例如，上海人工智能实验室已发布并开源"书生"科学多模态大模型，在"通专融合"方面进行了诸多探索 ——它融合了蛋白质序列、基因组、化学分子式、脑电信号等科学数据，经过科学推理专项优化，具备 严谨的逻辑推理能力和科学多模态能力。目前，基于"书生"大模型，我们推出了科学发现平台，并与国 内多家科研单位合作，成功研发基于人工智能的量子计算中性原子排布算法、专注于靶标发现与临床转 化评估的多智能体系统等。目前，该平台在量子计算、药物研发、高分子化学等方面均取得了进展。 人工智能驱动科学研究快速发展，这一趋势已成为科学界的共识。要通过"通专融合"，推动整个科研范 式的系统性变革 人工智能以其基础性、平台性和通用性特点，在多个维度上对经济社会产生深远影响，科学研究就是其 中的重要方面。国务院日前印发的《关于深入实施"人工智能+"行动的意见》提出加快实施六大重点行 动，其中之一就是"人工智能+"科学技术。近年来，人工智能在生命科学、数学、生物、材料科学等领 域发挥着越来越重要的作用。人工智能驱动科学研究快速发展，这一趋势已成为科学界的共识。 科学研究离不开相互影响的三个要素：研究者、研究工具和研究对象。从研究者角度看，人工智 ...

Core Insights - The article highlights the launch of the Intern-S1 multimodal model by Shanghai AI Laboratory, which is positioned as a leading open-source model in the field of scientific research, showcasing significant advancements in AI for science [5][12][17]. Group 1: Model Capabilities - Intern-S1 is recognized for its superior performance in scientific reasoning tasks, outperforming leading closed-source models like Grok-4, particularly in fields such as chemistry, materials science, and biology [12][17]. - The model integrates a 235 billion parameter MoE language model and a 6 billion vision encoder, trained on 5 trillion tokens, with over 2.5 trillion tokens specifically from scientific domains [25][21]. - Intern-S1 demonstrates a 70% improvement in compression rates for chemical formulas compared to previous models, indicating enhanced efficiency in processing complex scientific data [26]. Group 2: Technological Innovations - The model employs a dynamic tokenizer and temporal signal encoder to effectively handle various complex scientific modalities, addressing challenges posed by data heterogeneity and semantic understanding [26]. - Intern-S1's training costs for reinforcement learning have been reduced by tenfold due to collaborative breakthroughs in system and algorithm optimization [30]. - The model's architecture allows for a unique "cross-modal scientific analysis engine," enabling it to interpret complex scientific data such as chemical structures and seismic signals accurately [16][17]. Group 3: Open Source and Community Engagement - Since its initial release in 2023, the "ShuSheng" model family has been continuously upgraded and expanded, fostering an active open-source community with participation from hundreds of thousands of developers [32][33]. - The Shanghai AI Laboratory has launched a comprehensive open-source toolchain that includes frameworks for data processing, pre-training, fine-tuning, deployment, and evaluation, aimed at lowering barriers for research and application [32]. - The Intern-Discovery platform, based on Intern-S1, has been introduced to enhance collaboration among researchers, tools, and research subjects, promoting a new phase of scientific discovery [6][33].

Core Insights - DeepSeek has sparked widespread discussions across various industries regarding the "AI+" movement since the beginning of this year [1] - Digital China (000034) reported a revenue of 29.65 billion yuan from its cloud services and software business, driven by AI, marking an 18.75% year-on-year increase [2] - The overall revenue for Digital China in 2024 reached 128.166 billion yuan, a 7.14% increase, achieving a five-year high [2] Financial Performance - Digital China's net profit decreased by 35.57% to 777 million yuan due to asset impairment related to the International Innovation Center (IIC) [3] - Excluding the negative impact from IIC, the net profit was 1.305 billion yuan, showing positive growth [3] - Revenue from traditional IT distribution and value-added services was 124.451 billion yuan, up 6.84% year-on-year [4] AI Strategy and Developments - AI has become the core of Digital China's cloud integration strategy, with significant investments in AI capabilities [4] - The company launched the Shenzhou KunTai AI-native empowerment platform, reinforcing its position in the AI application sector [5] - Digital China is focusing on process re-engineering and optimization through AI to drive continuous innovation and breakthroughs for enterprises [2][6] Market Trends and Future Outlook - The current phase of AI application is described as the "beginning," with many enterprises yet to fully leverage AI's potential [6] - The integration of AI into business processes is expected to redefine core competitiveness, transitioning from traditional static operations to dynamic systems centered around intelligent agents [6][7] - Future AI applications for enterprises will likely involve heterogeneous computing and the integration of various models, supported by extensive internal data [7]