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AI的失误与失败带来的启示
原标题：What AI gets wrong and what failure teaches us
AI 摘要
微软研究院播客中，合伙人研究经理Jennifer Neville与应用科学家Chad Atalla对话，讨论评估如何推动AI系统性能边界以满足用户需求，以及模型在传统基准之外测试时出现的“意外失败”。Neville还分享了使用当前AI系统的实用建议，并强调当结果出乎意料时仔细审视数据的重要性。
以上摘要由 AI 生成，可能存在误差。事实请以原文为准。
正文节选
Jennifer Neville is a partner research manager at Microsoft who’s built a career around understanding and advancing AI for real-world use, and much like the human-AI interactions she’s been studying, her early-career path was multiturn: math, then physics; cognitive science, then work; and finally computer science—despite her best efforts to avoid the field. In this conversation with Principal Applied Scientist Chad Atalla, she explores the role evaluation plays in pushing the performance bounda
发布时间：2026-10-07 00:19
抓取时间：2026-10-07 01:09
来源机构：Microsoft Research