Core Insights - StepFun has completed a B+ round financing of over 5 billion RMB, setting a record for single financing in the large model sector over the past 12 months [1][2] - The company has announced the appointment of Yin Qi as the chairman, who has a significant background in AI and has previously founded Megvii Technology [2][10] - The core decision-making team of StepFun is structured in a "1+3" model, consisting of Yin Qi, CEO Jiang Daxin, Chief Scientist Zhang Xiangyu, and CTO Zhu Yibo [4][5] Group 1: Financing and Leadership Changes - The financing amount surpasses the IPO fundraising amounts of two other companies in the large model sector, Zhiyun and MiniMax [2] - Yin Qi's appointment is seen as a strategic move, given his extensive experience in both AI 1.0 and AI 2.0 eras, and his focus on integrating AI with hardware [12][13] - The "1+3" model is designed to cover four core capabilities essential for the implementation of large models: strategy, algorithms, systems, and engineering [9][36] Group 2: Team Member Profiles - Yin Qi has a unique background that includes founding Megvii Technology and leading it through various stages of AI development, emphasizing the importance of industrial application [11][12] - CEO Jiang Daxin is a globally recognized expert in natural language processing, with a strong engineering background from Microsoft, making him well-suited for leading large model applications [16][18] - Chief Scientist Zhang Xiangyu is known for co-authoring ResNet, a pivotal architecture in deep learning, and is focused on multi-modal models, which aligns with StepFun's strategic direction [20][25] Group 3: Strategic Focus and Market Position - StepFun emphasizes an "AI + terminal" strategy, which aims to integrate AI capabilities with hardware, positioning the company to capitalize on emerging market opportunities [7][52] - The company plans to leverage its unique team composition to address the challenges of algorithm development, system engineering, and commercial viability in the competitive landscape of large models [36][50] - The market for AI terminals is projected to exceed 300 million units by 2026, indicating a significant growth opportunity for companies like StepFun that focus on integrating AI with hardware [52]

Core Insights - The article discusses the recent significant developments at Jieyue Xingchen, including a record-breaking financing round and the appointment of a new chairman, Yin Qi, who brings extensive experience in AI and industry integration [2][3]. Group 1: Financing and Leadership Changes - Jieyue Xingchen completed a B+ round financing exceeding 5 billion RMB, setting a record for single financing in the large model sector over the past 12 months [2]. - Yin Qi has officially joined the core decision-making team as chairman, marking a strategic shift for the company [3]. Group 2: Team Composition and Strategy - The core team is structured in a "1+3" model, consisting of Yin Qi as chairman, CEO Jiang Daxin, Chief Scientist Zhang Xiangyu, and CTO Zhu Yibo, each bringing unique expertise [13][8]. - This team structure aligns with the four essential capabilities required for the implementation of large models: strategy, algorithms, systems, and engineering [15]. Group 3: Individual Contributions - Yin Qi has a notable background, being a co-founder of Megvii Technology and has experience in transitioning AI from research to practical applications [6][22]. - CEO Jiang Daxin is recognized for his contributions to natural language processing and has extensive experience in large-scale online systems, making him well-suited for leading the application of large models [28][30]. - Chief Scientist Zhang Xiangyu is a co-author of ResNet, a pivotal architecture in deep learning, and is focused on multi-modal models, which is a distinctive feature of Jieyue's approach [35][43]. - CTO Zhu Yibo has a strong background in AI infrastructure, having built significant AI systems at ByteDance and Google Cloud, which positions Jieyue uniquely in the competitive landscape [51][56]. Group 4: Market Position and Future Outlook - The AI+ terminal model is seen as a blue ocean opportunity, with projections indicating that by 2026, AI terminal shipments in China will exceed 300 million units, with a penetration rate expected to surpass 93% by 2027 [85]. - The company aims to achieve significant milestones by 2026, including having 1 million vehicles equipped with its intelligent driving system and developing top-tier foundational models [90].

Core Insights - The article provides a comprehensive guide for newcomers on how to improve the quality and acceptance rate of research papers in the field of deep learning, based on the author's personal experiences and reflections during the submission process [2][3]. Paper Production and Submission Process - The typical process for producing and submitting a deep learning paper involves generating a good idea or experimental results, expanding on them, and writing a structured paper according to the conference's requirements [3][4]. - After submission, if there are no serious issues, the paper enters the review stage, where feedback is provided by three reviewers, and authors must respond to comments, often leading to a significant number of papers being withdrawn from consideration [4][5]. Importance of Writing Quality - Writing a good paper is crucial as it serves as a vehicle for conveying ideas and can significantly impact an author's career; high-quality papers are more likely to be cited and recognized [7][8]. - The quality of a paper can reflect an author's research achievements, with a few outstanding papers often defining a scholar's career [7]. Innovation and Core Ideas - The concept of novelty is central to deep learning papers, where innovation can be measured by the impact of the problem addressed, the effectiveness of the solution, and the novelty of the methods used [10][11]. - Authors should clearly define their core ideas and potential impact when selecting topics and writing papers, ensuring that their contributions are well-articulated [11]. Writing Techniques - Effective writing in deep learning papers often follows a structured approach, where the title and abstract are critical for attracting readers and matching appropriate reviewers [13][14]. - The introduction should clearly present the importance of the problem and the proposed solution, while the experimental section should demonstrate the effectiveness of the approach [15][16]. Common Reviewer Feedback - Common negative feedback from reviewers includes perceived lack of understanding of the field, unclear contributions, and failure to respect prior work [22][24]. - Authors are encouraged to address potential issues before submission by considering common criticisms and ensuring their papers are well-structured and clearly articulated [22][24].

本期内容是拾象 CEO 李广密对大模型公司阶跃星辰首席科学家张祥雨的访谈。 张祥雨专注于多模态领域，他提出了 DreamLLM 多模态大模型框架，这是业内最早的图文生成理解 一体化的多模态大模型架构之一，基于这个框架，阶跃星辰发布了中国首个千亿参数原生多模态大 模型 Step-1V。此外，他的学术影响力相当突出，论文总引用量已经超过了 37 万次。 一直以来，业界都相当期待一个理解、生成一体化的多模态，但直到今天这个模型还没出现，如何 才能达到多模态领域的 GPT-4 时刻？这一期对谈中，祥雨结合自己在多模态领域的研究和实践历 程，从纯粹的技术视角下分享了自己对多模态领域关键问题的全新思考，在他看来，虽然语言模型 领域的进步极快，但多模态生成和理解的难度被低估了： • 接下来 2-3 年，多模态领域会有两个 GPT-4 时刻：多模态推理和自主学习； • o1 范式的技术本质在于激发出 Meta CoT 思维链：允许模型在关键节点反悔、重试、选择不同分 支，使推理过程从单线变为图状结构。 目录 01 研究主线： 重新回归大模型 • 多模态生成理解一体化难以实现的原因在于，语言对视觉的控制能力弱，图文对齐不精确， ...