据财联社报道，知名数据分析平台SimilarWeb的最新数据显示，OpenAI的GPT商店中用户自定义的GPT模型在ChatGPT网站的桌面访问量中仅占微不足道的1.5%。这一比例在2月份保持稳定，显示出用户对个性化GPT的使用热度并未出现显著增长。尽管OpenAI应用商店内存在各类创新应用，如能预测股市动态和规避剽窃检测的聊天机器人，但这些工具的受欢迎程度似乎并未能大幅提升整体流量。
值得注意的是，SimilarWeb的分析揭示了生成式人工智能在实际应用中的一个热点领域：教育。在ChatGPT网站访问量最高的GPT应用中，竟有五分之一声称其生成的内容能够逃避学术检测工具的识别，使用户可能在撰写论文或答案时规避人工智能的检测。这一现象引发了对人工智能在学术诚信问题上可能产生的影响的讨论，数百万人可能正在利用此类工具来应对学术任务。
这些数据表明，尽管生成式AI技术在多个领域展现出潜力，但在教育领域，其应用可能需要更加严格的监管和伦理考量，以确保学术的公正性和诚信。随着此类工具的普及，相关机构和学校可能需要升级检测机制，以应对这一新兴挑战。
英语如下：
News Title: “Uncovering the ChatGPT Traffic Surge: Only 1.5% Custom GPT, with避开 Detection Tools on the Rise”
Keywords: ChatGPT Traffic, Custom GPT, Avoidance Tools
News Content: According to Caixin Global, the latest data from the renowned analytics platform SimilarWeb reveals that user-customized GPT models from OpenAI’s GPT store account for a mere 1.5% of desktop traffic on the ChatGPT website. This percentage remained stable in February, indicating no substantial growth in the popularity of personalized GPT usage. Despite various innovative applications within the OpenAI app store, such as chatbots that predict stock market movements and evade plagiarism detection, these tools seem to have had limited impact on overall traffic.
Notably, SimilarWeb’s analysis highlights a key area of application for generative AI in practice: education. Among the top GPT applications on the ChatGPT website, one in five claims to generate content that can evade academic detection tools, potentially allowing users to bypass AI detection when writing papers or answering questions. This trend has sparked discussions about the potential impact of AI on academic integrity, with millions possibly relying on such tools for academic tasks.
These statistics suggest that while generative AI technology demonstrates potential across multiple domains, its application in education may necessitate stricter regulation and ethical considerations to uphold fairness and honesty in academia. As these tools gain popularity, institutions and schools might need to upgrade their detection mechanisms to address this emerging challenge.
【来源】https://www.cls.cn/detail/1633044
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