华人AI科学家视频系列之一 扎克伯格最近疯抢AI科学家，尤其是华人科学家，动不动就开出1亿美元甚至2亿美元的薪酬包。 有一位AI大神似乎被忽略了。 今年3月，Facebook首席AI科学家杨立昆在一次访谈中，提到了"一件不为人知的事"，科学（AI）领域 被引用次数最多的论文，是关于深度学习领域的，来自10年前的2015年，这篇论文起源于北京。 这篇论文的主要作者叫做，何恺明。 《自然》杂志给出了一个21世纪引用量最高的最新Top 25，排在第一位的就是"Deep Residual Learning for Image Recognition"， 是一篇关于ResNets研究的论文，作者包括何恺明、张祥雨、任少卿和孙剑。 何恺明是何方神圣？ 何恺明1984年出生于广州，他在执信中学的时候，因为获得全国物理竞赛一等奖，拿到了清华大学的保 送资格，但他还是参加高考来证明自己。以标准分900分的成绩，成为当年广东省9位满分状元之一。 2007年何恺明进入香港中文大学读研，师从汤晓鸥。港中大认识何凯明的，都说他是超级拼命三郎，早 上六点多出门晚上十二点回寝室，天才还这么拼命，"普通人没法玩"。2011年博士毕业后，进入 ...

文｜张子怡 算法研发要求同步升级。因此，上海交通大学交龙战队的步兵机器人搭载边缘计算模块，通过神经网络 实现敌方装甲板、能量机关扇叶识别，同步完成运动预测与弹道解算，支持远距离精准打击动态目标。 相关技术既提升实战性能，更推动深度学习等前沿视觉算法在复杂动态环境中的应用探索。 东北大学TDT战队的工程机器人采用关节式串联机械臂与逆运动学解算技术，结合自定义控制器这种符 合直觉的人机交互形式，高效完成三维特殊角度的矿石兑换，在比赛中持续积累经济优势，为高强度对 抗提供支撑。 中国科学技术大学RoboWalker战队基于激光雷达与先进算法，实现规划导航、避障及多机通讯，进行 自主追击、进攻、基地保护等智能决策，在赛场复杂环境下灵活应对"攻防"需求。相关技术能够引导学 生关注前沿AI决策算法，对接自动化工业生产、辅助驾驶、安防巡检等智能产业需求。 机甲大师赛初期引入空中机器人，以轻量化、高负载为目标，持续提升载重率要求，推动参赛队在轻量 化与动力载荷平衡上创新。25赛季中，中国石油大学（华东）RPS战队的空中机器人表现突出，整机重 量仅为12.4kg，可负载5kg官方物资，在7分钟的比赛时间中进行高效输出，紧贴应急 ...

Core Viewpoint - The article highlights the challenges faced by researchers in the AI field, particularly in the paper submission process, leading to a high rejection rate due to various issues such as writing quality, methodological flaws, and misalignment with journal focus [1][2]. Group 1: Submission Challenges - Pain Point 1: 60% of desk rejections are due to misalignment with the journal's focus [3]. - Pain Point 2: Lack of innovation is a critical issue, with reviewers criticizing submissions for not addressing relevant problems [3]. - Pain Point 3: 65% of rejections stem from methodological flaws, indicating that many experiments are not reproducible [3]. - Pain Point 4: 78% of papers are rejected due to poor writing structure, with many authors failing to effectively communicate their research [3]. - Pain Point 5: 23% of initial rejections occur due to formatting errors in the submission process [2]. Group 2: Support and Solutions - The company offers personalized guidance from over 300 experienced mentors in the fields of autonomous driving and embodied intelligence, with a high success rate of 96% for students [4]. - The mentoring process includes comprehensive support from topic selection to submission, ensuring that students are well-prepared for the publication process [11]. - The program aims to help students build a clear research framework, improve coding skills, and enhance their overall research capabilities [9][12].

Core Viewpoint - The article discusses the recruitment process and job responsibilities for a perception algorithm engineer in the autonomous driving industry, emphasizing the importance of skills in computer vision, deep learning, and sensor fusion technologies [1][5][6]. Group 1: Job Responsibilities - The role involves processing large amounts of autonomous driving data, building automated ground truth labeling systems, and designing cutting-edge AI and vision technologies [6]. - Algorithms and code developed will be deployed in millions of mass-produced vehicles [6]. - Key tasks include detecting static scene elements, tracking dynamic targets, and developing calibration methods for various sensors [10]. Group 2: Job Qualifications - Candidates should have a master's degree or higher in relevant fields such as computer science, automation, or mathematics [7]. - Proficiency in programming languages like C++ or Python, along with solid knowledge of algorithms and data structures, is required [7]. - Familiarity with multi-view geometry, computer vision, deep learning, and sensor technology applications is essential [7]. Group 3: Preferred Qualifications - Experience in developing perception algorithms for autonomous driving systems or ADAS, such as lane detection and obstacle tracking, is a plus [9]. - Candidates with experience in sensor fusion involving visual, LiDAR, and millimeter-wave radar are preferred [9]. - Publications in top conferences or journals in the fields of computer vision, machine learning, or robotics are advantageous [9]. Group 4: Community and Resources - The article mentions a community platform for job seekers in autonomous driving and robotics, providing resources such as interview questions, industry reports, and salary negotiation tips [12][13]. - The community aims to assist members in preparing for job applications and understanding industry trends [12][21].

亚军中国科学技术大学RoboWalker战队基于激光雷达与先进算法，实现规划导航、避障及多机通讯(雷 达与哨兵机器人间)，进行自主追击、进攻、基地保护等智能决策，在赛场复杂环境下灵活应对"攻 防"需求。相关技术引导学生关注前沿AI决策算法，对接自动化工业生产、辅助驾驶、安防巡检等智能 产业需求。 来源：中国新闻网 中新网深圳8月3日电 (记者 王东宇)3日，深圳湾体育中心"春茧"体育馆内，第二十四届全国大学生机器 人大赛 RoboMaster2025机甲大师超级对抗赛全国赛(RMUC2025)正式收官。上海交通大学交龙战队获得 全国总冠军；中国科学技术大学 RoboWalker战队、华南理工大学华南虎战队、东北大学TDT战队分别 获得亚、季、殿军。 8月3日，RoboMaster 2025 机甲大师超级对抗赛·全国赛(RMUC 2025)总决赛，上海交通大学交龙战队步 兵机器人正在飞坡。(赛事方供图) 作为赛事核心载体，各类型机器人的技术迭代成为本年度最大亮点。2025赛季，赛事通过升级赛场地形 (增设二级台阶、狭窄隧道等)，引导参赛队研发轮腿平衡底盘及运动控制算法，重点突破阶梯攀爬、跌 倒自主复位等关键技术 ...

Group 1 - Geoffrey Hinton, the AI pioneer, recently sat down comfortably in Shanghai, marking a significant moment in his life after nearly 18 years of discomfort that prevented him from sitting for extended periods [1][6][30] - Hinton's journey in AI began in 1972 when he chose to pursue neural networks, a path that was largely dismissed by his peers at the time [12][20] - His persistence in the field led to breakthroughs in deep learning, particularly during the ImageNet competition in 2012, where his team achieved a remarkable error rate of 15.3% [30][31][32] Group 2 - Hinton's contributions to AI were recognized with the Turing Award in 2019, which he received while standing, reflecting his long-standing discomfort with sitting [59][63] - Following his resignation from Google in May 2023, Hinton expressed concerns about the risks associated with AI, stating that he regretted his role in its development [67][68] - In recent interviews, Hinton has been able to sit for longer periods, indicating a potential improvement in his health, and he has been vocal about the dangers of AI, suggesting a 10%-20% chance of human extinction due to AI in the next 30 years [70][76]

Quantitative Models and Construction Methods 1. Model Name: Deep Learning Stock Return Prediction Model - **Model Construction Idea**: This model is based on a deep learning framework tailored to the current market environment. It integrates daily and minute-frequency inputs to predict stock returns and generate trading signals based on historical rolling thresholds[1][10][22] - **Model Construction Process**: - **Input Layer**: Combines 51 technical/sentiment daily features, 7 basic daily price-volume indicators, 10 enhanced style factors, and 52 minute-frequency features aggregated to daily frequency[22] - **Training Layer**: Utilizes meta-learning to adapt to new market data dynamically, avoiding overfitting to historical data[14] - **Output Layer**: Employs LinSAT neural networks to impose constraints on the output, ensuring specific objectives like controlling style and industry exposures[18] - **Loss Function**: Multi-period mean squared error (MSE) is used to stabilize predictions for timing strategies[22] - **Formula**: Multi-period return prediction as \( y = (n, 1) \), where \( n \) represents the number of stocks[22] - **Model Evaluation**: Demonstrates robustness in adapting to market changes and controlling exposures, with significant predictive power for timing strategies[10][22] 2. Model Name: SimStock - **Model Construction Idea**: SimStock uses self-supervised learning to predict stock similarities, incorporating both static and dynamic correlations. It leverages contrastive learning to dynamically capture time-series information beyond traditional industry and style classifications[2][47][48] - **Model Construction Process**: - **Input**: Past 40-day price-volume data, Barra style factors, and capital flow indicators[52] - **Positive and Negative Sample Construction**: Positive samples are generated as \( X_{pos} = X + (1-\alpha)X_{rand} \), where \( \alpha = 0.75 \) and \( X_{rand} \) is a random feature sample[52] - **Embedding**: LSTM initializes dynamic attention weights, and CLS tokens aggregate sequence information into stock attribute vectors[52] - **Similarity Calculation**: Stock similarity is measured using cosine similarity between attribute vectors[52] - **Model Evaluation**: Effectively identifies stocks with high similarity, primarily within the same industry, but without clear patterns in market capitalization or sub-industry[56] 3. Model Name: Improved GRU Model with SimStock Integration - **Model Construction Idea**: Enhances the GRU-based stock return prediction model by initializing hidden states with SimStock-generated stock attribute vectors, improving stability across different stock types[57][59] - **Model Construction Process**: - **Initialization**: SimStock attribute vectors replace the GRU model's initial hidden state[57] - **Training**: Retains the same training setup as the baseline GRU model, with adjustments to incorporate the new initialization[59] - **Model Evaluation**: Demonstrates improved predictive performance and stability, particularly in timing strategies across diverse stocks[60][63] 4. Model Name: Index Timing Model - **Model Construction Idea**: Aggregates individual stock signals into index signals using weighted predictions based on market capitalization, followed by threshold-based signal generation[77] - **Model Construction Process**: - **Aggregation**: Combines stock return predictions into index return predictions using market-cap weights[77] - **Signal Generation**: Uses the 60th percentile of past-year predictions as the buy threshold and the 40th percentile as the sell threshold[77] - **Holding Period**: Maintains positions for at least 5 trading days to reduce turnover[77] - **Model Evaluation**: Effective in generating excess returns, particularly in high-volatility sectors[79][82][84] --- Model Backtest Results 1. Deep Learning Stock Return Prediction Model - **Cumulative Excess Return**: 77% over 5 years[33] - **Annualized Return**: 27%[33] - **Excess Return vs. Stocks**: 11.3% (pre-cost)[33] 2. SimStock - **Cumulative Excess Return**: 109% over 5 years[60] - **Annualized Return**: 30%[60] - **Excess Return vs. Stocks**: 14.8% (pre-cost)[60] - **Daily Win Rate**: 57.4%[60] - **Holding Probability**: 45.7%[60] 3. Index Timing Model - **HS300**: Annualized Return 5.1%, Excess Return 5.6%, Max Drawdown 7.7%[79] - **CSI500**: Annualized Return 12.4%, Excess Return 12.2%, Max Drawdown 7.1%[82] - **CSI1000**: Annualized Return 15.1%, Excess Return 14.9%, Max Drawdown 11.3%[84] 4. Sector Timing - **Best Sector**: Electric Power Equipment & New Energy, Annualized Return 36%, Excess Return 31.1%[101] --- Quantitative Factors and Construction Methods 1. Factor Name: Reinforced Style Factor (PPO Model) - **Factor Construction Idea**: Uses PPO reinforcement learning to predict market style preferences, generating more interpretable and robust risk factors compared to traditional deep learning[12] - **Factor Construction Process**: - **Input**: Traditional style factors and recent stock price-volume data[12] - **Reward Function**: Stability-penalized market return goodness-of-fit[12] - **Output**: Enhanced style factor representing AI market preferences[12] - **Factor Evaluation**: Provides a stable and interpretable representation of market style dynamics[12] --- Factor Backtest Results 1. Reinforced Style Factor - **RankIC**: Weekly average of 4.5% since 2019[36] - **Annualized Return**: 23.2% for long-only portfolios, Excess Return 18.3% vs. CSI800[36]

Quantitative Factors and Construction Factor Name: GAN_GRU Factor - **Construction Idea**: The GAN_GRU factor is derived by processing volume-price time-series features using a Generative Adversarial Network (GAN) model, followed by encoding these time-series features with a Gated Recurrent Unit (GRU) model to generate a stock selection factor [4][13][41] - **Construction Process**: 1. **Input Features**: 18 volume-price features such as closing price, opening price, turnover, and turnover rate are used as input data. These features are sampled every 5 trading days over the past 400 days, resulting in a feature matrix of shape (40,18) [14][17][18] 2. **Data Preprocessing**: - Outlier removal and standardization are applied to each feature over the 40-day time series - Cross-sectional standardization is performed at the stock level [18] 3. **GAN Model**: - **Generator**: An LSTM-based generator is used to preserve the sequential nature of the input features. The generator takes random noise (e.g., Gaussian distribution) as input and generates data that mimics the real data distribution [23][33][37] - **Discriminator**: A CNN-based discriminator is employed to classify real and generated data. The discriminator uses convolutional layers to extract features from the 2D volume-price time-series "images" [33][35] - **Loss Functions**: - Generator Loss: $$ L_{G} = -\mathbb{E}_{z\sim P_{z}(z)}[\log(D(G(z)))] $$ where \( z \) represents random noise, \( G(z) \) is the generated data, and \( D(G(z)) \) is the discriminator's output probability for the generated data being real [24] - Discriminator Loss: $$ L_{D} = -\mathbb{E}_{x\sim P_{data}(x)}[\log D(x)] - \mathbb{E}_{z\sim P_{z}(z)}[\log(1-D(G(z)))] $$ where \( x \) is real data, \( D(x) \) is the discriminator's output probability for real data, and \( D(G(z)) \) is the discriminator's output probability for generated data [27] 4. **GRU Model**: - Two GRU layers (GRU(128,128)) are used to encode the time-series features, followed by an MLP (256,64,64) to predict future returns [22] 5. **Factor Output**: The predicted returns (\( pRet \)) from the GRU+MLP model are used as the stock selection factor. The factor is neutralized for industry and market capitalization effects and standardized [22] Factor Evaluation - The GAN_GRU factor effectively captures the sequential and cross-sectional characteristics of volume-price data, leveraging the strengths of GANs for feature generation and GRUs for time-series encoding [4][13][41] --- Factor Backtesting Results GAN_GRU Factor Performance Metrics - **IC Mean**: 11.43% (2019-2025), 10.97% (last year), 9.27% (latest month) [41][42] - **ICIR**: 0.89 [42] - **Turnover Rate**: 0.82 [42] - **Annualized Return**: 38.52% [42] - **Annualized Volatility**: 23.82% [42] - **IR**: 1.62 [42] - **Maximum Drawdown**: 27.29% [42] - **Annualized Excess Return**: 24.86% [41][42] GAN_GRU Factor Industry Performance - **Top 5 Industries by IC (Latest Month)**: - Home Appliances: 27.00% - Non-Bank Financials: 23.08% - Retail: 20.01% - Steel: 14.83% - Textiles & Apparel: 13.64% [41][42] - **Top 5 Industries by IC (Last Year)**: - Utilities: 14.43% - Retail: 13.33% - Non-Bank Financials: 13.28% - Steel: 13.23% - Telecommunications: 12.36% [41][42] GAN_GRU Factor Long Portfolio Performance - **Top 5 Industries by Excess Return (Latest Month)**: - Textiles & Apparel: 5.19% - Utilities: 3.62% - Automobiles: 3.29% - Non-Bank Financials: 2.56% - Pharmaceuticals: 1.47% [2][43] - **Top 5 Industries by Average Monthly Excess Return (Last Year)**: - Home Appliances: 5.44% - Building Materials: 4.70% - Textiles & Apparel: 4.19% - Agriculture: 4.09% - Utilities: 3.92% [2][43]

Summary of Key Points from the Conference Call Industry Overview - The conference call discusses the **artificial intelligence (AI)** industry, particularly focusing on the development and investment trends in large language models (LLMs) and deep learning technologies [1][2][3]. Core Insights and Arguments - **Investment Waves**: AI investment has experienced three significant waves over the past three years, with the latest wave showing longer duration, stronger momentum, and higher capital expenditure compared to previous waves [1][2][4]. - **Technological Advancements**: The introduction of deep learning and reinforcement learning has significantly enhanced the capabilities of LLMs, allowing them to perform complex tasks with improved logic and reasoning abilities [1][8][9]. - **Model Performance**: OpenAI's upcoming models, such as GPT-5, are expected to achieve generational improvements in logic processing and dynamic handling, while models like GROX and Google's Gemini series are noted for their impressive performance and balanced capabilities [10][12][14]. - **Cost of Model Training**: The cost of training models has been decreasing annually due to advancements in chip technology and training methodologies, which enhances training efficiency [22][23]. - **Market Dynamics**: The AI API market is competitive, with Google holding approximately 45% market share, followed by Sora and Deepseek. Domestic models like Kimi K2 are also gaining traction [30]. Additional Important Content - **Challenges in Deep Learning**: Deep reasoning models face challenges such as slow response times for simple queries, which impacts user experience. Future developments may focus on hybrid reasoning to improve performance [16]. - **Future Training Paradigms**: The evolution of training paradigms for LLMs will emphasize increased reinforcement learning time and the integration of high-quality data during training phases [17]. - **Domestic vs. International Models**: There is a noticeable gap of about 3 to 6 months between domestic and international models, but this gap is not expected to widen significantly. Domestic models are making strides in areas like programming capabilities [18][20]. - **User Interaction and Growth Potential**: AI technology has seen significant user penetration, particularly in Google Search, with potential for further growth as new applications are developed [27][28]. - **AGI Development**: Progress towards Artificial General Intelligence (AGI) is ongoing, with no major technical barriers identified. The integration of AI across various applications is enhancing overall efficiency [31]. This summary encapsulates the key points discussed in the conference call, highlighting the current state and future outlook of the AI industry, particularly in relation to large language models and their market dynamics.

Core Viewpoint - OpenAI has launched a new "Study Mode" for ChatGPT, designed to enhance learning by guiding users through problem-solving rather than simply providing answers [1][2]. Summary by Sections Introduction of Study Mode - The Study Mode is now available for free, Plus, Pro, and Team users, with ChatGPT Edu users to gain access in the coming weeks [2]. Educational Impact - Leah Belsky, OpenAI's VP of Education, emphasizes that using ChatGPT for teaching can significantly improve student learning outcomes, while merely using it as an "answer machine" may hinder critical thinking [4]. - Approximately one-third of college students are using ChatGPT to assist with their studies, raising concerns among educators and parents about potential academic dishonesty [4]. Learning Mode Features - The Study Mode does not provide direct answers; instead, it poses guiding questions to encourage users to think through problems and summarize concepts in their own words [12][15]. - The design of the Study Mode is a result of collaboration with educators and experts in teaching methodologies, incorporating long-term research in learning science [15]. Interactive Learning - Key features include: - Interactive questioning that promotes active learning through Socratic questioning and self-reflection prompts [16]. - Scaffolding responses that organize information into understandable parts, highlighting key connections between topics [16]. - Knowledge checks through quizzes and open-ended questions, providing personalized feedback to support knowledge retention [17]. Customization and Flexibility - The Study Mode adapts to the user's skill level and past interactions, breaking down complex information into manageable modules while maintaining contextual relevance [18]. - Users can toggle the Study Mode on or off based on their learning objectives [19]. Future Developments - OpenAI views the current Study Mode as an initial step, with plans to refine the model based on real student feedback and to incorporate clearer visual representations for complex concepts [23][24]. - Future improvements may include cross-dialogue goal setting and deeper personalization based on individual student needs [24]. Strategic Intent - OpenAI's CEO, Sam Altman, expresses skepticism about traditional education, suggesting a potential shift in educational paradigms over the next 18 years [26][28]. - This perspective indicates a strategic intent to fundamentally reshape future educational models through AI [28].