Report Industry Investment Rating No relevant content provided. Core Views of the Report - The report focuses on the empowerment of intelligent computing centers, elaborating on the latest development trends around demand scenarios, key capabilities, and implementation ecosystems to further release the empowerment effect of intelligent computing centers and promote the deep integration of AI and the real economy [5]. - Facing the "14th Five-Year Plan", the artificial intelligence computing infrastructure has three important development trends: clear demand scenarios for optimal resource allocation, focused key capabilities for improved service levels, and aggregated implementation ecosystems for accelerated value release [24]. - In the future, the demand scenarios of artificial intelligence computing infrastructure will become more diverse and complex, key capabilities will be more intensive and soft, and the implementation ecosystem will be more aggregated and collaborative [75]. Summary by Directory 1. Evolution Trend of Artificial Intelligence Computing Infrastructure - **Technological Innovation: Upgrading of Tri - in - One Intelligent Computing Facilities**: China's artificial intelligence computing infrastructure is evolving towards large - scale clustering, green and low - carbon development, and high - speed interconnection. For example, Huawei's Ascend 384 super - node and ZTE's Nebula intelligent computing super - node achieve high - speed interconnection of computing cards; the liquid - cooling technology in the China Mobile data center reduces energy consumption [12][13][14]. - **Layout Optimization: Coordinated Development of National Intelligent Computing Facilities**: Policy guidance promotes the high - quality development of intelligent computing centers. The scale of intelligent computing centers continues to grow, and regional intelligent computing is deployed in a more coordinated and intensive manner. For instance, as of June 2025, the total rack scale of computing centers in use in China reaches 1.085 million standard racks, and the intelligent computing scale is 788 EFlops [16][17]. - **Industrial Upgrade: Collaborative Development of the Entire Intelligent Computing Industry Chain**: The intelligent computing industry is growing rapidly, with upstream hardware achieving domestic breakthroughs, mid - stream facilities being built on a large scale, and downstream applications accelerating penetration into various industries. Three major operators and AI giants are actively deploying intelligent computing [18][19][20]. 2. Important Trends in the Empowerment of Artificial Intelligence Computing Infrastructure - **Clearer Demand Scenarios for Optimal Allocation of Intelligent Computing Resources**: The positioning of demand scenarios is becoming clearer, promoting the precise empowerment of intelligent computing centers. The construction of artificial intelligence computing infrastructure is shifting from "building well" to "using well", and the rights and responsibilities of all parties are becoming more explicit [25]. - **Focused Key Capabilities for Improved Intelligent Computing Service Levels**: The supply of key capabilities is being strengthened. In terms of basic support, innovation services, and operation guarantee, the service capabilities of intelligent computing centers are continuously improving, promoting the value - closed - loop and long - term development of intelligent computing centers [26][27]. - **Aggregated Implementation Ecosystems for Accelerated Release of Intelligent Computing Value**: The ecological system is being integrated, and the collaborative mechanism is being improved. The construction of artificial intelligence computing infrastructure is evolving towards an integrated solution of "computing power + algorithm + data + scenario + service", and a sustainable and high - value partner network is being initially established [28]. 3. Demand Scenarios of Artificial Intelligence Computing Infrastructure - **Large - Model Pre - training Scenario**: Training large - scale pre - trained models (with over a thousand billion parameters) requires high - end ten - thousand - card cluster centers with E - level computing capabilities. Domestic operators and AI manufacturers are actively building such clusters [30][31][33]. - **Large - Model Fine - tuning Scenario**: Small - scale intelligent computing centers (with a computing capacity of 100 PFlops) can effectively support the fine - tuning of industry models. Most domestic intelligent computing centers are focusing on this scenario [34][36]. - **Large - Model Inference Scenario**: Cloud - side inference dominates the current inference demand scenarios. Different inference application scenarios have different requirements for inference models and intelligent computing centers, and specialized intelligent computing centers for inference are emerging [37][39][40]. 4. Key Capabilities of Artificial Intelligence Computing Infrastructure - **Basic Support Capabilities**: Training scenarios focus on cluster computing power effectiveness, stability, single - cluster computing power scale, and compatibility with mainstream computing frameworks. Inference scenarios focus on throughput, latency, and the heterogeneity of intelligent computing cards [44][45][46]. - **Innovative Service Capabilities**: Training scenarios emphasize high - efficiency cloud services, efficient model migration, and diverse data governance. Inference scenarios focus on the pooling and scheduling capabilities of intelligent computing resources and efficient model migration and deployment [50][51][52]. - **Operation Guarantee Capabilities**: Both training and inference scenarios focus on the flexibility of computing power scheduling, the cost - effectiveness of computing power leasing, and security and compliance. Training scenarios also pay attention to the richness of cooperative partners [55][56][57]. 5. Implementation Ecosystem of Artificial Intelligence Computing Infrastructure - **Collaboration between Intelligent Computing and Data Elements**: Collaborating closely with high - value data is the core for intelligent computing centers to improve basic support capabilities. For example, the Wenzhou Artificial Intelligence Computing Center and the Guian New Area are promoting the transformation of high - quality data resources into intelligent computing ecological capabilities [60][61]. - **Collaboration between Intelligent Computing and Algorithm Models**: Collaborating with high - level algorithm models is the key for intelligent computing centers to improve innovative service capabilities. For example, the Chongqing Artificial Intelligence Innovation Center and the Wuling Mountain (Lichuan) Artificial Intelligence Computing Center are promoting the development and application of industry - specific models [63][64][65]. - **Collaboration between Intelligent Computing and Cross - domain Intelligent Computing**: Promoting cross - domain intelligent computing interconnection and collaboration is an important exploration for the improvement of intelligent computing center operation capabilities. Operators' intelligent computing centers have achieved practical breakthroughs in long - distance interconnection [66][67]. - **Collaboration between Intelligent Computing and Industry Scenarios**: Collaborating closely with industry scenarios is the core driving force for the continuous evolution and upgrading of the intelligent computing center ecosystem. The Chang'an Automobile Intelligent Computing Center and the Yunnan Communications Investment Intelligent Computing Center are typical examples of in - depth collaboration [68][70]. - **Collaboration between Intelligent Computing and Regional Industries**: Collaborating with regional industries is an important guarantee for intelligent computing centers to achieve multi - dimensional and full - scenario empowerment. Intelligent computing centers in Ningbo, Wuhan, and Dalian are promoting regional industrial development [71][73]. 6. Development Outlook - **More Diverse and Complex Demand Scenarios**: The demand scenarios of artificial intelligence computing infrastructure will become more diverse, complex, and deeply integrated. There will be higher requirements for computing power, storage, industry integration, and cloud - edge - end collaboration. Different stakeholders should play different roles [76][77]. - **More Intensive and Soft Key Capabilities**: The artificial intelligence computing infrastructure is shifting from extensive hardware stacking to refined service improvement, including large - scale clustering, resource pooling, open - source development, and service - orientation. Industry organizations and operators should take corresponding measures [78][79][80]. - **More Aggregated and Collaborative Implementation Ecosystems**: The implementation of artificial intelligence computing infrastructure empowerment depends on a more aggregated and collaborative ecosystem, including multi - party participation, joint innovation, and industrial cultivation. Government departments and operators should play their roles [81][82][83].

Core Insights - The "EVOLVE 2025" summit was held in Beijing, focusing on creating an open, connected, and sustainable "AI+" industry ecosystem through the "Super Connection" global ecosystem partner program [1] - Zhongguancun KJ announced a roadmap for enterprise-level intelligent agents and introduced a "3+2+2" product matrix, which includes various platforms and solutions for marketing, office, R&D, and production [1] - Zhongguancun KJ's products currently serve over 2,000 leading industry clients across more than 180 countries and regions [1] Group 1 - The summit featured participation from leading companies such as Huawei Cloud, Alibaba Cloud, Baidu Intelligent Cloud, and Amazon Web Services [1] - The "Super Connection" initiative aims to foster collaboration among industry leaders to enhance the AI ecosystem [1] - The intelligent agent product matrix includes the Dazhu model platform 5.0 and various intelligent application platforms tailored for different business needs [1] Group 2 - The event highlights the growing importance of AI in various sectors and the need for collaborative efforts to drive innovation [1] - Zhongguancun KJ's extensive client base indicates strong market demand for its AI solutions [1] - The initiative aligns with global trends towards digital transformation and the integration of AI technologies in business operations [1]

直播时间 12 月 10 日 20:00-21:30 直播主题 从 Chatbot 到 Action Agent，企业级落地最怕什么？是长程推理的显存天价成本，还是业务逻辑的"死循环"风险？如何利用 MCP 协议解决接口调用 的"信任危机"？本次直播集结值得买、商汤、明略三位技术专家拆解可信 Agent 的构建之道。 直播介绍 鲁琲 商汤科技大装置事业群 高级技术总监 王云峰 值得买科技 CTO 吴昊宇 明略科技 高级技术总监 企业 Agent 如何"可信"？ 直播嘉宾 主持人： 马可薇 RBC senior application support analyst 嘉宾： 直播亮点 大模型基础设施： 攻克 KV Cache 显存危机，异构集群如何承载 Agent 长程推理？ 可信 Agent 架构： 知识图谱 vs Long Context 记忆之争，设计防止死循环的业务"熔断按钮"。 MCP 协议实战： 解决接口调用"幻觉"与"误解"，实现 Agent 从对话到行动的精准对齐 如何看直播？ 扫描下图海报 【二维码】 或点击下方直播预约按钮，预约 AI 前线视频号直播。 可信 Agent 架构:知识图谱 vs ...

□ 本报记者 叶 真 "我们正在构建一个基于信号通路信息的单细胞转录AI基础模型，能够提升靶点发现的准确性与效率。 有了人工智能的支持，靶点的发现与筛选比原来快了好几倍。"不久前，在中国药科大学2025人工智能 药学大会上，该校教授骈聪透露。 人工智能和算力为药学发展按下"加速键"，已成为生物医药产业界的共识。当前，人工智能技术正深度 融入药物研发各个环节，从靶点发现到临床试验，为生物医药产业带来革命性变革。 助力新药研发提速 人工智能如何重塑药物研发的未来？智能药学将如何推动医药产业转型升级？在人工智能药学大会上， 这两个医药产业发展的核心命题成为与会专家探讨焦点。 "计算模拟和人工智能发展，为药物靶标发现提供了创新策略。"北京大学成都前沿交叉生物技术研究院 院长来鲁华认为，特别是在药物靶标识别、分子生成等核心环节，人工智能发挥着关键作用。 在药物设计环节，人工智能带来变革。"在开发新一代靶向药物时，我们采用了强化学习驱动增强采样 技术，最终成功动态捕捉了分子与靶标之间的结合与解离路径，并据此设计开发出一种新型化合 物。"中国药科大学教授邹毅分享他正在进行的课题：为了测试这种新型化合物的有效性，他又在体内 ...

Group 1: AI Keywords and Models - The article lists the top 50 AI keywords for the week, highlighting significant developments in the AI sector [2] - Key models mentioned include Trainium4 by Amazon, DeepSeek V3.2 by DeepSeek, and openPangu-R by Huawei, showcasing advancements in AI model technology [3] - Other notable models include Mistral 3 by Mistral AI, Qwen3-Learning by Alibaba, and various models from OpenAI, indicating a competitive landscape in AI model development [3] Group 2: AI Applications - Various AI applications are highlighted, such as Tencent's 混元3D Studio and ByteDance's 豆包手机助手, reflecting the diverse use cases of AI technology [3] - The article also mentions AI tools like AI数学家 by Harmonic Math and AI助盲眼镜 by 瞳行科技, emphasizing the societal impact of AI innovations [4] - New applications like Vidu Q2 by 生数科技 and AI眼镜Livis by 理想 demonstrate the ongoing integration of AI into consumer products [3][4] Group 3: Industry Insights and Opinions - The article includes insights from various thought leaders, such as Ilya Sutskever on scaling and 吴恩达 on training facility bubbles, providing a deeper understanding of industry challenges [4] - Perspectives on AI's evolution over three years by OpenAI and the importance of human-machine collaboration by McKinsey highlight the strategic direction of the industry [4] - The mention of pricing strategies by Stripe and productivity improvements by Anthropic indicates ongoing discussions about the economic implications of AI advancements [4]

嘻疯 发自 凹非寺 量子位 | 公众号 QbitAI 豆包升级上新，火山引擎带着 图 像创作模型 Doubao-Seedream-4.5 来了。 新模型有三个主打点。 一是强化了 原 图保持能 力 ，最大化保持原图的人脸、光影与色调、画面细节，可以用来P图。 例如"只保留绿线中的人物，将其他角色都删掉"： 再复杂一些，将白天变为黑夜： 二是重点强化了 多图组合生成能力 。 在官方展示中，输入8张参考图，并指定画面布局后，让它生成图画故事书封面： 童话故事书封面：小女孩与小狐狸站在发光森林小屋前，月亮巨大而梦幻，星尘在他们周围飘浮；萤火虫的光点点亮草地；小白花细致 点缀；雾气营造柔和深度；古铜色童话边框华丽包围整个场景；色调是蓝紫与暖金对撞；角色面部特征保持原图一致；整体梦幻、温 柔、魔法感强烈，适合作为儿童绘本封面。 把图片中的英文转成手写体中文： Seedream-4.5 能 精准执 行复杂指令，将多种元素精准识别提取出来 ，并自然融合： 同样地，让多个角色"拍"一张大合照： 模型也能生成无违和感的群像画面： 反过来，根据一张参考图，一次性生成6张海报，比例分别改成1:1、2:3、4:3、16:9、1:2、 ...

Core Insights - The 2025 Volcanic Engine Winter FORCE Conference is scheduled for December 18-19, focusing on Agentic AI's impact on industries, with three major releases planned: a complete refresh of the Doubao model family, upgrades to Agent development tools, and ecosystem expansion [3] Group 1: Conference Overview - The conference will feature three main forums and 20 specialized forums, covering topics such as AI in finance, education, and automotive sectors, as well as exploring the integration of Data and AI [2] - The event aims to provide a comprehensive analysis of the pathways for AI implementation across various industries [2] Group 2: Model Updates - The Doubao model 1.6 series will be highlighted, showcasing improvements in reasoning, mathematics, instruction adherence, and Agent capabilities, with a 63% reduction in comprehensive costs compared to Doubao 1.5 [3] - The conference will also focus on the further evolution of the Doubao model in deep thinking and multimodal integration, assessing the cost-performance optimization paths of the new generation [3] Group 3: Usage Metrics - As of May 2025, the daily token usage for the Doubao model exceeded 16.4 trillion, with a significant increase from 1.2 billion tokens in May 2024 to over 30 trillion tokens by September 2025, indicating a 253-fold growth [4]

Core Viewpoint - The article highlights the launch of Doubao-Seed-ASR-2.0 by Huoshan Engine, which significantly enhances voice recognition capabilities through advanced contextual understanding and multi-modal visual recognition [1] Group 1: Model Enhancements - The 2.0 version of the model features improved inference capabilities, allowing for precise recognition through deep contextual understanding [1] - Overall keyword recall rate has increased by 20%, indicating a substantial improvement in recognition accuracy [1] Group 2: Multi-modal and Language Support - The model supports multi-modal visual recognition, enabling it to understand both audio and visual inputs, which enhances text recognition accuracy [1] - It recognizes 13 foreign languages, including Japanese, Korean, German, and French, broadening its applicability in global markets [1] Group 3: Specialized Recognition - The model has been upgraded to better handle complex scenarios involving proper nouns, personal names, geographical names, brand names, and easily confused homophones [1]

Core Insights - The core viewpoint of the article is the launch of Doubao-Seed-ASR-2.0 by Huoshan Engine, which significantly enhances voice recognition capabilities through improved contextual understanding and multi-modal visual recognition [1] Group 1: Model Enhancements - The new model features a 20% improvement in overall keyword recall rate through enhanced contextual understanding [1] - It supports multi-modal visual recognition, allowing the model to not only "hear words" but also "see images," improving text recognition accuracy with single and multiple image inputs [1] - The model is capable of accurately recognizing 13 foreign languages, including Japanese, Korean, German, and French [1] Group 2: Technical Specifications - Doubao voice recognition model is built on the Seed mixed expert large language model architecture, maintaining the advantages of the 1.0 version's 2 billion parameter high-performance audio encoder [1] - The upgrade focuses on optimizing recognition in complex scenarios involving proper nouns, names, geographical locations, brand names, and easily confused homophones [1] - Enhanced contextual reasoning capabilities enable the model to achieve multi-modal information understanding and mixed-language recognition accuracy [1]

Core Viewpoint - The article reports the official launch of Doubao-Seed-ASR-2.0, a voice recognition model by Huoshan Engine, which enhances contextual understanding and recognition accuracy through advanced technology [1] Group 1: Model Features - The 2.0 version of the model has improved inference capabilities, achieving a 20% increase in overall keyword recall rate [1] - It supports multimodal visual recognition, allowing the model to understand both audio and visual inputs, thereby enhancing text recognition accuracy [1] - The model can recognize 13 foreign languages, including Japanese, Korean, German, and French [1] Group 2: Targeted Upgrades - The model has been specifically upgraded to handle complex scenarios involving proper nouns, personal names, geographical names, brand names, and easily confused homophones [1]