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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The snowballing of edtech in classrooms was associated with an emerging narrative on how tech impacts learning, Horvath said: Education was broken, and computers could provide adaptability to students’ differing learning needs. With knowledge at their fingertips, students could be empowered to learn all by themselves.，这一点在WPS下载最新地址中也有详细论述
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