We Stopped Measuring AI by Benchmark Scores. We Started Measuring Production Failures Instead.
中文摘要
AI系统的失败通常源于部署后的缺乏监测而非模型弱，因此评估重心应从基准分数转向衡量实际生产故障。
English Summary
AI failures often stem from post-deployment issues rather than model weakness. The focus is shifting from benchmark scores to measuring actual production failures.
Original Excerpt
Most AI systems don’t fail because the model is weak. They fail because nobody measured what actually happens after deployment. Continue reading on Stackademic »