三湘都市报全媒体记者 仝若楠 10月14日，现货黄金首次涨至4160美元/盎司关口，日内涨近1%，年内累计上涨超1500美元。金价走高 带动市民置换黄金的需求。近日，记者注意到，长沙多家商场内新增了一台黄金自动贩卖机，该设备兼 具自助购金与自助回收功能，市民无需人工协助，通过机器即可依据实时国际金价，完成黄金置换全流 程。 相较于品牌金饰店，黄金自助售卖机的黄金回收价格如何？其回收流程是否透明和易操作？缺少人工介 入，是否会增加设备故障带来的问题？10月14日，记者展开了走访和调查。 不过，也有消费者提出顾虑，担心这台设备出现故障。"万一我把黄金放进去后卡住了，要是金子因此 有了磕碰，后续也说不清楚。"对此，上述工作人员表示，可以通过机器上的电话联系工作人员操作取 出。 长沙有市民在驻足查看自助黄金购买机。仝若楠 摄 记者注意到，这台设备除了售卖黄金，还支持金条、金饰和K金的免费检测以及回收。"用户点击屏幕 完成自助下单后，将黄金放入指定区域，回收机便会自动检测克重与含金量，计算得出最终的回收价 格，待熔金冷却后，还会启动二次检测以确保结果准确。"工作人员介绍。 据设备提示，要求回收的黄金含金量超50%、重量 ...

Group 1 - The rapid development of Generative Artificial Intelligence (GAI) is infiltrating academic systems globally, with tools like ChatGPT and DeepSeek becoming integral to students' creative processes as "academic assistants" [1] - The emergence of GAI has raised significant concerns regarding academic integrity, prompting universities to enhance countermeasures and develop advanced detection technologies as central weapons in the AI "arms race" [1][3] - The user growth of ChatGPT has been unprecedented, reaching over 1 million users within a week of its launch and surpassing 100 million monthly active users by January 2023, making it the fastest-growing consumer application in history [2] Group 2 - Several top universities in the U.S. have classified AI-generated application essays as academic dishonesty, with penalties including disqualification from admission, while Yale University encourages the use of AI as a brainstorming tool under the condition of maintaining academic integrity [3] - In academic publishing, journals like Nature have established rules prohibiting the listing of AI tools like ChatGPT as authors to uphold standards of academic authorship and responsibility [3] - Traditional plagiarism detection methods are challenged by GAI, leading institutions to adopt more sophisticated tools like Turnitin and The Checker AI to mitigate risks associated with AI-generated content [4] Group 3 - Turnitin's technology compares student papers against a vast academic content database, but it is not the sole indicator of academic misconduct, as AI detection tools exhibit significant flaws in consistency, reliability, and transparency [4][5] - Many detection tools struggle with accuracy, often misclassifying human-written texts as AI-generated or failing to identify actual AI-written content, particularly when AI texts are edited or translated [5][6] - Human detection remains crucial, as studies indicate that university professors can accurately distinguish between original student papers and those generated by ChatGPT, with original works displaying more engagement features compared to AI outputs [6]

Core Viewpoint - The article discusses the increasing misuse of AI technology by both buyers and sellers in e-commerce, leading to a trust crisis and the need for better verification methods to combat fraud [2][10][21]. Group 1: Buyer Misuse of AI - Some buyers are using AI-generated images to falsely claim product defects in order to obtain refunds, exploiting the difficulty of verifying the condition of perishable goods like fruits [2][6]. - This practice has evolved from earlier methods where buyers used basic photo editing tools, making it harder for sellers to detect fraud due to the sophistication of AI-generated images [8][10]. - The phenomenon reflects a "tit-for-tat" mentality among buyers who have previously been deceived by sellers using AI-enhanced product images [10][21]. Group 2: Seller Misuse of AI - Sellers are also misusing AI to create misleading product images, over-enhancing ordinary items, and generating fake reviews, which contributes to the issue of "goods not matching the description" [10][24]. - The article highlights that sellers may use virtual models and AI-generated content to cut costs, further complicating the authenticity of product representations [10][24]. Group 3: Proposed Solutions - Various proposed solutions to combat this issue include requiring buyers to submit videos of defective products, taking multiple photos from different angles, and using in-app cameras to prevent the upload of AI-generated images [11][15][24]. - However, these solutions have limitations, as advanced AI tools can still generate convincing content, making it challenging to establish foolproof verification methods [11][15][23]. Group 4: Technological Innovations - The article suggests that implementing digital watermarking and content provenance technologies could help in identifying and tracing AI-generated content, thus enhancing trust in e-commerce [19][21]. - The development of standards like C2PA and tools such as Google's SynthID aims to embed invisible watermarks in AI-generated media, which could serve as a digital identity for content [19][21][26]. Group 5: Ongoing Challenges - The ongoing "cat-and-mouse" game between AI generation and detection technologies poses a continuous challenge, as both sides evolve rapidly [23][24]. - E-commerce platforms are exploring various strategies, including strengthening evidence chains and utilizing big data analytics to monitor user behavior and detect anomalies [24][26].

Core Viewpoint - The reliability of AI detection tools is under scrutiny due to significant discrepancies in their performance, leading to concerns about their application in academic and publishing contexts [2][9]. Group 1: AI Detection Tool Performance - A study evaluated 10 popular AI detection tools for text and image content, revealing inconsistent detection standards and high rates of false positives and negatives [2][3]. - Text detection tools showed a tendency to misclassify genuine articles as AI-generated, with nearly half of the tools failing to accurately identify AI content [3][4]. - The tool "茅茅虫" had the highest false positive rate, incorrectly identifying 99.9% of 老舍's "林海" as AI-generated, while others like 知网 and PaperPass performed accurately [4][5]. Group 2: Challenges in AI Detection - The detection of AI-generated content is complicated by the evolving nature of AI models and the potential for content to undergo modifications, which can obscure detection markers [9]. - Image detection tools performed well overall, but struggled with edited images, indicating a gap in recognizing modified content [8]. - Experts highlight that the current AI detection technology is still in an exploratory phase, necessitating ongoing development and a dual approach of technological advancement and regulatory frameworks [9][10].

Core Viewpoint - The increasing reliance on AI detection systems for academic integrity is leading to significant misjudgments, causing concern among students regarding the originality of their work [1][2][3] Group 1: AI Detection Issues - Many students report high AI detection rates in their theses, with some reaching as high as 90%, raising fears of misjudgment [1] - Classic literary works have also been flagged by AI detection systems, with instances of 100% AI generation likelihood, indicating a flaw in the technology [1] - The current AI detection methods, based on machine learning and natural language processing, have limitations that lead to frequent misjudgments [2] Group 2: Limitations of AI Detection Technology - The variability of language makes it difficult for AI systems to accurately determine the source of text, resulting in misjudgments [2] - As generative AI improves, its outputs increasingly resemble human writing, complicating the detection process [2] - Inadequate training data and simplified detection processes for efficiency can further reduce the accuracy of AI detection systems [2] Group 3: Recommendations for Improvement - There is an urgent need to enhance the accuracy and credibility of AI detection technologies through improved standards and regulations [3] - Establishing a standardized system that covers technical principles, data application, and result evaluation is essential for reliable detection [3] - Implementing regular audits by third-party organizations can ensure the transparency and accuracy of detection algorithms, preventing conflicts of interest [3]