Group 1 - A-shares indices opened higher on May 28, with the Computer ETF (159998) experiencing a slight decline of 0.47% and a trading volume exceeding 20 million yuan [1] - The Computer ETF (159998) has seen continuous capital inflow over the past 8 trading days, accumulating a net inflow of 112 million yuan, making it the top performer in its category [1] - The latest scale of the Computer ETF is 2.801 billion yuan, making it the largest computer ETF by on-market size [1] Group 2 - The Cloud Computing ETF (517390) has recorded net inflows on 4 out of the last 5 trading days, indicating strong investor interest [2] - The Computer ETF tracks the CSI Computer Index, which includes stocks from companies involved in information technology services, application software, system software, and computer hardware, with top holdings including Hikvision, iFlytek, Kingsoft Office, and others [2] - Huawei has launched the Ascend Super Node technology, which consists of 12 computing cabinets and 4 bus cabinets, achieving the industry's largest scale of 384-card high-speed bus interconnection [2] Group 3 - The AI sector is undergoing rapid evolution, shifting focus from model scale and benchmark performance to user experience and interaction innovation, leading to a new round of industry reshuffling [3] - Generative AI is transitioning from passive response to active execution of complex tasks, enhancing its practical application scenarios and commercialization potential [3] - Domestic AI computing power ecosystems, represented by Ascend, are improving through innovations in underlying architecture and tools, boosting the performance of related industries such as high-speed connectors and liquid cooling [3] Group 4 - As general reasoning capabilities advance, high-value applications in research and programming are expected to unlock first, benefiting software and internet sectors [3] - Hardware demand is anticipated to rise alongside advancements in multimodal technology, maintaining a positive outlook on investment opportunities in the AI computing power sector [3]

每经编辑｜毕陆名 近日，美国最大公共养老基金——加州公务员退休基金（CalPERS）对其投资组合进行了重大调整，大幅减持了苹果公司的股票。 美国证券交易委员会（SEC）披露的文件显示，一季度，CalPERS抛售了510万股苹果公司股票，持仓规模降至3470万股。与此同时，CalPERS增持了 Meta、AMD和麦当劳的股票，增持数量分别为57.9万股、32.5万股和49.4万股。 根据公开资料，CalPERS管理超过5400亿美元（约合人民币近4万亿元）的资产，以支持200多万加州公务员的退休福利。该养老基金一季度抛售苹果股票的 举动，可能预示着大型机构投资者对苹果的信心下降，也加剧了市场对苹果前景的担忧。 据证券时报，对于抛售苹果股票的报道，CalPERS回应媒体称："公司的公共资产投资是以指数为导向的，并使用系统和定量的投资策略进行优化，不受任 何单一时期事件的驱动。因此，我们通常不会对我们的个人持股或交易发表评论。我们的团队将继续评估整体市场状况。" 苹果公司的业务及股价受关税影响比较大。该公司5月初预计，将在第三财季（2025年3月30日至6月28日）因美国关税政策损失约9亿美元。另外，美国总统 特朗 ...

Group 1 - UAE becomes the first country to offer free access to ChatGPT Plus for all citizens, part of a collaboration with OpenAI [1] - Abu Dhabi will establish the Stargate UAE high-performance AI data center, supporting a 1 GW computing cluster with an initial target of 200 MW capacity [1] - The collaboration is part of OpenAI's "nation-focused" initiative, with UAE committing to match US funding, potentially totaling up to $20 billion [1] Group 2 - OpenAI has enabled singing capabilities for GPT-4o, seen as a response to Google's Gemini 2.5 Pro and Veo3 releases [2] - Google's Gemini 2.5 Pro has outperformed OpenAI and Claude models in several benchmark tests [2] - Analysts believe that the singing feature of GPT-4o is insufficient to regain market leadership, emphasizing the need for OpenAI to launch GPT-5 soon [2] Group 3 - Claude Opus successfully solved a stubborn bug that had troubled a veteran C++ engineer for four years, taking only a few hours [3] - The AI identified the root cause of the issue through analysis of code libraries and architecture comparisons, which had previously stumped other models [3] - Despite its debugging prowess, AI is still considered to be at a beginner level in writing new code [3] Group 4 - French non-profit AI research organization Kyutai launched Unmute, a modular voice AI system that can quickly add voice interaction capabilities to any text LLM [4] - Unmute features low latency (200-350 ms), streaming speech-to-text and text-to-speech, full-duplex interaction, and 10-second voice cloning, supporting over 70 emotional styles [5] - Kyutai plans to fully open-source Unmute in the coming weeks, including STT (1B parameters) and TTS (2B parameters) models and code [5] Group 5 - Alibaba Tongyi launched QwenLong-L1-32B, a large model addressing long-context reasoning issues, with a maximum context length of 130,000 tokens [6] - The team identified two core challenges: low training efficiency and instability, proposing progressive context expansion techniques and a mixed reward mechanism [6] - QwenLong-L1-32B outperforms models like OpenAI-o3-mini and Qwen3-235B-A22B, showing significant advantages in long document analysis [6] Group 6 - Mita AI Search introduced a new "Ultra" model, achieving a response speed of 400 tokens per second, with most queries answered within 2 seconds [7] - The new model utilizes kernel fusion on GPUs and dynamic compilation optimization on CPUs, achieving performance breakthroughs on a single H800 GPU [7] - Mita offers both "Ultra" and "Ultra·Thinking" modes optimized for different types of questions, along with a temporary speed test site for user experience [7] Group 7 - Thunderbird officially released the AI glasses X3 Pro, featuring a custom large model and full-color display, priced at 8,999 yuan [8] - The X3 Pro utilizes a 4nm Qualcomm Snapdragon AR1 platform and proprietary Firefly light engine with RayNeo waveguide technology, achieving a brightness of 3,500 nits (peak 6,000 nits) and weighing only 76g [8] - The product is available for pre-order and will ship on June 15, supporting AI Agent store and real-world navigation features [8] Group 8 - The core team of Meta's Llama faces significant talent loss, with 11 out of 14 core authors having left, leaving only 3 remaining [10] - Among the departed, 5 joined the French AI open-source startup Mistral, including two main architects of Llama [10] - Meta is under pressure from open-source models like DeepSeek and Qwen, despite investing billions, lacking a dedicated "inference" model [10] Group 9 - The Beihang University team proposed the "Flying-on-a-Word" (Flow) task, enabling drone control through language commands, filling a gap in low-level language interaction control research [11] - The team constructed the UAV-Flow benchmark dataset, containing 30,000 real-world flight trajectories across eight major movement types [11] - The research addressed drone computational limitations by performing model inference at the ground station and providing real-time feedback for control commands [11] Group 10 - NVIDIA experts recommend that students integrate multiple skills and enhance adaptability, not limited to computer science backgrounds, to stand out in the job market [12] - Job seekers should clarify their interests in the AI field, responsibly use AI tools, and build industry connections for career development opportunities [12] - Candidates can showcase their technical abilities, professional knowledge, and innovative thinking through project examples to excel in interviews [12]

以下文章来源于福布斯 ，作者Forbes 福布斯 . 福布斯中国集团是一家集咨询、社群于一体的企业。集团秉承企业家精神和创新精神，坚持专业、公 正、创新和进取的价值观。 作者丨 Richard Nieva 翻译丨 Lemin 图源丨 Nikkei Asia 走进旧金山历史悠久的渡轮大厦内的 Niantic 总部，访客首先会被一群巨大的宝可梦毛绒玩具包围： 在阶梯式剧场般的台阶上，一只巨大的卡比兽正在角落打盹，而妙蛙种子则仿佛随时准备扑向猎物。 不远处，一脸震惊的可达鸭茫然望着远方，似乎想要看清这家公司出人意料的未来。 来源丨福布斯（ID： forbes_china ） 今年 3 月， Niantic 宣布了一则重磅消息： 根据高德纳（ Gartner ）的数据， 2023 年空间计算的市场规模为 1100 亿美元，到 2033 年预计将 增至 1.7 万亿美元，增长动力来自 TomTom 等地图巨头以及谷歌等传统科技巨头的定位服务。 "这是 个巨大的机会。"高德纳新兴技术团队首席分析师阮祥（ Tuong Nguyen ，音译）表示。 但竞争也同样激烈。在空间 AI 领域， Niantic 面临一些强劲对手。 ...

"与 SAP 的合作，进一步增强了我们用世界级技术赋能全球企业的决心。"阿里巴巴集团董事会主席蔡 崇信表示，"通过将 SAP 的企业级软件与阿里云强大的基础设施和AI能力结合，我们正在助力客户打造 更智能、更敏捷的运营体系。" SAP全球CEO柯睿安（Christian Klein）表示："我们的联合市场拓展战略将为企业打开新局面，为他们 带来全方位的工具与服务。我们期待与阿里巴巴携手，帮助双方共同的客户推动创新、优化运营表现、 打造竞争优势，共同引领云技术驱动的数字化转型未来。"（文猛） 责任编辑：何俊熹 作为SAP云服务提供商，阿里云将支持企业部署SAP ERP云上及SAP ERP私有云版本，为各行各业的企 业级客户提供可扩展、安全且智能的解决方案。企业能够部署"SAP集成业务计划云"，并开启RISE with SAP和GROW with SAP旅程。阿里云也将加入最新的 SAP 基础设施即服务（IaaS）认证计划，全面支 持客户运行 SAP 企业解决方案。 该合作还在企业级AI方面迈出重要一步。SAP探索将通义千问大模型接入SAP AI Core中的生成式AI Hub， 使企业客户可以在SAP应用及定制 ...

Core Viewpoint - InSilico Medicine, a pioneer in applying generative AI to drug discovery, is facing significant challenges in commercializing its technology and managing its financial health despite its innovative platform and potential breakthroughs [1][2]. Financial Performance - InSilico Medicine's revenue grew from $30.15 million in 2022 to $85.83 million in 2024, with a compound annual growth rate of 68.7% [2]. - The company has accumulated losses of $591 million from 2021 to 2024, with a net loss of $17.1 million in 2024, a 92% year-on-year decrease, primarily due to one-time licensing fees [2][3]. - The revenue is heavily reliant on three candidate drugs, with slow progress in licensing agreements, exemplified by a $12 billion collaboration with Sanofi, where only 1.04% of the agreement has been realized [2][3]. Client Dependency - The top five clients contributed 90.6%, 94.1%, and 94.4% of the revenue from 2022 to 2024, with the largest client accounting for 76.2% at one point [3]. - If core clients reduce their investments or terminate collaborations, the company's performance may face a sharp decline [3]. Research and Development Costs - R&D expenses reached $91.89 million in 2024, exceeding total revenue by 7% [3]. - Clinical trials are the most expensive phase in drug development, accounting for about 80% of the total R&D costs, while InSilico's pipeline is still in preclinical or early clinical stages [3]. Pipeline Status - InSilico Medicine has 15 candidate drugs, all in preclinical or early clinical stages, with the fastest progressing drug, ISM001-055, only having completed Phase IIa trials [4][6]. Clinical Trial Risks - The lack of Phase II clinical data poses a significant risk, as this stage is critical for validating the potential of drug candidates and the company's technology [6]. - Historical examples in the AI drug development sector show that failures in key clinical trials can lead to drastic declines in company valuations [6]. Data Challenges - The company faces a "data island" challenge, where the fragmented and inconsistent quality of data hampers the effectiveness of its AI-driven drug discovery platform [7]. - The AI drug discovery industry is still in its early stages in China, with data barriers prevalent, making it difficult for companies like InSilico to access high-quality research data [7].

Group 1 - The core revenue for the company in Q1 2025 was $49.44 million, with non-live streaming revenue reaching $12.3 million, representing a year-on-year growth of 25.3% [2] - Live streaming revenue amounted to $37.13 million, with BIGO Live contributing $35.16 million. The company has established a diverse product matrix in live streaming, short videos, and instant messaging, creating a globally influential user community [2] - The live streaming segment enhanced regional user engagement through localized operational strategies, leading to increased user stickiness and improved paid conversion rates. Monthly active users for Bigo Live in North America grew over 7% year-on-year, with paid user numbers increasing approximately 4% quarter-on-quarter [2] Group 2 - In the non-live business segment, the BIGO Ads advertising platform experienced rapid growth, driven by AI-powered user insights, smart creativity, and precise targeting capabilities. The advertising business grew approximately 27% year-on-year in Q1 [3] - The company's chairperson and CEO, Li Ting, stated that 2025 marks the 20th anniversary of the company, and the results of its diversified growth strategy are becoming evident. The company plans to further advance its diversification strategy with the steady development of live streaming and the expansion of advertising and other business scales [3]

Group 1 - Red Hat has launched a new open-source project called llm-d to meet the large-scale inference demands of generative AI, collaborating with CoreWeave, Google Cloud, IBM Research, and NVIDIA [1][3] - According to Gartner, by 2028, over 80% of data center workload accelerators will be deployed specifically for inference rather than training, indicating a shift in resource allocation [3] - The llm-d project aims to integrate advanced inference capabilities into existing enterprise IT infrastructure, addressing the challenges posed by increasing resource demands and potential bottlenecks in AI innovation [3] Group 2 - The llm-d platform allows IT teams to meet various service demands for critical business workloads while maximizing efficiency and significantly reducing the total cost of ownership associated with high-performance AI accelerators [3] - The project has garnered support from a coalition of generative AI model providers, AI accelerator pioneers, and major AI cloud platforms, indicating deep collaboration within the industry to build large-scale LLM services [3] - Key contributors to the llm-d project include CoreWeave, Google Cloud, IBM Research, and NVIDIA, with partners such as AMD, Cisco, Hugging Face, Intel, Lambda, and Mistral AI [3][4] Group 3 - Google Cloud emphasizes the importance of efficient AI inference in the large-scale deployment of AI to create value for users, highlighting its role as a founding contributor to the llm-d project [4] - NVIDIA views the llm-d project as a significant addition to the open-source AI ecosystem, supporting scalable and high-performance inference as a key to the next wave of generative and agent-based AI [4] - NVIDIA is collaborating with Red Hat and other partners to promote community engagement and industry adoption of the llm-d initiative, leveraging innovations like NIXL to accelerate its development [4]

Group 1 - China's aviation engine "Taihang 2" pure hydrogen gas turbine has achieved a cumulative operation time of over 7000 hours for the first unit and over 5000 hours for the second unit, marking a successful commercialization of the 2MW pure hydrogen gas turbine [2] - The low-altitude economy is driving the popularity of drone pilots, with over 225,000 registered drone pilot licenses in China as of June 2024, and more than 2000 training institutions available [3] - Shenzhen has established its first pharmaceutical and medical device industry overseas joint venture, aiming to create a platform for local companies to connect with global markets [4] Group 2 - China's marine economy has surpassed 10 trillion yuan for the first time, showing a growth of 5.9% compared to the previous year, with the marine engineering equipment manufacturing sector maintaining the largest global market share for seven consecutive years [5] - The first large-scale lithium-sodium hybrid energy storage station in China has been put into operation, with a capacity of 400 MWh and a green energy ratio of 98% [6] - The Yukun high-speed railway's Ningjingli tunnel has been safely completed, contributing to the construction of the railway with 40 tunnels already completed in the Yunnan section [7][8] Group 3 - Yunding Technology has launched an industrial vision intelligent all-in-one machine in collaboration with Ascend, featuring high computing power and supporting over 100 channels of 1080P video processing [9] - A Chinese team has overcome challenges in the large-scale production of third-generation photovoltaic technology, achieving stable mass production of perovskite solar cells [10] - QQ Browser has introduced an AI tool named "AI Gao Kao Tong" to assist students in exam preparation and college application processes [11] Group 4 - Market rumors suggest that Sais Technology's humanoid robot prototype is ready for demonstration, although the company has not confirmed this information [12] - U.S. President Trump has threatened to impose tariffs of 50% on the EU and 25% on Apple, causing declines in Apple stock and U.S. stock futures [13] - Experts have commented on the impracticality of relocating iPhone production to the U.S., citing high costs and potential price increases for consumers [14] Group 5 - Japan's consumer price index for rice has seen a dramatic increase of 98.4% year-on-year in April, marking the highest increase since 1971 [15] - Nissan is considering selling its Yokohama headquarters as part of its restructuring plan, which may incur an additional 60 billion yen (approximately 418 million USD) in costs [16] - Elon Musk praised Google's new AI video generation model, Veo 3, during a developer conference, while also announcing his return to a 24/7 work schedule [17]

Core Insights - The emergence of large models in the financial industry presents unprecedented opportunities and challenges, acting as powerful tools for data analysis and decision-making [1] - Concerns regarding data security and algorithmic bias are prevalent as the industry navigates this transformation [1] Group 1: Current State of Large Model Applications - The financial industry in China is leading in the investment and application of large models, with an expected investment scale of 19.694 billion yuan in AI and Generative AI by 2024 [2] - While 18% of global enterprises have integrated Generative AI applications into production environments, only 3% of Chinese enterprises have done so, although 95% are investing or testing [2] Group 2: Mature Application Scenarios - Mature application scenarios for large models in financial institutions include intelligent customer service, internal operations, intelligent investment advisory, marketing, and risk management [3] - Different types of financial institutions adopt varying strategies based on their resources and goals, with larger institutions building comprehensive AI capabilities while smaller ones focus on high ROI scenarios [3][4] Group 3: Balancing Costs and Benefits - Financial institutions face high costs in training large models and must carefully select application scenarios that align with strategic goals to ensure high ROI [5] - Recommendations include using platform and toolchain approaches to reduce costs and improve efficiency in model inference [5] Group 4: Enhancing Data Quality and Model Interpretability - To improve data quality and mitigate AI hallucinations, financial institutions can employ data cleaning, fairness algorithms, and synthetic data generation [6] - Techniques such as LIME and SHAP can enhance model interpretability, providing clearer insights into model outputs [6] Group 5: Future Directions of the AI Industry - The rise of domestic foundational models and accelerated open-source processes are propelling the industrialization of AI applications in China [7] - A balanced approach between private deployment and market-scale applications is essential for fostering disruptive innovations in AI [7]