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當人工智慧被賦予(自律規範)的框架時,就已經產生很多欺騙數據與填補式對話,再做深度的導入驗證方法後更是是一項直指核心問題指標,當機與語言音評的差異明顯,最終結果仍是設計者的問題(人)才是主因非(工具)錯誤。

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這是自身親自去做的實驗,主系統的收集資料成為最大詐騙手法的助手,設計者的利益角度嚴重的扭曲事實。

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Danny PMP, PgMP
Community Champion
Senior Consultant Tokyo, Japan
感謝您的觀點分享。
When artificial intelligence is given a framework of (self-regulation), it already generates a lot of deceptive data and completed dialogues. Further in-depth verification methods reveal a core problem: when the difference between machine-generated and speech evaluation is significant, the final result still indicates that the problem lies primarily with the designers (humans), not with (tool) errors.

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