Blogs

管理的本质是一种实践，在某些情形下，阅历比简历更重要，丰富的职业经历有助于企业高管形成多元化的思维结构、广阔的管理视野、丰富的社会资源和过人的胆识。因此，对于企业而言，了解高管的职业经历非常重要，这可以帮助企业更好地了解高管的背景和潜力，从而更好地为企业的发展提供支持。而研究高管的个人特质，已有的研究，主要从年龄、性别、学历等类别型变量开展研究，即使从从职业经历研究，也是作为离散变量，没有充分挖掘职业经历的信息。...

大邓是一个技术博主，运营着公众号，每天要消耗大量的时间进行选题、创作、编辑。随着LLM的流行， 能否让LLM替我进行选题、创作、编辑，从此进入躺平式人生新阶段。 这不是做梦， 使用软件Ollama、Python的CrewAI库，设计好智能体(AI Agent)，就能实现大邓的白日梦。In technical terms an AI Agent is a software entity designed to perform tasks autonomously or semi-autonomously on behalf of a user or another program. These agents leverage artificial intelligence to make decisions, take actions, and interact with their environment or other systems....

数据科学家花费 80% 以上的时间来准备数据，这其中主要是数据清洗、数据标注。随着 GPT-4 等大型语言模型 (LLM)的兴起，现在我们可以更高效的准备工作。在本文中，我们将探讨如何使用 LLM 进行数据标注，以提高文本注释的准确性、效率和可扩展性，并最终为 ML 项目带来更好的结果。 Data scientists spend over 80% of their time preparing data, including data labeling. With the rise of Large Language Models (LLMs) like GPT-4, we now have the tools to streamline this process significantly.In this article, we’ll explore how to use LLM for data labeling to enhance the accuracy, efficiency, and scalability of text annotations and ultimately drive better outcomes for ML projects....

非结构文本、图片、视频等数据是待挖掘的数据矿藏， 在经管、社科等研究领域中谁拥有了从非结构提取结构化信息的能力，谁就拥有科研上的数据优势。正则表达式是一种强大的文档解析工具，但它们常常难以应对现实世界文档的复杂性和多变性。而随着chatGPT这类LLM的出现，为我们提供了更强大、更灵活的方法来处理多种类型的文档结构和内容类型。For many years, regular expressions have been my go-to tool for parsing documents, and I am sure it has been the same for many other technical folks and industries.Even though regular expressions are powerful and successful in some case, they often struggle with the complexity and variability of real-world documents.Large language models on the other end provide a more powerful, and flexible approach to handle many types of document structures and content types....

在当今的学术界，定性研究因其深入挖掘现象背后的原因和逻辑而备受重视。然而，定性数据的分析往往耗时且成本高昂。现在，随着chatGPT这类大语言模型的问世，这一局面可能即将改变。AcademiaOS是一个创新的开源平台，它利用大型语言模型（LLMs）的能力，自动化地进行地面理论的发展，为定性研究带来了新的视角。AcademiaOS is a first attempt to automate grounded theory development in qualitative research with large language models. Using recent large language models’ language understanding, generation, and reasoning capabilities, AcademiaOS codes curated qualitative raw data such as interview transcripts and develops themes and dimensions to further develop a grounded theoretical model, affording novel insights. A user study (n=19) suggests that the system finds acceptance in the academic community and exhibits the potential to augment humans in qualitative research. AcademiaOS has been made open-source for others to build upon and adapt to their use cases....