Core Viewpoint - The article emphasizes the growing importance of tabular data in AI applications across various sectors, including finance, healthcare, education, recommendation systems, and scientific research [1]. Group 1: Background and Importance of Tabular Data - Tabular data is fundamentally a structured representation of information, offering inherent advantages in organizing and expressing complex data relationships [3]. - The rise of deep learning has led to significant advancements in fields like computer vision and natural language processing, making the application of deep neural networks (DNN) to tabular data a research hotspot [6]. Group 2: Deep Learning Approaches to Tabular Data - The research categorizes deep learning methods for tabular data into three types: specialized methods, transferable methods, and general methods, reflecting the evolution of deep learning technology and the enhancement of model generalization capabilities [7][19]. - Specialized methods are the earliest and most widely used, focusing on obtaining high-quality representations from feature and sample levels [9]. - Transferable methods leverage pre-trained models to improve learning efficiency and reduce reliance on computational resources and data scale [12]. - General methods extend the generalization ability of pre-trained tabular models to various heterogeneous downstream tasks without additional fine-tuning [19]. Group 3: Challenges in Tabular Data Learning - Tabular data presents unique challenges, including feature heterogeneity, lack of spatial or sequential structure, low-quality and missing data, and the importance of feature engineering [22][23][25][26]. - The presence of class imbalance in many tabular datasets can lead to biased predictions, necessitating specific strategies for model training [27]. - Scalability to large datasets poses additional challenges, particularly as dimensionality increases, raising the risk of overfitting [28]. Group 4: Evaluation and Benchmarking - The article discusses the importance of robust evaluation methods for tabular models, highlighting the need for diverse benchmark datasets to assess model performance across different tasks and feature types [36]. - Performance evaluation metrics for classification tasks include accuracy, AUC, and F1 score, while regression tasks typically use MSE, MAE, and R² [32][33]. - Recent research emphasizes the need for comprehensive benchmarks that include semantically rich datasets to enhance the evaluation of tabular models [38][39].

Core Insights - The article discusses the transition of large models into the second half of their development, emphasizing the importance of redefining problems and designing real-use case evaluations [1] - It highlights the need for effective measurement of ROI for Agent products, particularly for startups and companies looking to leverage AI [1] - SuperCLUE has launched a new evaluation benchmark, AgentCLUE-General, which deeply analyzes the capabilities of mainstream Agent products [1] Group 1 - The blog post by OpenAI's Agent Researcher, Yao Shunyu, has sparked discussions on the shift from "model algorithms" to "practical utility" [1] - There is a focus on how existing evaluation systems can effectively measure the ROI of Agent products [1] - SuperCLUE maintains close connections with various model and Agent teams, showcasing its expertise in model evaluation [1] Group 2 - An invitation is extended to join an online sharing session featuring SuperCLUE's co-founder, Zhu Lei, discussing core challenges in evaluating large models and Agents [2] - The session is scheduled for May 15, from 20:00 to 22:00, with limited spots available for registration [3] - Additional reading materials are suggested, covering topics such as pricing AI products, insights from the Sequoia AI Summit, and the importance of product design in AI applications [4]

今天凌晨， OpenAI 发布了新模型 GPT-4.1 ，相对比 4o，GPT-4.1 在编程和指令遵循方面的能力显 著提升，同时还宣布 GPT-4.5 将会在几个月后下线。 不少人吐槽 OpenAI 让人迷惑的产品发布逻辑——GPT-4.1 晚于 4.5 发布，以及混乱的模型命名，这 些问题，都能在 OpenAI CPO Kevin Weil 最近的一期播客访谈中得到解答。 在访谈中，Kevin Weil 分享了 OpenAI 在产品方面的路线规划，以及所拥护的产品发布哲学「迭代 部署」，对于近期火热的 4o 图片生成功能，也做了内部的复盘。 Kevin Weil 表示，「我们尽量保持轻量级，因为它不可能完全正确。我们会在半路放弃一些不正确 的做法或研究计划，因为我们会不断学习新的东西。 我们有一个哲学叫做迭代部署，与其等你完全 了解模型的所有能力后再发布，不如先发布，即使不完美，然后公开迭代。 」 背景：Kevin Weil 是 OpenAI 的首席产品官，负责管理 ChatGPT、企业产品和 OpenAI API 的开发。在加入 OpenAI 之前，Kevin 曾担任 Twitter、Instagram ...