华为云CMO条线的组织有所变化，据了解，周秋硕接过了相应职责，他此前负责华为云营销服海外能 力中心，同时相较于此前的Marketing，更高一级的组织华为云MSSD（Marketing and Solution Sales Department，Marketing与解决方案销售部）被赋予了更多销售职责，其负责人为刘飞，此前为华为云拉 美总裁。华为云广东总经理徐卫星调任泛政企拓展部部长，负责相关企业的销售拓展。 华为手表将接入DeepSeek 华为WATCH 5将接入DeepSeek，并融合盘古大模型与运动健康专业模型，可精准理解个人数据与专业 知识。据悉，用户可通过腕上小艺智能分析20+运动健康领域近200项指标。 雷军：汽车工厂正在试用机器人相关能力，小米汽车芯片预计很快推出 小米创始人雷军在小米投资者大会上披露，最新推出的小米YU7售价不可能是网传的23.59万元，正式 定价要1-2天前才能确定。雷军称五年前小米就开始投资研发机器人领域，目前汽车工厂正在试用相关 能力，小米的汽车芯片正在研发中，预计也将很快推出。 图片系AI生成 【产业互联网周报是由钛媒体TMTpost发布的特色产品，将整合本周最重要的企 ...

要闻提示 NEWS REMIND 1.字节跳动内容质量负责人李彤离职，CQC业务变化折射AI冲击人工审核 2."疯狂的石头"再出发！石头科技冲刺港股二次上市 3.比亚迪李云飞回应"车圈恒大""常压油箱"事件：油箱合规、财务没问题 4."一年能省几千万"！周鸿祎：准备干掉360整个市场部 5.吉利李书福：有些企业的竞争方式令人难以启齿 6.小红书估值350亿美元，朱啸虎：没有股东愿意卖 7.特斯拉人形机器人Optimus项目负责人宣布离职：希望更多陪伴家人 8.扎克伯格豪赌AI，Meta拟斥资超百亿美元投资Scale AI 今日头条 HEADLINE NEWS "一年能省几千万"！周鸿祎：准备干掉360整个市场部 6月6日晚间，360集团创始人、董事长周鸿祎在其个人自媒体账号发文称，"我准备干掉360整个市场 部，这样一年可以给公司省下几千万。"他表示，从当天起，要做一个挑战，一个人完成一场完整的新产 品发布会。"听起来像天方夜谭，但这次我准备动真格的了。" 周鸿祎称，过去做一个产品发布会，需要市场部几十号人，忙活大半个月，费时费力费钱，还让他不满 意，这次他自己一个人全包了。周鸿祎还称，这不是一拍脑袋就做的决 ...

"我准备干掉 360 整个市场部，这样一年可以给公司省下几千万 元 。" 6 月 6 日， 360 集团创始人、董事长兼首席执行官周鸿祎在其个人自媒体账号上宣布。 周鸿祎表示，这听起来像天方夜谭，但是他准备动真格了。 "从今天起我要准备做一个挑 战，我一个人要完成一场完整的新产品发布会——测试公司 6 月 11日 即将发布的纳米 AI 超级搜索智能体。"周鸿祎称。 往期回顾 据周鸿祎早前视频介绍，他提到的即将发布的纳米 AI 超级搜索，是一款 AI 搜索类的超级 智能体，不同于以往 AI 搜索只能生成文字答案，纳米 AI 超级搜索可以在没有人工干预的 情况下自主进行思考、直接调用浏览器和外部工具自动执行任务等。 去年 12 月， 360 曾在互动平台表示， 2024 年来， 公司相继发布了 " 纳米搜索（原 360AI 搜索） " 、 "360AI 浏览器 " 和 "360AI 办公 " 三大 AI 原生产品，并持续进行产 品迭代升级，用户体验和用户黏性持续保持高速增长。 另据报道，今年 4 月 23 日， 360 旗下纳米 AI 正式发布 "MCP （大模型上下文协议）万 能工具箱 " ，周鸿祎介绍，万能工 ...

Core Viewpoint - The CEO of 360 Group, Zhou Hongyi, announced a personal challenge to conduct a complete product launch for the upcoming Nano AI Super Search Intelligent Agent, aiming to save millions by eliminating the marketing department's involvement [2][4]. Group 1: Company Strategy - Zhou Hongyi plans to replace the traditional marketing department's extensive involvement in product launches, which typically requires dozens of staff and significant time and resources, with a streamlined approach led by himself [4]. - The challenge aims to validate the potential of AI to transform individuals into "super individuals" capable of directing multiple intelligent agents to perform tasks, potentially replacing many traditional employee roles [4][6]. Group 2: Product Development - The upcoming Nano AI Super Search is designed to autonomously think and execute tasks without human intervention, distinguishing it from previous AI search tools that only generated text-based answers [6]. - In December 2023, 360 announced plans to release three AI-native products in 2024, including "Nano Search," "360 AI Browser," and "360 AI Office," with ongoing product iterations aimed at enhancing user experience and engagement [6].

Group 1 - The strategic cooperation agreement between the Guangxi Zhuang Autonomous Region government and 360 Group focuses on artificial intelligence and digital security industries, aiming to develop a series of strategic collaborations targeting Guangxi and ASEAN [2] - Key areas of the agreement include the establishment of 360 Group's ASEAN headquarters, the creation of an "AI + digital security" industry ecosystem, and the promotion of mature applications like nano AI search and secure cloud services [2] - The partnership will explore "AI + infrastructure" research, enhance regional digital economy through smart port scenarios, and establish a talent cultivation system through research institutes and academies [2][3] Group 2 - 360 Group's founder emphasized Guangxi's role as a frontier for China's cooperation with ASEAN, highlighting the rich application scenarios for artificial intelligence and the broad cooperation potential [3] - The company plans to leverage the cooperation agreement to enhance investments in AI security and industry development in Guangxi, accelerating the construction of the ASEAN headquarters and AI application and security research institutes [3] - 360 Group aims to participate in the establishment of the China-ASEAN AI Innovation Cooperation Center and the China-ASEAN AI Security Demonstration Center, focusing on creating exemplary projects in education, healthcare, and tourism [3]

Core Insights - The article discusses the core technologies of multimodal large models, focusing on representation learning, translation, alignment, fusion, and collaborative learning [1][2][7][11][14]. Representation Learning - Representation learning is fundamental for multimodal tasks, addressing challenges such as combining heterogeneous data and handling varying noise levels across different modalities [1]. - Prior to the advent of Transformers, different modalities required distinct representation learning models, such as CNNs for computer vision (CV) and LSTMs for natural language processing (NLP) [1]. - The emergence of Transformers has enabled the unification of multiple modalities and cross-modal tasks, leading to a surge in multimodal pre-training models post-2019 [1]. Translation - Cross-modal translation aims to map source modalities to target modalities, such as generating descriptive sentences from images or vice versa [2]. - The use of syntactic templates allows for structured predictions, where specific words are filled in based on detected attributes [2]. - Encoder-decoder architectures are employed to encode source modality data into latent features, which are then decoded to generate the target modality [2]. Alignment - Alignment is crucial in multimodal learning, focusing on establishing correspondences between different data modalities to enhance understanding of complex scenarios [7]. - Explicit alignment involves categorizing instances with multiple components and measuring similarity, utilizing both unsupervised and supervised methods [7][8]. - Implicit alignment leverages latent representations for tasks without strict alignment, improving performance in applications like visual question answering (VQA) and machine translation [8]. Fusion - Fusion combines multimodal data or features for unified analysis and decision-making, enhancing task performance by integrating information from various modalities [11]. - Early fusion merges features at the feature level, while late fusion combines outputs at the decision level, with hybrid fusion incorporating both approaches [11][12]. - The choice of fusion method depends on the task and data, with neural networks becoming a popular approach for multimodal fusion [12]. Collaborative Learning - Collaborative learning utilizes data from one modality to enhance the model of another modality, categorized into parallel, non-parallel, and hybrid methods [14][15]. - Parallel learning requires direct associations between observations from different modalities, while non-parallel learning relies on overlapping categories [15]. - Hybrid methods connect modalities through shared datasets, allowing one modality to influence the training of another, applicable across various tasks [15].

Core Insights - The EDR concept has seen a significant increase of 4.75%, ranking fifth among concept sectors, with 19 stocks rising, including notable gainers like Derun Electronics and Qiming Information, which hit the daily limit up [1][2] Market Performance - The top-performing concept sectors today include: - Digital Currency: +6.50% - Electronic ID: +5.62% - Cross-border Payment (CIPS): +5.33% - Mobile Payment: +5.30% - EDR Concept: +4.75% [2] Capital Flow - The EDR concept sector experienced a net inflow of 752 million yuan, with 17 stocks receiving net inflows, and 6 stocks exceeding 50 million yuan in net inflow. The leading stock in net inflow is Xingmin Zhitong, with 165 million yuan [2][3] - The top stocks by net inflow rate include: - Qiming Information: 39.59% - Derun Electronics: 34.86% - Xingmin Zhitong: 34.76% [3] Stock Performance - Key stocks in the EDR concept and their performance: - Xingmin Zhitong: +9.97%, turnover rate 10.33%, net inflow 164.89 million yuan - Qiming Information: +10.02%, turnover rate 4.64%, net inflow 140.35 million yuan - Derun Electronics: +10.09%, turnover rate 8.60%, net inflow 109.55 million yuan [3][4]

Core Insights - The multi-modal large model market in China is projected to reach 15.63 billion yuan in 2024, an increase of 6.54 billion yuan from 2023, and is expected to grow to 23.48 billion yuan in 2025, indicating strong market demand and government support [1][6][19] Multi-Modal Large Model Industry Definition and Classification - Multi-modal large models are AI systems capable of processing and understanding various data forms, including text, images, audio, and video, using deep learning technologies like the Transformer architecture [2][4] Industry Development History - The multi-modal large model industry has evolved through several stages: task-oriented phase, visual-language pre-training phase, and the current multi-modal large model phase, focusing on enhancing cross-modal understanding and generation capabilities [4] Current Industry Status - The multi-modal large model industry has gained significant attention due to its data processing capabilities and diverse applications, with a market size projected to grow substantially in the coming years [6][19] Application Scenarios - The largest application share of multi-modal large models is in the digital human sector at 24%, followed by gaming and advertising at 13% each, and smart marketing and social media at 10% each [8] Industry Value Chain - The industry value chain consists of upstream components like AI chips and hardware, midstream multi-modal large models, and downstream applications across various sectors including education, gaming, and public services [10][12] Competitive Landscape - Major players in the multi-modal large model space include institutions and companies like the Chinese Academy of Sciences, Huawei, Baidu, Tencent, and Alibaba, with various models being developed to optimize training costs and enhance capabilities [16][17] Future Development Trends - The multi-modal large model industry is expected to become more intelligent and humanized, providing richer and more personalized user experiences, with applications expanding across various fields such as finance, education, and content creation [19]

金融界5月27日消息，上证指数低开震荡，中证软件服务指数 (中证软件，930601)下跌0.94%，报5572.6 点，成交额126.59亿元。 从中证软件服务指数持仓样本的行业来看，信息技术占比100.00%。 资料显示，指数样本每半年调整一次，样本调整实施时间分别为每年6月和12月的第二个星期五的下一 交易日。权重因子随样本定期调整而调整，调整时间与指数样本定期调整实施时间相同。在下一个定期 调整日前，权重因子一般固定不变。特殊情况下将对指数进行临时调整。当样本退市时，将其从指数样 本中剔除。样本公司发生收购、合并、分拆等情形的处理，参照计算与维护细则处理。 数据统计显示，中证软件服务指数近一个月上涨0.11%，近三个月下跌19.66%，年至今上涨0.07%。 据了解，中证软件服务指数选取30只业务涉及软件开发、软件服务等领域的上市公司证券作为指数样 本，以反映软件服务产业上市公司证券的整体表现。该指数以2004年12月31日为基日，以1000.0点为基 点。 从指数持仓来看，中证软件服务指数十大权重分别为：科大讯飞（11.77%）、金山办公（8.94%）、同 花顺（7.48%）、润和软件（5.62%）、 ...

据悉，360目前已独立发现并披露57个境外APT组织。（文猛） 责任编辑：杨赐 据警方调查掌握，此次发起攻击的黑客组织近年来频繁针对中国大陆地区10余个省份的1000余个重要网 络系统（涉及军工、能源、水电、交通、政府等）开展大规模网络资产探查，搜集相关系统基础信息和 技术情报，并通过大范围发送钓鱼邮件、公开漏洞利用、密码暴力破解、自制简易木马程序等低端网攻 手法实施了多轮次网络攻击。特别是去年以来，该黑客组织针对我境内目标的攻击规模和攻击频次均有 明显提升，骚扰破坏意图明显，用心极其险恶。 360集团创始人周鸿祎介绍，此次360通过网络安全大数据和网络安全智能体的配合，很快辨认出此次攻 击来自于中国台湾省的一个APT（高级持续性威胁）组织。周鸿祎表示，360对中国台湾省APT情况掌 握较早，360发现的APT组织第一个编号就是以中国台湾省的APT组织命名，目前，360已独立发现并命 名了5个中国台湾省APT组织。经过十余年与这些APT组织进行实战对抗的技术积淀和经验，360的安全 团队已全面掌握相关组织的武器库和技战术特征，建立起基于行为模式分析的战术推演模型。 新浪科技讯 5月27日上午消息，广州市公安局 ...