- The high-frequency skewness factor showed strong performance with long-short returns of 0.9%, 4.93%, and 22.69% for the past week, September, and 2025, respectively[5][9] - The intraday downside volatility proportion factor had long-short returns of 0.77%, 5.18%, and 18.23% for the past week, September, and 2025, respectively[5][9] - The post-open buying intention proportion factor had long-short returns of 1.11%, 3.65%, and 19.98% for the past week, September, and 2025, respectively[5][9] - The post-open buying intention intensity factor had long-short returns of 1.62%, 3.28%, and 25.81% for the past week, September, and 2025, respectively[5][9] - The post-open large order net buying proportion factor had long-short returns of 0.34%, 1.51%, and 20.7% for the past week, September, and 2025, respectively[5][9] - The post-open large order net buying intensity factor had long-short returns of 0.38%, 1.51%, and 12.86% for the past week, September, and 2025, respectively[5][9] - The intraday return factor had long-short returns of 0.98%, 1.26%, and 20.66% for the past week, September, and 2025, respectively[5][9] - The end-of-day trading proportion factor had long-short returns of 1.25%, 4.18%, and 17.74% for the past week, September, and 2025, respectively[5][9] - The average single outflow amount proportion factor had long-short returns of 0.29%, 0.26%, and -0.54% for the past week, September, and 2025, respectively[5][9] - The large order-driven price increase factor had long-short returns of 0.09%, 2.88%, and 8.88% for the past week, September, and 2025, respectively[5][9] - The GRU(10,2)+NN(10) deep learning factor had long-short returns of 1.33%, 8.73%, and 41.75% for the past week, September, and 2025, respectively, with long-only excess returns of 0.71%, 3.42%, and 8.08%[5][9] - The GRU(50,2)+NN(10) deep learning factor had long-short returns of 1%, 7.98%, and 42.75% for the past week, September, and 2025, respectively, with long-only excess returns of 0.63%, 2.99%, and 7.91%[5][9] - The multi-granularity model (5-day label) factor had long-short returns of 0.99%, 6.15%, and 53.09% for the past week, September, and 2025, respectively, with long-only excess returns of 0.5%, 2.56%, and 19.48%[5][9] - The multi-granularity model (10-day label) factor had long-short returns of 0.81%, 5.2%, and 49.1% for the past week, September, and 2025, respectively, with long-only excess returns of 0.37%, 2.97%, and 20.1%[5][9] - The weekly rebalanced CSI 500 AI enhanced wide constraint portfolio had excess returns of -0.99%, -4.8%, and -0.06% for the past week, September, and 2025, respectively[5][11] - The weekly rebalanced CSI 500 AI enhanced strict constraint portfolio had excess returns of -1%, -2.32%, and 2.66% for the past week, September, and 2025, respectively[5][11] - The weekly rebalanced CSI 1000 AI enhanced wide constraint portfolio had excess returns of -1.48%, -1.06%, and 7.53% for the past week, September, and 2025, respectively[5][11] - The weekly rebalanced CSI 1000 AI enhanced strict constraint portfolio had excess returns of -0.79%, -0.12%, and 13.11% for the past week, September, and 2025, respectively[5][11]

Core Insights - The article discusses the author's experience in job searching and interviews, highlighting the challenges and changes in the job market, particularly in the computer vision (CV) and deep learning sectors [4][6][8]. Job Search Experience - The author experienced a high volume of interviews, averaging six per day over a month, with some days reaching eight interviews, indicating a competitive job market [4][5]. - The author transitioned from a role in a delivery company focused on CV to seeking opportunities in more stable and specialized areas, reflecting a shift in personal career focus [6][8]. Market Trends - There has been a significant increase in job opportunities compared to previous years, with many large and mid-sized companies actively hiring [8]. - The demand for traditional CV roles has diminished, with a notable shift towards large models, multi-modal applications, and end-to-end models in the autonomous driving sector [8][10]. Interview Preparation - The author prepared for interviews by reviewing popular coding problems, particularly from LeetCode, indicating a trend where companies now require candidates to demonstrate coding skills more rigorously than in the past [9][10]. - The author noted that many interview questions were derived from the "Hot100" list of coding problems, emphasizing the importance of algorithmic knowledge in technical interviews [11]. Career Transition - After several interviews, the author received offers from companies like Kuaishou, Xiaomi, and Weibo, but faced challenges in securing positions at larger firms like Alibaba and Baidu [10]. - Ultimately, the author accepted a position at a foreign company, which was described as a significantly better work environment compared to previous domestic companies, highlighting the differences in corporate culture [10][12]. Technical Skills and Trends - The author observed a shift in technical skills required in the job market, with a growing emphasis on large models and multi-modal technologies, suggesting that professionals in the field need to adapt to these changes to remain competitive [13].

Core Insights - Chi Jin, a Chinese scholar, has been promoted to tenured associate professor at Princeton University, effective January 16, 2026, marking a significant milestone in his academic career and recognition of his foundational contributions to machine learning theory [1][4]. Group 1: Academic Contributions - Jin joined Princeton's Department of Electrical Engineering and Computer Science in 2019 and has rapidly gained influence in the AI field over his six-year tenure [3]. - His work addresses fundamental challenges in deep learning, particularly the effectiveness of simple optimization methods like Stochastic Gradient Descent (SGD) in non-convex optimization scenarios [8][12]. - Jin's research has established a theoretical foundation for two core issues: efficient training of large and complex models, and ensuring these models are reliable and beneficial in human interactions [11]. Group 2: Non-Convex Optimization - One of the main challenges in deep learning is non-convex optimization, where loss functions have multiple local minima and saddle points, complicating the optimization process [12]. - Jin has demonstrated through multiple papers that even simple gradient methods can effectively escape saddle points with the presence of minimal noise, allowing for continued exploration towards better solutions [12][17]. - His findings have provided a theoretical basis for the practical success of deep learning, alleviating concerns about the robustness of optimization processes in large-scale model training [18]. Group 3: Reinforcement Learning - Jin's research has also significantly advanced the field of reinforcement learning (RL), particularly in establishing sample efficiency, which is crucial for applications with high interaction costs [19]. - He has provided rigorous regret bounds for foundational RL algorithms, proving that model-free algorithms like Q-learning can maintain sample efficiency even in complex settings [22]. - This theoretical groundwork not only addresses academic inquiries but also guides the development of more robust RL algorithms for deployment in high-risk applications [23]. Group 4: Academic Background - Jin holds a Bachelor's degree in Physics from Peking University and a Ph.D. in Electrical Engineering and Computer Science from the University of California, Berkeley, where he was mentored by renowned professor Michael I. Jordan [25]. - His academic background has equipped him with a strong foundation in mathematical and analytical thinking, essential for his theoretical research in AI and machine learning [25]. Group 5: Recognition and Impact - Jin, along with other scholars, received the 2024 Sloan Award, highlighting his contributions to the field [6]. - His papers have garnered significant citations, with a total of 13,588 citations on Google Scholar, indicating the impact of his research in the academic community [27].

Core Viewpoint - The updated CS230 Deep Learning course at Stanford, taught by Andrew Ng, emphasizes the importance of artificial intelligence, likening it to electricity, and introduces new content reflecting the latest advancements in AI, particularly focusing on the GPT-5 model [1][4]. Course Structure and Content - The course adopts a flipped classroom model where students must watch Coursera's deeplearning.ai videos before attending in-person classes [3]. - Since its inception in 2017, the course has maintained a similar core framework but has integrated updates relevant to recent AI developments, including a new chapter on GPT-5 [4]. - The course enhances the discussion on generative models and incorporates popular technologies like RAG and AI Agents, using GPT-5 for case studies [6]. - CS230 aims to provide comprehensive knowledge in deep learning, covering both theoretical foundations and practical skills necessary for building and applying deep learning models [10][12]. Key Topics Covered - The course covers a wide range of topics, including: - Basics of neural networks and deep learning [20]. - Optimization techniques such as regularization, Adam optimizer, hyperparameter tuning, Dropout, and Batch Normalization [20]. - Strategies for constructing machine learning projects from conception to successful deployment [20]. - In-depth understanding of Convolutional Neural Networks (CNN) and their applications in image classification and detection [20]. - Mastery of Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) networks for sequence tasks [20]. - Exploration of advanced topics like Generative Adversarial Networks (GANs) and deep reinforcement learning [20]. - Insights from industry and academia, along with practical career development advice in AI [20]. Course Schedule - The 2025 fall course will run for approximately 10 weeks, starting at the end of September [15]. - Weekly topics include introductions to deep learning, neural network basics, CNNs, RNNs, optimization algorithms, generative models, and advanced topics related to GPT-5 [16].

Core Viewpoint - The article emphasizes the importance of exploring and learning in the AI field as a means to contribute to society and the nation, highlighting the current opportunity for investors, practitioners, and researchers to deepen their understanding of technological trends and advancements in AI [1]. Group 1: AI Research Papers Overview - A collection of 30 influential AI papers recommended by Ilya Sutskever is presented, covering nearly 15 years of milestones in AI development, structured around the themes of "technical foundations, capability breakthroughs, and practical applications" [4]. - The selected papers span key transitions in AI from "perceptual intelligence" to "cognitive intelligence," including foundational works on CNNs, RNNs, Transformers, and cutting-edge research on RAG and multi-step reasoning [4][5]. Group 2: Learning and Application - The compilation breaks down complex technical terms like "residual mapping" and "dynamic pointer networks," aiding non-technical investors in understanding AI model capabilities, while providing practitioners with practical references for implementation [5]. - The article encourages readers to study the recommended papers during the holiday period to systematically understand the evolution of AI technology and to gain deeper insights into the opportunities and challenges in the current AI industry [5]. Group 3: Importance of the Recommended Papers - Ilya Sutskever stated that mastering the content of these 30 papers would provide a comprehensive understanding of 90% of the key knowledge in the current AI field [8]. - The papers cover a range of topics, including the effectiveness of recurrent neural networks, the structure and function of LSTM networks, and the introduction of pointer networks, all of which contribute to advancements in AI applications [8][9][10].

Group 1 - The core viewpoint is that the artificial intelligence revolution is seen as the last technological revolution for humanity, with significant implications across various sectors including defense, healthcare, and finance [1][3] - The development of artificial intelligence relies on core technologies such as machine learning, deep learning, and large models, aiming to achieve autonomous decision-making capabilities [3] - The revolution is expected to unfold over decades or even centuries, potentially accompanied by breakthroughs in energy fusion [1][3] Group 2 - There are known sacrifices in the advancement of artificial intelligence technology, particularly in defense applications, exemplified by the case of Feng Yanghe, a prominent expert who tragically died in a car accident while on duty [5][7] - Feng Yanghe's contributions to military artificial intelligence, including enhancing intelligent decision-making capabilities in defense systems, highlight the risks faced by researchers in this field [5][7] - The official narrative attributes his death to an accident, without evidence of external interference, emphasizing the inherent risks in pioneering technological advancements [7] Group 3 - The ethical, legal, and security challenges associated with the development of artificial intelligence are critical considerations as the technology continues to evolve [3][7] - Ensuring the ethical protection and technical safety of researchers is essential as artificial intelligence applications deepen in sectors like defense [7]

Core Insights - The article discusses the intricacies of job interviews in the autonomous driving sector, particularly focusing on the detailed role divisions within companies like NV and the technical expectations from candidates [3][4][5][8][11][12][14]. Group 1: Interview Process - The interview process for positions in autonomous driving involves multiple rounds, including technical assessments and coding challenges, with a focus on specific skills such as dynamic programming and algorithm optimization [4][5][8][11][12]. - Candidates are expected to demonstrate their understanding of advanced concepts like Model Predictive Control (MPC), Simultaneous Localization and Mapping (SLAM), and various optimization techniques [5][8][12][14]. - The coding challenges often include data structure manipulations, such as linked lists and dynamic programming problems, which are critical for assessing a candidate's problem-solving abilities [6][11][14]. Group 2: Technical Skills and Knowledge - A strong grasp of algorithms, particularly in the context of planning and control for autonomous vehicles, is essential. Candidates are often asked to explain complex algorithms like hybrid A* and kinodynamic-RRT [12][14]. - Knowledge of deep learning, especially in image processing and object detection, is increasingly important in the autonomous driving field, reflecting the industry's shift towards integrating AI technologies [11][12][14]. - Candidates are also evaluated on their ability to communicate technical concepts clearly, indicating the importance of both technical and soft skills in the hiring process [8][11][12]. Group 3: Industry Trends - The autonomous driving industry is experiencing a convergence of technology stacks, with a move towards unified models and higher technical barriers, which may impact job roles and required skills [22]. - There is a growing community focused on sharing knowledge and resources related to job opportunities and industry developments, highlighting the collaborative nature of the field [19][22]. - The article emphasizes the importance of networking and community engagement for professionals seeking to advance their careers in autonomous driving [22].

Core Viewpoint - Magic Vision Technology (Shanghai) Co., Ltd. has submitted its listing application to the Hong Kong Stock Exchange after completing eight rounds of financing, despite having incurred cumulative losses exceeding 660 million yuan over the past three years and not yet achieving profitability [1][12]. Company Overview - Founded in 2015, Magic Vision is an AI-driven intelligent driving solution provider, offering integrated hardware and software solutions with L0-L4 level autonomous driving capabilities to OEMs and tier-one suppliers [5][7]. - The founder, Yu Zhenghua, has extensive academic and industry experience, holding a PhD in pattern recognition from Shanghai Jiao Tong University and previously serving in various senior roles in the AI field [5][6]. Financial Performance - Revenue is projected to grow from 118 million yuan in 2022 to 357 million yuan in 2024, representing over a twofold increase, with the first half of 2025 achieving 189 million yuan, a year-on-year growth of 76.4% [11]. - However, net losses are also increasing, from 200 million yuan in 2022 to an expected 233 million yuan in 2024, with a continued loss of 112 million yuan in the first half of 2025 [11]. Market Context - The Chinese market for L0 to L2+ intelligent driving solutions is rapidly expanding, projected to grow from 21.6 billion yuan in 2020 to 91.2 billion yuan in 2024, with a compound annual growth rate (CAGR) of 43.3%, and expected to reach 228.1 billion yuan by 2029, with a CAGR of 20.1% [9]. - The market is relatively fragmented, with the top ten participants accounting for approximately 15.2% of the market share by revenue in 2024 [10]. Competitive Landscape - The intelligent driving industry is experiencing a shift in the value chain, with OEMs increasingly moving towards in-house development, which intensifies competition among tier-one suppliers like Magic Vision [12]. - The company aims to transition to sustainable profitability by deepening relationships with existing clients, expanding its customer base, enhancing R&D efficiency, controlling costs, and improving operational efficiency [12].

Core Insights - Magic View Intelligent Technology (Shanghai) Co., Ltd. has submitted its listing application to the Hong Kong Stock Exchange after completing eight rounds of financing, marking its entry into the capital market [1][3] - Despite delivering over 3.3 million solutions across 92 vehicle models, the company has incurred cumulative losses exceeding 660 million RMB over the past three years and has yet to achieve profitability [1][5] Company Overview - Founded in 2015, Magic View Intelligent is an AI-driven provider of intelligent driving solutions, offering integrated hardware and software solutions with L0-L4 level autonomous driving capabilities [3][4] - The founder, Yu Zhenghua, has extensive academic and industry experience, previously serving in various prestigious roles, and recognized the potential of autonomous driving during his first entrepreneurial venture [3][4] Market Position and Performance - The company launched its first generation of deep learning-based embedded ADAS in 2016 and has established partnerships with major automotive manufacturers such as BYD, Geely, and GAC [4][5] - According to its prospectus, Magic View is projected to rank eighth among third-party solution providers in China's intelligent driving solutions market by revenue in 2024, with a market share of approximately 0.4% [4][5] Financial Performance - Revenue is expected to grow from 117.8 million RMB in 2022 to 356.8 million RMB in 2024, representing over a twofold increase, while net losses are projected to rise from 200 million RMB in 2022 to 233 million RMB in 2024 [6][7] - The company reported a revenue of 189 million RMB in the first half of 2025, reflecting a year-on-year growth of 76.4%, but continues to face significant losses [7] Industry Context - The Chinese market for L0 to L2+ intelligent driving solutions is rapidly expanding, projected to grow from 21.6 billion RMB in 2020 to 91.2 billion RMB by 2024, with a compound annual growth rate (CAGR) of 43.3% [5] - As the automotive industry transitions from electrification to intelligence, competition is intensifying, with traditional manufacturers increasingly investing in in-house development of autonomous driving technologies [7]

Core Insights - Stanford University and Elsevier jointly released the "Stanford 2025 Global Top 2% Scientists List," highlighting the significant achievements of Chinese scholars, with Tsinghua University ranking fourth globally with 746 scholars included [1][2][3]. Summary by Categories Overall Rankings - A total of 1,435 individuals from China made it to the lifetime "Stanford 2025 Global Top 2% Scientists List," while 2,270 were included in the annual list [2]. - Tsinghua University is ranked fourth globally, just behind the University of Oxford and ahead of Stanford University, with 746 scholars recognized [3][5]. Notable Individuals - Zhou Zhihua from Nanjing University and Zhang Zhengyou from Tencent both entered the global top 1,000, ranked 526 and 969 respectively [5][6]. - Zhou Zhihua is noted for his contributions to artificial intelligence and machine learning, with over 100,000 citations on Google Scholar [9]. - Zhang Zhengyou, a prominent figure in computer vision and robotics, has over 80,000 citations and is recognized for his innovative contributions in the field [12][14]. Methodology of Ranking - The list identifies the top 2% of scientists based on standardized citation metrics across 22 scientific fields and 174 subfields, ensuring a fair representation of research impact [20]. - The composite score (c-score) used for ranking considers multiple citation metrics, emphasizing meaningful impact rather than mere productivity [20].