Core Insights - Google DeepMind's AlphaFold has significantly impacted protein structure prediction, driving advancements in scientific research over the past five years [1][4] - AI is reshaping scientific research, particularly in life sciences and biomedicine, due to rich data availability and urgent societal needs [1][3] Group 1: AI in Scientific Research - AI models and tools have achieved breakthroughs in basic research, including protein structure prediction and the discovery of new biological pathways [1][3] - The paradigm of "foundation models + research agents + autonomous laboratories" is emerging in AI-driven scientific research [3][13] Group 2: Advancements in Biology - DeepMind's AlphaFold has solved the protein structure prediction problem, earning the 2024 Nobel Prize in Chemistry and establishing itself as a digital infrastructure for modern biology [4] - The C2S-Scale model, developed by Google and Yale University, has generated new hypotheses about cancer cell behavior, showcasing AI's potential in formulating original scientific hypotheses [8] Group 3: AI in Drug Development - AI-assisted pathology detection has expanded to new disease scenarios, with the DeepGEM model achieving a prediction accuracy of 78% to 99% for lung cancer gene mutations [10] - The AI-optimized drug MTS-004 has completed Phase III clinical trials, marking a significant milestone in AI-driven drug discovery [10] Group 4: AI in Other Scientific Fields - AI applications in materials science are gaining momentum, with startups like Periodic Labs and CuspAI focusing on discovering new materials [11] - DeepMind's WeatherNext 2 model has surpassed traditional physical models in accuracy and efficiency for weather predictions [5] Group 5: Future of AI in Science - The evolution of scientific intelligence technologies is expected to accelerate, with AI foundational models and robotics enhancing research efficiency [19] - The integration of AI into scientific discovery is anticipated to lead to significant breakthroughs, with predictions of achieving near-relativistic level discoveries by 2028 [19]

11月27-28日，被誉为"年度科技与商业风向标"的36氪WISE2025商业之王大会，在北京798艺术区传导空间落地。 今年的WISE不再是一场传统意义上的行业峰会，而是一次以"科技爽文短剧"为载体的沉浸式体验。从AI重塑硬件边界，到具身智能叩响真实世界的大门； 从出海浪潮中的品牌全球化，到传统行业装上"赛博义肢"——我们还原的不仅是趋势，更是在捕捉在无数次商业实践中磨炼出的真知。 我们将在接下来的内容中，逐帧拆解这些"爽剧"背后的真实逻辑，一起看尽2025年商业的"风景独好"。 大朋友： 而过去我们几乎所有的生产环节都变成流水线了，为什么科学发现不能变成流水线呢？ 以下为对话内容，经36氪整理编辑： 冯大刚：好的，非常高兴能够邀请到孙总。我们知道在过去的一年中，毫无疑问是AI应用爆发的一年，但是我想有很多的应用是我们能够感受到的，比如 说各种AI的游戏、AI对话、AI陪伴，但是还有很多是我们没有那么明显感知到的，我想这些东西并不是不重要，而是它们对整个行业，甚至我想说它对整 个人类的命运都有很大的影响。那AI for Science，我不知道我们有没有这样一个解释，就是为科学研究而去做的AI开发，我想这是 ...

Core Insights - The article discusses a new method called Context-Aware Kernel Evolution (CAKE) for Bayesian Optimization, which utilizes large language models (LLMs) to dynamically design optimal Gaussian Process (GP) kernel functions during the optimization process [5][6][14]. Group 1: Methodology - CAKE reimagines the kernel design problem as an "evolutionary process," using LLMs to generate new kernel functions based on existing observational data [17]. - The system maintains a "population" of kernel functions and employs genetic operations such as crossover and mutation to evolve these kernels [19]. - BIC-Acquisition Kernel Ranking (BAKER) is introduced to rank kernel functions based on their model fit and sampling potential, balancing optimization and exploration [21][22]. Group 2: Experimental Results - CAKE was tested against three baseline methods: Fixed (using a single SE or M5 kernel), Adaptive (random selection or BIC selection), and Compositional methods [25]. - In hyperparameter optimization tasks, CAKE achieved the highest final accuracy across all tested machine learning models, demonstrating high sample efficiency, especially in the early stages of optimization [27]. - In dynamic simulation tasks, CAKE outperformed all baseline methods, showing robustness to environmental changes and successfully achieving high scores in challenging tasks [28]. Group 3: Advantages and Future Directions - CAKE offers significant interpretability, allowing for human-readable explanations of kernel structures generated during optimization [34][37]. - The framework is expected to evolve further by incorporating more general kernel function syntax and extending its core ideas to other machine learning tasks, such as SVM and kernel PCA [42].

Focus Review - The hard technology sector, particularly semiconductors and chips, remains mainstream with significant financing activities. For instance, Yanwei Semiconductor completed hundreds of millions in Series A financing, backed by notable investors like Yongxin Ark and Jinyuan Capital [4][15]. - In the health sector, medical devices are gaining attention, exemplified by Jingwei Vision's recent financing exceeding 100 million, supported by multiple investment funds [5][25]. - The internet sector is seeing advancements in AI, with Deep Principle securing over 100 million in Series A financing, led by Alibaba's fund and Ant Group [5][34]. Hard Technology - Yanwei Semiconductor completed hundreds of millions in Series A financing with investments from Yongxin Ark and Jinyuan Capital [4][15]. - Liding Microelectronics raised nearly 100 million in Series A financing, led by CMB International Capital [5][20]. - Hypershell, a consumer-grade exoskeleton company, announced a successful completion of 70 million USD in Pre-B and B rounds, achieving a post-financing valuation of nearly 400 million USD [7]. Health Sector - Jingwei Vision completed a new round of financing exceeding 100 million, with investments from various funds [5][25]. - Jiangsu Zhenyi Medical Technology announced the completion of several hundred million in Series C financing, backed by multiple investment institutions [5][30]. - Weike Biotechnology secured nearly 100 million in Series A financing, led by Shenzhen Capital Group [5][28]. Internet/Enterprise Services - Deep Principle, a pioneer in AI for Science, completed over 100 million in Series A financing, with participation from several major investors [5][34]. - Vision Future, a visual large model company, announced nearly 100 million in angel round financing, led by a listed company [5][35]. - Kulan Dream completed a million-level angel round financing, with investments from notable figures in the gaming industry [5][37].

Core Insights - The Beijing Artificial Intelligence Industry White Paper (2025) predicts explosive growth in various AI agents capable of serving as personal assistants, automating enterprise processes, and acting as scientific research assistants [1][3] - The development of embodied intelligence will enable a transition from information processing to physical tasks [3] Industry Overview - Beijing has registered 183 large models, maintaining its position as the national leader [2] - The AI core industry in Beijing is projected to reach a scale of 215.22 billion yuan in the first half of 2025, reflecting a year-on-year growth of 25.3% [2] - The total industry scale is expected to exceed 450 billion yuan by the end of 2025, with over 2,500 AI companies operating in the region [2] Technological Advancements - Various innovative entities in Beijing are producing leading-edge results, including the launch of FlagOS by the Beijing Zhiyuan Artificial Intelligence Research Institute and the introduction of "Tongtong 2.0" by the Beijing General Artificial Intelligence Research Institute [3] - The establishment of the world's first AI research platform covering literature review, computation, experimentation, and multidisciplinary collaboration has been achieved with the launch of the Bohr Research Space Station [3] Future Trends - The white paper outlines future trends in the AI industry, indicating that AI agents will experience significant growth and that embodied intelligence will bridge the gap between information processing and physical operations [3] - The development of world models is expected to enhance the generalization capabilities and reliability of AI systems [3] - The "AI for Science" initiative is anticipated to accelerate scientific discovery and lead to breakthroughs across various fields [3]

Core Insights - The article discusses the evolving landscape of AI competition leading into 2026, highlighting trends such as the concentration of power among a few key players and the rise of new entrants in the open-source community, particularly from China [3][7][8] - It emphasizes the limitations of current large language models (LLMs) in achieving super intelligence and the challenges in generalization capabilities [15][18][22] - The article also explores the implications of open-source versus closed-source models, talent attraction, and the importance of policy support for fostering innovation in the AI sector [33][40][41] Group 1: AI Competition Trends - The AI industry is witnessing a concentration of power among a few core players due to the availability of computational resources, which will be a significant topic in 2026 [7][11] - There is a notable emergence of new laboratories in China producing high-quality models, which has prompted a resurgence of open-source initiatives in the U.S. as a response to China's advancements [8][9] - Companies seeking to explore new AI applications are increasingly turning to open-source models, as closed-source systems impose limitations [8][10] Group 2: Limitations of Current AI Models - Current LLMs exhibit weaker generalization capabilities than previously expected, leading to a ceiling effect that hinders the achievement of super intelligence [15][18] - The article posits that while AI can serve as a valuable research assistant, it struggles to define new research questions, which is crucial for groundbreaking scientific discoveries [20][22] - The notion that expanding model size will naturally lead to greater intelligence is challenged, with the argument that true innovation requires more than just scaling [22][24] Group 3: Open-source vs Closed-source Dynamics - The choice between open-source and closed-source models is influenced by various factors, including the need to attract top talent and the cultural context of the research environment [36][37] - In the U.S., closed-source models are becoming more attractive for researchers, while in China, open-source models are preferred [37][39] - The article suggests that policy support for open-source initiatives is crucial for maintaining a competitive edge in AI development [40][41] Group 4: Business Model and Future Directions - Hugging Face is transitioning its business model to focus on enterprise solutions, providing tools for organizations to manage and deploy AI models securely [50][51] - The company has entered the robotics field, emphasizing the importance of open-source ecosystems in this domain and launching affordable entry-level robotic products [52][58] - The introduction of a low-cost robotic arm and the Ritchie Mini robot aims to enhance human-robot interaction and make robotics more accessible [58][59]

Group 1: AI and Technology Transformation - AI is evolving from a tool for process optimization to a core productivity driver that reshapes industry logic, moving towards a symbiotic relationship with humans [3][4][5] - The emergence of embodied intelligence signifies a paradigm shift where robots can autonomously learn and operate in diverse environments, integrating AI's value into real-world applications [4][5] - The concept of "Agentic AI" is introduced, where AI transitions from a passive tool to an active collaborator, enhancing decision-making and operational efficiency [11] Group 2: Consumer Behavior and Market Dynamics - The consumer market is undergoing a profound value reconstruction, with consumers balancing rational price-performance assessments and emotional connections to brands [25][27] - Companies are shifting from a focus on mass appeal to creating unique value for specific consumer segments, as seen in the "胖东来模式" and the rise of domestic brands [26][27] - The importance of quality and cultural narratives in consumer choices is emphasized, indicating a trend towards brands that resonate with cultural identity and emotional experiences [25][27][42] Group 3: Innovation and Business Strategy - The focus of innovation is shifting from merely adding features to creating tangible value, with a growing emphasis on effective innovation that simplifies choices for consumers [52] - Companies are encouraged to adopt a long-term perspective, prioritizing sustainable growth and trust-building over short-term gains [39][44] - The integration of AI into business operations is seen as a critical factor for driving efficiency and enhancing customer experiences, marking a transition from traditional practices to data-driven strategies [40][41] Group 4: Health and Biotechnology - The intersection of AI and biology is leading to personalized health solutions, moving towards a model that decodes individual health needs rather than offering generic solutions [33][34] - The concept of "biological intelligence" emerges, highlighting the potential of AI to enhance our understanding of complex biological systems and improve healthcare outcomes [34][37] Group 5: Globalization and Collaboration - The globalization of AI is evolving from a one-way technology transfer to a two-way empowerment ecosystem, fostering collaboration across borders [16] - The narrative of global brands is shifting from competition to co-creation, emphasizing the importance of trust and shared values in building sustainable business relationships [38][39]

来源：联想创投 近日，联想创投被投企业「深度原理 Deep Principle」完成超亿元人民币A轮融资。本轮由戈壁创投管理的阿里巴巴创业者基金大湾区基金(简称AEF大湾 区基金)与蚂蚁集团共同领投，现有股东联想创投、Taihill Venture 超额加注，BV百度风投继续加注，多家机构参与。 本轮融资将主要用于三大方向：加速 Agentic AI for Materials Discovery 材料发现智能体 Agent Mira™的研发与升级；推进 L4 高通量自主实验室 AI Materials Factory™与其研发管线的建设与布局；深化与国际和国内头部客户的合作，巩固技术落地领先优势。 顶尖团队驱动模型持续突破 联想集团副总裁、联想创投管理合伙人王光熙表示，联想创投本轮加注深度原理，核心是持续看好 AI for Science 引领的产业变革机遇，更认可深度原理 的技术硬实力与商业化闭环能力。团队以扩散模型与大语言模型的前沿突破为根基，将材料研发从"试错式实验"推向"智能化设计"，凭借显著技术领先 性，成立仅一年便斩获超千万订单，展现了团队将顶尖学术成果向产业解决方案转化的超强效率。其提出的 E ...

Investment Rating - The report does not explicitly state an investment rating for the industry Core Insights - The U.S. "Genesis Mission" aims to establish a national-level technology initiative comparable to the "Manhattan Project," focusing on integrating scientific datasets, supercomputing resources, and AI models to enhance the U.S.'s global technology leadership [12][14] - The initiative emphasizes the application of AI in scientific discovery as a national security priority, indicating a shift from commercial AI applications to a more integrated national research approach [13][14] - The plan outlines six priority sectors: advanced manufacturing, biotechnology, critical materials, nuclear energy, quantum information science, and semiconductors, which will guide federal budget allocations and industrial policies [14][15] Summary by Sections Event Overview - The Genesis Mission was officially launched on November 24, 2025, by President Trump, with the goal of accelerating scientific breakthroughs in critical fields [12] - The initiative is led by the Department of Energy and aims to create a unified "American Science and Security Platform" [12][14] Strategic Shift - The focus has shifted from regulation to national research empowerment, establishing "AI for Science" as a strategic priority [13] - The government intends to leverage national resources to overcome scientific bottlenecks in key areas [13] Infrastructure Development - The plan includes building a national-level hardware and software infrastructure that integrates supercomputers, cloud-based AI environments, and extensive federal scientific data [14] - This infrastructure aims to create significant barriers to entry for competitors, positioning entities with high-quality data and computing capabilities at the core of future technology ecosystems [14] Priority Sectors - The six identified priority sectors are expected to drive demand for upstream computing infrastructure and benefit industry leaders that can integrate AI into their R&D processes [14][15] - Specific sectors include semiconductors, biotechnology, and clean technology, which are poised for growth due to the initiative [14] Public-Private Partnerships - The initiative emphasizes collaboration with private sector companies and universities, allowing technology firms to participate in national projects [15] - Clear execution milestones have been set, including resource audits and initial platform capabilities, which are expected to accelerate technology transfer from research to application [15]

Core Insights - ByteDance has been actively exploring technology since its inception, focusing on large-scale machine learning systems for recommendation algorithms [1][5][34] - The company has made significant advancements in AI, particularly with its AI dialogue assistant "Doubao" and its leading position in the Chinese MaaS market through Volcano Engine [2][34] - ByteDance is investing heavily in XR technology, aiming to enhance user experience through improved hardware and software solutions [22][30] Group 1: Technology Development - In 2014, ByteDance set an ambitious goal to develop a recommendation system with a feature scale of one trillion, leveraging large-scale machine learning [5][9] - The company initially underestimated the potential of large language models, but quickly pivoted to invest in this area starting in 2022, leading to successful applications [34][35] - ByteDance has developed a stable training system called MegaScale, achieving a floating-point operation utilization rate exceeding 55%, which is 1.3 times higher than mainstream open-source frameworks [34] Group 2: AI and Machine Learning - The company has recognized the importance of large-scale data for creating valuable models and algorithms, particularly in the context of real-world applications [10][34] - ByteDance's AI dialogue assistant "Doubao" has become the most popular in China, showcasing the company's success in AI applications [2][34] - The company is also exploring advanced AI models, including the Seed Edge plan, which focuses on cutting-edge research in large models [35] Group 3: XR Technology - ByteDance acquired the Pico team in 2021 to enhance its XR capabilities, focusing on both content and foundational technology [22][30] - The company aims to achieve a pixel density (PPD) of nearly 4000, significantly higher than existing products, to improve clarity in XR experiences [26][29] - ByteDance is developing a dedicated consumer electronics chip to address processing bottlenecks in mixed reality applications, achieving a system latency of around 12 milliseconds [31][30]