Group 1 - The 27th China Association for Science and Technology Annual Conference will be held from July 1 to July 31 in Beijing, featuring 1 main forum, 98 thematic forums, and 4 parallel forums [1] - The main forum is scheduled for July 6, with notable speakers including five vice presidents of the China Association for Science and Technology, discussing topics such as quantum technology, biomanufacturing, deep-sea technology, artificial intelligence, and agriculture [1] - The conference aims to release major scientific issues, engineering challenges, and industrial technology problems for 2025, establishing a "guiding standard" for the continuous output of original and disruptive scientific achievements [1] Group 2 - The "Frontiers in Pattern Recognition and Artificial Intelligence" thematic forum will take place on July 6, focusing on the development of pattern recognition and artificial intelligence, including breakthroughs and governance [2] - The 2025 China Science and Technology Journal Development Forum, one of the four parallel forums, will be held on July 10, highlighting the growth of high-impact journals in China, with 56 journals ranked in the top three internationally and 24 ranked first in their fields [2] - The forum will feature five key experts presenting reports and will include four thematic discussion sections to explore innovative development paths for Chinese scientific journals [2]

这家公司的名字，你也许没听过，但这家公司的董事长，想必你一定有所耳闻。 源：Kira Learning官网截图 美国的K12教室也正在通过AI助教进行技术升级。近日，美国教育科技初创公司Kira Learning面向K12学 校推出AI智能体。 图 从左至右：Kira董事长吴恩达、联创兼CEO Andrea Pasinetti、联创Jagriti Agrawal 据介绍，Kira能够高效处理各种教学数据，包括文本、音频、视频和图像，并提供即时反馈。无论是评 估学生的论文、分析课堂讨论，还是评估视频，Kira的AI智能体都能在几秒钟内提供分析，帮助教师做 出更快、更明智的教学决策。 Kira的董事长是机器学习和在线教育领域的先驱吴恩达，他还担任Google Brain创始人、Coursera董事长 兼联合创始人、DeepLearning.AI创始人、AI Fund董事合伙人、斯坦福大学教授和AI研究员。 美国教师也受日常繁琐任务的困扰，这些任务通常会占用教师数小时时间。据介绍，Kira的AI智能体会 执行重复性任务，包括打分、课程规划和课堂讨论分析，还会提供学生哪方面做得好、哪方面有困难的 分析，同时还支持一对 ...

Core Viewpoint - The article discusses the launch of Stanford University's CS336 course "Language Models from Scratch," which aims to provide a comprehensive understanding of language models through practical development and implementation [5][7]. Course Overview - The course focuses on the foundational aspects of language models, which are essential for modern natural language processing (NLP) applications. It emphasizes the importance of understanding language models for scientists and engineers in the fields of AI and ML [5][7]. - The course is structured into five major modules: Foundations, Systems, Extensions, Data, and Alignment & Reinforcement Learning [7]. Course Requirements - Students are expected to have proficiency in Python, as most assignments will require extensive coding. The course will provide minimal scaffolding, resulting in a higher volume of code written by students compared to other AI courses [7]. - A background in deep learning and system optimization is necessary, particularly familiarity with PyTorch and basic system concepts like memory hierarchy [7]. - Foundational knowledge in calculus, linear algebra, probability, and statistics is required, along with a basic understanding of machine learning principles [7]. Assignments - The course includes several assignments that cover various aspects of language model development, such as implementing a BPE tokenizer, training models on specific datasets, and optimizing performance on GPUs [8]. - Assignments are designed to simulate real-world challenges, including data processing and model alignment, with a focus on practical application and hands-on experience [8]. Course Schedule - The course is structured with a detailed schedule that outlines topics, materials, and deadlines for assignments, ensuring a systematic approach to learning [9].

Core Viewpoint - The article announces the launch of Stanford University's CS336 course "Language Models from Scratch" for Spring 2025, which aims to guide students through the entire process of developing their own language models [1][8]. Group 1: Course Overview - CS336 is designed to help students gain a comprehensive understanding of language models by guiding them through various stages, including data collection, model construction, training, and evaluation [8]. - The course structure consists of 5 units and 19 lectures, with a focus on practical implementation and hands-on experience [10]. Group 2: Instructors - Tatsunori Hashimoto, an assistant professor at Stanford, has a strong background in machine learning and has received over 30,000 citations for his research [2]. - Percy Liang, an associate professor and director of the Center for Research on Foundation Models (CRFM), has over 100,000 citations and extensive experience in AI research [6][7]. Group 3: Course Requirements - Students are expected to have proficiency in Python, deep learning, and system optimization, as well as a solid understanding of calculus, linear algebra, and basic probability and statistics [11]. - The course emphasizes minimal scaffolding, requiring students to write significantly more code compared to other AI courses [11].

真正的技术突破在1967年才到来，美国通用电气公司研制的"Hardiman"外骨骼机器人原型机横空出世。这款 原型机采用半仿生构型设计，通过液压驱动，并且存在力量反馈系统，包含30多个动力关节，能辅助普通 人轻松举起一百多公斤的物体。然而，"Hardiman"680公斤的自重、迟缓的动作节奏和惊人的能耗，严重限 制了该机器人项目的落地。不过，它的诞生依然为外骨骼机器人的未来探索指引了方向。 在泰山十八盘的陡峭石阶上，一位白发登山者轻松越过年轻游客的队伍。他腰腿都包裹着流线型金属支架，步 伐稳定而轻快——这不是科幻电影里的场景，而是泰山景区内常见的真实画面。80元租用3小时的外骨骼机器 人，正让曾经遥不可及的"机械战甲"走进普通人的生活。 所谓外骨骼机器人，是一种通过机械结构与人体关节紧密耦合，增强或替代人体上肢、下肢运动能力的智能辅 助设备，宛如为人体安装了"物理外挂"，赋予人们应对各类体力挑战的非凡能力。 就如电影《钢铁侠》中，托尼·斯塔克的能量战甲让他成为名副其实的钢铁侠，《流浪地球》中的动力装甲为人 类在极端环境下的生存和工作提供了强大的支持，在现实中，除了户外运动，外骨骼机器人还被应用至工业、 医疗、 ...

Core Viewpoint - The research published in Nature highlights the design of a new ductile FeNiCoAlTa alloy with unprecedented strength and plasticity, achieved through machine learning techniques, setting a new record for yield strength and tensile ductility in alloys [2][4]. Group 1: Research Findings - The alloy composition is Fe 35 Ni 29 Co 21 Al 12 Ta 3, which demonstrates a remarkable combination of 1.8 GPa yield strength and 25% true uniform elongation [4]. - The study emphasizes the use of machine learning models based on domain knowledge to design multi-principal element alloys, achieving high strength and high ductility [4]. Group 2: Microstructural Innovations - The research team enhanced the alloy's strength by maximizing microstructural heterogeneity, featuring large coherent L1 2 nano-precipitates and incoherent B2 micro-particles [6]. - The B2 micro-particles contribute to a multi-component structure that can accumulate dislocations, thereby maintaining a high strain hardening rate and extending uniform elongation [6].

Group 1 - The event aimed to deepen the construction of science and technology clubs, fostering well-rounded talents with both cultural and scientific backgrounds [1] - A diverse team of experts provided professional guidance to the school’s science and technology club, focusing on art integration, technical practice, and industrial application [1] - Interactive sessions with students encouraged innovative thinking, with discussions on practical applications of electrical components and technology [2][3] Group 2 - Teachers presented various technological concepts, including robotics, artificial intelligence, and drone applications, using engaging methods such as animations and real-life examples [2][3][4] - The importance of safety and technical specifications was emphasized during discussions about drone operations and the principles of algorithms in resource optimization [3][4] - The event concluded with a focus on the integration of science and art, encouraging students to explore their creativity while grounding their ideas in scientific principles [5]

在LSEG"与专家面对面"系列网络研讨会的最新一期中，深入剖析了实施全面合规计划的重要性，并为制定 更具主动性的风险管理策略提供了一系列最佳实践方面的见解。 以更少的资源做更多的事 如今，受监管实体正深陷一场"完美风暴"：法规不断演变，尽职调查工作量与成本急剧攀升，而资源却极为 有限。据LSEG的研究显示，90%的受访者表示，过去三年间，他们所处理的增强尽职调查（EDD）请求数 量呈上升态势。 这些不断攀升的工作量给预算和资源带来了巨大压力。与此同时，合规团队必须确保客户准入流程以及交易 决策过程快速、无缝且具备成本效益。 所有这些情况都凸显出"以更少资源达成更多成效"的迫切需求。借助恰当的数据与技术手段，您便能够驾驭 这一复杂多变的风险局面。 5项最佳实践见解 01 采用基于风险的方法 采用基于风险的方法至关重要，因为资源是有限的。即使是最大的组织也没有无限的资源，这意味着您应该 将可用的预算、时间和精力投入到潜在风险最高的领域。 筛查是识别潜在风险的重要初始环节。一旦怀疑或发现风险，便需要以增强尽职调查（EDD）的形式开展 更深入的尽职调查工作。 增强尽职调查（EDD）的力度应与怀疑的风险程度相匹配，并应 ...

Group 1 - The report titled "2025 Financial Industry Intelligent Risk Control Practice White Paper" focuses on the intelligent risk control practices in the financial sector, aiming to provide references for the industry [1][12] - The report highlights the urgent need for financial institutions to enhance their intelligent risk control capabilities due to the increasing severity of fraud risks and the evolving landscape of financial crime [1][18] - The current state of intelligent risk control in the financial industry is characterized by a shift towards technology-driven solutions, with traditional models facing limitations in data and algorithmic capabilities [2][20] Group 2 - The report identifies several challenges in the construction of intelligent risk control systems, including difficulties in data sharing, data quality issues, and the evolving tactics of criminal enterprises [2][29] - Proposed solutions include broadening data dimensions, automating analysis, and developing more refined model strategies to enhance risk management [2][36] - Practical case studies from various financial institutions demonstrate the effectiveness of intelligent risk control applications, such as the use of blockchain technology and integrated intelligence systems [2][26] Group 3 - The future of intelligent risk control in the financial industry is expected to focus on collaborative defense, ecosystem co-construction, and technology empowerment to improve overall risk management levels [2][36] - The report emphasizes the importance of high-quality data and dynamic risk assessment models in achieving effective intelligent risk control [2][36] - The financial sector is urged to adapt to new fraud techniques and enhance monitoring capabilities to address the challenges posed by evolving criminal methods [2][31]

其产品组合包括贡献主要收入的组织分子分析解决方案MI Profile，以及2024年一季度推出的血液分子 检测方案Caris Assure。此外，公司还运营药物发现业务，利用检测数据和基因组数据集识别潜在药物 靶点并研发疗法。 财务数据显示，2025年一季度Caris营收1.209亿美元，净亏损1.27亿美元;去年同期则是8070万美元营收 和1.341亿美元亏损，亏损幅度收窄。 招股书显示，上市后哈尔伯特将持股41.7%，Sixth Street Partners和私募公司JH Whitney Capital Partners 的关联机构分别持股9.8%和6.8%。 医疗科技公司Caris Life Sciences(CAI.US)于美东时间周三登陆纳斯达克市场，在美股首次公开募股(IPO) 中筹资4.94亿美元，发行价高于原定区间上限。该股一经上市便高涨至近40%，周三收涨33%于28美 元。 由Sixth Street Partners支持的Caris以每股21美元定价发行2350万股，较最初19至20美元的定价区间再度 上调。按此前备案文件中的流通股计算，这家总部位于得州欧文的公司市值达79亿美元。 ...