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].

Investment Rating - The industry investment rating is "Positive" (maintained) [2] Core Insights - The report highlights the continuous innovation in AI applications across various sectors such as social media, publishing, and e-commerce, with significant advancements in AI models and their commercial viability [4][32] - The gaming sector is experiencing a surge with new game launches and IP products, indicating potential revenue growth for companies involved [5][12] - The report suggests a focus on AI applications and their commercialization, recommending specific companies for investment based on their market positioning and product offerings [4][5] Industry Data Overview - The mobile game "暴吵萌厨" ranked first in the iOS free games chart in mainland China, while "王者荣耀" topped the iOS revenue chart [12][17] - The film "水饺皇后" achieved the highest box office revenue for the week, indicating strong performance in the film sector [27] - The report notes that the A-share media sector outperformed major indices, suggesting a positive market trend [9] Industry News Summary - AI technology continues to evolve, with breakthroughs in generative AI and applications in various fields, including gaming and entertainment [32] - The report emphasizes the importance of new game releases and IP product launches as key drivers for revenue growth in the gaming sector [5][12] - The report also discusses the performance of various media products, including TV dramas and variety shows, highlighting their market share and audience engagement [28][29][30]

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 ...

Core Viewpoint - The cloud gaming industry continues to lead the market, with significant gains in the information security and big data sectors, as evidenced by the performance of related ETFs [1] Group 1: ETF Performance - Several ETFs related to big data have shown a half-day increase of over 1%, with specific ETFs like the Information Security ETF (159613) and the Big Data Industry ETF (516700) rising by 1.59% and 1.51% respectively [2][3] - The Information Security ETFs (159613 and 562920) have small scales, with half-day trading volumes below 10 million yuan, tracking the CSI Information Security Theme Index [3] Group 2: Index Composition - The CSI Information Security Theme Index includes listed companies involved in information security technology, products, and services, reflecting the overall performance of securities in the information security sector [4] - Major stocks in the CSI Information Security Theme Index include Inspur Information (5.78%), Unisoc (5.39%), and Hikvision (5.02%) [5] Group 3: Big Data Sector - The Big Data Industry ETF (516700), Big Data ETF (515400), and Data ETF (516000) also experienced gains exceeding 1%, with the Big Data ETF (515400) having a large scale of 2.3 billion shares and a half-day trading volume of 96.73 million yuan [6] - The CSI Big Data Industry Index selects companies involved in big data storage, analysis, operations, and applications, reflecting the overall performance of securities in the big data sector [6][7] - Key stocks in the CSI Big Data Industry Index include iFlytek (9.23%), Inspur Information (8.72%), and Unisoc (6.22%) [7]

Core Insights - The Chinese cybersecurity industry market size is projected to exceed 95 billion yuan in 2024, following a market size of over 85 billion yuan in 2023, with a compound annual growth rate (CAGR) of 18.94% over the past five years [1][3]. Market Segmentation - The cybersecurity market can be divided into cybersecurity products and cybersecurity services, with cybersecurity software products accounting for over 50% of the market share in 2023, and cybersecurity software products alone making up more than 30% of the overall market size [3]. Industry Competition - The Chinese cybersecurity industry has established several leading domestic companies, including 360, Anheng Information, and Qi An Xin, which have a broad product layout. Popular product areas include firewalls, endpoint management, and vulnerability scanning [5].

Core Insights - The article discusses the development and support of the multimodal large model industry in China, highlighting various policies and initiatives at both national and local levels aimed at enhancing AI capabilities and applications [1][4][11]. Policy Development Timeline - In 2023, local policies began to emerge, focusing on computational power to encourage the development of large model technology and innovative application scenarios, starting with Guangdong, Beijing, and Shanghai. By 2024, more regions are expected to introduce relevant policies aimed at improving administrative efficiency [1]. - By 2025, government work reports will emphasize the ongoing promotion of the "Artificial Intelligence +" initiative, with a focus on supporting the widespread application of large models [1]. National Policy Summary - The Chinese government has implemented several measures to support the AI industry, particularly multimodal large models, which are seen as crucial products within the AI sector. The State Council has identified embodied intelligence as a future industry, promoting the integration of digital technology with manufacturing and market advantages [4][5]. - Key national policies include the "Guidelines for the Development of Artificial Intelligence Industry" and the "Three-Year Action Plan for Data Elements," which aim to enhance data utilization and promote high-quality economic development through data-driven initiatives [11][13]. Local Policy Highlights - Various provinces have introduced specific policies to support the development of AI large models. For instance, Guangdong aims to develop a comprehensive technology system for large models with trillion-parameter capabilities, while Beijing targets the creation of 3-5 advanced, controllable foundational model products by the end of 2025 [13][15]. - Local initiatives also include the establishment of intelligent computing centers and the promotion of AI applications in various sectors, such as manufacturing, healthcare, and urban governance [13][14]. Key Development Directions - The article outlines that provinces like Guangdong, Beijing, and Shanghai have set ambitious goals for the development of large models, focusing on creating a robust ecosystem for AI innovation and application [15]. - The emphasis is on fostering collaboration between government, industry, and academia to drive advancements in AI technologies and their practical applications across different sectors [15].