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腾讯研究院AI速递 20250821
腾讯研究院· 2025-08-20 16:01
生成式AI 一、 Meta重组AI部门,将超级智能实验室拆分为四个团队 https://mp.weixin.qq.com/s/GzaD8SM82Sy5IdQ6wBjl2g 二、 实测最新DeepSeek V3.1 Base,不止拓展上下文长度 1. DeepSeek V3.1相比V3不仅拓展上下文长度至128k,在编程表现、创意写作、翻译水平和回答语气等方面均有明 显提升; 2. 实测表明,V3.1在代码能力方面更全面周到,考虑了更多可能性并主动提供使用说明,支持更激进的压缩策略; 3. V3.1在Reddit测试中获得71.6%分数,成为非推理模型SOTA,比Claude Opus 4高1%但价格便宜68倍。 https://mp.weixin.qq.com/s/x0X481MgH_8ujjB0_XL4SQ 三、 智谱发布AutoGLM 2.0,为Agent配置云手机/云电脑 1. Meta将超级智能实验室拆分为TBD Lab(研究新版Llama)、FAIR(长期研究)、产品应用团队和基础设施四个 部门; 2. 新团队已讨论将Meta下一代AI模型改为闭源模式,可能放弃Llama 4转而从头开发新模型,动摇 ...
美股大跌的导火索,这篇MIT的报告有什么特别?
水皮More· 2025-08-20 09:31
Core Viewpoint - A recent MIT report reveals that up to 95% of companies are not seeing any returns from their investments in generative AI, raising concerns about the sustainability of the AI hype and its ability to translate into profits for businesses [5][6][9]. Group 1: Market Reaction - The report has led to a significant sell-off in the tech sector, with the Nasdaq Composite Index dropping 1.4%, marking its largest single-day decline since August 1 [6]. - Major beneficiaries of the AI boom, such as Nvidia, saw a decline of 3.5%, while companies like Palantir and Arm experienced drops of 9.4% and 5%, respectively [6]. - Defensive sectors like consumer staples, utilities, and real estate saw gains, indicating a shift of funds away from high-risk tech stocks [6]. Group 2: Findings from the MIT Report - The report titled "The Generative AI Gap: The State of Business AI in 2025" indicates that despite high expectations, most generative AI projects fail to deliver financial impact [9]. - Only about 5% of AI pilot projects have achieved rapid revenue growth, while the majority have stagnated without measurable effects on profit and loss statements [10]. - The report attributes the failures not to the quality of AI models but to internal organizational issues and integration strategies [10]. Group 3: Success Factors and Strategies - Successful AI implementations often involve identifying a specific pain point and executing well, with some startups reportedly increasing their revenue from zero to $20 million within a year [12]. - Over half of the generative AI budgets are allocated to sales and marketing tools, but the highest ROI comes from back-office automation [12]. - Purchasing AI tools from specialized vendors and forming partnerships has a success rate of about 67%, compared to only one-third for companies building their own systems [13]. Group 4: Valuation Pressures and Market Sentiment - The report's release coincides with growing concerns over high valuations in the tech sector, with the Nasdaq 100 index's expected P/E ratio at 27, significantly above its long-term average [15]. - Sam Altman's warning about potential investor losses and the possibility of an AI bubble has further fueled market anxiety [15]. - The market has shown sensitivity to negative news regarding AI, with past incidents causing notable fluctuations in stock prices [15].
当大模型实现 3D 实时互动,AI 娱乐的未来是什么?|科技早知道
声动活泼· 2025-08-20 08:48
Core Viewpoint - The article discusses the rapid advancements in AI technologies, particularly in the realm of interactive entertainment, highlighting the emergence of AI-native startups that redefine content, social interactions, and entertainment forms [2][3]. Group 1: AI in Interactive Entertainment - The integration of AI in gaming and interactive entertainment is becoming a central topic among players and investors, as seen at the ChinaJoy exhibition [4]. - Users have high expectations for new interactive entertainment forms, with traditional gaming being gradually deconstructed by AI, leading to faster content consumption and higher demands for emotional value [4][5]. - The blending of AI with gaming, video, and social elements is deepening, driven by advancements in AI-native technologies and large model capabilities [6]. Group 2: Startup Insights and Product Development - Startups like Feeling AI are focusing on creating products that facilitate user-generated 3D content, emphasizing co-creation between AI and users [8][10]. - The company aims to allow users to create unique characters and narratives, fostering social interactions and collaborative storytelling [9][10]. - The importance of understanding user needs and narrative demands is highlighted, with a focus on structured storytelling as a core product feature [11]. Group 3: Future Trends and Market Fit - The article emphasizes the need for startups to find their Product-Market Fit (PMF) by experimenting across various sectors, including gaming, e-commerce, and education [30][31]. - The evolution of AI technologies is expected to redefine industry standards, with a call for companies to embrace innovative models and maintain agility in their approaches [37]. - The future of interactive entertainment is envisioned as a space where users can engage in immersive experiences, potentially transforming traditional content consumption into collaborative creation [40][41].
ChatExcel完成千万天使轮融资,常垒资本、武汉东湖天使基金投资 | 融资首发
Sou Hu Cai Jing· 2025-08-20 08:13
ChatExcel 团队近日已完成近千万天使轮融资。此次融资由上海常垒资本、武汉东湖天使基金投资。本轮资金主要用于加速产品研发迭代,以及全球化市场 运营推广,进一步提升ChatExcel在数据智能体(DataAgent)领域的领先地位。 ChatExcel作为AI Native团队,由北京大学团队创业成立,是国内领先的生成式AI 表格处理与数据智能体,累计服务用户超千万次。获得央视《赢在AI+》 创业大赛智能办公组第一名等多项荣誉,对AI技术保持前沿的探索和商业化落地的落地交付能力。目前已启动PreA轮融资。 一、ChatExcel定义AI DataAgent,打造数据全链路商业闭环平台 ChatExcel凭借其深厚的学术背景与卓越的技术研发能力,在AI 表格处理与DataAgent 技术上取得了突破性进展。用户仅通过对话,即可处理Excel和数据分 析,将用户从繁琐的公式与运算中解放出来,有效降低了Excel和数据使用门槛。 "我们很高兴能够获得这笔天使轮融资,这不仅是对 ChatExcel 团队技术实力和创新理念的认可,更为我们的未来注入了强大的发展动力。" ChatExcel 创始 人逄大嵬表示,"我们 ...
IDC:2024年中国大模型开发平台市场规模达16.9亿元人民币
Zhi Tong Cai Jing· 2025-08-20 05:57
Market Overview - The market size of China's large model development platform is projected to reach 1.69 billion RMB in 2024, driven by various applications aimed at enhancing productivity in both state-owned and private enterprises [1] - The market growth is supported by the development of AI applications, with internet companies favoring public cloud platforms for API integration in entertainment applications [1] Key Players - The top six companies in the market include Baidu Smart Cloud, Alibaba Cloud, SenseTime, Zhipu AI, Telecom AI, and Xiyu Technology [1] - Other notable companies include Zhongshu Ruizhi, which focuses on RAG technology and enterprise-level intelligent self-optimization, as well as Shenzhou Digital and Ruijie Technology, which have launched large model platforms earlier [1] International Expansion - The outbound market for China's large model platforms is expected to reach 860 million RMB in 2024, with applications in generative AI gaining global popularity [3] - Users primarily utilize OpenAI GPT on Azure and Claude on Amazon Web Services, while many Chinese companies also opt for Alibaba Cloud for their large model platform needs [3] Future Development - The construction of large model platforms is currently focused on application development, with a need to lower usage barriers by providing low-code flexible development tools [5] - There is also a demand for high-code development tools aimed at professionals to enhance platform capabilities [5]
重压之下的陈立武:能否复刻格鲁夫式“死亡之谷”的穿越?
首席商业评论· 2025-08-20 04:26
Core Viewpoint - Intel is facing significant challenges under CEO Pat Gelsinger, including a projected net loss of $18.8 billion in 2024 and a nearly 60% drop in stock price, leading to its removal from the Dow Jones index [3] Group 1: Leadership and Management Challenges - Pat Gelsinger's leadership has been marked by a dramatic political episode, where he was publicly called to resign by Trump due to alleged conflicts of interest, but later received praise after a meeting [2] - Gelsinger has initiated a major restructuring effort, including a 50% reduction in management layers and a global workforce reduction of approximately 25,000 employees [6] - The historical context reveals that Intel has repeatedly missed critical opportunities over the past two decades, such as rejecting the acquisition of Nvidia and OpenAI, which has contributed to its current struggles [3][4] Group 2: Organizational and Cultural Reforms - Gelsinger has identified the company's bureaucratic structure and rigid management as key issues, stating that the organization is "too slow, too complex, and stuck in its ways" [6] - The new strategy emphasizes a cultural shift towards "engineering-first" principles, focusing on innovation, speed, and execution [6] - Gelsinger's approach reflects the management philosophy of former CEO Andy Grove, who advocated for a flat organizational structure and the elimination of bureaucracy to enhance agility and decision-making [7][8] Group 3: Strategic Focus and Future Outlook - Gelsinger's reforms include pausing non-core capacity expansion projects and focusing on core chip design capabilities, indicating a strategic pivot [6] - The emphasis on direct reporting from key departments to the CEO aims to streamline communication and decision-making processes [6] - The effectiveness of Gelsinger's strategies remains uncertain, as he faces the daunting task of navigating Intel through its current crisis, reminiscent of Grove's challenges in the past [10]
AI“烧钱大战”仍然如火如荼! AI初创公司吞下1220亿美元 一己之力带动VC复苏
智通财经网· 2025-08-20 04:13
Core Insights - The global AI startup funding has reached an astonishing $122 billion since the beginning of the year, with the US market accounting for $104.3 billion, representing 85.5% of the total raised [1] - The AI funding landscape continues to grow, with a projected $110 billion in 2024 and significant investments from major players like Meta and Anduril [1][5] - Despite a slight decrease in total investment from the previous quarter, AI-related funding remains at historically high levels [4][5] Investment Trends - In Q2, global AI startup funding totaled $50 billion, nearly half of the total VC investment of approximately $101.5 billion during the same period [1][5] - The largest funding round this quarter was Meta's $14.3 billion investment in Scale AI, which resulted in CEO Mark Zuckerberg acquiring a 49% stake [5] - There is a notable shift towards AI projects with "intensive infrastructure," supported by significant public and private sector investments [6] Market Dynamics - The AI-driven venture capital market has shown resilience, with a year-over-year growth of 7.28% from 2023 to 2024 and 9.26% from 2024 to 2025, totaling a 17.22% increase over two years [5] - Major VC firms like SoftBank, Andreessen Horowitz, and Sequoia continue to dominate the AI startup funding landscape [7] - The concentration of capital in leading AI startups has created a challenging environment for smaller companies seeking funding [7] Future Projections - OpenAI plans to invest trillions in core AI infrastructure, including AI chips and advanced power systems, indicating a long-term commitment to AI development [8] - Analysts predict that major tech companies will spend over $350 billion on AI infrastructure in 2023, with expectations of nearly 50% growth in 2024 [8] - Morgan Stanley forecasts that the AI investment boom could add $13 to $16 trillion in value to the S&P 500 index, representing a potential 30% increase [9][10]
【点击报名】xMEMS Live - Asia 2025 | 技术研讨会
Cai Fu Zai Xian· 2025-08-20 02:13
2024年11月,xMEMS推出了一款具有突破性的音频新品——Sycamore近场微型扬声器。这款全频、全 硅近场微型扬声器基于xMEMS革命性的"超声波声音"平台打造,凭借仅1mm的超薄厚度即可呈现全频 声音。Sycamore可满足开放式耳机、智能手表、智能眼镜、虚拟现实头戴设备等对高品质近场音频的需 求。在9月份的技术研讨会上,xMEMS将带来Sycamore的设计与应用方案、失真测量等主题分享。 此外,在研讨会上,xMEMS还将分享耳机通过MEMS扬声器实现空间音频的竞争优势,以及使用 Cypress / Alta 设计的主动降噪耳机方案。 xMEMS将于2025年9月16日(台北)、9月18日(深圳)举办【xMEMS Live - Asia 2025】技术研讨会。活动 现场将会呈现独家主题演讲,并提供与音频行业伙伴面对面交流的机会,共同探讨xMEMS高保真音频 解决方案以及PiezoMEMS平台在生成式AI领域的应用等前沿技术,助力市场产品提升音频品质和释放 AI潜能。 在本次技术研讨会上,xMEMS的高管团队、资深技术专家以及企业生态合作伙伴等将齐聚一堂,深入 探索xMEMS固态高保真音频解决方案在 ...
中国零售消费行业生成式AI及数据应用研究报告
3 6 Ke· 2025-08-20 01:37
Core Insights - The retail industry is transitioning from rapid growth to stock competition, necessitating a digital transformation of "people, goods, and scenarios" to enhance operational efficiency and consumer engagement [1][2] - The integration of generative AI and data provides a comprehensive solution for retail companies, enabling them to optimize user operations, internal decision-making, and global expansion [1][52] Industry Growth Dynamics and Trends - Retail consumption is shifting from high-speed growth to stock competition, with a focus on digital reconstruction of consumer touchpoints to match supply and demand accurately [2] - Companies must leverage digital technologies to enhance sales conversion rates and inventory turnover while reducing operational costs [2] Demand-Side Transformation - Post-pandemic, consumers are more rational, leading companies to shift focus from traffic-driven strategies to membership economies [4] - Businesses need to create detailed user profiles and utilize digital tools to effectively target high-intent consumers, thereby increasing customer lifetime value [4] Supply-Side Transformation - The retail market is projected to reach approximately 49 trillion yuan in 2024, with online sales channels continuing to grow [7] - Retail companies must establish efficient data processing systems to support digital integration and leverage AI for precise customer acquisition and operational efficiency [7] Sector-Specific Insights: Beauty Industry - Domestic beauty brands have rapidly increased market share from 43.7% in 2022 to 55.7% in 2024, utilizing KOL evaluations and UGC content to establish a marketing loop [10] - Chinese beauty brands are expanding into Southeast Asia, the Middle East, and Europe, enhancing brand presence through local partnerships and offline stores [10] Sector-Specific Insights: Footwear and Apparel Industry - The footwear and apparel market is experiencing intense competition, requiring companies to develop strong product R&D capabilities and brand recognition [13] - Leading firms are focusing on consumer insights to create differentiated products and using content marketing to enhance brand loyalty [13] Sector-Specific Insights: Home Furnishing Industry - The home furnishing market is transitioning to a replacement phase, with companies seeking growth through international expansion [16] - Firms are building omnichannel operations to enhance customer experience and are increasingly focusing on establishing their own brands overseas [16] Generative AI and Data Applications - The synergy between generative AI and data governance is crucial for maximizing AI value, with high-quality data being essential for effective AI implementation [21] - 71% of companies plan to enhance data-driven decision-making, with generative AI primarily applied in marketing and customer service scenarios [25] Cloud Services and AI Integration - Companies are encouraged to choose cloud service providers with comprehensive data and AI capabilities to lower the barriers to generative AI application [28] - Nearly 90% of companies prefer to engage external service providers for AI development, indicating a strong reliance on cloud vendors for diverse model capabilities [30] Marketing and User Journey - Over 90% of retail companies have adopted generative AI in marketing, addressing high costs and fragmented consumer demands [55] - Generative AI significantly reduces content production costs by approximately 30%, enhancing sales conversion rates [58] Internal Decision-Making and Governance - 93% of companies are building knowledge bases across multiple scenarios, with generative AI enhancing data governance and decision-making efficiency [63] - The integration of generative AI allows for real-time data analysis, shifting decision-making from experience-based to data-driven approaches [49] International Market Expansion - 93% of retail companies are pursuing international business, focusing on high-potential markets in Asia-Pacific, Europe, and North America [74] - Generative AI aids in overcoming language and cultural barriers, facilitating localized marketing and efficient customer service [75]
最新综述!扩散语言模型全面盘点~
自动驾驶之心· 2025-08-19 23:32
Core Viewpoint - The article discusses the competition between two major paradigms in generative AI: Diffusion Models and Autoregressive (AR) Models, highlighting the emergence of Diffusion Language Models (DLMs) as a potential breakthrough in the field of large language models [2][3]. Group 1: DLM Advantages Over AR Models - DLMs offer parallel generation capabilities, significantly improving inference speed by achieving a tenfold increase compared to AR models, which are limited by token-level serial processing [11][12]. - DLMs utilize bidirectional context, enhancing language understanding and generation control, allowing for finer adjustments in output characteristics such as sentiment and structure [12][14]. - The iterative denoising mechanism of DLMs allows for corrections during the generation process, reducing the accumulation of early errors, which is a limitation in AR models [13]. - DLMs are naturally suited for multimodal applications, enabling the integration of text and visual data without the need for separate modules, thus enhancing the quality of joint generation tasks [14]. Group 2: Technical Landscape of DLMs - DLMs are categorized into three paradigms: Continuous Space DLMs, Discrete Space DLMs, and Hybrid AR-DLMs, each with distinct advantages and applications [15][20]. - Continuous Space DLMs leverage established diffusion techniques from image models but may suffer from semantic loss during the embedding process [20]. - Discrete Space DLMs operate directly on token levels, maintaining semantic integrity and simplifying the inference process, making them the mainstream approach in large parameter models [21]. - Hybrid AR-DLMs combine the strengths of AR models and DLMs, balancing efficiency and quality for tasks requiring high coherence [22]. Group 3: Training and Inference Optimization - DLMs utilize transfer learning to reduce training costs, with methods such as initializing from AR models or image diffusion models, significantly lowering data requirements [30][31]. - The article outlines three main directions for inference optimization: parallel decoding, masking strategies, and efficiency technologies, all aimed at enhancing speed and quality [35][38]. - Techniques like confidence-aware decoding and dynamic masking are highlighted as key innovations to improve the quality of generated outputs while maintaining high inference speeds [38][39]. Group 4: Multimodal Applications and Industry Impact - DLMs are increasingly applied in multimodal contexts, allowing for unified processing of text and visual data, which enhances capabilities in tasks like visual reasoning and joint content creation [44]. - The article presents various case studies demonstrating DLMs' effectiveness in high-value vertical applications, such as code generation and computational biology, showcasing their potential in real-world scenarios [46]. - DLMs are positioned as a transformative technology in industries, with applications ranging from real-time code generation to complex molecular design, indicating their broad utility [46][47]. Group 5: Challenges and Future Directions - The article identifies key challenges facing DLMs, including the trade-off between parallelism and performance, infrastructure limitations, and scalability issues compared to AR models [49][53]. - Future research directions are proposed, focusing on improving training objectives, building dedicated toolchains, and enhancing long-sequence processing capabilities [54][56].