郭晓东曾迫切地想摆脱家乡留在他身上的烙印。刚来北京时，他与一位副导演见面，随口说了一句，“我要去拿我的褂子。”副导演皱起了眉，他很快意识到，这个词是“错的”，之后开始认真学习普通话。但在写《那个地方》时，他发现，很多心底的意思无法用普通话准确表达出来，比如“厨房”无法代替“锅屋”，“大大”也比“父亲”更生动鲜活。
还没有等到其为跑到赛道前列而庆祝，整个赛道甚至都可能要被掀翻。这对于过去砸下数十亿研发费用、积累三十年行业经验与产品架构的金蝶来说，才是最致命的问题。
Source: Computational Materials Science, Volume 267，详情可参考体育直播
Image by Mat Smith for Engadget
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The threat extends beyond accidental errors. When AI writes the software, the attack surface shifts: an adversary who can poison training data or compromise the model’s API can inject subtle vulnerabilities into every system that AI touches. These are not hypothetical risks. Supply chain attacks are already among the most damaging in cybersecurity, and AI-generated code creates a new supply chain at a scale that did not previously exist. Traditional code review cannot reliably detect deliberately subtle vulnerabilities, and a determined adversary can study the test suite and plant bugs specifically designed to evade it. A formal specification is the defense: it defines what “correct” means independently of the AI that produced the code. When something breaks, you know exactly which assumption failed, and so does the auditor.
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