with the launch of new large language models (LLMs) by companies like Google, Anthropic, and DeepSeek, intensifying competition in the industry [1] - The report emphasizes the continued relevance of the "scaling laws" in model performance, indicating that computational power will remain a critical factor in determining the AI competitive landscape [1] Model Performance - The latest generation of models has shown significant breakthroughs, with Gemini 3 Deep Think and Claude Opus 4.5 achieving multi-step reasoning task scores of 45% and 38%, respectively, surpassing previous models that scored between 10%-20% [2] - This performance aligns with the effectiveness of the AI model pre-training scaling laws, where increased computational investment leads to non-linear improvements in model capabilities [2] Chip Technology Competition - Google’s Gemini 3 Pro is trained entirely on self-developed TPU chips, sparking discussions about the competition between GPUs and AI-specific ASIC chips [2] - ASIC chips are noted for their higher efficiency in specific AI tasks, while GPUs maintain a 90% market share in data center chips due to their flexible architecture and extensive software ecosystem [2] - The collaboration between OpenAI and Broadcom, as well as Anthropic and Google, is expected to enhance the focus on ASIC chips, with both chip types anticipated to coexist in the future [2] Market Trends - The introduction of next-generation chips like NVIDIA's Blackwell and Rubin is expected to sustain the competition for computational expansion, leading to an upward revision of AI capital expenditure forecasts by UBS [3] - The advancements from Google, Anthropic, and DeepSeek are increasing competitive pressure on companies like OpenAI, driving the AI industry towards a multi-model and multi-vendor landscape, a trend expected to persist at least until 2026 [3]

机器之心报道 机器之心编辑部 如今，强化学习（RL）已成为提升大语言模型（LLM）复杂推理与解题能力的关键技术范式，而稳定的训练过程对于成功扩展 RL 至关重要。由于语言具有强烈 的上下文属性，LLM 的 RL 通常依赖序列级奖励 —— 即根据完整生成序列给一个标量分数。 然而，主流 RL 算法（如 REINFORCE 与 GRPO）普遍采用基于 token 的优化目标。这种「奖励在序列级、优化在 token 级」的不匹配引发了对于它们理论健全性 与训练稳定性的担忧，因此已经有研究尝试直接使用序列级优化目标。 此外，token 级优化目标在混合专家（MoE）模型的 RL 训练中带来了新的挑战，比如 MoE 的动态专家路由机制可能破坏 token 级重要性采样比的有效性。由此引 出的关键问题是：在什么条件下，用 token 级目标优化序列级奖励是合理的？有效程度又是怎样的？ 针对这些问题， 阿里千问团队提出了一种针对 LLM 的全新 RL 公式化方法 。核心洞察是： 为了优化序列级奖励的期望值，可以使用一个替代（surrogate）token 级目标作为其一阶近似 。这一近似在以下两种偏差都足够小的条件下才成立 ...

AI之争就是生态之争 作者 | 吕敬之 来源 | #融中财经 11月20日，Gemini3推出两天后，在被称为"硅谷投资人春晚"的Cerebral Valley AI峰会上，OpenAI就被 选成了"第二大可能倒闭的AI独角兽"。 同一天，Sam Altman推送了一条内部备忘录，承认了OpenAI在预训练上已经落后于谷歌的表现。 十天后，在12月1日，Altman再次推送全员内部信，这一次，口气更加严厉，发起内部"红色预警"叫停 广告商业化、AI agent的所有尝试，把所有人的所有注意力重新调回到ChatGPT性能提升上。硅谷知名 投资人Deedy Das在X上评价，Gemini3上线十五天，ChatGPT的日均访问量已经掉了约1200万，这也是 OpenAI拉响红色警报的真正原因。 随着谷歌的穷追猛打，用户和投资人也开始意识到，AI之争，争的不只是用户数据、快速商业化，而 是长远的生态之争。 被谷歌抢走1000万流量的ChatGPT 在Gemini3上线的第十六天，OpenAI传出发布新的大模型开启反击战的消息。 据The information最新报道，OpenAI在近几周的人工智能开发竞赛中似乎已落 ...