近日，全球人工智能和计算机科学学术圈被一条消息刷屏： arXiv，这个领域内最重要的预印本服务器，突然宣布了一项重大的实践更新。 即日起，所有投稿至 arXiv 计算机科学分类的综述性文章（review/survey）和立场性文章（position paper），必须首先通过期刊或正式会议的同 行评审，并附上已获接收的证明，否则很可能会被拒绝。 消息一出，科研圈一片哗然。arXiv 明确指出，这场风波的源头正是生成式 AI 与大语言模型。 然而，就在 arXiv 官方拉响警报的前几周，一篇中稿 2025 年 NeurIPS P osition Paper Track 的立场性文章恰好也登上了 arXiv。它的标题精准地概括了这场危机： 《Stop DDoS Attacking the Research Community with AI-Generated Survey Papers》。来自上海交通大学的研究团队精准地预见、识别、量化并命名了这场由大模型 引发的学术界危机。该论文的作者团队来自上海交通大学，第一作者为安泰智能计算研究院助理教授林江浩，通讯作者为计算机学院张伟楠教授。 AI 综述不仅是洪水漫灌， ...

Core Viewpoint - The article discusses the evolution of academic ranking systems, emphasizing the shift from quantity-based metrics, such as the number of published papers, to quality-based assessments that reflect true academic impact and influence [2][12]. Group 1: Issues with Current Ranking Systems - Traditional ranking systems like USNews rely on subjective surveys, while CSRankings uses objective metrics like publication counts, leading to a competition focused on quantity rather than quality [2][3]. - The reliance on citation counts to measure academic influence has its drawbacks, as not all citations indicate significant contributions to the field [3][4]. Group 2: New Approaches to Measuring Impact - A new academic ranking system has been developed by researchers from Oregon State University and the University of California Santa Cruz, utilizing large language models (LLMs) to assess the impact of academic papers [5][7]. - The LLM analyzes top AI conference papers from 2020-2025 to identify the five most important references cited by each paper, aiming to uncover the foundational works that drive innovation in the field [7][8]. Group 3: Implementation of the New Ranking System - The new system maps the identified key references back to their authors and institutions, assigning academic influence points based on how often a paper is cited as a key reference by new research [10][12]. - This approach rewards institutions that contribute to groundbreaking discoveries and foundational research, shifting the focus from mere publication counts to genuine academic influence [12][13]. Group 4: Results and Rankings - The resulting rankings highlight institutions that have significantly impacted their fields, showcasing a more nuanced understanding of academic contributions [12][14]. - The article provides specific rankings of institutions based on their impact scores, illustrating the effectiveness of this new methodology in recognizing true academic excellence [16][21].

Core Viewpoint - The article highlights the importance of science popularization (popular science) efforts by Chinese academicians, emphasizing their role in enhancing public understanding of science and fostering interest in scientific knowledge [6][7][11]. Group 1: Importance of Science Popularization - Academicians play a crucial role in science popularization, helping the public understand scientific knowledge and improve scientific literacy [6][7]. - Engaging in popular science activities is seen as an obligation for academicians, contributing to the cultivation of scientific talent and innovation [8][9]. - The article discusses the positive impact of popular science on correcting misconceptions and providing accurate information about scientific developments [8][9]. Group 2: Targeted Approaches in Science Popularization - Different approaches are necessary for various audiences, such as middle school students, university students, and professionals in government and enterprises [9][10]. - For middle school students, interactive methods are emphasized, while university students benefit from broader interdisciplinary knowledge [9][10]. - Professionals require practical applications of scientific knowledge relevant to their fields [9][10]. Group 3: Diverse Formats for Science Communication - Science popularization can take many forms, including books, videos, lectures, and online platforms [11][13]. - The integration of scientific discoveries into educational materials, such as textbooks, is highlighted as an effective way to disseminate knowledge [13][14]. - The article mentions the significance of using various media to engage the public and enhance their understanding of scientific concepts [13][14]. Group 4: Personal Experiences and Insights - Academicians share their personal experiences in research and the importance of maintaining curiosity and resilience in scientific endeavors [15][17]. - The significance of igniting interest in science among students is emphasized, showcasing how storytelling can make science relatable [15][17]. - The article illustrates memorable moments from popular science activities, highlighting the positive reactions from students when they connect scientific principles to real-life scenarios [15][17].

铁饭碗。 作者 | 新智元 来源 I 新智元 （ID：AI_era） 在过去十多年里，计算机科学被奉为「最稳的选择」。 高薪、有前景、容易就业——这是无数学生和家长的信念。 但到了202 5年，这个神话突然破碎。 CS毕业生的失业率飙到6.1%，几乎是哲学的两倍。 所谓的「铁饭碗」，正在眼前裂开。 铁饭碗不香了 名校也不能避免 扎心的是，这不是个别现象，而是整个专业的坍塌。 数据显示，虽然CS毕业生的起薪依旧最高——8万美元，但他们的失业率却在所有专业 中排第七。 计算机工程更惨，失业率高达7.5%。 | 主要 | | 失业 就业不 | 工资中位数早期职业生 | 职业生涯中期工资中位 | 与研究生学位分享 | | --- | --- | --- | --- | --- | --- | | | 家 | 足率 | 准 | 数 | | | 1 | A | | 1 | 1 | 1 | | V | | | | > | | | 人类学 | 9.4% | 55.9% | 42,000 美元 | 70,000 美元 | 46.7% | | 物理 | 7.8% | 35.0% | 70,000 美元 | 100,000 美元 ...

Core Points - The first IEEE E-CARGO and its Applications International Symposium and the third IEEE E-CARGO and its Applications International Summer School were held at Guangdong University of Technology, focusing on expanding the application of the E-CARGO model and Role-Based Collaboration (RBC) methodology [1][8] - The event attracted over 170 participants, including experts and scholars from more than 20 universities across China, Canada, the United States, Australia, and Japan [1] Group 1 - The opening ceremony featured a presentation by the Vice President of Guangdong University of Technology, highlighting the university's development history, recent high-quality growth, and future plans to become a high-level technical teacher training university rooted in the Bay Area and serving globally [3] - Experts from various institutions delivered speeches on cutting-edge topics such as artificial intelligence, low-altitude economy, robotics, smart education, smart healthcare, digital twins, autonomous intelligence, brain-like intelligence, multi-agent systems, and cyber-physical social systems [3] Group 2 - A special forum for young scientists was organized, featuring presentations from notable scholars, including recipients of the National Excellent Youth Fund and EU Marie Curie Scholars [5] - The symposium included a Best Paper Award selection process, resulting in the recognition of six outstanding papers, and discussions on the development and application of the E-CARGO model [6] Group 3 - The international summer school, held over three days, included seven tutorial sessions focused on the theme of "E-CARGO and its Applications," attracting over 40 participants from nearly 20 universities [8] - The event was organized by the IEEE Systems, Man, and Cybernetics Society and Guangdong University of Technology, with support from various international and local academic institutions [8]

Core Insights - Chen Xiang, a PhD student at Nanjing University of Science and Technology, focuses on computer vision enhancement technology in harsh environments, having published 18 papers and received over 900 citations on Google Scholar [2][4] - He has been recognized as a "Huawei Terminal Camera Academic Star," a title awarded to only 14 individuals globally, and has participated in Huawei's talent selection program [10] Group 1: Academic Achievements - Chen has consistently ranked first in his program for two consecutive years and has received multiple scholarships and awards, including the National Scholarship and the CASC Public Welfare Scholarship [2][4] - He has been invited to serve as a chair for the International Joint Conference on Neural Networks (IJCNN) and has been a reviewer for over 40 international SCI journals and conferences [4] Group 2: Research Contributions - Chen is involved in a National Natural Science Foundation project focused on image and video enhancement for ground targets in adverse conditions, addressing challenges in battlefield environments [5] - He has proposed an all-weather visual imaging enhancement technology and collaborates with military and defense organizations to innovate reconnaissance methods using AI [5] Group 3: Community Engagement - Chen actively participates in academic conferences, sharing his research with over 48,000 attendees across various platforms, and has received awards for his presentations [9] - He emphasizes the importance of balancing hard work with visionary thinking, aiming to contribute to national development through his research [9]

Core Viewpoint - The article emphasizes the significant impact of artificial intelligence (AI), particularly generative AI, on various industries and the increasing demand for AI-related content and research. Springer Nature is actively embracing these trends by publishing insightful research and developing AI tools and services to enhance academic and industrial collaboration [1]. Group 1: AI Resources and Services - Springer Nature is set to launch a new collection of AI books in 2025, covering topics from machine learning algorithms to neural network research impacting healthcare. This collection aims to provide a comprehensive resource for researchers, educators, and professionals [2]. - The AI book collection includes diverse resources such as monographs, conference proceedings, textbooks, and manuals, catering to different levels of readers and facilitating a deeper understanding of the rapidly evolving AI field [2]. Group 2: Core Research Areas - The AI book collection focuses on core research areas, including machine learning, deep learning, natural language processing, computer vision, and AI ethics, among others [3]. Group 3: AI-Driven Services - Springer Nature offers AI-driven services, including text and data mining (TDM) tools that help researchers extract valuable information from large datasets, enhancing their ability to discover patterns that traditional analysis may miss [46]. - The Methods Muse platform simplifies experimental design and optimization for life sciences research, integrating with protocols.io to streamline the research process [50]. - Nature Research Intelligence provides data-driven insights to help academic, governmental, and corporate researchers make informed decisions, track research trends, and enhance their academic impact [53][55].

Core Insights - The article emphasizes the rapid transformation of the world through artificial intelligence and programming technologies, highlighting the significance of Auckland University's computer science undergraduate program as a platform for students passionate about these fields [1]. Group 1: Program Advantages - Auckland University's computer science program boasts exceptional academic resources and a strong faculty, with the department recognized internationally for its research in artificial intelligence, data science, and cybersecurity [3]. - The faculty comprises professors from around the globe who have made significant academic contributions and maintain close collaborations with major tech companies like Google and Microsoft, integrating the latest industry trends into the curriculum [3]. - The university provides advanced learning resources, including high-performance computing clusters and virtual reality equipment, facilitating complex programming experiments and AI project development [3]. - Partnerships with numerous tech companies offer students internship and employment opportunities, allowing them to engage with real-world business projects during their studies [3]. Group 2: Application Requirements - Applicants to the computer science undergraduate program must meet specific academic and language criteria, with international students typically required to achieve an average high school score of over 80%, particularly excelling in mathematics and physics [4]. - For Chinese students, the Gaokao score is a critical reference, generally requiring a score above the provincial first-tier line; alternative qualifications like A-Level or IB scores are also accepted [4]. - Language proficiency is essential, with a minimum IELTS score of 6.5 (no individual score below 6.0) or a TOEFL score of 90 (with writing no less than 21) required for admission [4]. Group 3: Curriculum Content - The curriculum is diverse and designed to build a solid theoretical foundation and practical innovation skills, starting with introductory courses in computer science, programming basics (Python and Java), and discrete mathematics in the first year [6]. - As students progress, they encounter more specialized courses such as data structures and algorithms, computer systems principles, and database systems, deepening their understanding of computer science fundamentals [6]. - Elective courses in artificial intelligence, machine learning, computer graphics, and cybersecurity allow students to explore cutting-edge areas of interest, while project-based courses enable teamwork and problem-solving through real programming projects [6].

Core Insights - A significant breakthrough has been made in computer science after a 50-year stagnation regarding the relationship between time and memory in algorithms [1][8]. Group 1: Breakthrough Discovery - MIT scientist Williams discovered that memory is more powerful than previously thought, indicating that a small amount of memory can be as valuable as a large amount of time in computations [2][4]. - Williams proved that there exists a mathematical program that can convert any algorithm into a form that occupies less space [4][7]. Group 2: Historical Context - The problem stems from the intuition that space can be reused, but time cannot, leading to a half-century challenge in proving the relationship between time and space in computational complexity theory [8][10]. - The complexity theory, established in the 1960s, categorizes problems based on the resources (time and space) required to solve them, with P representing problems solvable in reasonable time and PSPACE representing those solvable with limited space [11][13]. Group 3: Theoretical Implications - The relationship between P and PSPACE is a core issue in complexity theory, with scientists historically believing that space is a more powerful computational resource than time [15][19]. - Williams' results suggest that some problems cannot be solved unless more time is used than space, hinting at a potential resolution to the long-standing P vs. PSPACE question [33][34]. Group 4: Personal Journey of the Researcher - Williams has been fascinated by this problem since his university days and has pursued various avenues, including studying logic and philosophy, to find inspiration [27][42]. - His breakthrough was influenced by a 2010 advancement in understanding computational memory, which led him to realize that data could be compressed, allowing for significant reductions in space usage [28][31].