摘要 第四范式提供先知 AI 平台、SHIFT 智能解决方案平台和 AIGS 服务，通 过 AutoML 和迁移学习技术，构建低门槛 AI 开发套件及 AIOS 平台，赋 能企业快速构建定制化模型基座，并深挖细分场景，构建多行业解决方 案。 2018 年至 2023 年，第四范式营收高速增长，2023 年达 42 亿元，同 比增长 36.4%。标杆用户收入占比约 60.77%，数量从 18 个增至 139 个，每用户平均收入从 3.9 百万元提升至 8.38 百万元。 第四范式在金融、能源电力等行业营收占比较高，但行业覆盖集中度较 低，抗风险能力较强。2022 年能源电力和金融行业分别占总营收的 20.3%和 16.9%，呈现均衡化发展态势。 公司销售费用率、管理费用率和财务费用率逐年下降，研发投入保持高 位，2023 年研发费用 17.69 亿元，占营收比例 42.08%，持续构建长 期竞争护城河。 公司亏损持续收窄，2023 年归母净利润亏损 9.09 亿元，同比减少 7.36 亿元。通过控制费用和增加高毛利项目，公司未来有望实现盈利。 Q&A 第四范式在 AI 时代企业数字化转型中的角色是什么？ 第四范 ...

Core Viewpoint - The "2025 Intelligent Robot Key Technology Conference" will be held in Qiqihar City from July 22-24, 2025, focusing on advancements in intelligent robotics and their applications across various industries [1]. Group 1: Embodied Intelligence Forum - The "Embodied Intelligence Forum" will take place on the afternoon of July 23, 2025, emphasizing core technological innovations and cross-industry applications in embodied intelligence [2]. - The forum will feature expert reports and PhD flash presentations aimed at promoting the full-chain development of embodied intelligence from theoretical breakthroughs to industrial implementation [2]. Group 2: Expert Reports - Key presentations include: - "Cognitive Navigation Technology for Embodied Intelligence" by Professor Yue Yufeng from Beijing Institute of Technology, addressing dynamic environment perception and autonomous decision-making [3]. - "High-Quality Development Path for Mining Embodied Intelligent Robots" by Wang Lei, focusing on intelligent solutions for specialized scenarios [3]. - "Dynamic Locomotion Control of Legged Robots" by Professor Zhang Guoteng, innovating adaptive technologies for complex terrains [3]. - "Human-Machine Collaboration Driven by Cross-Modal Embodied Intelligence" by Associate Professor Yang Kun, exploring multi-modal perception integration and operational optimization [3]. - "Fall Prediction Research Based on Transfer Learning and Attention Fusion ResNet" by Professor Wu Chuanyan, enhancing intelligent health monitoring systems [3]. - "Skill Learning for Robot Manipulation of Flexible Objects" by Fu Tianyu, tackling challenges in unstructured environments [3]. Group 3: PhD Flash Presentations - The forum will also showcase young scholars presenting cutting-edge research on the application innovations of embodied intelligence in industrial and medical fields, highlighting the youthful energy driving technological implementation [4]. Group 4: Health and Rehabilitation Robots Forum - The "Health and Rehabilitation Robots Forum" will be held on the morning of July 24, 2025, addressing technological solutions to aging challenges [6]. - Expert reports will cover topics such as: - "Robot Empowerment Paths for China's Aging Population" by Zhang Jianhua, outlining technological routes to address aging society issues [6]. - "Technological Innovation in Elderly Care Services and Applications of Care Robots" by Lan Zhi, discussing care scenarios across institutions, communities, and homes [6]. - "Key Technologies and Clinical Research of Lower Limb Rehabilitation Exoskeleton Robots" by Guo Zhao, revealing new mechanisms for gait reconstruction and neural compensation [6]. - "Intelligent Gait Analysis and Clinical Applications" by Ji Bing, driving innovations in AI-enabled rehabilitation assessment paradigms [6]. - "Design and Implementation of Acupuncture Robot Systems" by He Zhaoshui, overcoming automation challenges in traditional therapies [6]. - "Bionic Arm Systems with Multi-Modal Tactile Perception" by Zhang Ting, exploring fine manipulation challenges in human-robot interaction [6]. - "Personalized Rehabilitation Assessment and Motion Control Optimization Driven by Muscle Coordination" by Sheng Yixuan, pioneering personalized functional reconstruction solutions [6]. Group 5: Youth Innovation Reports - The forum will feature flash presentations from young scholars on topics such as: - "Minimum Impact Trajectory Planning for Lower Limb Rehabilitation Robots" by Wang Xincheng [7]. - "Cardiovascular Health Risk Perception Technology Based on Multi-Sensor Fusion" by Xie Shiqin [7]. - "Design and Analysis of Multi-Posture Lower Limb Rehabilitation Robots" by Yu Hongfei [7]. - "Development of Portable Multi-Channel fNIRS Systems" by Xiang Jiayao [7].

Core Viewpoint - The article discusses the evolution and challenges of artificial intelligence (AI) in gaming and virtual environments, emphasizing the importance of interactive learning experiences over traditional pre-training methods. It critiques the limitations of large language models (LLMs) and highlights the need for more effective learning frameworks in AI development [16][18][19]. Group 1: Background and Development - Id Software, founded in the 1990s, played a significant role in the development of iconic games that contributed to GPU advancements and the modern AI landscape [3]. - The author has extensive experience in various tech companies, including Armadillo Aerospace and Oculus, focusing on the development of virtual reality technologies [6][8]. Group 2: Learning and AI Models - The article critiques the effectiveness of LLMs, arguing that many people do not fully understand their limitations, particularly in learning from new environments [16]. - It emphasizes the importance of interactive learning, suggesting that AI should learn through experiences similar to how humans and animals do, rather than relying solely on pre-trained models [16][18]. Group 3: Gaming and AI Interaction - The author notes that traditional gaming AI often relies on internal game structures, which can lead to cheating, while cloud gaming could mitigate this issue [18]. - The article discusses the limitations of current AI models in learning from games, highlighting that significant amounts of experience (e.g., 200 million frames) are required to reach human-level performance [20][34]. Group 4: Challenges in AI Learning - The article identifies ongoing challenges in continuous, efficient, and lifelong learning within AI, which are tasks that even simple animals can accomplish easily [20]. - It points out that many AI systems struggle with learning in complex environments, and traditional reinforcement learning frameworks may not be suitable for all scenarios [30][32]. Group 5: Future Directions - The author proposes a mixed approach to learning environments, combining passive and interactive content to enhance AI learning capabilities [22]. - The article suggests that new benchmarks should be established to evaluate AI performance across various games, focusing on long-term learning and retention of skills [95][97].

Core Points - The third United Nations Ocean Conference, co-hosted by France and Costa Rica, opened in Nice, France on June 9 [1] - The China National Ocean Information Center led a side event titled "Smart Ocean: Innovative Science Leading Action for a Sustainable Future" [1] - The Ministry of Natural Resources of China publicly released the China Global Ocean Fusion Dataset 1.0, which integrates over 40 different data sources and includes China's independent ocean observations [1] Summary by Categories Dataset Features - The China Global Ocean Fusion Dataset (CGOF1.0) has a time span of up to 60 years and a spatial resolution of 10 kilometers [1] - The dataset incorporates advanced AI technologies such as deep learning, transfer learning, and machine learning, resulting in improved accuracy compared to mainstream foreign datasets [1] International Collaboration - The event highlights China's commitment to international collaboration in ocean data sharing and sustainable ocean management [1] - The integration of diverse data sources reflects a global effort to enhance oceanic research and monitoring [1]

2025 年 4 月 29 日,上海交通大学人工智能与微结构实验室李金金教授接受第一财经采访,在直播间"财经 夜行线"探讨人工智能如何重构新型工业化。当前工业面临动态数据解析难、数据标注瓶颈、算力成本 高企等难题,李教授提出需加强多学科交叉,建立专业标注体系。其团队研发的 AI 自控系统引入 "时间维 度",采用轻量化设计,推动工业从 "经验驱动" 转向 "智能驱动"。 在科技飞速发展的当下,人工智能与工业化的融合已成为推动产业变革的核心力量。国内积极探索 AI 与 工业化结合路径,从产业基础再造、产品技术攻关到供应链管理优化等多个方向发力,正逐步改写工业生 产的格局。 在这场变革浪潮中,上海交通大学李金金教授团队脱颖而出,其研发成果为行业发展带来全新突破。在发 酵行业,他们研发的 "基于迁移学习和物理可解释的小样本 AI 工业自动控制系统",创新性地将 "时间维 度" 引入工业控制领域。生物发酵过程中,微生物生长阶段差异大、代谢动态变化受时间影响显著,传统 依赖固定参数和人工经验的生产方式难以应对。而该系统成功攻克生物发酵复杂动态过程的实时预测与 调控难题。企业应用后,可根据实际情况动态调控参数,实时生成最 ...