亲吻数又叫牛顿数，是希尔伯特第十八问题（球体堆积）的局部形式，和通信技术中的"比特拥挤"问题是同一套底层逻辑。 闻乐 发自 凹非寺 量子位 | 公众号 QbitAI 情人节到了… 那咱也来应应景，讲讲亲吻这件事—— AI的打开方式。 你或许知道，数学上有个正经问题叫做 亲吻数（Kissing Number Problem） ，卡了人类300多年，但就在最近，被 中国AI 狠狠推了一 把。 简单说，它研究的是：在n维空间中，一个球体周围，最多能有多少个和它大小相同的球体，刚好与它相切（kiss），不重叠的那种 。 它源自于1694年，牛顿和格雷戈里两位大佬的争吵： 在三维空间里，一个球周围到底能放12个，还是13个同款球？牛顿坚持12，格雷戈里不服，结果谁也没能当场辩过谁。 直到1953年，数学家用了 258年 时间才严格证明牛顿是对的。 就连2022年获得 菲尔兹奖 的玛丽娜·维亚佐夫斯卡， 正是凭借解决8维和24维空间的最密球体堆积问题，摘得桂冠。 但再往高维走，人类的直觉就崩了。在过去近50年里，亲吻数构造仅有7次实质性进展，而且每一次的方法都完全不同，在临近维度上难以迁 移与复用。 现在，僵局被打破了。 ...

Pacific Biosciences of California (NasdaqGS:PACB) Q4 2025 Earnings call February 12, 2026 05:00 PM ET Speaker9Good day, and welcome to PacBio's fourth quarter and full year 2025 earnings conference call. All participants will be in listen-only mode. Should you need assistance, please signal a conference specialist by pressing the star key followed by zero. After today's presentation, there will be an opportunity to ask questions. To ask a question, you may press star then one on your telephone keypad, and t ...

Core Insights - The article discusses a month-long live demonstration of an AI system named FARS, which aims to autonomously conduct the entire research process, producing 100 complete research papers without human intervention [1][20]. Company Overview - Analemma, the company behind FARS, was founded less than a year ago and has secured tens of millions of dollars in angel funding from notable investors such as Sequoia China and Meituan [1]. - The founder, Tianxiang Sun, was a key developer of MOSS, a significant model in the AI field, which gained attention for its capabilities [11][12]. Technology and Architecture - FARS, or Fully Automated Research System, is a multi-agent system composed of four modules: Ideation, Planning, Experiment, and Writing, which collaborate in a shared file system [2][4]. - The system utilizes APIs from various closed-source models, including Claude, GPT, and Gemini, along with self-developed models for certain tasks [5]. Research Focus and Methodology - FARS focuses on AI research itself, allowing for fully automated experiments that do not require physical laboratories [8]. - The system is designed to produce "short papers" that emphasize clear hypotheses and reliable validation, diverging from traditional academic publishing norms [7]. Quality Control and Evaluation - Each paper produced by FARS will undergo review by at least three team members with over five years of research experience before being uploaded to arXiv, ensuring a level of quality control [8]. - The team plans to invite peer reviews rather than submitting to traditional academic conferences, focusing on the practical citation and value of the results [8]. Competitive Landscape - FARS is part of a growing trend in automated research systems, competing with others like Sakana AI's AI Scientist and AI-Researcher from Hong Kong University [17][19]. - Unlike its competitors, FARS aims for real-time, large-scale, and fully transparent public deployment, which is a bold move in the field [19]. Future Directions - The live demonstration of FARS will begin on the company's website and social media platforms, marking a significant step in evaluating the system's capabilities [20]. - The results of this experiment could provide insights into the potential of AI to conduct research autonomously, a question that remains to be answered through the quality of the 100 papers produced [20][21].

Core Insights - The most challenging phase in the industrialization path of biomanufacturing/biopharmaceuticals is the pilot scale-up process, where over 90% of laboratory results fail to transition successfully [6][7]. Group 1: AI's Role in Biomanufacturing - AI is reshaping traditional processes in biomanufacturing, leading to geometric efficiency improvements, particularly in drug discovery and production [2][3]. - AI-driven pharmaceutical companies can achieve efficiency gains of 30% to 50% in specific areas like lipid nanoparticle screening [3]. - While AI can generate numerous protein structures, it cannot validate these ideas without the capability to implement them, highlighting the need for human expertise in critical stages [4][5]. Group 2: Challenges in Transitioning from Lab to Factory - The transition from laboratory to industrial production involves significant differences in operational requirements, including efficiency, delivery, cost, and environmental compliance [6][7]. - The gap between "product" and "commodity" requires a deep integration of commercial logic, which is often overlooked by principal investigators [6][7]. - Successful navigation of this transition may involve collaboration between scientists and entrepreneurs, with the latter addressing engineering, financing, and regulatory challenges [7]. Group 3: Market and Globalization Challenges - After commercialization, biomanufacturers face challenges in market penetration and global expansion, where compliance with international standards like EU CE and FDA certifications is crucial [8]. - The ability to meet safety, accessibility, and efficacy standards is more important than technological advancement in global competition [8]. Group 4: Collaboration and Resource Integration - Successful industrialization in biomanufacturing requires collaboration among various stakeholders, with an emphasis on understanding each party's needs and business logic [9][10]. - There is often a disconnect between government resources, industrial parks, and corporate needs, which can hinder market entry for many companies [9]. - Many innovative ideas remain uncommercialized due to a lack of professional teams to drive products to market, necessitating joint efforts from capital and industry teams [9].

这是一场关于"真理"与"概率"的博弈。 编译 | 王启隆 来源 | youtu.be/Z5GKnb4H_bM 出品丨AI 科技大本营（ID：rgznai100） 在数学界，陶哲轩（Terence Tao）的名字本身就代表着一种"确定性"。 这位菲尔兹奖得主、被誉为"数学界的莫扎特"的天才，过去几十年的工作是和最纯粹的逻辑、最绝对的真理打交道。但在 2026 年初，他做了一个看 似"反直觉"的决定——他要以此身为桥梁，去拥抱那个充满了概率、幻觉和不确定性的 AI 世界。 就在昨天，陶哲轩联合创立的 SAIR（科学与 AI 研究基金会） 正式浮出水面，宣告这位大神入局 AI for Science。 ▲ 右上角居然还有 B 站和抖音官号，这就和外国的机构不一样了 这件事的信号意义极强。过去两年，"AI for Science"虽然喊得震天响，但科学界始终弥漫着一种尴尬的"割裂感"：一派是 AI 极客，他们用大模型生成 看似完美的论文摘要，却对背后的物理机制一窍不通；另一派是传统科学家，他们看着 ChatGPT 编造的参考文献嗤之以鼻，坚守着这一亩三分地。 可能比较看得清的，还属弄出了 AlphaFold 的诺奖得 ...

Core Insights - The article discusses the limitations of traditional industrial simulation methods and highlights the emergence of a new company, Jiying Technology, which aims to revolutionize the field through AI-driven physical modeling [6][12][85]. Group 1: Industry Context - In July 2025, a significant acquisition occurred in the industrial software sector, with Synopsys acquiring ANSYS for $35 billion, marking the most expensive deal in the history of industrial software [2][73]. - Concurrently, AI industrial software companies like PhysicsX and Neural Concept secured funding of around $100 million, indicating a growing consensus in the capital market about the need to revalue the ability to predict the physical world in the AI era [3][74][75]. - Traditional physical simulation in sectors like semiconductors and aerospace is still hindered by outdated paradigms, often requiring days for complex calculations, which traps engineers in tedious tasks like mesh generation and parameter tuning [4][76]. Group 2: Company Overview - Jiying Technology was founded to address the inefficiencies in physical simulation, with its founder, Gao Xin, drawing from personal experiences in simulation and AI research [5][77]. - The company has successfully completed seed and angel funding rounds totaling several million yuan, with notable investors including Qiji Chuangtan and Yuanhe Puhua [5][77]. - The founding team consists of experienced professionals with over 30 years of combined expertise in physical simulation and software development, specifically targeting the demanding fields of semiconductors and aerospace [6][78]. Group 3: Technological Innovations - Jiying Technology aims to break through the limitations of traditional industrial simulation by focusing on a unified modeling approach that adheres to fundamental physical laws, such as conservation of mass and energy [8][80]. - The company has developed a physical world model that significantly reduces feedback cycles from days to seconds, achieving a response speed that is 100 times faster than traditional methods [9][82]. - This innovative approach has garnered interest beyond industrial applications, with gaming companies like Mihayou exploring the potential for creating credible physical boundaries in virtual worlds [10][83]. Group 4: Future Prospects - Gao Xin envisions a future where the ability to accurately map the real world could lead to the capacity to create entirely new worlds, representing a significant philosophical and technological leap [12][85]. - Investors view Jiying's 1.0 physical simulation model as a groundbreaking innovation that addresses long-standing industry pain points related to multi-physical field simulations [13][86]. - The company is positioned to solve traditional numerical simulation challenges, with applications spanning industrial research, embodied intelligence, and scientific inquiry [14][87].

（文章来源：每日经济新闻） 创新药ETF国泰（517110）跟踪的是SHS创新药指数（931409），该指数从中国A股市场中选取涉及创 新药研发、生物制药等领域的上市公司证券作为指数样本，以反映创新型医药相关上市公司证券的整体 表现。 华泰证券指出，AI for Science（AI4S）的研究范式打破了传统"实验发现"或"手工推导方程"的局限，正 通过赋能量子、原子与连续介质系统中的高级建模、仿真与预测，引领科研革命。该机构持续看好AI 制药在2026年的商业化前景，预计行业将呈现小分子药物合作深化与大分子抗体领域合作爆发的双轮驱 动格局。其中，抗体等大分子领域有望成为2026年最大的增长亮点，AI能够高效探索广阔的蛋白质序 列空间，设计出具有更佳特性的新型抗体。预计AI新材料将成为AI4S的重点应用与投资方向，AI加速 材料发现，并通过数字化工艺优化直接推动产业化，是实现制造产业升级的核心引擎。 ...

马健说："今年是深圳的APEC机遇之年，我们的企业要有更大的格局、更高的战略，用于布局未来产 业、布局科技前沿的技术。" 孙迎彤：以AI思维吸引全球人才，推动深圳成AI创新高地 深圳市人大代表，国民技术股份有限公司董事长、总经理孙迎彤围绕深圳新兴产业的发展情况分享了自 己的看法。他表示，深圳的新兴产业发展环境已经处于第一阵营，在刚过去的2026年CES展4000家参展 企业中也包含了将近400家深圳企业，占比约10%。这离不开深圳坚持的市场化、法治化、国际化，激 发了全球创业者来深圳扎根创业发展。 孙迎彤提出，深圳正从"深圳速度"转向"深圳质量"，在努力创建中国特色社会主义现代化强国的城市范 例、打造粤港澳大湾区国际科技创新中心的进程中，深圳正锻造"引领"的DNA。 2月9日，深圳市七届人大七次会议举行首场"代表通道"集中采访活动，来自不同领域的3位市人大代表 走上通道，围绕人工智能、生物医药、人才战略与文化发展等关键议题建言献策。深圳市人大代表、晶 泰科技CEO马健提出聚焦"三个跨越"，推动AI与生物医药深度融合，助力产业向系统性创新转型；市人 大代表、国民技术（300077）董事长孙迎彤强调以AI思维吸引 ...

如果底层范式不改变，工业仿真将成为工程创新的天花板。 作者｜周悦 编辑｜王博 2025年7月，硅谷完成了工业软件史上最昂贵的一笔交易：半导体设计软件龙头新思科技以350亿美元收购仿真巨头ANSYS。 几乎同期，PhysicsX、Neural Concept等AI工业软件公司相继完成1亿美元级融资。 这意味着资本正在达成共识：AI时代，预测物理世界的能力需要被重新定价。 在半导体、航空航天等领域，物理仿真仍受困于传统范式。一轮复杂计算往往耗时数日，工程师被困在网格划分与参数调试中。 正是这一长期低效，催生了物理世界模型公司极映科技。 今天，「甲子光年」独家获悉，极映科技连续完成了数千万元的种子轮及天使轮融资。其中种子轮由奇绩创坛投资，天使轮由元禾璞华领投，未来光锥跟 投。远山资本担任独家财务顾问。 这家公司并非从风口起步，而是源于创始人高鑫十年前的切身体验。作为迈阿密大学博士、密西根大学博士后，高鑫一直从事仿真与AI研究工作。但在 早年为了跑通数值算法，他曾需对着医学影像手动点击上千次鼠标，清洗"脏"数据。 这种对耐心的极致消耗，让他逐渐确认了一件事：如果底层范式不改变，工业仿真将成为工程创新的天花板。 为了击 ...

Group 1: AI and Economic Integration - The year 2025 is highlighted as a critical point for Shenzhen's AI and biomedicine industries, marking a transition to high-quality development [2] - Shenzhen's innovation cluster, comprising Shenzhen, Hong Kong, and Guangzhou, is projected to rank first globally in the innovation index by 2025, attributed to its strong innovation ecosystem [2] - Emphasis on three focal points for technology-driven industry leadership: ecological collaboration, industry value realization, and ecosystem cultivation [3] Group 2: Talent Development and Industry Competitiveness - Shenzhen is recognized as a leading global hub for emerging industries, with a strong emphasis on quality over quantity in manufacturing [4] - The city has a high R&D investment intensity of 6.67%, with 93% of this funding coming from enterprises, enhancing its competitive edge [4] - There is a call for increased focus on talent development in the AI sector, aiming to attract global talent and cultivate AI-thinking professionals from the existing engineering workforce [6] Group 3: Cultural and Tourism Integration - Shenzhen has established numerous world-class cultural facilities, contributing to its cultural and technological foundation [8] - The development of cruise economy and AI-enabled cultural landmarks is proposed to enhance the cultural tourism experience [8][9] - The integration of various cultural narratives and community involvement is essential for creating a vibrant cultural ecosystem in Shenzhen [9]