多模态 AI 技术正以前所未有的速度重塑内容创作领域。 从2024年 OpenAI Sora 点燃全球想象，到近期，吉卜力风图片席卷全网。这个一度被视为 AI 终极想象力边界 的领域，正以前所未有的速度冲破技术壁垒。 视频生成作为技术难度与应用潜力并存的关键环节，也吸引了全球范围内的广泛关注和投入。 在追求更长时长、更高分辨率、更惊艳视觉效果的同时，内容一致性难以保证、生成过程可控性不足、以及高 昂的计算成本等核心挑战，依然限制了其在专业领域、大众娱乐领域的规模化应用。 在此背景下，由生数科技研发的视频生成模型 Vidu，展现出一条差异化的发展路径。在多模态视频生成技术 的早期发展阶段，通过集中资源解决专业用户的核心痛点，如一致性、可控性、效率，建立起差异化优势和用 户基础，尤其是在动画等特定领域形成壁垒。 根据生数科技廖谦在近期访谈中的阐述，Vidu 的核心定位是"全球领先的AI内容生产平台 "，这也意味着 ，除 了追求基础生成能力的提升，也需要优先解决实际工作流中的关键痛点。 比如，生数科技敏锐的发现，纯粹的文生视频因为难以控制一致性，应用者并不多 。而 Vidu 推出的"参考 生"（Reference ...

Core Viewpoint - DeepSeek has released a new model named DeepSeek-Prover-V2-671B, which focuses on formal mathematical reasoning, addressing a significant challenge in AI and opening up high-value commercial opportunities [1][2]. Group 1: Model Development and Impact - DeepSeek-Prover series models combine the generalization capabilities of large language models (LLMs) with formal tools like Lean, achieving large-scale end-to-end conversion from natural language descriptions to machine-verifiable proofs [2]. - This breakthrough could potentially enhance the efficiency of mathematical research several times over and create new possibilities for AI applications in fields that require mathematical rigor, such as financial modeling, chip verification, and cryptography [2]. Group 2: Event Information - A cross-ocean dialogue event will take place on May 9, 2025, featuring DeepSeek's former member Xin Huajian, who will discuss the formal mathematical revolution in the era of large language models [3][4]. - The event will also include a presentation by Zang Tianyu from Jinqiu Capital on AI investment trends for 2025 [3][4]. Group 3: Organizers and Participants - Jinqiu Capital focuses on AI investments and has a 12-year long-term fund, actively supporting early-stage entrepreneurs with a strategy of aggressive follow-on investments [6]. - The Cambridge China AI Association aims to connect the Chinese AI industry with global academia and industry, facilitating efficient resource flow between China and the UK [7].

Core Viewpoint - The article emphasizes the importance of genuine dialogue and face-to-face interactions in fostering valuable insights and potential collaborations among entrepreneurs and investors in the AI sector [4]. Group 1: Event Organization - The company initiated a series of closed-door dinner discussions with entrepreneurs starting from February 26, hosting nine sessions across Beijing, Shenzhen, and Shanghai [2][3]. - The aim is to create a high-quality social environment without formalities, focusing on authentic conversations rather than presentations [3]. Group 2: Discussion Topics - Key topics discussed include the latest trends in technology, products, capital, and industry, as well as real experiences in the generative AI wave [6]. - Specific discussions have covered AI product opportunities, challenges in AI coding, and the current state of AI agents [10][15][24]. Group 3: Insights on AI Applications - Current AI products face challenges in forming a closed-loop data flywheel, with user behavior data not significantly enhancing application outcomes [12]. - The importance of brand perception is highlighted as a critical barrier for AI products at this stage [12]. Group 4: Market Dynamics - The article notes that startups should focus on niche markets overlooked by larger companies, emphasizing speed, iteration, and building brand identity in specific fields [18]. - The discussion also touches on the limitations of current AI models in handling complex tasks and the need for breakthroughs in model theory or architecture [19]. Group 5: Hardware Innovations - The article discusses the emerging opportunities in hardware innovations driven by AI, particularly in voice interaction and personalized devices like smart glasses [43][44]. - It highlights the potential for AI-driven hardware to enhance user experience and engagement, with predictions of significant market growth in the coming years [44]. Group 6: Investment Insights - The article provides insights into the investment landscape, noting a shift in market dynamics between Hong Kong and the US, with increased optimism for AI applications in the Chinese market [56]. - It emphasizes the cautious approach towards consumer-facing AI agents while being more optimistic about vertical agents with clear tasks and data barriers [56].

Core Insights - OpenAI's Deep Research focuses on integrating search, browsing, filtering, and information synthesis into the model's core capabilities through reinforcement learning, rather than relying solely on prompt engineering [1][3][4] Group 1: Origin and Goals of Deep Research - The team shifted from simpler transactional tasks to tackling knowledge integration, which is deemed essential for achieving AGI [3][6] - Emphasis is placed on data quality over quantity, with a preference for expert-annotated high-value examples and reinforcement learning to optimize strategies [3][5] - The ultimate vision is to create a unified intelligent agent that autonomously determines the appropriate tools and maintains continuity in memory and context [3][14] Group 2: Development Process - The development process involved creating a demonstration version based on prompt engineering before focusing on data creation and model training [7][8] - The team utilized human trainers for data handling and designed new data types to train the model effectively [8][10] - Iterative collaboration with reinforcement learning teams allowed for significant improvements without the pressure of rapid product releases [7][8] Group 3: Reinforcement Learning Fine-Tuning (RFT) - RFT can enhance model performance for specific tasks, especially when the task is critical to business processes [9] - If a task is significantly different from the model's training, RFT is advisable; otherwise, waiting for natural model upgrades may be more beneficial [9] Group 4: Role of Human Expertise - High-quality data creation requires domain expertise to assess the validity and relevance of sources [11] - OpenAI's approach involves engaging experts across various fields to create diverse synthetic datasets [11] Group 5: Path to AGI and the Role of Reinforcement Learning - The resurgence of reinforcement learning has bolstered confidence in the path to AGI, though significant work remains to ensure models can effectively utilize tools and evaluate task outcomes [12][13] - A strong foundational model is essential for the success of reinforcement learning efforts [12] Group 6: User Trust and Interaction - Establishing user trust is crucial, necessitating explicit confirmations for significant operations during initial interactions [16] - As models improve, users may gradually allow more autonomy, but initial safeguards are necessary to prevent errors [16][17] Group 7: Future of Intelligent Agents - Future intelligent agents must address complex security issues, especially when accessing sensitive user data [17][19] - The goal is to create agents capable of executing long-duration tasks while effectively managing context and memory [17][21] Group 8: Performance and User Expectations - Users expect instant responses, but Deep Research requires time for in-depth analysis, leading to potential delays [29] - OpenAI plans to introduce products that balance the need for quick responses with the depth of research [29][30] Group 9: Applications and User Feedback - Users have found Deep Research valuable in fields like medical research and coding, validating its effectiveness [25][26] - The model excels in handling specific queries and generating comprehensive reports, making it suitable for detailed research tasks [27]

Core Insights - The article emphasizes the rapid acceptance and integration of AI models in the automotive industry, particularly focusing on the development of intelligent agents and their applications in vehicles [2][4][7]. Group 1: Current Trends and Developments - All major manufacturers have reached a consensus on the application of agents in vehicles, marking a significant shift in the industry's approach to AI technology [4][7]. - The acceptance speed of large model technology by manufacturers has exceeded expectations, with a clear consensus forming among mainstream automakers by early 2024 [8]. - The focus of applications has shifted towards intelligent voice enhancement, multimodal interaction breakthroughs, and the integration of visual foundational models in intelligent driving [8][9]. Group 2: Challenges and Technical Bottlenecks - Key challenges include high inference latency, online inference costs, and the need for significant development to adapt existing hardware for large models [10][12][16]. - Data collection across the vehicle remains difficult due to the current centralized architecture, which leads to inefficiencies in data transmission and limits model training [11][12]. - The existing chips are not designed for large models, leading to computational bottlenecks and challenges in deploying models effectively in vehicles [12][16]. Group 3: Core Capabilities of AI Agents - AI agents are expected to autonomously complete tasks, significantly enhancing user experience compared to traditional assistants [18][20]. - The agents exhibit multimodal perception and understanding, enabling them to recognize various environmental factors and user states [19][22]. - The interaction style has shifted towards voice-driven commands, reducing reliance on complex app interfaces [20][22]. Group 4: Future Directions and Integration - The future of automotive AI will focus on creating a unified AI model that supports both cabin interaction and intelligent driving functions, leading to a more integrated vehicle experience [9][68]. - The development of a central computing architecture will facilitate deeper information sharing and functional collaboration between cabin systems and intelligent driving systems [67][68]. - The industry is moving towards an AI-defined vehicle paradigm, where AI will reshape the entire automotive ecosystem from design to service delivery [69][70].