Group 1 - The core viewpoint of the articles highlights the challenges and risks associated with the improper use of new technologies, emphasizing the need for careful implementation to avoid inconveniences for users [1][2][3] - The first news story illustrates a "blocking effect" where the introduction of facial recognition technology created barriers for a blind individual attempting to obtain a mobile phone card, contrasting with the previous ease of the process without such technology [1] - The second news story discusses the issue of over-reliance on new technologies, such as generative AI and autonomous driving, which can lead to risks if users blindly trust these tools without understanding their limitations [2] Group 2 - The articles stress that new technologies are still in their early stages, and their effectiveness largely depends on how they are utilized and the depth of their development [2] - It is noted that the quality of results from AI products can vary significantly based on the input provided, indicating the importance of user engagement and understanding in leveraging these technologies effectively [2] - The articles advocate for the use of new technologies to enhance user experience and convenience, rather than merely for the sake of efficiency in service delivery [1][2]

Group 1 - The core issue revolves around the reliability of AI detection systems, as classic literary works like Zhu Ziqing's "Lotus Pond Moonlight" and Liu Cixin's "The Three-Body Problem" are flagged with high AI generation probabilities, raising doubts among netizens about the accuracy of these tools [1][2] - A practical test conducted by reporters showed that the AI generation probability for "Back Shadow" was 18.21%, while "Ball Lightning" had a probability of 32.05%. Other AI detection websites reported even lower rates for "Back Shadow," with some showing less than 1% [2][4] - AI detection models are based on training with large datasets of both human and AI-generated texts, but they can misjudge due to the evolving nature of AI-generated content. Continuous updates to detection models are necessary to reduce misjudgment rates [4][6] Group 2 - The discussion on social media regarding the high AI detection rates in academic papers suggests that educators should not rely solely on these results as a measure of student performance, but rather consider them as part of a broader evaluation system [6]