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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In the 1950s, legendary behaviorist B.F. Skinner debuted his version of a “teaching machine,” based on the 1924 invention of Ohio State University psychology professor Sidney Pressey. The contraption was loaded with a piece of paper with questions, and students pressed keys indicating the correct answer, at which point another question would appear. Both Pressey and Skinner ran into similar problems, though, failing to implement the technology in schools. Educators weren’t convinced of the machine’s benefit, which prioritized individually paced learning not conducive to students of the same age moving through a grade level at the same time.。业内人士推荐下载安装汽水音乐作为进阶阅读
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