当前，全球经济与技术格局正处在剧烈变动之中，而人工智能无疑是推动这场变革的核心力量。11月13 日，太湖世界文化论坛·钱塘对话在杭州举办，人工智能是当天嘉宾最为关注的话题之一。 当天上午，北京大学国家发展研究院院长黄益平与诺贝尔经济学奖得主、斯坦福大学荣誉教授迈克尔· 斯宾塞（Michael Spence）展开了一场深度对谈。迈克尔·斯宾塞也分享了对于人工智能投资热潮、全球 人工智能竞争格局变化等诸多问题的观察和思考。 据迈克尔·斯宾塞观察，全球股市的"疯狂"主要源于对数字领域尤其是人工智能的热情和投资驱动。不 仅科技巨头，各类企业乃至整个资本市场都在大规模投入人工智能模型研发与相关基础设施，包括量子 计算、数据中心以及电力供应等领域。从标普500指数来看，超过30%的市值集中在全球前七大科技公 司。 他坦言，市场中确实存在一定泡沫，但这种"狂热"背后反映的并非非理性，而是竞争压力下的理性选 择。"从博弈论的角度看，投资不足的代价远大于投资过度的代价。科技企业若在AI竞赛中落后两三 步，就可能被淘汰出局。"正因如此，中美两国都在不断加大投入，不愿在战略竞争中失去先机。 迈克尔·斯宾塞进一步指出，中美在人工智能 ...

转自：北京日报客户端 学习时报报记者：王翠娟 采访嘉宾： 郭贵春 山西大学原校长 曾大军 中国科学院自动化研究所副所长 何 哲 中央党校（国家行政学院）国家治理教研部教授 传统以人为主导的科研运行逻辑被打破 学习时报：当前，"人工智能驱动的新型科研范式"是一个热点话题。从辅助工具升级至新型范式，这个 跨越的依据是什么？ 曾大军：科研范式大致经历了三次重大变革。一是以观察实验为核心的经验范式，强调对自然现象的描 述、记录、总结和归纳。二是以数理模型为基础的理论范式，强调通过数学建模对自然规律的抽象和推 演。三是以仿真模拟为标志的计算范式，强调利用电子计算机仿真科学实验。这三种科研范式都遵 循"观察—假设—验证"的传统研究逻辑。随着海量数据的涌现和算力的飞速提升，AI正推动科研逻辑发 生根本性转变，催生出以"数据密集—智能涌现—人机协同"为特征的智能化科研新范式。首先，AI实现 了以智能挖掘替代假设检验，从海量数据中自主发现人类难以直观捕捉的规律与关联，进而提出新假 设。其次，AI擅长多元知识耦合，能够打破传统学科壁垒，在多学科融合中激发新知识的"智能涌现"。 最后，AI驱动形成了"人类提出需求—AI生成路径—机 ...

Core Insights - Demis Hassabis, CEO of Google DeepMind, has been featured on the cover of TIME100 for 2025, highlighting his influence on AI technology and ethics as the field evolves [1][2] - DeepMind is shifting its focus from general artificial intelligence (AGI) to a strategy centered on scientific discovery, termed "AI for Science (AI4S)" [10][11] - The company has made significant advancements, including the development of AlphaGo and AlphaFold, which have had a profound impact on AI and life sciences [6][9] Group 1: Achievements and Recognition - Hassabis has been recognized for his contributions to AI, particularly in deep learning and its applications in scientific research [2][4] - The acquisition of DeepMind by Google in 2014 for approximately £400 million (around $650 million) provided the company with enhanced resources and computational power [6] - AlphaFold's success in predicting protein structures has been acknowledged as one of the most influential scientific achievements, earning Hassabis the 2024 Nobel Prize in Chemistry [9][10] Group 2: Strategic Direction - DeepMind is now prioritizing AI4S, aiming to leverage AI to accelerate scientific discoveries rather than merely mimicking human intelligence [10][11] - The launch of Gemini 2.5 and the Project Astra digital assistant are part of DeepMind's efforts to advance its AI capabilities while maintaining a focus on scientific applications [11][12] - Hassabis emphasizes that the goal of AGI should be to enhance human understanding and address global challenges, rather than to replace human roles [10][11] Group 3: Ethical and Controversial Aspects - Despite the accolades, Hassabis and DeepMind face scrutiny regarding the ethical implications of their work, particularly concerning military applications and the concentration of AI technology within a few corporations [12][16] - Internal dissent has emerged within DeepMind regarding its partnerships with military entities, with employees expressing concerns over the potential ethical ramifications [16][19] - The balance between technological advancement and ethical responsibility remains a critical issue for Hassabis and the broader AI community [20]

Core Viewpoint - The article discusses how Henan province aims to achieve high-level technological self-reliance and seize new development opportunities as it approaches the "14th Five-Year Plan" period, emphasizing key areas such as original innovation, core technology breakthroughs, and digital economy construction [1]. Group 1: Original Innovation and Core Technology - Henan is focusing on enhancing original innovation and tackling key core technologies, particularly in the field of artificial intelligence and drug development, to address challenges in data accuracy and integration [2]. - The "ranking and answering" mechanism has been established to foster collaboration between industries, enterprises, and research institutions, leading to breakthroughs in critical technologies [3]. - The province's R&D expenditure reached 127.51 billion yuan in 2024, marking a 5.2% increase from the previous year, with R&D intensity stabilizing at 2.01% [4][5]. Group 2: Integration of Technological and Industrial Innovation - The successful launch of advanced tunneling machinery by China Railway Engineering Equipment Group highlights the importance of independent innovation in the shield tunneling industry [6]. - Henan has recognized 39 innovation alliances to enhance collaboration between leading enterprises and research institutions, driving the transition from manufacturing to creation [7]. - The establishment of technology transaction markets and the increase in technology transfer contracts demonstrate the province's commitment to integrating technological innovation with industrial needs [8]. Group 3: Education and Talent Development - The introduction of new academic programs in response to industry demands reflects the province's efforts to align education with emerging sectors such as low-altitude economy and aviation [9][11]. - By 2027, Henan plans to cultivate over 200 new interdisciplinary programs to support strategic emerging industries, with a focus on artificial intelligence and biotechnology [11]. - The increase in higher education institutions and the number of graduates is expected to provide a steady supply of skilled talent to drive innovation [12][13]. Group 4: Digital Economy Development - The Zhengzhou Coal Machinery Smart Park exemplifies the integration of advanced technologies like IoT and cloud computing, resulting in significant improvements in production efficiency [14]. - The digital economy in Henan is projected to exceed 2 trillion yuan by 2024, accounting for over 30% of the GDP, with a forecasted growth of over 40% by 2025 compared to 2020 [15][16]. - The province is actively promoting digital transformation across industries, focusing on the application of artificial intelligence to enhance productivity and innovation [16].

Core Insights - The article discusses the tension between the rapid advancement of AI technologies and the potential risks associated with them, highlighting the contrasting approaches of major tech companies like Google, Microsoft, and Meta towards AI development and commercialization [1][10][14]. Group 1: AI Development and Corporate Strategies - Major tech companies are racing to develop AGI (Artificial General Intelligence), with significant investments and talent acquisition, but they differ in their approach to speed and safety [8][10]. - Google tends to be more cautious in its AI rollout, ensuring technologies are ready before launch, while Microsoft is perceived as more aggressive [8][10]. - OpenAI occupies a middle ground, balancing between caution and the urgency to capture market share [8][10]. Group 2: Energy and Resource Constraints - The article emphasizes that energy may become a critical bottleneck for AI development, despite the U.S. having advantages in chip technology and AI training [10][14]. - The competition for AI supremacy is not solely about capital and talent but increasingly about energy resources [10]. Group 3: The Future of AI and Human Longevity - There are indications that AI may soon exhibit recursive self-improvement, leading to rapid advancements that could result in an "intelligence explosion" [14][17]. - Breakthroughs in biomedical AI could significantly extend human lifespans, with predictions that children today may have a 50% chance of living to 200 years old [26][32]. Group 4: Societal Implications of AI and Robotics - The potential for robots to take over household tasks could lead to a society where humans have more leisure time, but it also raises concerns about societal engagement and productivity [33][37]. - The future may see a divergence in societal outcomes, with one scenario leading to creativity and prosperity, while another could result in widespread complacency and entertainment addiction [39][40].

Core Insights - Yoshua Bengio is recognized as the most cited computer scientist globally, with a total citation count of 987,920, and has seen a significant increase in citations since winning the Turing Award in 2018 [5][6][29] - Geoffrey Hinton, another prominent figure in AI, is approaching 1 million citations, currently at 972,944, and is expected to become the second individual to surpass this milestone [2][5] - The rise in citations for these AI pioneers reflects the explosive growth of AI research and its integration into various fields, particularly since the introduction of deep learning techniques [14][17][26] Group 1 - Yoshua Bengio's citation metrics include an h-index of 251 and a 110-index of 977, indicating his significant impact in the field of machine learning and deep learning [1][5] - The citation growth for Bengio and Hinton aligns with the overall increase in AI-related publications, which have tripled from 2010 to 2022, highlighting the growing importance of AI in computer science [26][14] - The deep learning community is dominated by a few key figures, with Bengio, Hinton, and Yann LeCun being recognized as the "three giants" of deep learning, all of whom received the Turing Award in 2018 [3][29] Group 2 - The AI research landscape has seen a dramatic increase in the number of papers published, with AI papers constituting 41.8% of all computer science papers by 2023, up from 21.6% in 2013 [26][14] - The introduction of the Transformer model in 2017 and subsequent advancements in generative AI have further accelerated the citation rates of foundational papers in the field [21][23] - The citation counts of leading researchers like Ilya Sutskever and Kaiming He also reflect the growing influence of deep learning, with Sutskever exceeding 700,000 citations and He surpassing 750,000 [34][31]

Collaboration & Development - EMBL-EBI renews collaboration with Google DeepMind to develop the AlphaFold Database [1] - The collaboration aims to support protein science worldwide [1] - The AlphaFold Database has been synchronized with UniProtKB release 2025_03 [1]

Core Insights - The unprecedented awarding of the Nobel Prizes in Physics and Chemistry to scientists in the AI field in 2024 highlights the rapid advancements AI models have made in scientific research, with predictions that AI could independently make Nobel-worthy discoveries by 2030 [2][5][6] - The "Nobel Turing Challenge" initiated by Sony AI's CEO aims for AI systems to achieve discoveries comparable to top human researchers by 2050, although current AI models still require significant human intervention [4][10] - While AI has shown potential in assisting scientific research, there are concerns about its limitations and the risks of over-reliance on AI in the scientific process, which could lead to a decrease in innovation and opportunities for young scientists [9][10] Group 1: AI's Role in Scientific Discovery - AI models have made breakthroughs in analyzing experimental data, designing experiments, and proposing new scientific hypotheses, leading to speculation about AI's future capabilities [2][5] - The 2024 Nobel Prizes awarded to pioneers in machine learning and AI applications in protein structure prediction signify the growing intersection of AI and scientific achievement [5][6] - AI tools are increasingly being used in various stages of scientific discovery, from data analysis to experimental design, showcasing their potential as collaborators in research [6][7] Group 2: Challenges and Limitations of AI - Current AI systems are primarily trained on existing human knowledge and may struggle to generate novel insights, necessitating significant changes in AI development and funding [4][9] - Despite some successes, AI's ability to autonomously conduct complete research projects remains limited, with a drastic drop in success rates when attempting to generate ideas and execute experiments [8][9] - The lack of real-world experience in AI systems hinders their ability to propose innovative questions or provide new insights, emphasizing the importance of human involvement in scientific inquiry [9][10] Group 3: Future Directions and Considerations - Developing AI systems capable of making Nobel-level discoveries requires advancements in their reasoning capabilities, allowing them to evaluate and adjust their own thought processes [10] - There is a debate within the scientific community regarding the implications of relying heavily on AI for discoveries, with concerns about potential negative impacts on research diversity and opportunities for emerging scientists [10]

Group 1: Core Insights - The ML-Summit 2025 focuses on the development and application of AI Agents, highlighting their evolution through various stages, including symbolic agents, reactive agents, reinforcement learning-based agents, and large language model (LLM)-based agents [6][25]. - AI Agents are expected to play a significant role in material research and development, with projections indicating that 2025 will mark the commercialization year for AI Agents, and the market size is anticipated to exceed $100 billion by 2030 [1][25]. Group 2: AI Agent Development - The development of AI Agents has progressed through several phases, with the current state being characterized by LLMs that enhance the agents' reasoning and planning capabilities [6][25]. - The technical framework of AI Agents consists of five main modules: perception, definition, memory, planning, and action, which collectively enable the agents to interact with their environment effectively [10][22]. Group 3: Applications and Trends - AI Agents are being applied in various fields, including materials research, where they serve as intelligent research platforms and expert assistants, demonstrating significant advancements in efficiency and effectiveness [34][41]. - The trend towards multi-agent collaboration and vertical domain investment is expected to shape the future landscape of AI applications, particularly in specialized fields [1][25]. Group 4: Technological Breakthroughs - Recent advancements in multi-modal perception capabilities, such as Google's Gemini and OpenAI's GPT-4o, have significantly enhanced the ability of AI Agents to process and understand diverse types of data, including text, images, and audio [16][18]. - The planning module of AI Agents has evolved to include task decomposition and reflective capabilities, allowing for more sophisticated problem-solving approaches [21][22]. Group 5: Market Dynamics - The traditional materials R&D process is lengthy and often reliant on imported materials, creating a strong demand for intelligent technologies to enhance efficiency and reduce costs [42][41]. - AI technologies are expected to accelerate all subprocesses in materials research and development, significantly shortening the R&D cycle and improving the overall effectiveness of material discovery [43][47].

Core Insights - Biomap, a biotechnology start-up co-founded by Baidu's Robin Li, claims to have surpassed AlphaFold in commercializing AI models for drug discovery [1][2] - Wei Liu, Biomap's CEO, noted a "reverse" in the technological gap between Biomap and AlphaFold, indicating a shift in competitive dynamics [1] - While AlphaFold is recognized for its academic influence, Biomap asserts its lead in commercialization and project development [2] Company Developments - Biomap's xTrimo models reportedly demonstrated greater accuracy than AlphaFold 3 in analyzing interactions between 70 antibodies and 44 single-domain antibodies [4] - The company received funding from the Hong Kong Investment Corporation in June last year, supporting its growth and development [4] - Biomap has initiated an accelerator program, BioMap InnoHub, aimed at enhancing Hong Kong's biotech ecosystem, with plans to expand to over 50 projects and 20 companies by 2030 [5] Strategic Partnerships - Biomap co-founded BioGend Science with Legend Capital, focusing on developing drug pipelines utilizing Biomap's AI platform and local research resources [6] - The partnership aims to further Biomap's commercialization efforts in bringing new drugs to market [5][6]