Core Insights - The Chinese government has prioritized "Artificial Intelligence +" in its recent policy, emphasizing the need to accelerate the exploration of AI-driven research paradigms and major scientific discoveries [1] Group 1: AI for Science (AI4S) Concept - The concept of "AI for Science" (AI4S) was introduced by a Chinese Academy of Sciences academician, highlighting AI's potential to provide new solutions for scientific calculations [2] - Beijing University has been actively exploring AI4S, establishing various initiatives such as the "Deep Research" student recruitment plan and the world's first AI4S Academy [2][3] - The upcoming 2025 Xili Lake Forum will focus on AI4S, featuring multiple specialized forums and the launch of new initiatives like the "Boya AI4S Talent Program" [2] Group 2: Infrastructure Development - The establishment of AI-driven research infrastructure is crucial for the implementation of AI4S, with the launch of the "Bohr Research Space Station," the first global AI research platform covering literature reading, computation, experimentation, and interdisciplinary collaboration [5][6] - Several AI4S infrastructure projects were announced at the Xili Lake Forum, including the "Shenzhen Science Navigation" platform and the AI4S LAB, which aims to provide a comprehensive cloud-based research experience [6][7] Group 3: Talent Development - The AI4S era necessitates an updated talent cultivation model, with the launch of the "Boya AI4S Talent Program" aimed at training interdisciplinary talents proficient in both AI and specific scientific fields [9][10] - The establishment of the Science Intelligence Academy at Beijing University marks a significant step in promoting AI4S, focusing on creating a talent pool with a deep understanding of AI and foundational sciences [10] Group 4: Collaborative Efforts - The signing ceremony for the Shenzhen University Town International Campus signifies a collaborative effort among several universities to enhance the region's capacity for industry-academia-research synergy and open innovation [11]

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 2024 Beijing Science and Technology Awards recognized 38 scientists and 193 achievements, with over half of the awarded projects involving enterprises for six consecutive years [1][3] - Basic research achievements accounted for 29.5% of the total awards, indicating a growing emphasis on foundational scientific work [1][2] Summary by Categories Basic Research Achievements - Basic research is highlighted as the source of innovation, with significant contributions in fields like new information technology, new materials, and health [2] - Notable awardees include Professor Deng Hongkui for developing new strategies in stem cell therapy and researcher Dong Jin for leading the development of the "Chang'an Chain," a domestically controllable blockchain technology [2] Enterprise Innovation - The project "Advanced Oxide Semiconductor Technology for Intelligent Display" won the first prize in scientific and technological progress, showcasing the critical role of enterprises in innovation [3] - Companies like BOE Technology Group have demonstrated a commitment to R&D, with a focus on talent retention and long-term investment in technology development [3] Young Talent - Over 50% of the awardees are under 45 years old, reflecting a vibrant pool of young talent in the scientific community [4] - Young scientists like Liu Ying and Chang Kai are making significant contributions in life sciences and quantum materials, respectively, indicating a strong future for innovation in these fields [4][7] Award Overview - The awards included 57 natural science achievements, 24 technological invention awards, and 112 scientific and technological progress awards, with a total of 15 first prizes in natural sciences and 29 first prizes in technological progress [6]

Core Insights - The Chinese government has issued an opinion to accelerate the implementation of "Artificial Intelligence +" actions, emphasizing the need for new research paradigms driven by AI to enhance scientific discoveries [1] - Beijing is positioned as a leader in AI4S (AI for Science), leveraging its rich educational, technological, and talent resources to foster innovation and industrial transformation [1][5] - The establishment of the Beijing Institute of Scientific Intelligence marks a significant step in integrating AI with scientific research, aiming to create a new research paradigm and infrastructure [3][9] Group 1: AI4S Development and Impact - AI4S is recognized globally as a new paradigm that enhances scientific research efficiency and effectiveness, moving from theoretical concepts to practical applications [2][6] - The DeepFlame Rocket software exemplifies the transition from research to commercial applications in aerospace, significantly reducing simulation times and costs in rocket engine development [4][5] - Beijing has produced significant original achievements in AI4S, including a large atomic model and a new generation of research knowledge databases, contributing to the emergence of innovative companies [5][9] Group 2: Infrastructure and Ecosystem - The Bol Research Space Station serves as a foundational infrastructure for AI4S, addressing key challenges in literature management, interdisciplinary knowledge discovery, and experimental calculations [7][8] - The establishment of a collaborative ecosystem involving over 30 organizations in the OpenLAM project aims to enhance micro-scale design in various industries, including materials and pharmaceuticals [10][11] - The "Action Plan" outlines a roadmap for developing AI4S in Beijing, targeting the establishment of high-quality scientific databases and a competitive industrial cluster by 2027 [12][13] Group 3: Future Directions - The focus on breaking down disciplinary boundaries and enhancing collaboration between research and industry is expected to drive original innovations and improve research efficiency [6][10] - The ongoing development of large atomic models and other AI-driven tools is anticipated to revolutionize traditional research methodologies and accelerate the pace of scientific discovery [11][12] - Beijing's strategic initiatives aim to solidify its position as a hub for scientific intelligence, fostering an open and collaborative innovation ecosystem [13]

Core Viewpoint - The article discusses the integration of artificial intelligence (AI) into scientific research, highlighting Beijing's role as a leader in this transformation through the AI for Science (AI4S) initiative, which aims to enhance research efficiency and innovation [1][2][6]. Group 1: AI4S Development and Impact - AI4S is recognized as a new paradigm that accelerates scientific research, moving from theoretical concepts to practical applications, with a global consensus on its importance [2][4]. - The establishment of the Beijing Scientific Intelligence Research Institute in 2021 marks a significant step in combining AI with scientific research, aiming to create a new research infrastructure [3][5]. - The DeepFlame Rocket software exemplifies the transition from research to commercial applications in AI4S, significantly reducing the time and cost of rocket engine development [4][5]. Group 2: Infrastructure and Ecosystem - Beijing has developed a comprehensive innovation and industrial chain in AI, producing significant original achievements such as a large atomic model covering over 90 elements and a new generation of research knowledge databases [5][9]. - The "Bohler Research Space Station" serves as a foundational infrastructure for AI4S, providing tools for literature management, interdisciplinary knowledge discovery, and experimental calculations [7][8]. - The OpenLAM project aims to create a large atomic model to facilitate micro-scale design in various industries, demonstrating the collaborative efforts in building research infrastructure [10][11]. Group 3: Future Plans and Goals - The "Action Plan" outlines Beijing's strategy to establish a scientific foundation model and high-quality scientific databases by 2027, targeting over 10 million users and fostering an open-source ecosystem [12][13]. - The plan emphasizes the importance of collaboration among research institutions and enterprises to enhance the AI4S innovation ecosystem and drive significant scientific advancements [12][13].

Core Viewpoint - The article introduces SciMaster, the world's first general scientific intelligence agent, developed by Shanghai Jiao Tong University and DeepMind Technology, which serves as an expert-level research assistant for various scientific inquiries and everyday problems [1][42]. Group 1: Features and Capabilities - SciMaster integrates resources from the internet and 170 million scientific documents to assist users in overcoming research challenges [2]. - It offers two modes: a "general assistant" mode for quick insights and a "deep research" mode for comprehensive reports, including references and links [22][25]. - The tool can automatically match and utilize various scientific tools based on user queries, enhancing its functionality [28]. Group 2: Research and Application - SciMaster's core function is expert-level deep research, leveraging the Innovator model with multimodal capabilities [5]. - It can conduct extensive searches across the internet and scientific literature, employing methods like WebSearch, WebParse, and PaperSearch to gather relevant data [7][14]. - The tool has demonstrated its ability to refine search strategies based on initial results, leading to more relevant findings [10][15]. Group 3: Industry Impact and Future Prospects - SciMaster aims to reshape the research paradigm in universities, moving beyond traditional teaching and research methods [45]. - The collaboration between DeepMind Technology and various universities is expected to foster innovation and broaden the application of AI in scientific research [44][46]. - The ultimate goal of SciMaster is to become a leading platform in the AI for Science (AI4S) field, akin to Hugging Face in its domain [47][48].

Group 1 - The core viewpoint of the article emphasizes Beijing's commitment to integrating artificial intelligence with scientific research, aiming to establish itself as a global hub for scientific intelligence innovation by 2027 [1][2] - The "Action Plan for Accelerating the High-Quality Development of Artificial Intelligence Empowering Scientific Research (2025-2027)" outlines goals to create at least 10 high-quality scientific databases and serve no less than 10 million users [1] - Significant achievements include the launch of the DeepFlame Rocket software, which utilizes AI to simulate various physical processes, thereby reducing R&D costs and time [1] Group 2 - Beijing has developed the world's first AI research platform that integrates literature review, computation, experimentation, and multidisciplinary collaboration, known as the Bohr Research Space Station [2] - The city is fostering innovative companies such as DeepSense Technology and Baitu Biotechnology, focusing on applications in basic scientific research, healthcare, and industrial intelligence [2] - Future plans include establishing a specialized working mechanism for AI, launching major project clusters, and enhancing investment in AI industry funds to create a competitive industrial cluster [2]

Group 1 - The core viewpoint of the news is the official release of the "Beijing Action Plan for Accelerating AI Empowerment in Scientific Research for High-Quality Development (2025-2027)", marking a strategic roadmap for AI for Science in Beijing and the first local policy in China focused on this area [1] - AI for Science is recognized globally as a new paradigm to accelerate scientific research, with Beijing positioning itself as a leader in this field by establishing the Beijing Academy of Scientific Intelligence in 2021 [2][6] - The Action Plan aims to integrate AI with scientific research, focusing on breakthroughs in fundamental theories and interdisciplinary collaboration, with goals to build at least 10 high-quality scientific databases and serve over 10 million users by 2027 [3] Group 2 - The Beijing Municipal Development and Reform Commission plans to enhance the overall planning and systematic layout of scientific intelligence, targeting key disciplines such as materials and life sciences to develop specialized models [4] - The Beijing Economic and Information Technology Bureau emphasizes the importance of AI in accelerating scientific innovation across various sectors, including biomedicine and new materials, while acknowledging existing challenges in the industrial application of scientific intelligence [5] - The Haidian District aims to strengthen the foundation for scientific intelligence development by supporting advanced research elements and creating a comprehensive support system for scientific innovation [6] Group 3 - Significant achievements in the field of AI for Science include the "Bohler Research Space Station," which integrates literature review, computation, and experimentation, currently utilized by over 900,000 users across more than 40 universities and companies [7] - The "DPA Large Atom Model," developed by over 30 organizations, aims to enhance the understanding of atomic interactions, achieving world-leading stability and predictive performance while reducing data computation costs by 90% [8] - The "Uni-Lab-OS" intelligent laboratory operating system transforms traditional labs into autonomous "AI scientists," improving efficiency and data sharing among instruments [9] - The "DeepFlame" software enables comprehensive numerical simulations of rocket engines, significantly reducing development costs and time by allowing for virtual testing of extreme conditions [10]