Core Viewpoint - The article discusses the emergence of AI for Science as a transformative direction in scientific research, highlighting the establishment of companies like Xaira Therapeutics and the initiatives by OpenAI and DeepMind in this field. It emphasizes the potential of AI to accelerate scientific discoveries and the journey of Chinese entrepreneurs Zhang Linfeng and Sun Weijie in founding DeepMind Technology, which focuses on applying AI to scientific research and industrial applications [3][4][5]. Company Background - DeepMind Technology was founded in 2018 by Zhang Linfeng and Sun Weijie, with initial funding of 12 million RMB from a disruptive technology innovation competition, rather than venture capital [4][5]. - Zhang Linfeng developed the Deep Potential Molecular Dynamics (DeePMD) method during his PhD at Princeton, which later won the prestigious Gordon Bell Award [4][5]. Technological Innovation - DeePMD integrates AI to optimize the long-standing issue of solving first-principles calculations, expanding the range of quantum mechanical calculations from hundreds of atoms to billions, thus enabling the discovery of new materials and drugs [5][6]. - The method allows for significant computational efficiency, achieving over six orders of magnitude acceleration, enabling complex simulations that were previously only feasible on supercomputers to be run on standard laptops [21][24]. Vision and Goals - The founders aim to create an open-source system that spans scientific research to industrial development, aspiring to contribute to a shared human destiny [9][30]. - The company has set a goal to become a leading technology firm originating from China, with a vision to influence global scientific research [8][30]. Product Development - DeepMind Technology has launched several platforms, including the Hermite drug design platform and various pre-trained scientific models, serving notable clients such as CATL, BYD, and others [8][30]. - The company’s first product, Hermite, was developed in response to the existing market needs in drug discovery, differentiating itself by incorporating machine learning methods [30][31]. Market Positioning - The founders identified a significant opportunity in the pharmaceutical and materials sectors, where understanding atomic interactions can lead to breakthroughs in drug development and material science [31][32]. - The company aims to build a comprehensive platform that can serve multiple research directions and stages, rather than focusing solely on vertical applications [50][51]. Educational Initiatives - DeepMind Technology emphasizes the importance of cultivating a new generation of interdisciplinary talent, integrating knowledge from physics, chemistry, and engineering to address complex scientific challenges [27][34]. - The company has developed a unique educational framework to train young talents, fostering a community that encourages collaborative learning and innovation [36][37]. Future Directions - The article suggests that the next phase for AI in science will involve the development of AI scientists, capable of autonomously conducting research and integrating various scientific tools [42][44]. - The integration of pre-trained models and multi-agent systems is expected to enhance research efficiency and redefine the roles of researchers in the scientific process [47][49].

Core Insights - The development of artificial intelligence (AI) is significantly supported by advanced computing power and innovative technologies, as highlighted in the recent policy recommendations from the Chinese government [1] - China's computing power centers have reached a total scale of 10.85 million standard racks, with intelligent computing power at 788 billion billion operations per second, positioning the country among the global leaders in AI infrastructure [1] Group 1: AI in Research and Innovation - AI is driving a transformation in research paradigms, enabling faster and more accurate scientific discoveries, particularly in fields like computational biology [2][3] - The collaboration between universities and computing companies is accelerating the application of intelligent computing in research, enhancing the efficiency of model training and inference [2][3] Group 2: Diverse Applications of Intelligent Computing - Companies like Yili are leveraging AI and cloud computing to create smart health management systems for livestock, improving operational efficiency and product quality [4][5] - The integration of AI in manufacturing processes, such as in the production of high-speed trains, has significantly reduced simulation times and improved design accuracy [5] Group 3: Industry Collaboration and Innovation - The establishment of the "Supernode Computing Cluster Innovation Alliance" aims to enhance collaboration among companies in chip development, system design, and AI applications [8] - Innovations in computing architecture, such as the development of ultra-node servers, are addressing the challenges of high energy consumption and system scalability in AI applications [7][8]

Core Insights - The development of intelligent computing power, based on the latest AI theories and advanced computing architectures, is significantly supporting the advancement of artificial intelligence [1] - China's computing center has reached a total scale of 10.85 million standard racks, with intelligent computing power at 788 billion billion times per second, and storage capacity exceeding 1,680 exabytes, positioning the country among the global leaders in AI model development [1] Group 1: AI in Research and Innovation - Intelligent computing is driving a paradigm shift in scientific research, accelerating the generation of original innovative results [2] - Researchers at Westlake University are utilizing intelligent computing technology to efficiently analyze sequencing data related to non-coding RNA, significantly speeding up research processes [2] - The collaboration between universities and computing companies is enhancing the application of intelligent computing in scientific research [3] Group 2: Diverse Applications and Industry Integration - The State Council's policy emphasizes the need for a coordinated approach to intelligent computing supply, making it accessible, economical, and environmentally friendly [4] - Companies like Yili are leveraging AI computing to create health profiles for dairy cows, improving monitoring and management efficiency in production [4] - The development of over 800 intelligent agents at Yili has optimized supply chain scenarios, reducing risks related to raw material shortages and excess [5] Group 3: Technological Advancements and Collaborations - Baidu's AI foundation, built on the Wenxin model and PaddlePaddle deep learning platform, is enhancing efficiency across various industries [6] - Inspur Information has introduced AI computing systems that significantly reduce the cost and time of model training and inference [7] - The establishment of the "Super Node Computing Cluster Innovation Alliance" aims to advance standards and applications in intelligent computing [8]

Core Points - The event "Implementation of the Future Pact University Dialogue" was successfully held in Beijing, marking the 80th anniversary of the United Nations, focusing on the role of universities in promoting the Future Pact and accelerating the 2030 Sustainable Development Agenda [1][2] - The dialogue attracted representatives from universities worldwide, discussing how educational cooperation, research innovation, and policy advocacy can drive sustainable development goals [1][2] - A significant outcome of the dialogue was the release of the "University Implementation of the Future Pact Action Plan," which aims to establish mechanisms for systematic implementation of the Future Pact in talent cultivation, research collaboration, and policy advocacy [3] Group 1 - The dialogue emphasized the importance of multilateralism in a chaotic and uncertain era, with universities playing a crucial role in translating words into action [2] - The Chinese Ministry of Education expressed support for deepening cooperation between universities and international partners, highlighting the proactive role of Beijing Foreign Studies University in the Future Pact [1][2] - The dialogue included discussions on artificial intelligence governance, interdisciplinary collaboration, and changes in research paradigms, addressing challenges faced by future universities [2] Group 2 - The "Future University Alliance" and "Future Learning Excellence Center" are proposed as part of the action plan to enhance collaboration among universities [3] - The event also initiated the preparation process for the "Sustainable Development Goals Series Dialogue," showcasing the youth's creativity and concern for sustainable development through performances [3] - The dialogue marked a significant step for global higher education in multilateral cooperation and sustainable development, aiming to create a more inclusive, equitable, and resilient future [3]

Core Insights - The article discusses the transformative impact of artificial intelligence (AI) on scientific research and innovation, highlighting the launch of the "Three-Body Computing Constellation" by Zhijiang Laboratory as a significant step in this paradigm shift [5][9]. Group 1: Impact of AI on Scientific Research - AI is revolutionizing scientific research methodologies, making traditional approaches increasingly unsustainable due to time and cost constraints [6][8]. - The integration of AI into research is not merely a tool revolution but a scientific revolution that transcends disciplinary boundaries, enabling the discovery of complex information previously unseen by humans [8][11]. Group 2: The "Three-Body Computing Constellation" - The "Three-Body Computing Constellation" consists of a thousand-satellite scale space computing infrastructure aimed at real-time data processing to overcome traditional satellite data processing inefficiencies [9][10]. - This constellation will facilitate inter-satellite communication, significantly enhancing operational efficiency and reducing costs, while also improving response times for emergency situations [10]. Group 3: Future of Scientists in the AI Era - AI is viewed as a tool to extend human creativity rather than replace scientists, emphasizing the importance of creativity in research [11][12]. - The ability to utilize AI in research will become a critical skill, with those who adapt likely to outpace those who do not [12][13].

Group 1: Impact of Artificial Intelligence on Scientific Research - Artificial intelligence (AI) is fundamentally changing the methodology of scientific research, leading to a paradigm shift in how research is conducted [2][4][7] - Traditional research methods are becoming increasingly unsustainable due to high costs and long timelines, making AI a necessary tool for efficiency and innovation [2][5] - AI serves as a universal language that transcends disciplinary boundaries, facilitating collaboration and enhancing creativity in scientific endeavors [2][6][7] Group 2: Technological Advancements and Innovations - The launch of the "Three-Body Computing Constellation" by Zhijiang Laboratory represents a significant advancement in space-based computing infrastructure, aimed at improving data processing efficiency [4][5] - The interconnectedness of satellites in space will enhance their operational value, reduce costs, and improve response times for critical applications such as disaster relief [5][6] - AI is not merely a tool but a revolutionary force in science, enabling new forms of discovery and innovation [2][4][7] Group 3: Future of AI in Scientific Community - The integration of AI in research is seen as essential for scientists to remain relevant, with those who adopt AI likely to outperform those who do not [6][7] - Ethical considerations surrounding AI usage in research are acknowledged, emphasizing the need for responsible governance and risk management [6][7] - The current era is characterized by rapid technological change, presenting significant opportunities for both individuals and nations to contribute to scientific advancements [7][8]

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].