三体计算星座指挥大厅，科研团队在开展相关研究测试。 之江实验室供图 计算卫星星座发射现场。 汪江波摄 网友：不久前，看到之江实验室把"计算"送上太空的新闻，觉得很有意思。这些年，人工智能深度融入 社会发展和日常生活，也给科技创新带来了很大影响。我很好奇，这样的变革对创新来说意味着什么？ 人工智能时代，科学家应该怎么做科研？未来，人工智能有没有可能替代科学家甚至替代人类？ 今天科研中遇到的许多问题，如果不用人工智能，也是不可持续的。举个例子，如果按照过去做结构生 物学的方法来研究蛋白质结构，会花费大量时间。新药研发也是如此，如果按照传统的方法，难以在短 期内研发出成果。传统的方法论在科学研究上变得越来越不可行了。 因此，人工智能作为方法论，就变得非常通用，已成为科学、技术、工程等领域中绕不开的话题。如果 不掌握新的科研范式，不管科技创新还是工程创新，都将举步维艰。 不要把人工智能当作一个简单的工具。人工智能不是一次工具的革命，而是一种科学革命的工具——它 已经成为一种能够打破学科壁垒的通用语言，而不仅是对科学研究的简单"赋能"。 许多高度复杂的问题，人工智能都可以提供帮助。比如，围棋棋盘由纵横各19条直线交叉组 ...

网友：不久前，看到之江实验室把"计算"送上太空的新闻，觉得很有意思。这些年，人工智能深度融入 社会发展和日常生活，也给科技创新带来了很大影响。我很好奇，这样的变革对创新来说意味着什么？ 人工智能时代，科学家应该怎么做科研？未来，人工智能有没有可能替代科学家甚至替代人类？ 人工智能怎么影响科技创新 尽管人工智能历史很短，但对人类社会的影响很大。人工智能是技术发展到一定阶段产生的，与计算和 互联网密切相关。从某种意义讲，人工智能就是在互联网作为基础设施的条件下，集数据、模型、算力 为一体的产物。我们常说的数据大都是互联网上的数据，算力的出现则是因为半导体的发展。半导体和 互联网的发展，以及模型关键技术的突破，共同带动人工智能的革命性突破。 人工智能对于科技创新的影响是方方面面的。科研范式经历过几次重大变革，人工智能引领科研范式的 变革，使得科研的方法论发生变化。 大家都在说科技创新的引领和驱动作用，但往往容易忽视科技创新是最需要变革的一个领域。科学的发 展时常会遇到瓶颈。比如，发现新材料需要花费十几年乃至更长时间，成本也很高。能不能大幅缩减研 究的时间和成本？这不单是钱的问题，更事关科研范式的变革。任何一个科学发 ...

Group 1 - The 2025 Suzhou International Elite Entrepreneurship Week attracted a total of 2,388 intended cooperation projects, including 2,267 entrepreneurial investment projects and 121 innovation cooperation projects [1] - The proportion of high-end equipment projects reached 19.4%, new generation information technology projects accounted for 18.5%, new materials represented 11.9%, software and information services made up 9.9%, and new energy projects constituted 6.2% [1] - The event facilitated the gathering of high-level talent and contributed to the construction of the "1030" industrial system in Suzhou [1] Group 2 - The event featured a 4,000 square meter exhibition showcasing over 1,300 technological achievements from 24 universities and 41 enterprises, attracting more than 4,200 visitors [2] - The first "Chunhui Innovation Training Camp" was held, attracting over a hundred overseas talents for offline engagement [2] - The 2025 Sino-foreign Academician Frontier Technology Forum included over 30 top academicians and 60 leading scholars in AI and interdisciplinary fields discussing research paradigm shifts and technological innovation paths driven by AI [2] Group 3 - The main opening ceremony of the Entrepreneurship Week was combined with the University Technology Transfer and Transformation Conference and the Second Suzhou International Science and Technology Innovation Conference [3] - The establishment of the "Billion Talent Fund" was announced, with sub-funds focusing on artificial intelligence, low-altitude economy, biomedicine, cultural creativity, and youth entrepreneurship [3] - Three "AI+" work platforms were launched to assist talent in job searching, policy matching, and service access [3]

Core Insights - The integration of AI technology into scientific research is accelerating, as evidenced by the awarding of the 2024 Nobel Prizes in Physics and Chemistry to scholars involved in AI-related studies [1] - The Third International Forum on Frontier Technology held at Soochow University focused on "AI for Science and Technology," highlighting the transformative impact of AI on research paradigms and technological innovation [1] Group 1: AI in Biomedical Research - AI is expected to revolutionize biomedical research, transitioning it from qualitative to quantitative science, as proposed by the Chinese Academy of Sciences [2] - The "Smart Simulation in Biomedical Research" initiative aims to quantitatively describe life phenomena and disease mechanisms using mathematical language [2] - AI is becoming a core driver for enhancing infrared imaging quality, with ongoing collaborations to apply this technology in clinical settings for surgical navigation [2] Group 2: AI in Manufacturing and Data Analysis - The introduction of AI in manufacturing processes is crucial for improving measurement accuracy and product quality, especially as product lifecycles shorten [3] - AI's capabilities in data processing and analysis, prediction, and innovation are significantly benefiting bioanalytical chemistry research [3] - In seismic data interpretation, AI has improved processing efficiency by approximately 70 times, reducing the time required from months to just 1-2 days while handling petabyte-scale data [3]

Group 1 - Artificial intelligence (AI) is recognized as a strategic technology leading a new round of technological revolution and industrial transformation, emphasizing its strong "leading goose" effect [1] - The development of AI is accelerating, driven by advancements in mobile internet, big data, supercomputing, and brain science, reshaping the fundamental logic and methodology of scientific research [1][2] - AI is transitioning from being an "auxiliary tool" to becoming a "research主体," forming a human-machine collaborative research model that enhances research efficiency [3][4] Group 2 - The historical evolution of research paradigms includes three major transformations: the empirical paradigm, the theoretical paradigm, and the computational paradigm, each emphasizing different methodologies [2] - The emergence of AI large models, such as ChatGPT and DeepSeek, marks a new phase in AI development, with "data-driven" and "computational power-driven" approaches becoming core features of the new research paradigm [3] - AI's ability to mine hidden patterns from vast datasets is revolutionizing scientific innovation, as exemplified by AlphaFold's prediction of nearly 200 million protein structures [3] Group 3 - AI is fostering a shift from "island innovation" to "distributed intelligent networks," transforming traditional research organizations into collaborative networks that enhance knowledge production [5] - The integration of AI with various disciplines is creating a cross-disciplinary innovation ecosystem, improving research efficiency and stimulating new discoveries [3][5] - The development of AI is also pushing for a more open and inclusive research paradigm, enhancing fairness in scientific research through open-source models and collaborative platforms [6][7] Group 4 - China is actively exploring pathways for AI-driven research paradigm transformation, focusing on modular research organization capabilities and dynamic team formations for urgent national strategic tasks [7] - The rich application scenarios in China are being leveraged to enhance AI data augmentation, improving innovation capabilities and model accuracy [7] - The integration of Chinese culture with AI modeling thinking is expanding research horizons and application boundaries, as seen in the digital construction of traditional medical theories [7] Group 5 - The rapid development of AI presents both opportunities and challenges, including data security, ethical considerations, and the need for new evaluation systems for AI-generated research outcomes [8] - Establishing a national research computing power network is essential for supporting AI development, ensuring high-level computing resources are available for research innovation [9][10] - Promoting international collaboration in research through open innovation ecosystems can enhance innovation capabilities and improve the global research environment [11]

Core Insights - The article highlights the emergence and capabilities of "machine chemists," which utilize artificial intelligence to revolutionize chemical research and enhance efficiency in scientific experiments [2][3][7]. Group 1: Development of "Machine Chemists" - The traditional chemical research paradigm relies heavily on trial and error, leading to long cycles and high costs for new material creation [3]. - In 2013, a team at the University of Science and Technology of China (USTC) began exploring the use of big data technology to innovate chemical research, addressing issues of low efficiency and data dispersion [3][6]. - After three years of data collection, the "machine chemist" named "Xiao Lai" was developed, integrating mobile robots and intelligent chemical workstations, capable of performing 2,000 precise operations daily, equivalent to the work of five to six researchers [6][8]. Group 2: Achievements of "Xiao Lai" - "Xiao Lai" demonstrated remarkable capabilities in researching Martian oxygen catalysts, identifying optimal solutions in just six weeks, a task that would take human researchers 2,000 years [7]. - The research findings were published in the prestigious journal "Nature Synthesis," showcasing the potential for in-situ chemical production in extraterrestrial environments [7]. Group 3: Advancements with "Xiao Lin" - The second-generation "machine chemist," "Xiao Lin," was introduced, featuring enhanced efficiency and the ability to autonomously design and optimize experiments using generative models [8][11]. - "Xiao Lin" successfully reduced the material screening time for energy-absorbing materials from ten years to seven months, showcasing its advanced analytical capabilities [11]. Group 4: Future Plans and Vision - The research team plans to construct a "machine chemist building" to accommodate hundreds of robots and thousands of intelligent workstations, aiming for a daily experimental capacity of one million operations [12]. - Future iterations of "machine chemists" will include advanced sensory capabilities, allowing them to analyze molecular structures and chemical differences, further enhancing their research capabilities [12].