国际知识产权学院落地于粤港澳大湾区，旨在服务国家知识产权强国战略，助力大湾区打造高水平知识 产权人才高地，为全球知识产权治理与创新发展提供智力支持与人才保障。未来，该学院将面向未来科 技与国际知识产权治理需求，开展跨学科、跨领域的高层次人才培养与创新研究，推动知识产权规则与 科技、产业发展的深度融合。 新京报讯 据北京大学国际法学院消息，11月15日，以"AI for Science"为主题的2025西丽湖论坛开幕式暨 主论坛在深圳大学城国际会议中心举行。作为本届论坛的重要环节，由国家知识产权局与北京大学共同 建设的国际知识产权学院正式成立。 该学院由北京大学国际法学院具体承建，充分发挥北大法学的深厚基础和深圳的独特区位功能，通过南 北联动，实现优势互补，努力打造集教育、科研、智库和国际合作于一体的、具有国际影响力的知识产 权人才培养高端平台。 ...

Core Viewpoint - The future development of artificial intelligence (AI) is centered around achieving satisfactory general artificial intelligence (AGI), which will significantly impact various sectors including science, strategy, and economic competition [2][3]. Group 1: Directions Towards AGI - The journey towards AGI will inevitably focus on four key directions: continuous evolution of large models, embodied general intelligence, AI for science, and AI safety governance [5][8]. - In the past five years, China has made remarkable progress in large model development, reaching a competitive level internationally [7]. - Embodied intelligence is crucial for enhancing robots' capabilities, allowing them to perform tasks that were previously difficult due to their rigid nature [8]. - AI for science is expected to revolutionize scientific research methodologies within the next 5 to 10 years, making collaboration between scientists and AI essential for competitive advantage [9]. Group 2: Risks and Governance - The development of AI poses significant safety risks, as it can potentially lead to loss of control and conflict with human intentions [10][11]. - AI algorithms inherently possess characteristics such as lack of robustness, uncertainty, and non-interpretability, which can impact societal values and ethics [11]. - Addressing the "survival risk" associated with AI requires the development of provably safe AI systems, leveraging theories from cryptography and game theory [12]. Group 3: Future of Robotics - The next decade is anticipated to transform robots from mere tools into life partners, capable of understanding the world and performing various tasks [14][17]. - Robots will increasingly collaborate with humans in industrial settings and provide assistance in community services, such as elderly care [17]. - The robotics industry will benefit from open-source collaboration to accelerate technological advancements and reduce innovation costs [17]. Group 4: Market Potential - The AI market is projected to reach a trillion-dollar scale as it empowers various industries, with open-source initiatives playing a crucial role in fostering commercial growth [19][20]. - The focus on intelligent terminals as potential AI entry points highlights the importance of integrating AI into everyday life, particularly in the automotive sector [22].

Core Insights - The rapid rise of AI for Science in scientific research is a significant topic, emphasizing the integration of AI with traditional technologies [1] - AI can assist quantum physicists in constructing quantum error correction decoders, enhancing quantum computing research [1] - The Google quantum chip Willow has achieved a significant reduction in quantum error correction errors, improving the large-scale usability of quantum computing [1] - Future applications of AI are expected to greatly benefit the accuracy and speed of quantum computing [1]

Core Insights - The 2025 Xili Lake Forum opened on November 15, focusing on "Accelerating Scientific Discovery, Defining the Future of Industry" with an emphasis on "AI for Science" [1][3] Group 1: Forum Structure and Participants - The forum featured a "1+N+X" model, including one main forum, 28 specialized forums, and various supporting activities throughout November, aimed at building an open and integrated innovation ecosystem [3] - Notable participants included academicians from the Chinese Academy of Sciences and the Chinese Academy of Engineering, as well as representatives from leading universities, research institutions, and media [3][11] Group 2: Key Initiatives and Achievements - Three major initiatives were announced: the establishment of the International Intellectual Property Academy in collaboration with Peking University, the launch of the "Boya AI4S Top Talent Program," and the signing of the Shenzhen University Town International Campus Phase I [7][10] - Seven significant achievements of the X9 Alliance were unveiled, including the Shenzhen Scientific Navigation platform, a new generation AI database, and the AI4S LAB, which is the world's first one-stop digital life sciences research platform [10] Group 3: Discussions and Insights - The forum included discussions on the integration of AI across various industries, with industry leaders exploring the transformative potential of AI in reshaping technology and industrial ecosystems [4][11] - Keynote speeches highlighted insights into AI's role in revolutionizing scientific research paradigms and driving new industrial transformations [11][13]

Core Insights - The article discusses the evolution of organizations and individuals in the context of increasing machine capabilities, emphasizing the need for a shift in how human value is perceived and utilized [4][11][22] Group 1: Organizational Evolution - The concept of "Frontier Organizations" is introduced, which challenges traditional views on human value, suggesting that machines excel at standardization while humans should focus on providing "outliers" that machines cannot replicate [9][11] - The structure of organizations is shifting from rigid departmental divisions to fluid, task-based teams, necessitating new management approaches [12] - The foundation of "Frontier Organizations" is the cultivation of "Super Individuals," whose value is no longer defined by traditional resumes but by their digital footprints and contributions [13][15] Group 2: Future of Work - Recruitment processes are expected to evolve, with organizations analyzing candidates' digital presence rather than relying on resumes, marking a significant shift in hiring practices [16] - The article raises critical questions about the future of technology and its impact on human roles, questioning whether current trends are leading to meaningful advancements or merely fleeting phenomena [19][20] - The ultimate inquiry posed is about individual agency in the face of advancing technology, urging individuals to choose to be active contributors rather than passive participants [22][23]

Summary of Key Points from the Conference Call Company Overview - **Company**: 金财控股 (Jincai Holdings) - **Industry**: Biotechnology and AI for Science Core Business and Model - 金财控股 focuses on using AI and robotics to empower drug discovery and new materials, operating as a research and development service platform [2][7][9] - The company has over 300 global clients, including 17 of the top 20 multinational pharmaceutical companies, showcasing its industry-leading technology platform [2][8] Recent Developments - A significant collaboration with 礼来 (Eli Lilly) was established, valued at approximately $350 million, involving AI-enabled large molecule antibody drug development [3][17] - Two AI-developed molecules for hair growth have been registered as cosmetic ingredients in the U.S. and received FDA approval, indicating successful application of AI technology in consumer products [2][3] Financial Performance and Market Position - The company went public on June 13, 2024, becoming the first listed company in the specialized technology and AI for Science sector, with over 43% of shares held through the Hong Kong Stock Connect [2][6] - Major institutional investors, including Vanguard and BlackRock, have invested in the company, reflecting confidence in its long-term growth [2][6] Competitive Advantage - Unlike traditional Chinese pharmaceutical companies that transfer pipelines to large overseas firms after significant investment, 金财控股 shares R&D costs and outcomes with clients, ensuring stable long-term development [5][9] - The company’s unique fee structure covers the entire lifecycle of drug development, significantly shortening the R&D cycle from 4-6 years to approximately 2 years [9][11] AI and Robotics in Drug Development - AI enhances drug discovery by overcoming human experience limitations, allowing for multi-target and multi-property optimization, which significantly reduces time and costs while increasing success rates [10][11] - The 24/7 operation of robotic laboratories improves experimental efficiency and accumulates high-quality data for further AI training, creating a rapid iterative feedback loop [11][12] Market Potential and Future Outlook - The hair growth product developed showed an 80% effectiveness rate in a trial with 100 participants, indicating substantial market potential, as approximately 2.5 billion people globally suffer from hair loss [15] - The company anticipates maintaining a growth rate of 50-70% annually, excluding unexpected large orders [17] Impact of External Factors - The company is insulated from U.S.-China tensions as it focuses on preclinical research and does not handle sensitive clinical trial data [18] - The company has received recognition for its data capabilities, winning a national award for data elements, which is crucial for enhancing AI algorithms [19][23] Conclusion - 金财控股 is positioned as a leader in the biotechnology sector, leveraging AI and robotics to innovate drug discovery and development, with a robust business model that emphasizes collaboration and shared success with clients. The company's strategic partnerships and technological advancements suggest a promising future in both pharmaceutical and materials science markets.

Core Viewpoint - The 2025 Quzhou Industrial Capital Investment Conference and Fluorine-based New Materials Industry High-Quality Development Conference aims to explore new investment opportunities and models in the context of technological transformation, emphasizing the deep integration of five chains for collaborative development [1][2]. Group 1: Industrial Development - Quzhou has achieved systematic restructuring of its industrial, enterprise, and power structures through the deep integration of five chains, transitioning from basic chemicals to new materials and extending from upstream materials to downstream finished products [2]. - The city has introduced 35 projects with investments exceeding 5 billion yuan during the 14th Five-Year Plan, including 15 projects worth over 10 billion yuan [2]. - By the end of this year, the new materials industry is expected to become Quzhou's first trillion-yuan industrial chain, positioning the city as "China's Fluorine Valley" [2]. Group 2: Expert Insights - Jiang Xiaojun highlighted the new requirements for the integration of technology and industry in the 14th Five-Year Plan, emphasizing that enterprises are becoming the main body of innovation in the digital age [2][3]. - Xu Xiaolan pointed out that humanoid robots represent a typical case of the integration of technological and industrial innovation, which will be a key driver for upgrading the manufacturing industry [3]. Group 3: Investment and Capital - Quzhou's industrial fund cluster has expanded from 15 billion yuan to over 100 billion yuan, leveraging nearly 150 billion yuan of social capital [7]. - The city aims to create a favorable business environment to attract more quality capital and facilitate the interaction between industry and capital [8]. - The "China Fluorine Valley" initiative was launched during the conference, marking a new chapter for the high-quality development of the fluorochemical industry [5][6]. Group 4: AI and Material Science - AI is seen as a driving force for the transformation of material science, with discussions indicating that the industry is entering a "no man's land" of research and development [11]. - Experts believe that the integration of AI and materials will accelerate breakthroughs in new materials, particularly in biomedicine and high-performance materials [11]. - The next five years are anticipated to be a pivotal period for "AI for Science," leading to significant advancements in various fields [12]. Group 5: Robotics Industry - The robotics industry is at a critical juncture, with advancements in AI and humanoid robots expected to drive significant growth [17]. - Quzhou is positioned to capitalize on opportunities in the robotics sector, particularly in humanoid and flexible robots [17][18]. - Investment opportunities are emerging in mobile equipment and facilities, especially as new companies integrate AI concepts into their operations [18].

Core Insights - The event highlighted the transformative role of AI in the materials industry, emphasizing its potential to drive innovation and reshape research paradigms [1][3]. Group 1: AI's Impact on Material R&D - AI is fundamentally changing the way materials are developed, moving from imitation to independent innovation [3]. - Key challenges in material R&D include identifying suitable molecular structures for specific applications and optimizing synthesis routes [3]. - Companies are investing in AI capabilities, such as establishing computational simulation labs and collaborating with universities and software developers, aiming to create industry-specific models and databases by 2026 [3]. Group 2: Investment Opportunities - Investment opportunities in the materials sector are identified in two areas: materials with significant production value and import substitution potential, and those leveraging AI for breakthroughs in performance and cost efficiency [3][4]. - The integration of AI and big data is expected to unlock vast potential in biomedicine and new materials development [4]. Group 3: Future Trends and Expectations - The next five years are anticipated to mark the beginning of "AI for Science," progressing towards a cycle of AI-driven research and applications [5]. - The industry is expected to reach a tipping point in four to five years, leading to a surge in material innovation as data accumulates [4].

Core Insights - The forum "AI for Science" held during the 2025 World Internet Conference focused on how AI is reshaping scientific research paradigms and stimulating new productivity [2][3][4] Group 1: AI Applications in Scientific Research - AI is becoming a crucial tool to overcome long-standing challenges in materials research, such as measurement limitations, as highlighted by Chen Lidong from the Shanghai Institute of Silicate [3] - AI models have shown significant potential in enhancing catalyst performance by 50% through iterative experimentation and modeling, demonstrating the efficiency of AI in material discovery [3] - The concept of "AI for Materials" and "Materials for AI" emphasizes a reciprocal relationship between AI and materials science [4] Group 2: AI in Healthcare - AI brain-machine interfaces are being applied in managing neurodegenerative diseases, with advancements allowing for quicker detection of seizures compared to traditional methods [5] - The accuracy of language decoding in AI has improved significantly, particularly in recognizing Chinese phonetics, achieving over 70% accuracy [5] Group 3: AI's Impact on Innovation - Generative AI is optimizing product design and team collaboration in open innovation, while its direct impact on disruptive innovation remains limited, underscoring the importance of human creativity [7] Group 4: Future Directions in AI and Science - The "scientific intelligence" concept is seen as a pathway to superintelligence, with significant advancements in drug design for diseases lacking clear targets, achieving a 50-fold improvement in molecular design [9][10] - The demand for computational power in AI for science is growing exponentially, necessitating a unique capability to couple high and low precision in scientific calculations [11] - The release of the "Global AI Standards Development Report" calls for collaboration among international organizations, governments, and industries to establish responsible global standards [13] Group 5: AI Infrastructure and Talent Development - The "Panshi V1.5" platform aims to empower scientific research across disciplines, covering the entire research process from hypothesis to discovery [18] - The forum concluded with discussions on the role of AI in empowering young scientists, emphasizing the need for interdisciplinary collaboration and the evolution of AI from a tool to a collaborator [25]

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].