北京商报讯(记者陶凤王天逸)1月9日，中科曙光（603019）宣布，近日国家工业信息安全发展研究中心 发布《2024—2025年中国智能体应用研究报告》，收录了曙光云为甘肃省"5G+智慧公路"云平台项目提 供的智能体服务，该服务支撑了"公路交通建设智能决策"，成为智能体技术在交通基础设施领域深度应 用的代表性实践。 ...

2、2026年大陆高校面向港澳研究生招收工作今日启动 1月4日，2026年内地面向港澳地区研究生招收程序启动。考生须以实名注册"学信网"账号，并用"学信 网"账号在4日至11日登录"内地（祖国大陆）高校面向港澳台招生信息网"进行报名，报名期间系统24小 时开放，考生须按要求填写及上传个人信息。 （原标题：今日看点｜韩国总统李在明访华） 1月4日重点关注的财经要闻与资本市场大事： 1、韩国总统李在明访华 据外交部网站消息，韩国总统李在明将于2026年1月4日至7日对中国进行国事访问。此访是李在明就任 后首次访华，他也将是新年首位来访的外国元首。 据了解，每位考生只限报读一所招生院校的一个专业，逾期不得补报，也不得修改信息。网上确认阶段 于1月15日至19日进行，考生须按要求登录系统上传个人身份证明和学历证明。 3、2026全国智能体开发者大会将举行 1月4日，2026全国智能体开发者大会在常州举行。本次大会是学会推动人工智能技术扎根地方、赋能实 体的重要举措，也是常州立足雄厚产业基础抢抓智能体技术风口的关键一步。 经济观察网 编辑 王俊勇 整理 ...

人民财讯1月4日电，据"常州发布"，2026年1月4日，2026全国智能体开发者大会在常州盛大启幕。本次 大会是学会推动人工智能技术扎根地方、赋能实体的重要举措，也是常州立足雄厚产业基础抢抓智能体 技术风口的关键一步。 ...

Core Insights - The launch of the Doubao mobile assistant in late 2025 has created significant waves in the smartphone industry, being recognized by users as a "truly AI phone" due to its autonomous cross-application operation capabilities [1][5] - However, the Doubao mobile assistant faced usage restrictions in multiple applications, leading to an official statement on December 5 regarding adjustments to its AI operation capabilities [1][3] - The challenges faced by Doubao reflect broader issues in the AI phone sector, particularly concerning user privacy and data rights, which require collaborative efforts from various stakeholders [3][7] Group 1: Technology and Development - The rapid improvement of domestic open-source large models has provided a solid foundation for the iterative development of AI phones, shifting focus from cloud-based models to offline capabilities [4][13] - The announcement of the open-sourcing of the AutoGLM model by Zhiyu on December 9 signifies a move towards "technological equality" in the mobile agent space, potentially lowering development barriers [3][12] - The evolution of AI phones is not just about technological advancements but also involves a complex interplay of patience, restraint, and ecosystem collaboration [4][17] Group 2: Market Dynamics and Competition - The Doubao mobile assistant's features are not significantly different from previously demonstrated smart applications by other manufacturers, indicating that the market is still in the early stages of realizing cross-application autonomous capabilities [5][6] - The competition in the AI phone market is fundamentally about ecosystem development, requiring a shift from traditional app models to a more integrated approach that allows for cross-application automation [17][21] - The emergence of AI agents is seen as a potential battleground for user engagement and control over data, with manufacturers needing to navigate the complexities of user habits and regulatory compliance [8][17] Group 3: Industry Challenges - The current landscape reveals a "standard vacuum" regarding user authorization and data rights, complicating the implementation of AI agents that require cross-application functionality [7][18] - The transition to AI agents necessitates a reevaluation of existing operational models, as traditional single-operation authorization methods are inadequate for the demands of AI-driven tasks [7][18] - The industry faces challenges related to memory and power consumption as the capabilities of end-side models expand, necessitating advancements in hardware to support these new demands [18][20] Group 4: Future Outlook - The AI phone competition is evolving from a focus on hardware specifications to a comprehensive race involving end-side capabilities, ecosystem openness, and user experience [21][22] - The integration of AI capabilities into hardware is expected to reshape traditional consumer electronics business models, moving towards subscription-based software services [20][22] - The developments surrounding the Doubao mobile assistant highlight the need for industry-wide collaboration to address the challenges of rights, interests, and standards in the evolving AI landscape [20][22]

深圳商报·读创客户端首席记者 王海荣 12月17日，毕马威中国大湾区科技咨询合伙人李良接受深圳商报·读创客户端记者采访时表示，过去依 赖土地增值和高速周转的"外延式"增长已难以为继，未来的核心必然转向通过好产品和精准投入来实 现"内涵式"发展。AI正是实现这一转向的关键工具，它让房企有能力像精密仪器一样去理解客户、设计 产品和测算投资，从"凭经验"转向"凭数据做精品"。 组合应用多维技术推动AI 与不动产融合 随着AI智能体逐步承担起巡检、客服、数据分析等标准化任务，不动产管理岗位的角色将从任务的直 接执行者转变为人机协同的规划者与决策者。未来的管理者更像一位AI训练师，其核心工作不再是亲 力亲为处理日常事务，而是精准定义运营中需要被优化的复杂问题，为AI设定清晰的目标和规则，并 对其输出的结果进行专业判断与纠偏。 图片说明：毕马威中国大湾区科技咨询合伙人李良（左一）17日在深圳主持主题为"XIN链接·构建以用 户为中心的AI基座"的圆桌讨论。 （受访单位供图） 李良认为，深圳作为科技与不动产深度融合的前沿，在培养新型管理者方面应充分发挥产业生态优势。 他建议采取"打破壁垒，在真实场景中淬炼"的核心路径，比如 ...

此外，驿站充分融合汇金国际商务社区"科技基因"，将智能体技术与区域内就业服务场景深度融 合，创新开发JobSeek就业智能助理，通过AI赋能有效链接供需，实现人岗精准匹配、政策精准推 送。"JobSeek不仅能像智能导航一样为求职者推荐合适的岗位，还集合了查询政策补贴、推送楼宇活 动、归纳周边信息等多种功能，是一个可以满足员工多维需求的'智慧百科'。"企航驿站负责人介绍。 据悉，在长庆·汇金"企航驿站"，不仅有现场招聘会，还在楼宇内设置固定招聘服务点，每月安排 企业现场招聘，并为重点群体提供简历优化、面试技巧指导等一对一服务。据悉，"企航驿站"平均每场 提供岗位超200个，累计匹配意向人才1500余人次。 今年以来，驿站聚焦新业态发展趋势，开展直播电商、数字营销、人工智能应用等领域的技能培训 累计28期，培训超1200人次。驿站同步推动"技能培训+创业孵化"模式，已成功孵化小微企业5家、带动 就业超30人，实现"技能提升促就业，创业带动稳就业"的良性循环。 不仅如此，这里还有"零跑腿"窗口，可以实现全周期服务，比如人才落户、社保补贴、创业扶持、 劳动争议调解等业务的咨询与办理，真正成为楼宇企业和员工的就业服务 ...

面对日益复杂的网络电信诈骗形式，抖音持续加强反诈能力建设，除持续升级的验证助手外，再添智能化反诈新工具。据悉，抖音官方智能防护助手账 号"抖音反诈"已上线新功能，新增全天候智能服务。 作为抖音官方反诈账号，"抖音反诈"核心内容包括教育与预警新型骗局，分享实用防骗技巧，不断加强用户防诈意识等。为让用户在遭遇风险时能第一 时间获得帮助，抖音依托智能体技术与平台长期累积的反诈治理经验，近日推出智能服务，全天候在线为用户提供防护。 当用户在平台遭遇可疑情况时，只需通过抖音搜索"抖音反诈"，进入账号，关注账号后打开私信页面，描述问题并发送即可获得反诈相关内容的回应。 智能体将基于抖音反诈知识库与高发骗局识别模型，对用户发送的问题进行实时分析、响应，提供具体的甄别建议与验证方式，实现从咨询、验证到举 报的一站式服务。 此功能是抖音在反诈工具上的持续升级。早在2024年12月，针对仿冒客服诱导共享屏幕等诈骗，抖音就推出了官方信息验证工具"验证助手"，用于甄别 可疑来电号码、短信或网址。今年以来，该功能历经多次升级，陆续上线了"一键查询官方联系记录""官方动态验证口令"等功能，并于今年11月进一步 新增了"会员服务"与"仿冒 ...

Core Insights - The article emphasizes that AGI is not the endpoint but the starting point towards ASI, with Alibaba Group's CEO categorizing the evolution into three stages: intelligent emergence, autonomous action, and self-iteration, currently in the autonomous action phase [2][3] Group 1: AI Development Stages - The current phase of AI is characterized by a shift from perception and generation to decision-making and action, driven by intelligent agent technology [3] - The transition to autonomous action is seen as a critical bridge towards self-iteration, enabling AI to create real-world value [3][19] Group 2: Technological Breakthroughs - Continuous breakthroughs in technology are essential for releasing AI's value, focusing on building foundational capabilities such as computing power, basic models, and technical ecosystems [4] - The integration of cloud computing and AI is creating a full-stack technology ecosystem, addressing resource and cost bottlenecks for scalable AI deployment [5][6] Group 3: Model Innovations - Large models are evolving from single-modal to multi-modal capabilities, enhancing AI's application scope across various fields such as education and healthcare [9][10] - Innovations like reinforcement learning from human feedback (RLHF) are improving models' abilities to solve complex tasks autonomously [10] Group 4: Application and Ecosystem Development - The rise of intelligent agents is reshaping software ecosystems, enabling dynamic decision-making and task execution [11][16] - Open-source initiatives are crucial for democratizing AI technology, with Alibaba contributing over 300 open-source models to lower development costs [13][14] Group 5: Industry Transformation - AI is driving systemic innovation across industries, enhancing operational efficiency and consumer experiences [20] - The global collaboration in AI innovation is reshaping industry structures and optimizing resource allocation, facilitated by AI cloud platforms [21] Group 6: Responsible AI Development - The article highlights the importance of a governance framework to ensure AI's sustainable development, addressing challenges like data privacy and algorithmic bias [25][26] - A collaborative approach involving industry, academia, government, and the public is essential for achieving responsible AI development [27]

Core Viewpoint - The company has confirmed the integration of the Model Context Protocol (MCP) into its Qiko intelligent platform, enhancing its capabilities in big data operations and service integration [1]. Group 1: Technology Integration - The Qiko intelligent platform is fully compatible with the MCP, allowing for efficient transformation of capabilities within intelligent agents and workflows [1]. - The technology has been applied across multiple product lines, including public safety big data and digital transformation for government and enterprises [1].

Core Insights - Nvidia reported Q3 revenue of $57.006 billion, a 62% year-over-year increase and a 22% quarter-over-quarter increase, with net profit rising 65% year-over-year to $31.910 billion [1] - The company is on track to meet its $500 billion revenue target related to high-performance computing data centers by 2026, with $150 billion of products already delivered [2] - Nvidia's supply chain is well-prepared to meet the growing demand for AI infrastructure, with partnerships across the technology sector [3] Financial Performance - Q3 revenue reached $57.006 billion, marking a 62% increase year-over-year and a 22% increase quarter-over-quarter [1] - Net profit for the quarter was $31.910 billion, up 65% year-over-year and 21% quarter-over-quarter [1] - Adjusted net profit, not in accordance with GAAP, was $31.767 billion, reflecting a 59% year-over-year increase [1] Market Demand and Supply - Nvidia's management confirmed that the demand for GPU products remains high, with full order books despite concerns about AI infrastructure investment returns [3] - The company has secured additional agreements, including a deal with Saudi Arabia for 400,000 to 600,000 GPUs over the next three years [2] - The transition from general computing to accelerated computing is ongoing, driven by the rise of generative AI [4] AI Infrastructure and Applications - Generative AI applications are expanding rapidly, with tools like code assistants becoming widely used across various roles beyond software engineering [5] - The training of AI models is progressing well, with significant advancements in performance and quality noted in Google's Gemini 3 model [6] - The industry is experiencing a shift towards accelerated computing, with generative AI replacing traditional machine learning methods [6] Future Growth and Investment - Nvidia's product architectures are expected to drive significant value growth in data centers, with each generation of products improving performance and cost efficiency [7] - The company anticipates that the capital expenditures required for its $500 billion target can be covered by customer cash flows, particularly from large-scale data center providers [7] - Global infrastructure funding will not be limited to large data center providers, as various industries are beginning to invest in AI technologies [8]