Now back to reality, LLMs are never that good, they're never near that hypothetical "I'm feeling lucky", and this has to do with how they're fundamentally designed, I never so far asked GPT about something that I'm specialized at, and it gave me a sufficient answer that I would expect from someone who is as much as expert as me in that given field. People tend to think that GPT (and other LLMs) is doing so well, but only when it comes to things that they themselves do not understand that well (Gell-Mann Amnesia2), even when it sounds confident, it may be approximating, averaging, exaggerate (Peters 2025) or confidently (Sun 2025) reproducing a mistake. There is no guarantee whatsoever that the answer it gives is the best one, the contested one, or even a correct one, only that it is a plausible one. And that distinction matters, because intellect isn’t built on plausibility but on understanding why something might be wrong, who disagrees with it, what assumptions are being smuggled in, and what breaks when those assumptions fail
「某種程度上，中國別無選擇，」美國布魯金斯學會中國中心研究員陳凱欣（Kyle Chan）博士對BBC中文說，「隨着追趕型增長和房地產繁榮終結，中國必須尋找新的、可持續的增長引擎。」，详情可参考新收录的资料
，推荐阅读新收录的资料获取更多信息
据此前报道，雷军在今年两会期间建议，应该加快建设汽车智能化技术标准和优化机动车驾驶考核项目，提升智能网联汽车相关内容在驾考中的权重；
В России ответили на имитирующие высадку на Украине учения НАТО18:04。关于这个话题，新收录的资料提供了深入分析
이란 대통령 “사과” 몇 시간 만에 또 공습…걸프국 “보복 경고”