腾讯研究院

Search documents
AI的落地难题、应用案例和生产率悖论
腾讯研究院· 2025-05-27 08:06
一、AI的企业应用仍处于初期阶段 人工智能的2C应用进展很快,2024年美国居民生成式AI的渗透率已达39.6% (来源:圣路易斯联储) 。然 而,当前的模型厂商还热衷于评分打榜、技术炫耀,企业应用尚处于早期阶段。迫切需要找到丰富落地 场景,加快推进AI和各行各业的深度融合。 国联证券对A股上市公司财报中提及AI的情况进行了梳理,近年提及数量迅速增加,从2020年的172家 上升至2023年的超过1200家,然而在所有A股上市公司的占比仍然不高,2023年还不到20%。根据国家 经济研究局 (NBER) 数据,截止2024年2月,美国AI企业采用率仅有5.4%。根据欧盟统计局数据,2024 年欧盟各国AI企业普及率在3.1%-27.6%之间,总体为13.5%。如下图所示。各国对问题的定义和调研方 法有所不同,以上数据不能简单横向比较,但都反映出AI的企业应用还处于初期阶段。 图 2024年欧盟的AI企业普及率 来源:根据欧盟统计局数据整理,2025 闫德利 腾讯研究院资深专家 二、信息密度越高,AI应用越易越深 AI的企业应用具有明显的行业差异,它与信息密度有关。大体是信息密度越高,AI应用越容易越深入; 信 ...
腾讯研究院AI速递 20250527
腾讯研究院· 2025-05-26 15:53
生成式AI 一、 海光信息与中科曙光 突发重大并购:两大算力巨头"合体" 1. 海光信息将通过换股方式吸收合并中科曙光,两家企业总市值合计超4000亿元; 2. 海光为国产CPU及GPU龙头,中科曙光为服务器及算力基础设施龙头,两家有频繁关联交 易; 3. 此次重组旨在抢抓信息技术产业发展机遇,将实现产业链互补,形成多元算力业务整合。 https://mp.weixin.qq.com/s/6ruj7Mc1EMFtbDZRW0z7Zw 二、 Lilian Weng自曝公司首个产品?一篇论文未发估值90亿 1. OpenAI前安全副总裁Lilian Weng分享其新公司Thinking Machines的产品——一种用于AI 训练的手动调参仪表盘; 2. Thinking Machines由多位OpenAI核心员工组建,虽未发表论文但估值已达90亿美元; 四、 AI老师上线!VideoTutor:2分钟搞定K12课程,还能定制 1. VideoTutor是一款面向K12教育的AI工具,用户输入问题或主题后可自动生成类似可汗学 院风格的短视频课程; 2. 该工具提供结构化脚本、动态视觉效果和专业旁白,支持100多种 ...
“AI的真正价值不在于有多酷,而在于多有用、多可靠”
腾讯研究院· 2025-05-26 09:02
郭凯天认为,AI应当尊重人类作为价值源头的独特性, AI的真正价值不在于"看起来多酷",而在于"用 起来多好用、多可靠", 为此,腾讯高度重视开源透明的技术生态,倡导开放、参与、监督并行的治理 模式,推动建立AI时代的信任基础。他也表示,AI文明的篇章才刚刚开启,腾讯愿与各方携手,共同塑 造一个技术与人文并重、开放包容的未来。 生成式AI加速发展,治理需同步演进 5月22日下午,由腾讯研究院和新加坡管理大学数字法研究中心(SMU Centre for Digital Law)联合主 办的AI与社会研讨会——" 生成式 AI 进展:应用、治理与社会影响 ",在新加坡管理大学顺利召开。 近百名来自中国和新加坡的业界、学界专家参加了会议,围绕生成式AI的技术趋势、产业应用、监管治 理、社会伦理等议题展开分享与讨论,为构建开放共享、健康可持续的AI发展生态和AI社会探寻对策思 路。 腾讯集团高级副总裁郭凯天代表主办方作欢迎致辞,他提出, AI不仅是一次技术革命,更是一场关于 人类、社会与智能之间关系的深刻变革。 我们正站在一个技术飞跃的关键节点,大模型技术的快速演进 正推动人工智能从"会认知"迈向"会行动",成为人类 ...
腾讯研究院AI速递 20250526
腾讯研究院· 2025-05-25 15:57
生成式AI 一、 H20之后,英伟达全新「阉割版」的Blackwell GPU曝光 1. 英伟达因美国出口管制在中国AI芯片市场份额从95%暴跌至50%,被国产芯片抢占市场; 2. 为应对困局推出新款阉割版Blackwell GPU,售价6500-8000美元,远低于H20的1-1.2万 美元; 3. 新芯片采用GDDR7内存技术,内存带宽约1.7TB/秒,以符合出口管制限制要求。 https://mp.weixin.qq.com/s/62VnkP-TrmhSd18CmDLWBA 二、 Claude 4如何思考?资深研究员回应,RLVR已得到验证 1. Claude 4采用可验证奖励强化学习(RLVR)范式,在编程和数学等有清晰反馈信号的领域取 得突破; 2. 当前AI Agent发展受限于高可靠性不足,但预计明年将出现能独立完成实际工作的软件工 程Agent; 3. 研究员预测到2026年底,AI将具备足够的"自我意识",能执行复杂任务并判断自身能力边 界。 https://mp.weixin.qq.com/s/0mQ9xEKdGiSMsFqyXMJVgg https://mp.weixin.qq.com/ ...
腾讯研究院AI每周关键词Top50
腾讯研究院· 2025-05-23 09:10
AI前沿每周关键词Top50 ( 0519-0523) 事件 正式收购io OpenAI 扫码加入ima知识库 ( 腾讯研究院ima AGI知识库二维码) 推 荐 阅 读 闫德利 : 《 技术创新的性质》 点个 "在看" 分享洞见 每周50关键词 把握全局AI动态 点击 关键词 可查看资讯概述 | 类别 | Top关键词 | 主体 | | --- | --- | --- | | 算力 | 阿布扎比数据中心 | OpenAI | | 算力 | GB300等 | NVIDIA | | 算力 | CloudMatrix 384等 | 华为 | | 算力 | TPU应用 | Google | | 模型 | SWE-1模型 | Windsurf | | 模型 | BGE向量模型 | 智源研究院 | | 模型 | 模型矩阵更新 | 腾讯 | | 模型 | Gemini Diffusion | 谷歌 | | 模型 | Devstral | Mistral | | 应用 | Codex | OpenAI | | 应用 | 混元图像2.0 | 腾讯 | | 应用 | 新增生图功能 | Manus | | 应用 | LightL ...
探元计划香港站|AI 赋能历史溯源,解码九龙寨城中华文脉基因
腾讯研究院· 2025-05-23 07:47
Core Viewpoint - The "Exploration Plan 2024" aims to integrate culture and technology to promote the digital preservation of cultural heritage, with a focus on the "In Kowloon City, Witness Hong Kong" project, which highlights the historical significance of Kowloon City and its cultural narratives [3][10]. Group 1: Project Overview - The "In Kowloon City, Witness Hong Kong" project is a collaboration between Hong Kong United Publishing Group, Electronic Publishing Co., and Huacui Starlight (Beijing) Intelligent Technology Co., utilizing advanced technologies like large model agents and 3D virtual spaces to recreate the cultural essence of Kowloon City [3][4]. - The project was selected from 81 cultural demand scenarios as one of the six key cultural co-creation scenes under the "Exploration Plan 2024" [4]. Group 2: Technological Innovations - The project team is developing a multimodal knowledge intelligent agent that supports bilingual and trilingual interactions, enhancing user engagement with Kowloon City's historical culture [4]. - An AI interactive narrative game is being designed to create immersive learning experiences, encouraging public interest in Kowloon City's history [4]. - A 3D virtual space of Kowloon City will be constructed to allow users to experience different historical periods and cultural customs [4]. Group 3: Expert Insights and Discussions - Experts from various sectors, including cultural institutions and universities, discussed the importance of technology and culture working together to enhance cultural dissemination and user engagement [11]. - The discussions emphasized the need for a shift from one-way cultural output to a collaborative and shared approach, utilizing gamification and user-generated content to stimulate cultural transmission [11]. - The project aims to create sustainable development models by integrating educational and cultural tourism resources, focusing on local schools and Kowloon City Park as pilot sites [11]. Group 4: Future Events and Exhibitions - The results of the "In Kowloon City, Witness Hong Kong" project will be showcased at the Shenzhen Cultural Expo from May 22 to 26 and at the Hong Kong Book Fair from July 16 to 22 [13].
大模型巨浪的下一个方向:AI Ascent 2025的十个启示
腾讯研究院· 2025-05-23 07:47
Core Insights - AI is expected to create trillion-dollar market opportunities, with all necessary elements in place for an imminent explosion in AI development [3][7] - The leap in AI capabilities, such as coding, indicates a shift towards a "bountiful era" where labor becomes cheap and abundant, while "taste" may become a new scarce asset [3][9] - The number of foundational large models will be limited, with companies investing more in reinforcement learning to enhance model capabilities [3][4] Group 1 - AI models may become more sparse and specialized, focusing on different areas of expertise and allowing for dynamic resource allocation [4][17] - Intelligent agents will possess improved working capabilities, including better memory and self-guidance, enabling longer autonomous operation [5][18] - User engagement with AI products may evolve into a new business model where personal background information is used for logging into multiple AI services [6][22] Group 2 - Innovation in the AI era is occurring at the blurred lines between model research and product development, advocating for a bottom-up exploration approach [4][21] - Organizations developing software products will face challenges from AI code generation, necessitating structural and operational changes [5][24] - Companies need to adopt a "stochastic mindset" to manage the uncertainties of AI, shifting from strict rule-driven approaches to dynamic adaptability [5][8] Group 3 - The competition in AI applications is expected to intensify, leading to the formation of an "agent economy" [6][9] - Startups should focus on solving complex problems that require human involvement, building data flywheels linked to specific business metrics [8][9] - AI's impact on the economy will be profound, reshaping companies and the overall economic landscape [8][9] Group 4 - OpenAI emphasizes maintaining organizational agility and aims to become a "core AI subscription" service [10][12] - The potential of models is believed to have a 10-100x growth space, with a focus on reinforcement learning to enhance model capabilities [10][11] - The vision includes creating an AI application ecosystem that provides powerful tools and services for developers and users [12][13] Group 5 - Google's approach focuses on hardware-software synergy to enhance model development, predicting significant advancements in AI capabilities within the next few years [14][15] - The future of models may involve mixed expert models to improve computational efficiency and continuous learning [17][18] - AI's transformative potential in scientific research is highlighted, with expectations for AI to replace traditional simulation methods [18][19] Group 6 - Anthropic advocates for a bottom-up approach in AI product development, emphasizing the importance of user needs over technical showcases [20][21] - The next generation of AI products will focus on autonomous agents capable of long-term operation and improved collaboration [22][23] - The rise of AI-generated content will necessitate new standards for content traceability and security [22][24]
腾讯研究院AI速递 20250523
腾讯研究院· 2025-05-22 15:09
Group 1: OpenAI Innovations - OpenAI's Responses API now supports MCP services, allowing developers to connect external services with simple configurations, significantly reducing development complexity [1] - The updated API enhances security controls through the allowed_tools parameter and permission management to ensure safe tool usage by agents [1] - New features include image generation, Code Interpreter, file search, background mode, inference summaries, and encrypted inference items [1] Group 2: Microsoft's Magentic-UI - Microsoft launched the open-source Web Agent project Magentic-UI, enabling automatic web browsing, file reading/writing, and code execution, with user monitoring and control [2] - The system employs a collaborative planning and execution mechanism, generating task plans for user confirmation and allowing real-time intervention during execution [2] - The project integrates innovative technologies like neural style engines, component DNA mapping, and performance prediction for intelligent style conversion and component reuse [2] Group 3: Mistral's Devstral Model - Mistral, in collaboration with All Hands AI, released the open-source language model Devstral, featuring 24 billion parameters and capable of running on a single RTX 4090 or a 32GB RAM Mac [3] - Devstral scored 46.8% on the SWE-Bench Verified benchmark, outperforming GPT-4.1-mini and other open-source models, showcasing excellent code understanding and problem-solving abilities [3] - The model is released under the Apache 2.0 license for commercial use, with pricing set at $0.10 per million input tokens and $0.30 per million output tokens [3] Group 4: xAI's Live Search API - xAI introduced the Live Search API, providing real-time data access for Grok AI, enabling retrieval of the latest information from X platform, web content, and breaking news [4][5] - The API offers flexible search control features, including enabling/disabling searches, limiting result numbers, and specifying time ranges and domains, combined with DeepSearch for inference display [5] - A Python SDK is available, with free beta testing until June 5, 2025, allowing developers to implement real-time information queries and research assistance [5] Group 5: OpenAI's Acquisition of Jony Ive's Team - OpenAI acquired AI device startup io for $6.5 billion, gaining a hardware team led by former Apple Chief Design Officer Jony Ive, with the deal expected to close by summer [6] - io is developing new forms of AI devices aimed at reducing screen time, including headphones, wearables, and AI home devices, with a projected release in 2026 [6] - The associated company LoveFrom will continue to operate independently while taking on more design responsibilities for OpenAI, including ChatGPT interface and voice interaction products [6] Group 6: Kunlun Wanwei's Skywork Super Agents - Kunlun Wanwei launched the Skywork Super Agents, integrating five expert agents and one general agent for one-stop generation of documents, PPTs, and spreadsheets [7] - The product's core is based on deep research technology, supporting deep information retrieval and traceable content generation at only 40% of OpenAI's costs, with the framework open-sourced [7] - System features include automated requirement clarification, information tracing, and personal knowledge base functionality, allowing users to upload various file formats to build knowledge bases [7] Group 7: Microsoft's Aurora Model - Microsoft introduced the first large-scale atmospheric foundation model, Aurora, trained on millions of hours of atmospheric data, achieving computation speeds 5000 times faster than the most advanced numerical forecasting systems [8] - Aurora excels in predicting air quality, wave patterns, tropical cyclone trajectories, and high-resolution weather, maintaining high accuracy even in data-scarce regions and extreme weather [8] - The model utilizes a 3D Swin Transformer architecture, allowing fine-tuning for different application areas, with a training cycle of only 4-8 weeks, and future expansion into ocean circulation and seasonal weather predictions [8] Group 8: Gartner's Principles for Intelligent Applications - Gartner identified that GenAI will drive enterprise software from auxiliary tools to intelligent agents, outlining five principles for building intelligent applications: adaptive experience, embedded intelligence, autonomous orchestration, interconnected data, and composable architecture [9] - Intelligent applications emphasize personalized experiences and proactive services, enabling cross-system tasks through natural language interactions, with AI capabilities deeply embedded in business logic for process optimization [9] - Enterprises need to maintain balanced investments in the five principles while upgrading foundational data, processes, architecture, and experiences to ensure intelligent applications transition from pilot demonstrations to scalable value applications [9] Group 9: a16z's Insights on AI Programming - The AI coding market has become the second-largest AI market after chatbots, valued at approximately $3 trillion, with developers rapidly adopting this tool as early technology adopters [10] - AI programming will not completely replace traditional programming; understanding foundational abstractions and system architecture remains crucial, with developer roles shifting towards product management or QA engineering [10] - New demographics and methods are fostering a new software paradigm, similar to the WordPress era, where AI lowers the barrier to "writing code," yet the depth and complexity of software development still require professional knowledge [10]
吴恩达:如何在人工智能领域打造你的职业生涯?
腾讯研究院· 2025-05-22 09:35
1.编码人工智能是新的读写能力 2.职业生涯发展的三个步骤 3.学习有前途的人工智能职业的技术技能 吴恩达 加州斯坦福大学计算机科学系和电机工程系的客座教授 本文节选自:How To Build Your Career in AI 【AI速读】 这篇文章探讨了如何在人工智能领域建立职业生涯。文章涵盖了从基础技能学习到项目实践,再到找到 合适工作的各个方面,并提供了具体的建议和步骤。以下是文章的主要内容: 4.你应该学习数学来获得人工智能的工作吗? 5.成功AI项目的范围 语言读写能力的演变:几百年前,语言读写能力并不普及,但随着时间的推移,它变得普遍并丰富 了社会。 代码的重要性:代码是人与机器之间最深入的交流方式,随着机器在日常生活中的重要性增加,编 程能力变得越来越重要。 人工智能和数据科学的应用:线性回归模型可以帮助披萨店老板优化需求预测和供应链管理。 学习基础技能:包括机器学习、深度学习、数学和软件开发。 从事项目工作:与缺乏AI专业知识的利益相关者合作,估计项目完成时间和投资回报。 找到一份工作:建立支持性社区,帮助你成长和找到工作。 基础机器学习技能:线性回归、逻辑回归、神经网络等。 深度学习:了 ...
腾讯研究院AI速递 20250522
腾讯研究院· 2025-05-21 15:01
生成式AI 一、 真碾压Sora!谷歌Veo 3直接「开口说话」 物理 遵循 性 更 好 1. Veo 3实现音画同步生成功能,能根据提示词同时生成视频画面、对白、唇动和音效,实 现完整的视听体验; 2. 基于V2A(Video-to-Audio)技术,模型能将视频像素转化为语义信号,配合文本提示生 成匹配音频; 3. 模型支持长提示词理解和多步骤事件流生成,但目前仅限8秒视频,面向美国Ultra订阅用 户开放,定价249.99美元/月。 https://mp.weixin.qq.com/s/rJFwZ1lLiWzFLE7jd4jGyA 二、 12秒1万token!谷歌文本「扩散模型」Gemini Diffusion 1. Gemini Diffusion采用扩散技术生成文本,速度达2000token/秒,12秒可生成1万 tokens; 2. 区别于传统自回归模型从左到右生成,通过逐步优化噪声学习生成输出,可快速迭代和错 误纠正; 3. 性能可与更大的模型Gemini 2.0 Flash-Lite相媲美,支持非因果推理,能一次生成整个标 记块。 https://mp.weixin.qq.com/s/paes ...